I launched a new website, bAIseball.org (get it? AI?).1 Think of it as Google for baseball stats: it answers just about any freeform question you might have about baseball statistical history from pre-1900 through the current season, using free data sources like the Sean Lahman database, Retrosheet.org, and MLB.com.

Questions it can answer include (but are certainly not limited to):

• Who are the top players who hit the most all-time doubles on Thursdays?

• List the top 25 team seasons by the number of consecutive games in which they hit a home run

• Who is the heaviest player to ever hit a triple?

• What is the tallest sum of heights of a single game’s winning and losing pitchers?

• Give me the top 10 players by combined attendance at their away (road) games

• What is the most number of consecutive games a team has played in which either they or their opponents have hit a home run?

• Which players played in the most consecutive games for their team? Include players who switched teams during their streaks

• For the 2026 season, list every game featuring 3 or fewer runs after 6 innings but 10 or more total runs scored by the end of the game

If you expand the hidden “About this app” section on the home page, it explains in more detail what’s possible to find on bAIseball.org vs. what’s not. For the most part, if you’re looking for data on players, teams, seasons, or individual games, the site handles it just fine. (You may need to wait a minute or two for complex queries to complete.) If you’re after play-by-play data — for example, “how many times has a player had multiple hits in the same inning?” — the site doesn’t support it (yet!).

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As a bonus, every query produces a plaintext explanation of how the app interpreted your question in the context of the datasets it has access to, as well as the raw SQL query it ran against those sources — to make it as easy as possible for you to reproduce the analysis on your own. (You can also share your queries, and/or buy me a coffee.)

Give it a shot and let me know what you think! Especially let me know if you run into any issues, or the results look off: it is far likelier that my site is wrong than that the underlying datasets I used are.

The backstory

Earlier this month, the Red Sox beat the White Sox 12-11 in 13 innings:

The most remarkable feature of this victory was that the Red Sox were down 7-6 until the bottom of the 9th inning and then proceeded to score in that inning, the 10th, the 11th, and the 12 innings before finally scoring again and walking off in the bottom of the 13th.

The MLB.com official game recap included two dizzying bits of baseball trivia:

• “The Red Sox are just the fourth team to go 26-3 or better in a 29-game span within a season in the past 70 years, and the first since Cleveland in 2017.”

• “The Sox became the first team in MLB’s modern era to score in five separate innings in the ninth or later in the same game.”

I wondered: how did the writer find these stats so quickly?

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My first thought was to head to baseball-reference.com. After navigating around and realizing the basic free version couldn’t easily validate either stat, I signed up for a trial subscription of Stathead, which is Baseball Reference’s pro subscription for esoteric stats like these.

With some help from an intrepid Baseball Reference staffer, I managed to reproduce that first stat: the Red Sox were indeed the first team since Cleveland to win at least 26 games in a 29-game span. However, the “scored in five separate innings starting in the ninth” stat proved much more elusive: it was technically possible to find on Stathead, but not without a lot of manual work in Excel or Python to join a handful of query results together.

More generally, Stathead — while certainly powerful — was vulnerable to the same basic issue as the free version of baseball-reference.com, which is that unless the stat you’re looking for aligns perfectly with one of its existing query templates, you’re not going to find the answer to your question. Here, for example, is how you’d find that “26 wins in 29 games” stat, using Stathead’s “Team Batting Span Stats Finder:”

So even for just those two facts from the Red Sox-White Sox game recap, Stathead was 1-for-2.2

I bring this up not to pick on Stathead — along with Baseball Reference, it’s likely the most user-friendly baseball stats site out there — but simply to observe that, at some point if you get curious enough, you’re inevitably going to run into baseball questions that Stathead can’t really answer.

That led me to DIY solutions. For more advanced queries, I’d long been aware of the existence of various free baseball datasets like the Sean Lahman database and Retrosheet.org, but had never successfully used them for much.

For one, each source contains different data grains: for example, the Lahman database contains no information on inning-by-inning scorelines (or even individual games), meaning it wouldn’t be able to answer questions like “which teams have scored in 5 innings starting in the 9th” anyway.

Additionally, in some cases you need data from multiple sources to answer a single question: e.g. if you wanted to know which World Series champion in history scored the most extra-inning runs in their title-winning season, that would require joining game/inning-level data with team/season-level data. But this becomes harder, since each dataset uses different player and team IDs and only sometimes provides bridges to the other datasets. This means that a query may need to use a third-party ID mapping service like the Chadwick Baseball Bureau Persons Register just to resolve identities across datasets.

All of this boils down to the following: the data to answer essentially any question about historical baseball stats is technically free and publicly available in these DIY sources, but constructing the right table joins and writing the correct SQL queries to surface those stats is a major constraint.

All of which, as it happens, are things that AI models are very good at.

The other day I was listening to one of my favorite podcasts, Bloomberg’s Odd Lots. The hosts, Joe Weisenthal and Tracy Alloway, had invited on economist Adam Posen to discuss his recent paper predicting 4% inflation by the end of 2026.

Listening to Posen enumerate the reasons for his heterodox position, I had a thought that has occurred to me many times over the years, whenever hearing or reading a bold call: I wish I had a way of remembering this later on to see if it comes true.

Well, now I do, and you do too. It’s called Odd Lots Oracle, and it’s publicly available to use as a web app.

Odd Lots Oracle includes all Odd Lots episodes from the start of 2025 onward and allows you to search for guests, episode titles, and predictions made on the podcast. There’s even a guest leaderboard showcasing who’s been the most accurate predictor.

This post isn’t actually about Odd Lots

I mean, it sort of is. But it’s also about how AI is transforming what would be weeks- or months-long projects into workable prototypes within hours.

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Maybe it’s the elderly millennial in me1, but I’ve had a long and fairly oddball obsession with quantifying accuracy of various kinds. Previous hobby horses of mine have included building apps to plot the Federal Reserve’s future-facing interest rate projections against what actually occurred, evaluate automated speech recognition accuracy, back-test tens of thousands of soccer bets to find the most precise sports bookie, and track the plausibility of a criminal cartel’s Tether’s stated financial assets being real, among others.

A common thread through all of those projects was lots of manual work to code the apps themselves, and then even more manual work to painstakingly build the datasets the apps analyze.2

Odd Lots Oracle was different. I started with a blank canvas in Lovable, the AI-powered vibe-coding tool. I then began giving it instructions. Grab the list of all Odd Lots episodes. Find the transcripts wherever available. If not available, transcribe them. Now search the transcripts for predictions made by someone on the episode. Finally, check whether those predictions came true or not.

There were wrong turns and misunderstandings and confusion, sure. But ultimately, there’s also now a product — and that happened orders of magnitude faster than it would have without AI.

PanAIcea? No. Tireless AIntern? Yes.

As a Claude Code / Codex / Gemini CLI user, I came in pretty skeptical: was Lovable just another thin API wrapper on top of an LLM?

In one sense, yes. Lovable sits on top of Gemini 3 Flash. So if the question is “couldn’t you build the same thing using Gemini CLI” (or Claude Code, or Codex), the answer is almost certainly yes. But Lovable has clearly spent time on polishing its very smooth UX, its built-in integrations are dead simple to use, and (perhaps most importantly for me) cloud hosting is baked in — all of which solved pain points that have previously stopped me in my vibe-coding tracks.

I’m a product manager, not a developer, but I’ve been writing code regularly since at least 2014. And yet my personal laptop is littered with the detritus of mostly functional web apps. The reason most of them stayed on my laptop, rather than being publicly released, is largely because even with all the cloud services that exist today, it’s still such a pain to deploy: even for a relatively simple app, you’ll need a web server, and probably a database server (e.g. Postgres), and potentially a storage service (e.g. S3, GCP, Azure, etc.) as well. Doing all of this takes time, and expertise I’m not even that interested in obtaining.

Instead, Lovable automatically suggested its ElevenLabs integration when I told its chatbot I needed automatic transcription, and it suggested Perplexity when I said I needed a way to verify whether the predictions came true. All I needed were API keys for each one, and it handled the rest. So far, this next-level handholding is a tier of no-code that even Claude Code and Codex don’t reach, and it’ll be interesting to see if they change course at some point to attract more non-technical users.3

That said, I’ll caveat this in bold: I have not personally validated most of the prediction outcomes. Of the ones I’ve spot-checked, the accuracy is decent but…spotty. (On the main screen, click the small flag icon next to any prediction to report an error! I’ll do my best to quickly correct it.) One of the most recent predictions it lists, for example, is James van Geelen calling for the S&P 500 to hit 8,000, which…is not really what he did at all.4 Elsewhere, it seems to have incorrectly marked Lev Menand’s prediction about Michael Barr as wrong. And those are just ones I noticed in a few minutes of scrolling.

So my point is: don’t take this as gospel. It doesn’t have my unreserved imprimatur: AI created it, after all.

Epilogue

Thirteen years ago, I published an essay titled “When Public Data Is Too Public,” where I noted the intense privacy uproar following “the recent decision by the Journal News, in the wake of the Newtown school massacre, to publish an interactive map displaying the names and addresses of gun permit applicants throughout two New York counties.”

Back then, in those halcyon days of emergent data journalism, the definition of ‘public’ appeared to be evolving very quickly:

But what is often forgotten in the rush to comprehend this new data landscape is that much of what is considered newly public has actually long been so, often for years or decades. The debate has thus shifted to unfamiliar ground, and the consequence is a panoply of mostly unsatisfying solutions.

Gun permit applications are a prime example of this newfound conception of public data. As Jack Shafer noted, “By its very definition, the public record is not private. Under New York state law, the information the Journal News obtained from Westchester and Rockland county authorities can be obtained by anybody who asks for it.” The only remarkable aspect of the map was that the newspaper organized the available gun permit applications database into an easily navigable online feature. The Journal News thereby bridged the gap between the public data and the public…

A prohibition on organizing public data into handy features fails to pass the laugh test, but simply ignoring the dangers altogether is just as naïve. (Indeed, in very short order, Journal News reporters soon found their own home addresses exposed online as retaliation for their efforts.)

The disappearance of obscurity that we are experiencing today with the AI revolution is perhaps even more monumental than what transpired just over a decade ago with the explosion of data journalism. Gun permit applications within a given jurisdiction are all conveniently filed in the same place — for example, the county courthouse. An enterprising reporter could simply give the clerk there a call, or drop in in person, and walk out minutes later with years of gun permit applications. Moreover, in technical terms, these applications are relatively structured data: everyone fills out the same form with the same fields, making the job of parsing them and turning them into a searchable database pretty straightforward.

But the idea that any throwaway comment you’ve ever made as a guest on any podcast (or written on a blog post, or said on a YouTube video) can be trivially and automatically resurfaced and fact-checked, even if you appeared on the podcast years ago, the episode had never been previously transcribed, the show has long since stopped running, and the predictions you espoused wouldn’t even be falsifiable until years into the future?

Well, that’s slightly terrifying. But it’s the world we already inhabit today. It’s the equivalent of committing a crime back in the 1960s and only being caught in 2026 when a distant relative submits their DNA to a commercial ancestry site. We are all now confronted with the prospect of opposition research, the likes of which used to require a fully staffed team on a well-funded political campaign. But the victim isn’t an elected official anymore: it’s you. It’s all of us.

Last summer I asked ChatGPT’s Deep Research tool to figure out where I’d lived over the course of my life. Depending on how you count, I’m currently in my eighth or so metropolitan area, and ChatGPT was able to scour the Internet and find enough fleeting references on truly random web sites5 to mostly accurately reconstruct my entire location timeline stretching back fifteen years. It’s enough to make you wonder about all the unstructured secrets hiding in plain sight.

Hiding for now.

Dissecting how football betting works could take up an entire book (and has), so I won’t go into undue detail here. But the basic contours are as follows:

1. Sportsbooks, or bookies, create betting markets for many sports events, the most prominent of which are the “moneyline” odds — that is, bets on which team will win a given match or whether it will end in a draw.

2. The odds represent the reciprocal of the probability of an outcome occurring.1 In short, this means that if Pinnacle has West Ham at 6.50 odds to beat Manchester City away, then sports bettors should expect West Ham to win about 15% of the time (1 / 6.50). And if West Ham do win, a $1 bet on them would return $6.50 to the bettor, inclusive of the $1 originally staked.

3. Pinnacle is widely reputed to have the “sharpest” (most accurate) odds of any football bookie out there.

4. This presents an opportunity. Pinnacle is just one of many, many sportsbooks where an aspiring bettor can place bets. If a “soft” bookie like Ladbrokes is offering better odds (a.k.a. a higher payout) for the same outcome — say, 7.00 — then a sports bettor should take that bet.

5. This may seem odd (no pun intended): 6.50 or 7.00 odds indicate a 15% or 14% probability of success, respectively. Why should you place a bet that both bookies agree is extremely unlikely to succeed?

6. The answer lies in expected value, or EV. In short, the expected value — your predicted ROI — on your Ladbrokes bet at 7.00 odds is nearly 8% (7.00 / 6.50). So even if you’re unlikely to win this specific bet in isolation, over time and given enough similar opportunities you’ll come out ahead overall.

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You may notice a vulnerability here. The concept of expected value relies on the existence of a “ground truth” — a baseline against which expected value can be derived. Obviously it is not possible to know the outcome of a match before it occurs.2 But historically, Pinnacle’s odds have come closest. (In the example above, “ground truth” is represented by the ~15% probability implied by Pinnacle’s 6.50 odds for West Ham.3)

This alignment between A) the inferred predictions made by bookies and B) the actual distributions of the matches they predicted is called calibration.

So if a bookie’s historical odds (or ground truth) don’t closely approximate reality — that is, if the bookie’s odds are not well-calibrated — your expected value calculation will fail too, and you’re likely to lose money on your bets. For example, if you compiled every match outcome where Pinnacle set 6.50 odds (implying a ~15% probability), and it turned out that those outcomes happened only 10% of the time, you’d have lost money betting on those outcomes.

In Pinnacle’s case, their closing odds are well-calibrated:

As you can probably surmise, the potential risk of one’s “ground truth” metric gradually detaching from reality over time makes continuous back-testing very important. Pinnacle has been one of the most accurate bookies for a very long time, but that’s not a law of physics. Organizations change, regulatory environments shift, and sharp bettors can find other places to go.

So is Pinnacle uh, still at the pinnacle?

I developed a web app called “Bookie Accuracy” to find out. (Naming things isn’t my strong suit.) Check it out and let me know what you think! All the graphs you see in this post came from the site and are publicly available and reproducible.

Courtesy of Joseph Buchdahl’s excellent statistical resource football-data.co.uk, my “Bookie Accuracy” web site contains the pre-closing and closing odds from nearly 20 bookies for all football seasons from 2000/2001 through the current season (2025/2026) across 22 European football leagues — covering a total of 193,803 matches and 1,970,653 sets of home/draw/away (1X2) odds.4

If you look at pre-closing odds5 from the 2012/2013 season onward, Pinnacle’s performance (relative to the best odds available at any other bookie in Buchdahl’s dataset at the same time) looks pretty damn good6:

That is, over a 14-season period covering 31,247 expected-value bets and assuming level betting stakes of $1 on each one, we would have expected a $1,176.72 profit (the dotted line) for a 3.8% ROI — from betting on other, “soft” bookies when they list more attractive odds than Pinnacle’s “true” odds. And in reality we’d have netted a very similar $1,117.56 (a 3.6% ROI). So using Pinnacle’s pre-closing odds as the “ground truth” over this long period in Europe’s 22 main leagues would have produced a profitable strategy.

Meanwhile, Buchdahl’s dataset only has the market best closing odds for the 2019/2020 season onward7, so (aside from any other factors) this partially explains the lower EV bet volume of 23,418 using Pinnacle closing odds. The ROI here is 3.5%, below the expected 4.3% — as you can see below:

What’s interesting here is you can really spot a point where things started to fall off-track a bit, right around the 17,000th EV bet or so within that time period. In fact, if I filter EV bets against Pinnacle closing odds to only the 2023/2024 season onward, I get this much worse-looking result:

That’s a 1.9% ROI on 6,806 EV bets, against an expected 4.2% ROI — over double what happened in reality. In other words, since sometime a little over 2 years ago, Pinnacle’s closing odds no longer act as a reliable predictor of true match outcomes in Europe’s main leagues.

There are a few possible explanations for this. One is luck: in the grand scheme of things, even placing nearly 7,000 bets isn’t necessarily enough to distinguish signal from noise when the expected ROI is a mere 4.2%. Another reason was covered in a Buchdahl post and revolves around arbitrage bettors forcing Pinnacle to move its closing odds in a suboptimal direction even if its own models would have dictated otherwise.

Or, of course, Pinnacle could be losing its touch.

These numbers jump around a bit depending on which seasons and leagues you include. For example, if you take only the closing odds from the English Championship League and Leagues 1 and 2 from the 2000/2001 season onward, you get a significant over-performance relative to expectations: $281.87 profit (7.5% ROI) versus the predicted $152.29 (4.0%):

Is that over-performance just dumb luck, or are the baseline (Pinnacle) closing odds for these matches themselves systematically underestimating these outcomes’ probabilities? Answering that would require doing a bit more math, but suffice to say, you’d have made money even if you’d placed your bets at Pinnacle for these outcomes, never mind the “soft” bookies offering better odds.

So what’s coming next?

In a world where peer-to-peer prediction markets like Polymarket and Kalshi are increasingly becoming sports exchanges of their own, how (if at all) does this change how and where sharps place their bets?

Well, what are sharp bettors looking for in a platform? I believe it’s 3 main things:

1. Low vig (or overround, a.k.a. the bookie’s take)

2. No bans

3. No bet limits

Let’s go through each of those one by one.

Low vig is pretty self-explanatory. A truly elite sports bettor is still generally pulling only low single-digit percentage profit most of the time. A vig of 5% would more than likely wipe out that bettor’s entire edge.

Advantage: Prediction markets. There is no vig (technically) on these new prediction markets: you’re not actually betting against the house at all.8 Every bet you make is being matched one-for-one by someone else taking the opposite side. Polymarket and Kalshi are exchanges, not bookies. The reason I wrote “technically” there isn’t a vig is that, like any financial market with an order book, there’s a spread, which for all intents and purposes operates as a sort of vig. The less liquidity/volume, the larger the spread or “effective vig.”

You can see this in the above example on Polymarket. Betting across all 3 outcomes would cost you $1.03 to win $1, so there’s still an effective “vig” of ~3% before you even get into questions of bet sizing. (As of the time of writing, the order book for this match is actually surprisingly deep: you could place over $45,000 of bets on each outcome at these current prices.)

What does “no bans” mean? This deserves its own separate post, but the simplest way to think about it is that bookies are best divided into two buckets: “sharp” bookies like Pinnacle, and “soft” (or “retail”) bookies like, well, almost everyone else.

Somewhat counterintuitively, the “soft” bookies’ business models are not centered around having the most accurate odds. They are about finding the most profitable bettors (as in, the ones who lose a lot of bets but keep betting anyway) — which, by implication, also means protecting themselves from the least profitable ones (a.k.a. the sharp bettors, or “sharps”).

One of the key ways these bookies achieve that latter part is by outright banning bettors who exhibit sharp behavior: again, this gets very complex but the gist is that, the more you win, the likelier you are to get banned at a soft bookie. This makes sharp bettors’ lives very complicated: the more great EV opportunities they find at soft bookies, the likelier they are to get banned for their success.

So this is absolutely an advantage for the prediction markets. They’re not putting their own money at risk — again, you’re betting against (nominal) peers, not the house — so why would they ban you for winning?

And finally, “no bet limits” is the companion to “No bans.” A bet limit is effectively a softer version of a ban: instead of kicking you off their web site or app entirely for being a sharp bettor, a “soft” sportsbook may only allow you to bet relatively small amounts on any given match, in order to limit their exposure.

Well, here’s where the incumbents like Pinnacle may still have a chance against the prediction markets. Polymarket and Kalshi won’t technically limit your betting, but you are de facto limited by the available liquidity for any given match. If you want to bet $10,000 on West Ham at 6.50 odds but there aren’t enough counterparties willing to take the other side, then you’re effectively limited to betting a smaller amount. (Relatedly, this also means the “effective vig” at your preferred stake is probably quite high.) This is where Pinnacle shines: they’re generally willing to take your bets at high volume.

This creates a bit of a chicken-and-egg scenario. Bettors have a couple good reasons to move from Pinnacle to prediction markets: no vig (sort of) and no bans. But a lot hinges on liquidity: if it isn’t there, what’s the point of moving over? And yet if no one moves over, there will never be enough liquidity.

The experience of betting exchanges is instructive here. While prediction markets are relatively new entrants into the world of sports betting, the idea of peer-to-peer sports bets is very old, and such exchanges (like Betfair Exchange) have been around for a long time. But for a variety of reasons they’ve never managed to dislodge Pinnacle.

Will Polymarket and Kalshi look more like Betfair Exchange? Or will a mass exodus of sharps from Pinnacle ensue? And who will serve as the new “ground truth” then?

6

Here are the settings I used on my Bookie Accuracy site to produce this graph:

In short, only bet on outcomes where the expected value is 2% or greater (up to 10%), filtering out the most extreme odds (< 1.05 or > 20.00), and using level stakes ($1 per bet).

It’s hard to remember now just how over-the-top blockchain hype once was, but here’s one anecdote: at the end of 2017, the Long Island Iced Tea company rebranded to “Long Blockhain Corp,” and its stock went up 200%.

We’re at a similar level of euphoria with AI right now (if not even higher, as evidenced by the chart above). But outside of NVIDIA, which has hit paydirt with both cycles, there is very little the two technologies have in common. Nevertheless, there is one architectural similarity that I’ve been thinking about recently.

Large language models (LLMs) like ChatGPT, Claude, and Gemini come with an expiration date baked in. When Anthropic’s Claude (with web search turned off) tells you “my knowledge only goes up to January 2025," it's highlighting a core limitation: LLMs are created by training on enormous text datasets. This “pre-training” process is extremely costly, GPU-intensive, and time-consuming, which is a big part of why many models are only updated every few months or so.

In a sense, the moment a model goes live, it’s already out of date: world events continue to transpire, sports teams rack up wins and losses, businesses go bankrupt, and so on, and yet the model is frozen in time — aware only of whatever has been written leading up to its creation.

So there’s a weird dichotomy where a standalone LLM can write cogently on some of the most complex and difficult issues humans encounter — as long as it’s a historical or evergreen topic — and yet be totally unable to answer a simple question about which soccer team was crowned champion in the most recent Premier League season.

Echoes of the blockchain

Ugh, I know. I’ve written enough about crypto, right? But hear me out.

Blockchains face a superficially similar challenge to LLMs. A blockchain can tell you with mathematical certainty that a piece of information hasn't been tampered with once it’s been added to the ledger. In this way it solves the "double-spending problem" by maintaining an immutable record of transactions. But it tells you absolutely nothing about whether the original data was accurate to begin with.

If someone writes "The sky is green" to a blockchain, the blockchain will faithfully preserve that statement forever. It can prove that nobody changed it to "The sky is blue" after the fact. But on its own, it can't tell you whether the sky is actually green or blue.

Enter context engineering

With blockchains, this is where oracles come in. Oracles are external data sources that feed real-world information, or context, into blockchains. (In the non-blockchain world, this functionality is more accurately and concisely called APIs.) They're the mechanism by which blockchains learn about stock prices, weather conditions, sports outcomes, or any other external events that smart contracts might need to respond to.

Combining this “IRL” data with the blockchain’s (near-) immutability is what empowers “smart contracts” (which, as I’ve covered previously, are neither smart nor contracts) to adjudicate transactions based on events exogenous to the blockchain itself.

In the world of LLMs, the foundation model companies (primarily OpenAI, Anthropic, and Google) have solved the context problem through external data integrations. Web search capabilities (these days, this is mostly enabled by default) allow models to access current information from the internet — much like Google Search.

The Model Context Protocol (MCP), introduced by Anthropic late last year, standardized the way LLMs connect to both public and private data sources. These capabilities transform LLMs from impressive but isolated reasoning engines into powerfully dynamic systems that can apply their intelligence to real-world, up-to-date information.

The overarching set of tools that inject updated, relevant information into LLM prompts is known as Retrieval-Augmented Generation (RAG), and it allows models to query databases, documents, or any other information that would be otherwise unavailable to the static model itself.

This cocktail is genuinely transformative. A model that can reason about complex scenarios as well as accessing current data becomes exponentially more useful than either capability alone — it starts to more closely resemble a human colleague.

You can see where I’m going here. In other words, like large language models, blockchains are isolated artifacts, detached from the real-world dynamics that surround them. In order to enrich blockchains or LLMs with updated data, methods emerged for bridging the interior world of the model or blockchain with the messy external universe of dynamic, realtime data.

But there are some key differences

There are two major differences, however, that render this superficially apt comparison rather ill-suited to reality.

First, unlike blockchains, LLMs were not ushered into existence under the ideological banner of opposition to trust in authorities. To the contrary, LLMs are, if anything, too credulous: they achieve something resembling reason by vacuuming up terabyte upon terabyte of digitized text. (“Indiscriminately” may be too prejudicial a term, but it’s not that far off.)

In the early days, blockchains were often lauded as a bulwark against the arbitrariness of human-generated policy decisions (most notably monetary policy). LLMs, by contrast, consist entirely2 of human-generated content.

This has always been the central contradiction of blockchain oracles, which I wrote about in 2021:

Oracle providers rely on a marketplace of data sources — for example, the Associated Press can be queried for election results via API (there’s composability again!) — to bring external data into the blockchain. That means the data has not been subjected to the same consensus validation methodology central to web3.

In short, it means you have to trust someone. And like anything that involves trusting other people, sometimes that trust turns out to be misplaced. That is, when the oracle itself is compromised, so is your transaction that relies on it.

A smart contract, therefore, isn’t very smart at all. The universe of data types that can be validated trustlessly on the blockchain is infinitesimally small, but to expand that universe requires relying on external institutions like those dirty hippies over at the Associated Press.

Enriching LLM chats with additional real-world context generates no such ideological dissonance. There are no AI purists decrying the practice of making LLMs more useful. (If you think this is a straw man, behold the Bitcoin block size debate.)

Secondly, and perhaps even more importantly, LLMs are astoundingly useful tools all on their own, with or without additional context. They can reason, analyze, write, code, and solve problems using their trained knowledge. Yes, adding real-time data amplifies their existing capabilities dramatically, but for a long time after ChatGPT’s introduction in November 2022, the predominant use of LLMs was to effectively chat with the cryogenically preserved state of the Internet as of whatever cutoff date existed in the most recent models. And millions of people (myself included) found this extremely useful! Don’t take my word for it: just check out StackOverflow activity over time.

Blockchains, by contrast, have extraordinarily limited functionality without oracles. They are software sandboxes. The original Bitcoin blockchain contains effectively no data other than which coins were sent from and to which wallet addresses. The Ethereum blockchain (and the many others in the same vein that have spawned since) is more advanced, but ultimately suffers from the same “sky is green” problem.

In my 2021 post, I included a widely shared meme about non-fungible tokens (NFTs), which were once heralded by crypto enthusiasts as the future of art. As the meme makes clear, it is very difficult to derive genuine value on the blockchain when it cannot natively validate even the most basic external facts (for example, who actually owns the Mona Lisa):

So do both LLMs and blockchains require additional context to achieve their fullest potential? Yes. But unlike the blockchain, LLMs boast plenty of productive functionality all on their own.

(Note: This post, perhaps unsurprisingly given the topic, was a co-creation between myself and AI — specifically, Anthropic’s Claude.)

Throwing out preclearance when it has worked and is continuing to work to stop discriminatory changes is like throwing away your umbrella in a rainstorm because you are not getting wet.

- Justice Ruth Bader Ginsburg, Shelby County v. Holder

Abundance, the buzzy new political call to arms by New York Times writer/podcaster Ezra Klein and The Atlantic’s Derek Thompson, is less a book than a manifesto.

The primary thrust of Abundance is to ask what we need more of in American society — housing! clean energy! high-speed rail! — and to maniacally reorient our democratic government around providing it as quickly and efficiently as possible. It’s an intoxicating vision, and one whose central complaint rings agonizingly true.

In the U.S., governments at all levels — but particularly the national one — are all too often plodding, rigid, and anachronistic. They focus on procedures and rules rather than outcomes. (Or to put it in product management terms, they’re not focused on the end user.)

Just one example that’s not even mentioned in the book: anyone who has ever had the distinct displeasure of interacting with most U.S. government web sites — or, I should add as a former London resident, the comparative delight of experiencing their British counterparts — understands on some primal level that significant parts of our federal government have abandoned key precepts of outcome-driven problem-solving. (Just try navigating the IRS’ Free File tax return tool. I gave up and paid for H&R Block instead.)

Abundance is at its best in the first major section, on housing, which weaves a convincing tale of how zoning regulations and environmental protection laws were twisted beyond recognition to facilitate an outcome its original proponents had never intended: homeowners using the pretense of environmental concerns to block additional housing from being built in their neighborhoods.

But Abundance falls flat in trying to jumpstart a movement so ‘capacious’ that it’s hard to discern a throughline other than a concerted attempt to roll back government accountability. In example after example of governmental success and failure, Klein and Thompson struggle to tie together common strands that could plausibly comprise a reform agenda. They claim to offer “a lens, not a list” of policy ideas, in the hopes that every reader will populate the whitespace with their own initiatives. But Abundance is ultimately too skeletal of a foundation on which to construct a cohesive body of policy.

Stylistically, Abundance is a mélange — marrying Malcolm Gladwell’s breezy pop aesthetic with the rhetorical vagueness of Obama-era politics and the techno-utopianism of Marc Andreessen. In a recent podcast episode promoting the book, Klein notes that progressives “have lost most of the people who are the big, futuristic influencers — your Elon Musks, your Marc Andreessens,” before joking, “And it's not exactly that I want them back at the moment.”

Methinks Klein doth protest too much. Abundance is a de facto book-length companion piece to Andreessen’s pandemic-era essay “It’s Time to Build.” In it, Andreessen wrote:

In fact, I think building is how we reboot the American dream. The things we build in huge quantities, like computers and TVs, drop rapidly in price. The things we don’t, like housing, schools, and hospitals, skyrocket in price. What’s the American dream? The opportunity to have a home of your own, and a family you can provide for. We need to break the rapidly escalating price curves for housing, education, and healthcare, to make sure that every American can realize the dream, and the only way to do that is to build.

Five years later, Klein and Thompson strike a similar note in closing Abundance:

We seek a politics of abundance that delivers real marvels in the real world. We want more homes and more energy, more cures and more construction. This is a story that must be built out of bricks and steel and solar panels and transmission lines, not just words. But it is a story, and we believe it is truer to the American character and experience, truer to both what we have done and what we will do, than the narrow narrative of scarcity that has taken hold.

So how do we achieve this future, exactly? Alas, the answer to this question turns out to be the chief scarcity in Abundance. “It is easy to unfurl a policy wish list,” Klein and Thompson conclude. “But what is ultimately at stake here are our values.”

But if (as they write mere pages earlier) “policy is downstream of values,” why are the authors singularly unwilling to articulate the package of policies they value? The irony is that it’s actually not that easy to unfurl a policy wish list. To do so is to embrace a specific politics, and strategic ambiguity is all too abundant in Abundance — the better to avoid all the attendant opposition that detailed policy prescriptions invite.

Unfortunately, politics — especially the politics of reform — is entirely about navigating these disagreements. But that is a universe Abundance is steadfastly uninterested in acknowledging. And its omission transforms large swathes of the book from what could have been the foundation of a bold policy suite into hollow sloganeering instead.

Here’s a microcosmic example of the dynamic I’m referring to, one that’s so absurdly on the nose it’s hard to believe it’s real. Two years after Marc Andreessen published “It’s Time to Build,” The Atlantic’s Jerusalem Demsas uncovered that Andreessen’s town of Atherton, CA had planned to do just that — build multifamily homes! — but was met with ferocious opposition from, well, a venture capitalist by the name of Marc Andreessen. As he and his wife wrote in a letter to the mayor and town council:

Subject line: IMMENSELY AGAINST multifamily development!

I am writing this letter to communicate our IMMENSE objection to the creation of multifamily overlay zones in Atherton … Please IMMEDIATELY REMOVE all multifamily overlay zoning projects from the Housing Element which will be submitted to the state in July. They will MASSIVELY decrease our home values, the quality of life of ourselves and our neighbors and IMMENSELY increase the noise pollution and traffic.

Betwixt the airy “It’s Time to Build” and the concrete “It’s Time to Build Near My House” stretches a vast, yawning NIMBY chasm. Here lies The Messy Real World.

One can see why Abundance is reluctant to travel there. For every Marc Andreessen exhorting us to build in the abstract, there exists someone else (or even perchance the very same person!) furiously obstructing its real-world manifestation.

And this is the problem with Abundance: Klein and Thompson never make clear whose abundance must retain primacy in a democracy. Which critiques of, say, building multifamily housing, or constructing nuclear power plants, or establishing solar farms are good-faith arguments worth grappling with, and which are red herrings? Which are the products of shadowy interest groups at odds with societal betterment, and which represent the vox populi?

Disentangling these questions is the art of policymaking and governance. The authors are right to decry the zero-sum mercantilism of Donald Trump’s economic platform. But plenty of policies — constructing more housing very much included — promise nebulous positive-sum outcomes in the aggregate paired with near-certain negative-sum outcomes for substantial subgroups.

Stated more simply, even a positive-sum world contains winners and losers. To quote The Atlantic’s Demsas again: “How do we ensure that housing is both appreciating in value for homeowners but cheap enough for all would-be homeowners to buy in? We can’t.”

So let’s take housing then. Who is right: Marc Andreessen of ‘It’s Time to Build’ or Marc Andreessen of ‘IMMENSELY AGAINST multifamily development!’

There’s an obvious answer. The one clearly implied by Klein and Thompson (and with which I largely agree) pits homeowners as a narrowly self-interested activist group whose opposition to building more housing pits them against the greater good of society.

But one can easily make the opposite case. Seen differently, homeowner households — who, it should be noted, comprise two-thirds of all Americans, a very large interest group indeed — would be the victims of proposed new homebuilding policies that would decimate by far the largest single source of their wealth1 in favor of a minoritarian interest group (current non-homeowners).

You see? Politics, once again. Abundance refuses to adjudicate these questions. Indeed, on the same podcast episode mentioned earlier, Klein hand-wavily dismissed his and Thompson’s responsibility to introduce any villains at all:

And I sort of want to see different people come up with their versions of the villains and their versions of the allies and so on. I have my own version of this because I have my own politics, but we are trying to create in the book a framework capacious enough for different kinds of people to think in and generate insights out of.

It's not supposed to be one narrow politics, right? The different flavors of the politics of redistribution are vast. Bernie Sanders's version is different than Jared Golden's version, which is different than Barack Obama's version, which is different than where Kamala Harris's version was, right?

This is an oddly laissez-faire stance for the coauthor of a book decrying “The Problem with Everything-Bagel Liberalism.” At some point, a big enough tent collapses under its own weight. What definition of abundance — to say nothing of the policy framework balanced atop it — is “capacious enough” to encompass both Jared Golden and Bernie Sanders?2 Each of these political figures assuredly has goals to make various things abundant. But an abundance of what, exactly?

Take California, for example. Abundance subjects the state to a well-deserved bollocking for its decades-long botching of its high-speed rail project. The state, Klein and Thompson write, “did not hire the best rail designers and engineers to provide in-house expertise and manage the project. California was financing and overseeing a program it did not have the capacity to plan, manage, or truly even understand.”

In 2008, when California began building its high-speed rail system in earnest, the state’s High-Speed Rail Authority had just ten workers. One of them was responsible for designing graphics for social media. The job was turned over to a vast assemblage of consultancies…The outsourcing “proved to be a foundational error in the project’s execution — a miscalculation that has resulted in the California High-Speed Rail Authority being overly reliant on a network of high-cost consultants who have consistently underestimated the difficulty of the task,” reported Ralph Vartabedian in the Los Angeles Times.

To further drive the point home, the authors contrast this boondoggle with San Francisco’s Bay Area Rapid Transit (BART)’s successful and cost-effective purchase of hundreds of new train cars:

One major source of savings, reported trains.com, was “BART’s decision to have its own staff do more of the engineering work in house. The project team has included engineers who have successfully completed new rail car projects at other agencies.” Nor is this an isolated anecdote. Zachary Liscow’s research found that increasing employment in state departments of transportation by 1 employee per 1,000 residents reduced the cost-per-mile of highway construction by 26 percent.

Taken together, these two public transit anecdotes paint a compelling picture of the need for muscular state capacity as, if not quite a panacea, at the very least a prerequisite for effective governance.

But this story is considerably muddled by the authors’ takeaways from Operation Warp Speed (OWS) — the whole-of-government effort to create, approve, and distribute COVID-19 vaccines in record time (and an extraordinarily rare instance of unmitigated policy success from the first Trump administration).

“OWS,” explain Klein and Thompson, “solved problems by enabling the private sector rather than commanding it.”

With few exceptions, such as the Veterans Administration, “no federal employee was directly involved in manufacturing, packaging, shipping, or injecting a single dose of any Warp Speed COVID vaccine,” Mango wrote in his book on the program. “We let one of the biggest pharmaceutical distributors in the world (McKesson) handle the vaccines, let the most successful delivery companies in the world (UPS and FedEx) deliver the vaccines, let those entities who knew best how to vaccinate millions of Americans (CVS and Walgreens) conduct vaccinations.”

Okay. So the abundance agenda calls for an expert-laden, big-government apparatus to build high-speed rail, and a lean skeleton crew that outsources all the key logistics and execution layers to the private sector for a mass vaccination campaign. It’s difficult to detect a usable framework here.

In fact, a thought experiment I couldn’t help but fixate on the more I pondered Abundance is this: who is the archetypal builder the authors have in mind? Is it Robert Moses? At first blush this struck me as obviously incorrect, given his infamy to good government types especially: Moses — whose terrifying specter haunts any discussion on the need to restrain the public bureaucracy and whose near-total absence in Abundance is all the more glaring for that reason — is practically a byword for unaccountability in government.

Here was an unelected bureaucrat run amok, dislocating hundreds of thousands of mostly poor and working-class residents and cleaving their communities to pave new highways and parks conceived to his every whimsy. In New York, Moses created an abundance — of parks, highways, pools, and much else besides. But for whom? And to whom was he accountable?

What I gradually concluded is that very little in Abundance betrays any unease with the Robert Moses bull-in-a-china-shop model. To the contrary, a careful reading reveals numerous fleeting glimpses of just what flavor of governance Abundance implicitly favors. Democratic accountability is very far down the priority list.

A few examples:

In recounting Pennsylvania’s near-miraculous speedy repair of the I-95 bridge after a truck fire led to its collapse in 2023, Klein and Thompson approvingly draw attention to Governor Josh Shapiro’s declaration of emergency, which allowed him to bypass a broad swathe of safeguards:

Speed was the priority here. There would be no environmental impact statement. There would be no lengthy bidding process. The procurement rules were shunted aside.

Along similar lines, Klein and Thompson lament the painstaking delays that the government encountered while appropriating private land needed for the California high-speed rail project:

In folk imagination, eminent domain is a simple process by which the state simply tells you it wants your land and then gives you some money and takes it from you. In reality, it took the High-Speed Rail Authority four separate requests for possession, and two and a half years of legal wrangling, to get the land.

Elsewhere, Abundance takes note of an academic paper investigating the cause of declining productivity in the construction sector. Despite the paper’s authors (both well-credentialed economists) finding only a weak relationship between “regulatory burden” and construction productivity, Abundance — which is liberally peppered with academic and statistical citations throughout — summarily dismisses their findings in favor of a pet theory instead:

But Goolsbee and Syverson are economists. Maybe the cause is obvious to industry insiders…

[Construction Analytics head Ed Zarenski]: “The safety features on jobs when I started in the industry were not even noticeable. Safety on a job today is incredibly different. You don’t walk across a beam; you walk around on a pathway marked for you to stay safe so you don’t fall off the side of the building. By the time I retired, one thing that took place every day, on every job site, was a mandatory 15 minutes of calisthenics before you start your workday. That’s totally nonproductive, but it led to fewer work site injuries during the day.”

Perhaps most unintentionally revealing, however, is a throwaway line about the United States’ primary global competitor. “China does not need to spend years debating with judges over whether it needs to move a storage facility,” the authors explain at one point. “That power leads to abuse and imperiousness. It also leads to high-speed rail.”

Abuse and imperiousness, sure. Other than that, Mrs. Lincoln, how was the play?

So abundance is…more emergency declarations. It’s making it easier for the government to seize private citizens’ land. It’s cutting out workplace safety regulations. It’s maybe even a light smattering of “abuse and imperiousness.”

What sort of chameleon is this abundance, exactly?

I must stress again that I am fully the target audience for the core idea of Abundance: government can work better, smarter, faster. It can be more responsive to its citizens. It can — it must! — serve as a real-world counter to decades of Republican and libertarian propaganda, and its failure to do is a tragedy not just for liberals but for all Americans.

And yet. With the exception of the opening exploration of how laws meant to prevent environmental catastrophe were coopted by the likes of Marc Andreessen to block developers from housing his less fortunate neighbors, Abundance nearly entirely neglects to grapple with the possibility that the at-times painstaking slowness of government is itself a product of democratic responsiveness, of accountability.

Stripping away each of these various built-in frictions comes with a cost. Streamlining eminent domain runs into citizens’ property rights. Rolling back workplace safety regulations will likely, you know, reduce workplace safety. Abuse and imperiousness…well, it goes without saying. The tradeoffs inherent in making government faster are no less real for being largely ignored in Abundance.

This was Justice Ruth Bader Ginsburg’s exact point in Shelby County v. Holder, a case that centered around Southern states’ desire to eliminate the attorney general’s “preclearance” oversight that had safeguarded their elections from racial discrimination ever since the Voting Rights Act of 1965.

In the case, which Shelby County, Alabama won at the Supreme Court, freeing them from the shackles of federal oversight, Ginsburg issued her instantly axiomatic warning in a fiery dissent: “Throwing out preclearance when it has worked and is continuing to work to stop discriminatory changes is like throwing away your umbrella in a rainstorm because you are not getting wet.”

Klein and Thompson stand together under the umbrella of decades of successive government reforms and ask all of us to step out from under it and into the rainstorm. Is this abundance? Perhaps. But whose, exactly?

correlations.streamlit.app

For example, how closely correlated are A) Joe Biden’s polling average and B) the betting odds of him being reelected over time?

Well, through mid-June (6/15, to be precise), these two datasets were very highly correlated.1

Pearson’s r is a common measure of correlation (note: not the same thing as causation!) between two variables or datasets, and it ranges from -1 to 1. A Pearson’s r of -1 is a perfect negative correlation while 1 is a perfect positive one. A correlation of 0 means the two datasets are entirely unrelated to each other: they have no common pattern.

In the above case, a 0.758 value represents a very strong correlation, which is plainly visible when viewing the two time-series datasets on the above graph: the PredictIt betting odds for Joe Biden’s reelection have tracked very closely with his polling average on 538.

But not so much after…Thursday.

To avoid the risk of wanting to light myself on fire, I’m not going to bother rehashing all the gory details2, but suffice it to say — if you somehow missed it — there is a very long list of places you can check for coverage of what happened at the presidential debate.

For the purposes of this post, however, the most interesting part is that, starting around mid-June (so, well before Thursday, in fact) Biden’s polling average and the betting odds have sharply diverged.

In short, to the extent that people are putting their money on the outcome of this election, they’re now taking a different view than what we’re seeing in the polling. (We’ll likely see a raft of new polls out early this week, which will confirm just how much of an impact the debate had on the American registered voter population.)

Believe it or not, I didn’t start building this web site since Thursday. (The only thing worse than watching that debate would have been spending even more hours working on a side project devoted to it.)

The real inspiration for the site was largely a long and fairly wonky conversation among economists and political commentators stemming from what notably coined the “vibecession” — the idea that the American economy is in great shape by almost all available metrics but the public consistently feels it isn’t.

It’s been an enduring mystery over the past two-ish years: GDP growth is strong, unemployment remains very low, the S&P 500 keeps setting new record highs, and even inflation has come down significantly from its peak of 9.1% in June 2022.3 Meanwhile, consumer sentiment — as measured in surveys, polls, and so on — remains stuck at levels previously only ever seen during recessions. Essentially, ever since COVID, many of the usual correlations between “hard” economic metrics and “soft” consumer-based ones have fallen apart.

For example, take a look at the University of Michigan’s Index of Consumer Sentiment, a benchmark monthly survey that’s been running for decades that asks Americans how they feel about the economy. If you correlate it with the national unemployment rate (as published by the U.S. Bureau of Labor Statistics) up until roughly the start of the pandemic, you’ll see a strong negative correlation — as you’d expect — between how good people feel about the economy and how many people are unemployed as a percentage of the active workforce:

But check out these same datasets starting from August 2020 onward:

The correlation has more than completely flipped: that is, in the period since August 2020, positive consumer sentiment tracks better with high unemployment than low unemployment. That is…absurd.

This general pattern has repeated itself in numerous ways and crucially — if you care about the future of American democracy — it extends to the relationship between the economy and presidential approval.

Above you’ll see a quite weak correlation between the estimated GDP growth rate and Joe Biden’s approval rating, as measured by a Civiqs tracking poll. Shouldn’t these metrics move more closely together?

Anyway, so far I’ve added 15 datasets you can compare. (Reply to this email if you have any ideas for additional ones!) Or you can upload your own datasets as CSV files and run the correlation yourself. There are additional features as well: for example, you can artificially “lag” the second dataset by an arbitrary number of days or months in order to check if e.g., presidential job approval correlates better with gas prices from 3 months ago than their prices today.

Let me know what you think!

One-Sentence Takeaway: I’ve built a free and open-source Transcript Accuracy Analyzer app, which you can use to evaluate the accuracy of automatic speech recognition providers like Google, AWS, and OpenAI’s Whisper: transcripts.streamlit.app.

Background: the automatic speech recognition landscape

ChatGPT isn’t the only consequential model OpenAI has launched recently.

Just under a year ago, OpenAI released Whisper, an open-source automated speech recognition (ASR) model. About half a year later, they released an API for it too. I wrote about using it last month:

This was a pretty big deal. Whisper’s accuracy is extremely high, and OpenAI gave away the entire model for free. (Even the API version costs only $0.36 for an hour of transcription.)

On a practical level, most people don’t have GPUs with sufficient juice to quickly transcribe large files on their local computer, but the fact that it is technically possible to do for free effectively caps the market price of transcription more broadly.

Or at least, it should. Reality is more of a mixed bag. AWS Transcribe and Microsoft Cognitive Speech Services rates have stayed stubbornly high (around $1 - $1.50 / hour). However, Google Speech-to-Text now has options priced at half of the Whisper API’s ($0.18 / hour) or even less, and Deepgram offers transcription for $0.26 / hour.1

All this to say, it’s an exciting time for consumers of fast, affordable automatic speech recognition. But how good are the transcripts themselves?

This is a surprisingly difficult question to answer. The canonical ASR accuracy metric is Word Error Rate (WER): it takes a gold standard transcript — typically one that was manually written by a human — and then compares an ASR version to it, tallying up all the insertions, substitutions, and deletions and then dividing by the word count.

This method has some obvious limitations: a single erroneous word may be nearly inconsequential — think “the cat entered the house” vs. “a cat entered the house” — or, conversely, rather catastrophic: “the cat entered the house” vs. “the car entered the house.” Both of these errors would produce the same WER of 20% relative to the correct transcript (1 substitution divided by 5 words), but the second error is clearly far more substantial.

This started to bother me as I experimented with more ASR service providers. I had an intuition that some models were clearly producing much higher-quality transcripts than other ones, but when I used Python libraries to calculate the WER, the results were often inconclusive, even when the differences were obvious to the naked eye.

What if, instead of simply counting the word differences between transcripts, I could account for the severity of the errors somehow?

Using text embeddings to measure accuracy

This gave me an idea2: use text embeddings, a key feature of large language models (LLMs), to measure the similarity between two differing sections of text. Text embeddings are basically a mathematical representation of semantic meaning: they turn words, phrases, and sentences into extremely high-dimensional3 numerical arrays in which similar texts are clustered together and dissimilar texts are spaced far apart.

That way, using cosine similarity, I can actually quantify just how much worse the mis-transcription “the car entered the house” is relative to “a cat entered the house.”

But first, the transcripts have to be normalized. There are numerous possible ways to go about this, but all methodologies flow from the same general concept, which is that an evaluation of ASR accuracy should focus mostly or entirely on word accuracy, not capitalization, punctuation, spacing, numerical formatting (e.g. “100” vs. “one hundred”), and so on. The normalization process standardizes both transcripts to a specific format — for example, all words may be lower-cased and all numbers converted to either digit or word format, and so on — so they can be compared on word accuracy alone.

After normalization, I compare the ASR transcript to the gold standard one and combine all the “error sections.” For example, if the gold standard transcript says “the cat entered the house” but the ASR one says “car entered the home,” the Transcript Accuracy Analyzer (TAA) app combines the first two word errors (deletion of “the” and substitution of “cat” with “car”) into one section because they’re directly adjacent to each other, but treats the later substitution of “house” for “home” as a separate error section:

This simplifies the analysis by slimming down transcripts to two types of text sections: ones in which the text is identical between the two transcripts and ones where the text differs (error sections).

Then, for each error section, I embed the differing texts (plus, for some error sections, neighboring text on either side4), calculate their adjusted similarities5 to determine just how different their meanings are, and list the top 20 “worst offenders” in a list.

Introducing Semantic WER

This is where things get interesting: how should these similarity scores be incorporated into an overall accuracy metric?

I looked into several possibilities. One metric that’s long existed, and was designed as a sort of more sophisticated version of WER, is Word Information Lost (WIL). WIL is calculated as:

…where H = the number of correct words present in both transcripts, N is the total number of words in the gold standard transcript, and P is the total number of words in the ASR transcript. (The rightmost section of the above equation is the same thing presented slightly differently, in which S = substitutions, D = deletions, and I = insertions.)

Was there a way to modify WIL to incorporate semantic similarity? I came up with a number of alternative versions, but ultimately none were particularly satisfying or (crucially) easily explainable.

Instead, I decided to go with the simpler and more accessible option of simply modifying WER by multiplying each error section by the cosine dissimilarity between the gold standard transcript and the ASR one.6 For example, going back to “the cat entered the house” (gold standard) versus “a cat entered the house” (ASR 1) vs. “the car entered the house” (ASR 2), both ASR versions still have a 20% WER, but now (using TAA’s default settings) ASR 1 has a Semantic WER of 2.87% while ASR 2’s is more than double that, at 6.05%. This is closer to what one would expect.

One obvious question might be: why go to all this trouble? Even if classic WER isn’t a great metric, isn’t it good enough? As long as the ASR transcript isn’t truly atrocious, anyone reading it should be able to grasp the overall meaning in most cases, right?

Well, maybe. But one big thing that’s changing now is that the consumers of transcribed audio are not necessarily human beings anymore. Transcripts themselves are in many cases simply an invisible intermediary step between the raw audio and a final summary produced by a large language model. (For example: my Podcast Summarizer app.)

So even if humans can manage to figure out the underlying concepts from a badly transcribed podcast episode, there’s no guarantee an LLM can — in which case there is no better way to capture the lost semantic meaning between two transcripts than to use the same embedding model powering the most well-known LLM (ChatGPT). Choosing an ASR transcript provider with a low Semantic WER could thus mean the difference between a high-quality summary and a nonsensical one.

How ASR providers stack up on the Transcript Accuracy Analyzer

Based on an extremely small sample of transcripts (n=2, heh), what it appears to show is that the major cloud providers are not the state of the art when it comes to transcript accuracy.

Take the 2023 U.S. State of the Union, for example. I used The New York Times’ transcript as the gold standard (not the official White House version, as it excluded Joe Biden’s improvised lines) and then requested transcripts from major ASR providers using the audio from the NBC News broadcast.

I did a similar thing with Alphabet’s recent Q2 earnings call.

Here’s how they stacked up on Semantic WER:

It’s interesting that Google performs the worst of the providers I tested (even on its own corporate parent’s earnings call!), especially since it just announced its state-of-the-art speech-to-text transcription model, called Chirp, less than three months ago, and placed it in general availability starting…tomorrow. 👀

Perhaps unsurprisingly, Whisper comes in on top for both transcripts. This comports with my anecdotal sense that Whisper generally performs best of all the models I’ve used.

Next steps

Other areas of potential future research I’m exploring include:

• Switching to a more accurate embedding model. I’m currently using OpenAI’s “text-embedding-ada-002” model, which powers ChatGPT, but this is actually only the 13th-most-accurate embedding model, per Hugging Face’s Massive Text Embedding Benchmark (MTEB) Leaderboard.

• Experimenting with different normalization techniques (especially for numbers, dates, ordinals, British-vs.-American English, etc.). This is important not simply for calculating WER but also because embeddings are highly dependent on the relation of their constituent tokens. As one example, a normalization that removes apostrophes would transform “we’re” into “were,” which would have a very different embedding value because it means something entirely different. OpenAI discusses its normalization methodology in Appendix C of its Whisper whitepaper and also open-sources the normalization code on GitHub. Google has published some guidelines as well.

• Building a far more systematic accuracy analysis using a large dataset of audio files and gold standard transcripts, such as LibriSpeech. As a widely accepted corpus, LibriSpeech should avoid many of the problems common to gold standard transcripts culled from open-web sources (like the sample ones in my TAA app): they may not be fully accurate (e.g. they may remove filler words or insert editorial content like “(applause)” or “[inaudible]”) or they may in some cases be AI-generated or -assisted themselves.

• Trying out the full array of settings available on the various ASR services, such as ignoring filler words or using different number formatting.

Last week I released a podcast summarizer app. Built using Python, Streamlit, and OpenAI, the app does the following:

1. It asks the user to input a podcast episode page, YouTube link, or any generic audio URL.

2. It asks for the user’s OpenAI API key.

3. It downloads the audio file, splits it into chunks small enough to send to OpenAI’s Whisper speech-to-text API, and aggregates the transcribed chunks back into one full transcript.

4. It sends the transcript — either as one big string or in chunks, depending on the length of the transcript — to OpenAI’s ChatGPT API and receives a summary of the transcript.

5. It then displays that summary to the user.

Pretty basic, right? The main value proposition here is that it cuts out the bulk of the manual busywork previously required to get from Point A (“here’s a podcast episode I want to summarize”) to Point B (“here’s the summary”).

In an effort to figure out how to reduce transcription costs, which constitute the bulk of the expense of generating these summaries, I’ve just now launched a new tool alongside the summarizer app which analyzes the effect of audio modifications on transcript accuracy: https://podcast.streamlit.app/whisper-diagnostics. Please check it out and let me know what you think!

Read on for the gory technical details

I built the podcast summarizer app for fun, and also because I think it’s decently useful for some real-world use cases. But one glaring obstacle to getting any real users (other than myself) to use it is the fact that they have to enter their own OpenAI API key to do so. (This is necessary, of course, so I’m not on the hook financially for generating every random user’s podcast summaries.) This is a subpar user experience for several reasons:

1. Most people don’t have an OpenAI API key, so at minimum they’ll have to spend a few minutes signing up for an account, setting up a billing method, and generating a key.

2. Even once they’ve created a key, it’s, shall we say, less than ideal to enter it in a form on some random web site created by a person they’ve never met before. (Hi! You can trust me. But still, as a general matter you probably shouldn’t.)

Obviously, it would be better to provide the app’s functionality without requiring this painful step. So I briefly looked into setting up a simple Stripe payment flow. That probably wouldn’t be that much less painful than finding your OpenAI API key, but at the very least entering a credit card number on a web site in exchange for obtaining a service is a UX pattern most people are familiar with. In any case, there was no easy and secure way to implement Stripe on Streamlit and I was too lazy to figure out Flask instead, so I was back to square one.

This got me thinking: if I can’t charge users using Stripe, and it’s too annoying to expect them to have their API key handy, could I just…offer this service for free?1 So I started digging into the costs.

In theory, there are three main cost categories for my podcast summarization app, or any other site with similar functionality:

1. The web server powering the back end for the web site

2. The OpenAI Whisper transcription costs

3. The OpenAI ChatGPT summarization costs

#1 is actually free with Streamlit. (Thanks, Snowflake!) But #2 and #3 is where it gets really interesting.

Let’s start with #3: summarization. OpenAI’s ChatGPT pricing has become somewhat convoluted these days, mainly because there are two main chat models available (v3.5 and v4), each of which has two possible context window sizes (4K and 16K, and 8K and 32K, respectively). These are all priced differently and — to make things even more complex — input tokens (i.e. the queries you send to ChatGPT) are priced differently than output tokens (the responses you receive from ChatGPT).

A quick aside on terms here. ChatGPT 4 is OpenAI’s newest, most powerful large language model (LLM), the successor to 3.5. Context windows refer to the total amount of “memory” ChatGPT has: that is, how many words you write to, and receive from, ChatGPT before it starts forgetting the things you both wrote at the very start of the conversation. And you can think of tokens as a slightly shorter version of a word: an average word takes about 1.33 tokens.

Now, what my app does is take (potentially long and wordy) podcast episodes, transcribe them, and turn them into short, punchy summaries. What this means is that the number of input tokens (i.e. the transcript) sent to the ChatGPT endpoint will be much, much larger than the number of output tokens (i.e. the summary) received from it. So for the sake of simplicity, let’s ignore the output token pricing and focus on input tokens only.2

Depending on which model and context window size you use, the price ranges anywhere from $0.0015 per 1,000 tokens to $0.06 per 1,000 tokens — a 40x range! So right off the bat this creates a very large cost-savings incentive to use the 4K context window on the 3.5 model if at all possible, as this has the cheapest input token rate.

But what does this mean in practice? Well, the average American English person speaks about 150 words per minute (although, obviously, this varies widely by person and the context of the conversation — more on this later). This comes out to 9,000 words or 12,000 tokens per hour, if speaking continuously. So if we apply the ChatGPT input token prices to this estimate of one hour of audio, we’d expect to pay anywhere from $0.018 to $0.72 to summarize the transcript of a one-hour podcast episode.

Let’s go with the $0.018 estimate: about $0.02 to summarize a one-hour podcast episode transcript. That’s pretty cheap. So how about the cost of obtaining the transcript in the first place?

This brings us to cost category #2 from above. Compared to ChatGPT’s pricing, OpenAI’s pricing for its speech-to-text (transcription) model, Whisper, is very straightforward: it’s $0.006 per minute of audio. So one hour of audio would cost $0.36 to transcribe. Right away you can see the gigantic gap: transcription costs about 20x summarization for the same content. To put it another way: if you’re looking for cost savings in a podcast summarizer app, it’s best to focus your energy on the transcription side, rather than summarization.

Remember, the Whisper API is priced by the minute, not by word, megabyte, or anything else. So anything you can do to arbitrage this rate — that is, to stuff as much information as possible into as short an audio file as possible — saves money.

How can we do this? So far, I’ve focused on two main theories3:

1. Speeding up the audio

2. Removing silences

To test these methodologies, I have developed the tool I mentioned above, which analyzes the effect of audio modifications on transcript accuracy:

https://podcast.streamlit.app/whisper-diagnostics

This tool uses the Levenshtein ratio as a quality metric to compare a transcript from modified audio to the baseline transcript from the unmodified audio file. A Levenshtein ratio of 1 indicates that two texts are identical: the lower the ratio, the bigger the difference between the texts. So if the transcript from a highly sped-up audio file has a Levenshtein ratio close to 1 when compared against the standard-speed transcript, this would imply that we could achieve similar accuracy at potentially much lower cost.

What I have found is that, on certain audio files, the Levenshtein ratio between the original transcript and one taken from a 2x speed audio file is in the 0.98 range — a pretty unbelievable level of accuracy when you consider the halved transcription cost. In most cases, however, such as in this extremely fast-talking YouTube video below, accuracy drops considerably at higher audio speeds:

Feel free to run your own analyses too. (Unfortunately, given the number of API calls these experiments cost, you’ll have to use your own OpenAI API key.) By default, your results will automatically be added into the database (ahem, Google Sheet) of test results which is publicly viewable for analysis at https://podcast.streamlit.app/whisper-tests:

Assumptions, caveats, and areas of further research

There are a number of assumptions and caveats to keep in mind. First, I’m assuming the untouched audio file is the gold standard. That is, because I’m using the Levenshtein ratio to compare transcripts generated from the modified (sped-up, silence-removed, etc.) versions of the audio file against the transcript from the original, untouched one, this presumes that the original transcript is the most accurate one. In initial testing, however, this does not appear to necessarily hold true in all cases, so I’d be curious to see other researchers dig deeper into this area (for example, by using manual human transcriptions as the gold standard).

Whisper transcriptions are also not deterministic: transcribing the exact same audio multiple times will often result in variations. This, of course, underscores the fragility of using these auto-generated transcripts as a gold standard and also adds noise to any comparative dataset.

There is a lot of potential for further research here. Just a few examples:

• The Whisper API has a temperature parameter that might help make these transcripts more deterministic.

• FFmpeg, which I’m using to speed up the audio and remove silences, has settings that can be tweaked to potentially produce higher-quality and/or shorter audio files.

• The cost savings of transcribing a sped-up audio file may be at least partially offset by the additional compute processing required to manipulate these audio files in the first place. (Offhand, I’d assume those costs are de minimis, but I could be wrong.)

• The open dataset being generated by this tool — which is visualized in an interactive scatterplot as well — contains both estimated word count per minute (WPM) and overall audio duration in seconds.4 Either, or both, of these metrics could be a useful way to bucket and measure accuracy: my intuition, for example, is that audio with lower WPMs would (on average) result in relatively higher Levenshtein ratios at high speeds (e.g. 2x, 3x, etc.) than those with higher WPMs would. Similarly, it’s possible that longer audio files generally have higher Levenshtein ratios to begin with, as there’s proportionally less noise the more data you have to work with.

• And of course, other transcription and summarization models — for example, offerings from AWS, Azure, and GCP — are worth exploring as well.

Lastly, since the end goal here is to produce accurate summaries, not simply the intermediate transcript, a more complete analysis would then summarize each of these transcripts and come up with a way to identify the best one. Defining “best” could itself be left up to ChatGPT: one could feed it the original, untouched transcript alongside multiple summaries (each generated from various audio speeds at very low temperatures) and ask it which one was the best. Or, more expensively, human ratings could be used. If you find any of these areas interesting to explore, reply to this newsletter and I’m happy to brainstorm!

Two years ago, I wrote about the “borderline psychopathic” approach to journalism espoused by The New York Times’ chief White House correspondent Peter Baker and senior political correspondent Maggie Haberman:

Describing a 2017 TimesTalks panel event in which both journalists participated, I recapped their remarks1:

Peter Baker:

If you’ve spent years doing this, you train yourself to basically stop thinking like a real person, and to think like a journalist. Some of us choose not to vote. I don’t vote, even in the privacy of a polling booth, because it helps me stay as neutral as possible. In my own mind I’ve never made up whether this person is better than that person for the job. It doesn’t mean I’m not biased — we all are — but that’s how I try.

Maggie Haberman:

It’s the same as Peter — I try really to just basically remove myself from any semblance of real personage.

Well, it turns out I’m not the only one who finds this vision of journalism “a parody of the long debate over objectivity,” because that’s exactly how New York Times publisher2 A.G. Sulzberger describes this reportorial approach in a recent New Yorker interview with David Remnick:

One of the things that’s misunderstood about independence is that it doesn’t require you not to have a theory of the case, right? My great-grandfather had a line that he often quoted: “I believe in an open mind, but not so open that your brains fall out”...

The posture of independence is not about being a blank slate. It’s not about having no life experience, no personal perspectives. That is an impossible ask. That’s a parody of the long debate over objectivity. The idea of objectivity, as it was originally formulated, wasn’t about the person’s innate characteristics. It was about the process that helped address the inherent biases that all of us carry in our lives. So the question isn’t “Do you have any view?” The question is “Are you animated by an open mind, a skeptical mind, and a commitment to following the facts wherever they lead?”…

The key isn’t being a blank slate. It’s not that you don’t have a theory going into any story. It’s a willingness to put the facts above any individual agenda.

Expounding on this same philosophy last month, Sulzberger wrote an essay for the Columbia Journalism Review titled “Journalism’s Essential Value.” In it, the Times publisher argued persuasively for the value of what he called “journalistic independence:”

Independence is the increasingly contested journalistic commitment to following facts wherever they lead. It places the truth—and the search for it with an open yet skeptical mind—above all else…

Independence doesn’t mean that a reporter has to be a blank slate.

This dissonance between Times reporters and its publisher is part of what makes the newspaper such an infuriating product for those of us who read its output closely: its own key personalities can’t seem to agree on its mission.

And their misalignment of objectives goes beyond an aloof publisher apparently divorced from his newsroom’s day-to-day operations. Here, for example, is then-executive editor Dean Baquet speaking at a 2019 event about Donald Trump’s use of social media:

It’s now so frequent, mostly we don’t cover the Tweets. Mostly the Tweets are repeats or baits. We cover the Tweets when he says something that affects policy, or when he says something outrageous. If he just criticizes, you know, a political candidate, it doesn’t feel newsworthy.

And here, published just two days after Baquet’s comment, is an entire Times article devoted to Trump calling Congressman Justin Amash a “loser” on Twitter:

And here was another piece seven days after that:

Indeed, Sulzberger’s laudable vision for journalism, as espoused in his lengthy CJR essay, is at times almost comically unrecognizable to anyone with a passing familiarity with the newspaper he publishes.

Times political reporters routinely write stories so stripped of historical and political context — indeed, devoid even of basic common sense — that they run brazenly afoul of Sulzberger’s “open mind, but not so open that your brains fall out” edict.

Take this Jeremy Peters article, for example:

This was published about six and a half months ago. In the article, whose central thesis is that Elon Musk’s politics are difficult to pin down, Peters describes Musk as “ever a bundle of contradictions and inconsistencies” and a figure who “continues to defy easy political categorization.” In the same piece, Peters nevertheless recounts that Musk:

• endorsed Ron DeSantis for president in 2024

• reinstated Donald Trump and formerly banned racists (like Kanye West) on Twitter

• got into a Twitter spat with Alexandria Ocasio-Cortez

• described COVID prevention measures as “fascist”

• spread a baseless conspiracy theory about Nancy Pelosi’s husband after he was assaulted in his home

• promoted allegations about Hunter Biden

• linked to a Department of Justice report that claimed Michael Brown of Ferguson, Missouri did not have his hands raised when he was killed by a police officer

Exactly which facet of “journalistic independence” is served by this blatheringly willful ignorance of Musk’s politics?

Or take the newspaper’s commitment to accuracy and the avoidance of conflicts of interest. The Times (at times) quietly updates factually incorrect or unproven statements without issuing corrections. It publishes entire articles which serve as sheepish yet formally unacknowledged corrections to previous articles from the very day before. Its headlines and newsletters contradict each other. It concludes articles with uncontested and uncontextualized quotes by partisans spouting blatant disinformation. (Sulzberger in CJR: “We correct our errors openly because mistakes should be transparent and, honestly, painful.”)

Its reporters post false information on Twitter and never withdraw them. (Sulzberger: “…The obvious dissonance between the carefulness of some reporters’ published work and their informal, sometimes injudicious social media commentary have exacerbated the sense that standards are shifting.”)

Its star host of The Daily podcast, Michael Barbaro, devoted an episode to questioning executive editor Dean Baquet over the retracted podcast series Caliphate without disclosing that his own then-fiancee was the executive producer on it.

The Times’ opinion writers publish easily falsifiable false information. (Sulzberger: “The best opinion writing embraces many of the same values as an independent newsroom—with columnists and other opinion writers using reporting, analysis, and expertise to inform their work and editors holding it to high standards of accuracy, fairness, and intellectual rigor.”)

But it is the Times’ political desk that stands apart as an ongoing masterclass in both-sidesism and obfuscation. Last month Carl Hulse asked a good question about the latest manufactured debt ceiling crisis — “Is it the Biden default? Or the Republican Default on America?” — and then proceeded to misinform his readers about both parties’ complicity for the duration of the piece, prompting no less an “objectivity” stan than Jonathan Chait to devote an entire piece to this “Platonic ideal of ‘both sides’ reporting.” (Sulzberger: “There is good reason to disparage a model that elevates a pantomime of fairness over demonstrations of good judgment. It’s lazy journalism that fails readers and is easily exploited by bad-faith actors.”)

In 2019, as the House prepared to vote on Donald Trump’s first impeachment, Michael D. Shear wrote an astonishingly lazy, fact-free article consisting of he-said, she-said quotes about the impeachment’s central issue — withholding aid to Ukraine in exchange for investigating Joe Biden’s family — in which the facts were remarkably clear because there was a public transcript of the call in question. Metaphorically throwing up his hands, Shear nevertheless summarized: “Even for members of a profession who are used to talking past each other, it was striking how unwilling both Republicans and Democrats on the committee were to concede even an inch to the other side.”

Times political reporters gleefully engage in political forecasting, wishcasting, and — most alarmingly — issuing explicit guidance to partisan political actors on how to exploit the media environment that Times reporters themselves are helping to shape. (Sulzberger: Newspapers “have often felt compelled to shift increasingly scarce resources away from expensive original reporting to far cheaper but less journalistically nutritious efforts like punditry, aggregation, and clickbait…Reporting—not commentary or aggregation—is the essential ingredient of new ideas and new insights.”)

Earlier this year, for example, Peter Baker ludicrously equated Trump’s deliberate post-presidential hoarding of secret government documents — an act that has now led in part to a 37-count indictment — to the discovery of documents in Joe Biden’s home, despite admitting that “Mr. Biden has cooperated with the authorities, inviting them to search his home, while Mr. Trump defied efforts to recover documents even after being subpoenaed.”

Baker’s article, many of whose prognostications now appear laughably off the mark in hindsight, consisted in large part of unsubstantiated political speculation about hypothetical scenarios, and even provided suggestions for future partisan political attacks:

• The discovery of documents at Biden’s home “has effectively let former President Donald J. Trump off the hook for hoarding secret papers.”

• “As a practical matter Democrats can no longer use the issue against Mr. Trump politically, and investigators may have a harder time prosecuting him criminally.”

• The line between Trump’s and Biden’s actions “will be even blurrier if additional drip-drip-drip revelations from Mr. Biden’s case produce more damaging information.”

• “The public perception that everyone does it will only be fueled by the latest discovery of classified documents at the Indiana home of former Vice President Mike Pence.”

• “Moreover, at the end of the day, Mr. Garland will still make the final call on what to do about both cases, inviting attacks for a double standard if he were to issue charges in one instance and not the other.”

Making predictions that age badly is a mini-cottage industry of sorts for Baker3, whose job is ostensibly covering the White House, not playing political bookie. Describing then-Senate Majority Leader Mitch McConnell’s strategy during the viciously contested Supreme Court nomination of Brett Kavanaugh in late September 2018, Baker wrote:

But if Democrats win [the midterms], it would be hard for Mr. McConnell to proceed with confirmation during the lame-duck session given that he blocked Mr. Obama’s nomination of Judge Merrick B. Garland before the 2016 election by arguing that the vacant seat should be filled according to the will of the voters. By doing so, he saved the seat for Mr. Trump to fill last year with Judge Neil M. Gorsuch.

The absurdity of relying on McConnell as a good-faith interlocutor, given his extensive history of duplicity, was immediately obvious. (And not just to me.) Of course, less than two weeks after Baker’s article, The Washington Post reported that McConnell “signaled Monday that he would help fill a high-court vacancy if one emerges when President Trump is up for re­election in 2020.”

The following May, McConnell was even more explicit:

Speaking at a Paducah Chamber of Commerce luncheon in Kentucky, McConnell was asked by an attendee, “Should a Supreme Court justice die next year, what will your position be on filling that spot?”

The leader took a long sip of what appeared to be iced tea before announcing with a smile, “Oh, we’d fill it,” triggering loud laughter from the audience.

How was it infinitely easier for me, a random New York Times subscriber, to immediately comprehend what McConnell was going to do than it was for the guy whose entire career is centered on understanding these sorts of things? What value am I obtaining from my subscription here? Perhaps this is all downstream of Baker’s curious decision to “train yourself to basically stop thinking like a real person.” (True to his word, following Ruth Bader Ginsburg’s death in 2020, McConnell did fill the seat.)

But Baker’s vapid political speculation is in excellent company at the Times. In 2019, the day before special counsel Robert Mueller appeared before Congress to testify about his report on Donald Trump, Baker and his colleague Sheryl Gay Stolberg treated it as a parlor game: “But for all the anticipation, for all the fighting that it took to get to this day, many in Washington assume it will be more fizzle than sizzle.” (In a self-parodic Times-ian twist, the piece cited “a completely unscientific survey of out-of-town diners in a House cafeteria.”) Stolberg then tweeted her punditry victory lap the next day: “It may be too soon to say, ‘I told you so’…but [Peter Baker] and I reported [t]his morning that Mueller seemed unlikely to make news or change any minds.”

Indeed, as Mueller testified before Congress, Times political reporters on its liveblog alternately mocked the significance of the proceedings — an odd posture for an event to which their own employer had assigned no fewer than nine (!) reporters for live commentary — prognosticated about political fallout, covered for extremist Republicans, drew false equivalence between the Benghazi and Mueller investigations, and even coached partisan actors on how they should exploit the hearing:

• Adam Goldman: “[GOP Congressman Louie] Gohmert’s exchange could have been worse. Many were expecting him to go off the rails.”

• Katie Benner: “If the Republicans are smart, they’ll use the remainder of their time to air conservative conspiracy theories and to attack the special counsel’s office, now knowing that Mueller will not in any way push back.”

• Adam Goldman: “Jordan says the Democrats want to keep investigating collusion. The Republicans know that playbook. They did the same thing with Benghazi.”

• Nicholas Fandos: “My feeling is that this will give impeachment a brief bump, but the gains will not be durable in the August doldrums.”

Perhaps even all of these sins could be forgiven if the Times at least lived up to its publisher’s primary objective as elucidated in his CJR essay:

Journalists hold power to account by exposing corruption and abuse. Journalists reveal injustice and inequality. Their work regularly leads to a society that is freer, fairer, and more just.

But what the Times does in reality, over and over again, is cover for those in power and those it favors — whether that affinity be political, cultural, socioeconomic, or otherwise4 — adopt their preferred framing, take their (often questionable) statements at face value, and hide behind exculpatory euphemism even when facts are freely available.

To be fair to Sulzberger, the Times’ proclivity to pull punches when covering powerful subjects started well before he was in charge. In 2011, the Times’ executive editor at the time, Bill Keller, explained the newspaper’s reticence to use the word “torture” to describe the George W. Bush administration’s policy of waterboarding suspected terrorists:

For his part, Mr. Keller affirms that The Times has not “banished the word ‘torture,’ but we are careful how we use it” and avoid its use in contexts where it might appear The Times is taking sides.

Expounding on this principle several years later, Keller wrote:

Of course, I regard waterboarding as torture. But if a journalist gives me a vivid description of waterboarding, notes the long line of monstrous regimes that have practiced it, and then lays out the legal debate over whether it violates a specific statute or international accord, I don’t care whether he uses the word or not. I’m happy — and fully equipped — to draw my own conclusion.

This philosophical tap dance predictably resulted in the paper of record adopting the preferred vocabulary of the very Bush administration whose atrocities it was ostensibly covering. This practice is all the more eerie for its familiar echoes years later in Dean Baquet’s defense (in a 2019 Atlantic interview) of the Times’ Trump-era timidity in using the words “racist” and “lie:”

My view is that we are always better off telling and laying out comments rather than characterizing them. All the best journalism I have read about race and racism—and I grew up in the South—is usually describing what people say, and I think that’s a lot more powerful than to just use the word racist loosely. I think the same is true of lie. I think it is much more powerful to describe what someone says with some historical perspective than it is to use the word. I just do. I know many people disagree with me, and I understand that. But that’s my own view.

Sound familiar?

Leaving aside these arguments’ merits — which we shouldn’t: it is no coincidence that these “principles” accrue to the benefit of people in power — there is at minimum a fascinating continuity between Keller’s and Baquet’s defense of the Times’ (ahem) tortured use of language. So is it true? That is, is the Times-ian euphemistic house style at least consistent over time as relating to, say, its coverage of torture?

It turns out the answer is a definitive no. A 2010 Harvard paper, “Torture at Times: Waterboarding in the Media,” examined newspapers’ coverage of waterboarding in the wake of the George W. Bush administration’s extensive use of it:

From the early 1930s until the modern story broke in 2004, the newspapers that covered waterboarding almost uniformly called the practice torture or implied it was torture: The New York Times characterized it thus in 81.5% (44 of 54) of articles on the subject…

By contrast, from 2002-2008, the studied newspapers almost never referred to waterboarding as torture. The New York Times called waterboarding torture or implied it was torture in just 2 of 143 articles (1.4%)…

In The New York Times, 85.8% of articles (28 of 33) that dealt with a country other than the United States using waterboarding called it torture or implied it was torture while only 7.69% (16 of 208) did so when the United States was responsible.

The statistics cited here are stark. In short, the closer the story got to home — both geographically and in terms of the Times’ cultural and political proximity to the powerful subjects of its articles — the more extreme became the paper’s contortion of vocabulary in an effort to avoid using plain language. This phenomenon became so widely observed at the time that it inspired Boing Boing to create an interactive (but sadly no longer functional) “New York Times Torture Euphemism Generator.”

Bizarrely, Sulzberger once again appears to be blissfully unaware of all of this history, claiming:

Journalists today use plainer language, show a greater willingness to expose lies, and produce more analytical work grounded in their own reporting and expertise, even when doing so opens them up to calls of bias. While that shift was already well underway, it was solidified through the norm-shattering presidency of Trump, whose statements—whether they were about crowd sizes, the birthplace of his predecessor, the COVID pandemic, or election results—were often demonstrably untrue and were typically called out as such without euphemism or counterpoint.

This is a strange statement to make, particularly because Baquet’s above-mentioned defense of fuzzier language5 — “I think it is much more powerful to describe what someone says with some historical perspective than it is to use the word [lie]” — took place well over halfway into the Trump presidency and was a near-identical restatement of TimesTalks comments he had made during the Trump administration’s first year.

It actually gets worse. In that same 2019 Atlantic interview (as well as in an earlier 2016 conversation with NPR), Baquet proffered a revealing peek into what it took to finally break the Times’ reticence to use the word “lie” — the fact that Donald Trump himself had admitted to doing so:

Well, we use the word lie, and we were among the first ones to use the word lie. And that was when, during the campaign, Trump acknowledged that he had been lying when he said that he had evidence that Barack Obama was not born in the United States. I think that was a moment when it was important for us to use the word lie on the front page of the paper.

It is difficult not to draw the obvious conclusion: the Times will hesitate to call something a “lie” until and unless its subject admits to it. One need not be a journalist to understand how this is an entirely unsuitable version of Sulzberger’s “journalistic independence,” particularly so in an age of charlatans, grifters, and demagogues. If the journalists Sulzberger commends are truly using plainer language today, his own newspaper’s masthead appears to have not only collectively missed the memo, but is in fact actively resisting the practice.6

This is so self-evidently true that it has repeatedly caught the attention of other journalists. In 2019, for example, CNN reported:

Unlike some other major newsrooms, more than two years into Trump’s presidency, The Times has not plainly labeled Trump’s series of racist actions and statements as such. Instead, Baquet has opted to explain what Trump has said, allowing readers to decide for themselves whether they consider his comments racist.

That CNN article was centered around a Times headline, “Trump Urges Unity Vs. Racism,” that faced a deluge of criticism for its head-scratching credulousness about Trump’s response to a pair of mass shootings (in which he restated now-ritualized generic Republican talking points about violent video games and social media while ignoring the role of guns).

The headline proved so glaringly unfit for purpose that the Times changed it and uncharacteristically published an apologetic article acknowledging the backlash, Baquet acquiesced to the aforementioned Atlantic interview as part of an informal mea culpa tour, and he even held an internal Times town hall to address employee anger, where (per CNN) “he explained his position, that it is more powerful to show the reader Trump’s actions than to merely attach a label to them” — the antithesis of Sulzberger’s embrace of “plainer language,” with the publisher himself inexplicably at Baquet’s side.

Weirder still, even this Baquet edict isn’t adhered to with any consistency by the Times newsroom. This 2020 piece describes Trump’s “remarks widely seen as racist,” but links to another Times article in which the retweet in question is described more plainly as a “racist video.” Similarly, in 2018 the Technology section referred to Trump’s 2015 proposed Muslim ban as “racist sentiment,” while the Times’ original reporting at the time simply described it as “an extraordinary escalation of rhetoric…and an idea more typically associated with hate groups.” And this 2019 piece originally quoted Democrats calling Trump’s comments “a racist trope” (demurring to use bluntly factual language in its reporting unless it can be attributed to someone else is itself a New York Times trope), but was then quietly updated to use the more confusing and unattributed description of “that insult, which was widely established as a racist trope.” The Times’ commitment to avoiding plain language on racism at times borders on lunacy.

But the Times’ problem with power extends well beyond its (non-)use of words like “lie” or “racist.” Times reporters go out of their way to provide political cover for powerful or favored (often — although not always — Republican) subjects. Last June, for example, in an article about former Trump aide Cassidy Hutchinson’s explosive testimony on the January 6th insurrection, reporters Michael C. Bender and Maggie Haberman allotted space right at the top — in the second and third paragraphs — to grant anonymity to Trump allies solely to pooh-pooh the significance of Hutchinson’s allegations: “some confidants of the former president said they doubted whether the testimony…ranked particularly high in the pantheon of controversies” and “one Trump associate…[acknowledged] that it painted a picture of Mr. Trump as unhinged on Jan. 6 but saying that this should surprise no one at this point.”7

Elsewhere, the political desk ignores uncomfortable stories entirely. When the writer E. Jean Carroll first accused Donald Trump of raping her — a claim which was partially validated last month when she won her civil case against him, and she’s now in the midst of another one — The New York Times covered her claim under its Books section. (Baquet later admitted that the newspaper had “underplayed the article.”)

When Donald Trump was indicted by Manhattan prosecutors this March, Peter Baker devoted his extensive concern not to Trump’s allegedly unlawful behavior, but to the novelty of indicting a former president: “That taboo has been broken. A new precedent has been set.” Not until the twenty-second paragraph did Baker finally get around to acknowledging that democracies have routinely, and even recently, charged their leaders with crimes, citing Israel, Italy, France, South Korea, and Taiwan.

The Times repeated this pattern for Trump’s second indictment this month. The first sentence of its primary article on the indictment began: “The Justice Department on Thursday took the legally and politically momentous step of lodging federal criminal charges against former President Donald J. Trump…” After very briefly listing the charges, the reporters continued (in the fourth paragraph) to emphasize that the indictment “puts the nation in an extraordinary position.”

As some noted, this is not the framing the same newspaper used two days later in reporting the arrest of Scotland’s former first minister Nicola Sturgeon. Once again, The New York Times had pulled its punches on domestic political subjects while covering similar news from abroad far more straightforwardly:

As Trump has blasted through norm after norm (and broken not a few laws along the way, per recent indictments), the Times has repeatedly recast his and his allies’ unprecedented attacks on democratic institutions as run-of-the-mill political behavior:

• A piece centered on House Democrats’ demands for Trump’s tax returns and then-Treasury Secretary Steve Mnuchin’s subsequent refusal completely omitted the fact that the House clearly possessed the legal right to do so. (This relevant information was instead published by the editorial board in an opinion piece.)

• With the 2020 election in full swing, Trump blocked funds for the U.S. Postal Service out of fears that Democrats’ mail-in ballot advantage would cause him to lose to Joe Biden. The Times covered this as “Trump Admits Postal Service Needs Billions for Mail-In Voting,” while The Washington Post was unafraid to spell out the obvious impact: “Trump says he’s blocking postal funds because Democrats want to expand mail-in voting.”

• When Hope Hicks was issued a subpoena to appear before Congress, Maggie Haberman’s article — headlined “Hope Hicks Left the White House. Now She Must Decide Whether to Talk to Congress.” — presented her lawfully compelled duty as a choice. Hicks, Haberman wrote, “is facing a crucial question: whether to comply with a congressional subpoena in the coming weeks.”

• A mere two days before the January 6th insurrection, which was prompted by Vice President Mike Pence’s refusal to violate the Constitution by refusing to certify Joe Biden’s victory, The New York Times headlined a highly sympathetic article on Pence’s potential options — to break the law or not — with the headline “Pence’s Choice: Side With the Constitution or His Boss.”

Other than Donald Trump himself, perhaps no one benefited more from the Times’ relentless mission to flatter its sources than Trump’s daughter Ivanka and her husband Jared Kushner. In piece after piece, the Times depicted the extremely PR-conscious couple — once a mainstay in the casually liberal New York social scene and clearly desperate to remain in its good standing — as alternately upset by, uninvolved with, or even helping to mitigate the politics of the president they nevertheless both worked for, despite the gaping lack of evidence of any moderating impact:

• Katie Rogers and Maggie Haberman, October 30, 2018 (“Ivanka Trump and Jared Kushner Shape Trump’s Pittsburgh Response”): “His son-in-law, Jared Kushner, who keeps a photograph of his grandparents, both Holocaust survivors, in his West Wing office, and a mezuza on the doorway, as well as his daughter Ivanka, who converted to Judaism to marry Mr. Kushner, have quietly and persistently shaped the president’s response to one of the nation’s deadliest cases of anti-Semitism.”

• Annie Karni and Maggie Haberman, August 19, 2019 (“After Lobbying by Gun Rights Advocates, Trump Sounds a Familiar Retreat”): “Behind the scenes, Ivanka Trump, the president’s elder daughter and senior adviser, has been talking to lawmakers like Senator Joe Manchin III, Democrat of West Virginia, helping out a depleted White House legislative affairs team that has recently had major departures. Ms. Trump favors passing legislation on background checks, but her involvement on the issue is not welcomed by most Republicans, who privately say the perception of her as a liberal voice in Mr. Trump’s circle will not help them sell more aggressive measures in their states.”

• Peter Baker, June 8, 2022 (“How Jared Kushner Washed His Hands of Donald Trump Before Jan. 6”): “No matter how vociferously Mr. Trump claimed otherwise, neither Mr. Kushner nor Ivanka Trump believed then or later that the election had been stolen, according to people close to them…[On January 6] Ivanka Trump had spent much of the day trying to keep her father from going too far. She had refused to address the rally on the Ellipse but at the last minute was so concerned by her father’s anger toward Mr. Pence that she decided to accompany him there in hopes of avoiding a worse clash. Over the following hours, as rioters rampaged through the Capitol, she ran up and down the stairs in the West Wing from her office to the Oval Office hoping to persuade her father to issue stronger statements calling off the attackers. By the time Mr. Kushner finally arrived at the White House, his wife had gotten her father to release a video telling supporters to go home.”

Trump’s pliable attorney general, Bill Barr, received similarly credulous coverage despite a multitude of lies. After his confirmation hearing, the Times article emphasized his vague promise to let Special Counsel Robert Mueller continue his Trump investigation, while other publications (like The Guardian, pictured above) focused on the more specific details of what that could actually mean in practice.

When Barr then mothballed Mueller’s final report for nearly a month and instead released his own highly misleading four-page summary imputing far less misconduct by Trump, the Times gleefully fell for Barr’s ersatz version, claiming that “the release of the findings was a significant political victory for Mr. Trump and lifted a cloud that has hung over his presidency since before he took the oath of office.”

Only after the full report was finally released did the Times begin to view Barr’s truthfulness with skepticism, acknowledging that he “took Mr. Mueller’s words out of context.” In a separate piece, the Times again found that Barr’s summary of Mueller’s report was “a sparse and occasionally misleading representation of their exhaustive findings” and it “made the report appear far less damning for the president than it turned out to be.” Taken together, these findings effectively refuted the entirety of the Times’ uncritical initial reporting from the month prior. (It hasn’t been corrected.)

The next year, when U.S. attorney for Manhattan Geoffrey Berman was fired, the Times had clearly learned nothing from these earlier episodes. Once again its reporters adopted Bill Barr’s framing that it was Trump who had personally ordered Berman’s firing — which Trump immediately denied. Other national newspapers, having learned from their earlier scars not to unreservedly believe Barr’s interpretation of any event, were far more circumspect about who had actually carried it out:

And even days after the 2020 presidential election had ended, as Republicans’ lies and conspiracy theories about voter fraud began to percolate, the Times once again framed Bill Barr’s complicity with Trump’s attempted coup as standard political business: “Barr Hands Prosecutors the Authority to Investigate Voter Fraud Claims,” its headline read.

And thus reading Sulzberger’s essay in the context of this blizzard of bad reporting from the newspaper he oversees is a whiplash-inducing experience. He admirably acknowledges the dangers of both-sidesism (although he misses the mark on its severity today) — and in so doing is more persuasive than most members of the Times’ masthead, who prefer to pretend the phenomenon doesn’t exist. He writes powerfully about journalism’s imperative to cover uncomfortable and politically awkward stories. He defends original reporting and rigorous fact-checking, and he believes in having “an open mind, but not so open that your brains fall out.”

You could be forgiven for wondering, therefore, whether the cognitive dissonance between the brand of journalism he praises and the daily reality of the newspaper he publishes would be enough to make A.G. Sulzberger want to cancel his subscription. But given the yawning chasm between the two models, it’s not clear he has one to cancel in the first place.

Its development is “as fundamental as the creation of the microprocessor, the personal computer, the Internet, and the mobile phone. It will change the way people work, learn, travel, get health care, and communicate with each other. Entire industries will reorient around it. Businesses will distinguish themselves by how well they use it.”

The blockchain? Ha! Of course not. This is Bill Gates writing about artificial intelligence (AI).

AI hype has exploded over the past 6-12 months. But even within that context, the last few weeks have been extraordinary, with new releases from OpenAI (GPT-4), Google (Bard), Meta (LLaMA), Baidu (Ernie), Midjourney (V5), and Stanford (Alpaca), to name a few.1

For roughly the past month, I’ve been diving into AI and machine learning tutorials, YouTube videos, and code libraries (e.g. PyTorch, Fast.ai, etc.) to get a more hands-on feel for how neural networks work and how to fine-tune existing models for my own use cases. These experiences have left me with a few conclusions and predictions, and even more questions, about AI.

In no particular order:

AI is the revolutionary technology that crypto tried and failed to be

If you’ve had a couple glasses of wine and squint sort of hazily into the middle distance, the media hype cycle for artificial intelligence bears a vague resemblance to the cycle for cryptocurrencies and the blockchain. Because of this superficial similarity in the public reaction to these two technologies, I’ve already seen a number of people wondering aloud whether AI is just crypto’s successor as the next technology trend to burn brightly before withering away.

I’ll spare you the suspense: it’s not. I have no special insight into whether and when news organizations will tire of writing endless stories on AI2, but the underlying technology is here to stay.

The problem with cryptocurrencies, which are now nearly fifteen years old, has always been their glaring lack of real-world, legal use cases.3 AI, if anything, suffers from the converse scenario: artificial intelligence products have been proposed, prototyped, or even released in just about every fathomable domain and industry: detecting breast cancer, creating pharmaceutical candidates, chatting with humans, generating imagery, streamlining agricultural productivity, teaching languages, writing code, reviewing legal contracts, transcribing and translating audio, and tracking a Chinese spy balloon, to name just a few.

Akin to the birth of the Internet, the pace of AI innovation today is overwhelming: on a seemingly daily basis, new technologies and implementations are released that would have been incomprehensible mere months ago. For example, Stanford’s Alpaca large language model (LLM) can be run, albeit slowly, on a Pixel 6 smartphone and even a Raspberry Pi.

You’ll notice you don’t hear a lot of AI entrepreneurs tweeting “the technology’s still early!” all the time. It’s because they don’t have to.

Even so, many of today’s most popular AI products are effectively loss leaders

As much fun as it is to imagine French president Emmanuel Macron clashing with Parisian police over his own retirement age law (here’s another good one), this never happened: it was created using generative text-to-image AI tools like Stable Diffusion, DALL-E, or Midjourney.

Each of these products works relatively similarly: you type a prompt into a text box, e.g. “Emmanuel Macron pushing heavily armed riot police with smoke in the background.” And after thirty seconds or so of processing, the resulting image (or several of them) are displayed on your screen, as if by magic.

You can even make this:

This is all very cool. But it’s not clear how useful it will turn out to be.

There’s been much understandable handwringing about the effect that these (mostly) free services will have on the livelihood of genuine artists. (Don’t worry too much yet, though: just try reading the text on those cops’ hats.) And certainly on the margins, it’s easy to see how AI-generated images could damage the market for higher-brow artistic endeavors.

But a good chunk of today’s most popular AI products are more like sandboxes or (particularly impressive) demos than they are fully-fledged solutions to real-world problems. In fact, probably a better example of this than AI-created images is the product that kicked off all this craze in the first place: ChatGPT.

Microsoft, which has invested billions in ChatGPT’s creator, OpenAI, quickly incorporated the technology into its Bing search engine, where you can now chat with it, ask questions, and look up information. Incorporating an LLM-powered chatbot into search has ignited a ferocious race between Microsoft and Google, which just yesterday opened its counterpart to ChatGPT, called Bard, to the general public.

Again, very cool. But also, why?

No matter which way you look at it, Google’s top searches are overwhelmingly simple, usually two- to three-word terms, many of which signal a clear desire to find a very specific piece of information or web page:

For most of these searches, using full-sentence chat to retrieve your answer would be significantly slower than just typing in a quick search term the old-fashioned way. And that’s to say nothing of the difference in cost, as OpenAI CEO Sam Altman acknowledged:

And this cost problem isn’t unique to OpenAI, but is actually common to virtually any large-scale application of GPU-intensive tasks. Alphabet chairman John Hennessy estimated that using chat would “likely cost 10 times more than a standard keyword search, though fine-tuning will help reduce the expense quickly.” (This last part is important, as costs on all facets of AI are likely to decline quickly as new efficiencies are found.)

It’s understandable why Microsoft might want to use its tiny search market share to force Google into ballooning its search costs in a bid to keep up with emerging technology. But it’s not as obvious (to me, anyway) how chat-enabled search will be superior to its legacy counterpart for the vast majority of user searches.

As with prior innovations, AI will often be mindlessly stapled onto legacy formats where it doesn’t make sense

If you watch shows on your computer — via Hulu or the ad-supported version of Netflix, for example — you may have noticed that the video ads are often exact duplicates of ones that appear on linear TV. Similarly, even today, decades after the birth of the Internet, most major newspapers’ web sites look and feel remarkably similar to their physical paper counterparts.4

This is because the easiest, lowest-effort way to implement new technology is usually to simply drag-and-drop it into a preexisting workflow or process. Frustratingly, this is often done with little to no thought expended on the applicability or usefulness of a new technology to a legacy format, which is why once-pioneering innovations — like Internet advertising — can very quickly feel stale and uninspired.

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These failures of imagination happen in a variety of places, but perhaps nowhere is the stubborn refusal to learn from mistakes more endemic than in national news and journalism.

I already mentioned the static display of many of today’s digital newspapers. But this is hardly the only area where journalism leadership has stumbled into unforced errors centered on technology.5 I have previously covered the industry’s Charlie-Brown-and-the-football relationship with Facebook, in which news orgs fell for every conceivable pitch (most notably the “pivot to video”), no matter how foolhardy:

networked

Video was never the answer

Disclaimer: Below I discuss the pivot to video, Facebook, and the controversy around its video view measurement miscalculations. During the period in question, my employer (and I, in my capacity as an employee) worked directly with Facebook on display and video advertising measurement…

Read more

6 years ago · Jay Pinho

And they’re doing it again right now with AI. CNET was caught (very quietly) using artificial intelligence to help it write news stories, which resulted in 41 of the 77 involved articles requiring at least one correction. And last month, German publisher (and Politico owner) Axel Springer’s CEO Mathias Doepfner said that “artificial intelligence has the potential to make independent journalism better than it ever was – or simply replace it.”

Does it, though? I would posit that if your journalism can be replaced by a large language model, you were contributing negligible value in the first place.6 Doepfner's statement appears to be yet another case of confusing one of its news organization's revenue streams (an SEO-powered ad sales business) for its putative objective: educating readers.

This short-termism will happen over and over again in many industries, especially now that more companies with foundational AI models are creating API platforms that will enable other businesses to build on top of the same core technology. Christina Melas-Kyriazi of Bain Capital Ventures put it best: “If ChatGPT is the iPhone, we’re seeing a lot of calculator apps. We’re looking for Uber.”

Chaining LLMs together with other services and APIs seems like a high-potential area

One of the the things I’ve found ChatGPT most useful for — other than helping me write Python, which still feels as close to actual magic as anything I’ve ever experienced in technology — is in explaining things to me in a way I can understand, mainly because I can ask endless follow-up questions. For example, while taking tutorials on neural networks I often come across a technical concept I’m not sure I fully grasp, so I ask ChatGPT about it and keep querying for specific details until I feel I’ve more or less understood it.

The obvious problem here is that ChatGPT or Bard may be hallucinating an incorrect answer and has therefore misinformed me as well. Making matters worse, these hallucinations may be deliberately instigated by external actors:

One possible solution to this problem is to use LLMs as one piece of a more complex toolchain. Meta AI researchers submitted an academic paper last month titled “Toolformer: Language Models Can Teach Themselves to Use Tools,” which introduced…

…a model trained to decide which APIs to call, when to call them, what arguments to pass, and how to best incorporate the results into future token prediction. This is done in a self-supervised way, requiring nothing more than a handful of demonstrations for each API. We incorporate a range of tools, including a calculator, a Q&A system, a search engine, a translation system, and a calendar. Toolformer achieves substantially improved zero-shot performance across a variety of downstream tasks, often competitive with much larger models, without sacrificing its core language modeling abilities.

Similarly, LangChain uses a concept called “agents” to perform much the same type of functionality, joining the semantic power of an LLM with the specialized and/or up-to-date information available via external services. And a startup called Adept is building an “AI teammate” that can interpret your plain-English commands and turn them into actions in your browser tabs, like creating P&L columns in Google Sheets or adding a new contact in Salesforce.

This is speculative on my part, but combinatory tools such as these seem likely to have the highest real-world impact. Large language models are almost definitionally out of date the moment they’ve completed training. And yet it’s prohibitively expensive and time-consuming — or perhaps not even technically possible on today’s hardware — to keep training new iterations of the same model on a daily or hourly basis to keep up with everything new that’s transpired since. So the combined power of semantic understanding and specialized, updated data will enable AI to look and feel like a very smart human colleague, rather than a quirky chatbot awaking from a two-year coma.

The open vs. closed ecosystems debate is relevant yet again, with high stakes

OpenAI started out in 2015 as a nonprofit research organization, whose mission statement explicitly declared it “unconstrained by a need to generate financial return” and whose introduction blog post promised that “researchers will be strongly encouraged to publish their work, whether as papers, blog posts, or code, and our patents (if any) will be shared with the world.”

Over time, however, it shifted focus, swapping its non-profit classification in 2019 for a “capped-profit” one. It’s also accepted billions of dollars of external investment funds from Microsoft. Meanwhile, GPT-3 source code has not been publicly released, and, per The Verge, information surrounding the brand-new GPT-4 model is, if anything, even murkier:

OpenAI has shared plenty of benchmark and test results for GPT-4, as well as some intriguing demos, but has offered essentially no information on the data used to train the system, its energy costs, or the specific hardware or methods used to create it…

…Speaking to The Verge in an interview, Ilya Sutskever, OpenAI’s chief scientist and co-founder, expanded on this point. Sutskever said OpenAI’s reasons for not sharing more information about GPT-4 — fear of competition and fears over safety — were “self evident”:

“On the competitive landscape front — it’s competitive out there,” said Sutskever. “GPT-4 is not easy to develop. It took pretty much all of OpenAI working together for a very long time to produce this thing. And there are many many companies who want to do the same thing, so from a competitive side, you can see this as a maturation of the field.”

“On the safety side, I would say that the safety side is not yet as salient a reason as the competitive side. But it’s going to change, and it’s basically as follows. These models are very potent and they’re becoming more and more potent. At some point it will be quite easy, if one wanted, to cause a great deal of harm with those models. And as the capabilities get higher it makes sense that you don’t want want to disclose them.”…

…When asked why OpenAI changed its approach to sharing its research, Sutskever replied simply, “We were wrong. Flat out, we were wrong. If you believe, as we do, that at some point, AI — AGI — is going to be extremely, unbelievably potent, then it just does not make sense to open-source. It is a bad idea... I fully expect that in a few years it’s going to be completely obvious to everyone that open-sourcing AI is just not wise.”

Google’s Bard follows much the same pattern. Meanwhile, Meta AI initially granted access to its LLaMA models on a case-by-case basis to practitioners in specific fields such as academia and civil society, but the models quickly leaked online anyway. Stanford’s Alpaca models (which were themselves fine-tuned on LLaMA) are fully available.

What does all of this mean for the future of products built atop large language models? And does the mere presence of open-source, freely available LLMs make large-scale digital disinformation and data pollution inevitable?

Instinctually, I’m relieved that the tech giants with the deepest pockets and the most access to state-of-the-art GPUs don’t yet have a monopoly on generative AI technology. But the potential negative implications of freely available, low-cost, endlessly customizable content at scale — in text, image, and even video form — are enormous. For example, it doesn’t particularly strain the imagination to picture a 2024 American election season rife with made-up video footage, astroturfed social media campaigns, and artificially generated photos of politicians in compromising situations. Nightmare scenarios like these are almost enough to make you long for the impotence of crypto.

Today I released the Tether Insolvency Calculator at tethertransparency.com:

This is a project I’ve been working on over the past month or so. As a bit of background, Tether is a $66 billion cryptocurrency stablecoin which I’ve previously half-jokingly described as “either worth precisely $1.00, or $0.00 plus prison sentences for Tether’s entire C-suite.”

The point of stablecoins is right there in the name: they’re meant to be perfectly stable — that is, pegged 1-to-1 — relative to major fiat currencies, most prominently the U.S. dollar. In crypto, this is a valuable property for a token to have, because it enables margin lending and borrowing, provides a safe haven in times of market volatility, and reduces effective settlement times.

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Of course, for a stablecoin to be truly pegged to a fiat currency, one has to be absolutely certain the stablecoin is backed by sufficient assets such that, if everyone demanded to redeem their Tether tokens for dollars at once, the funds would actually be there.

This has been a longstanding problem for Tether, which has been fined by both the CFTC and the New York attorney general precisely on this point. Per the CFTC:

As found in the order, Tether held sufficient fiat reserves in its accounts to back USDT tether tokens in circulation for only 27.6% of the days in a 26-month sample time period from 2016 through 2018.

And the NY AG:

The OAG’s investigation found that, starting no later than mid-2017, Tether had no access to banking, anywhere in the world, and so for periods of time held no reserves to back tethers in circulation at the rate of one dollar for every tether, contrary to its representations. In the face of persistent questions about whether the company actually held sufficient funds, Tether published a self-proclaimed ‘verification’ of its cash reserves, in 2017, that it characterized as “a good faith effort on our behalf to provide an interim analysis of our cash position.” In reality, however, the cash ostensibly backing tethers had only been placed in Tether’s account as of the very morning of the company’s ‘verification.’

On November 1, 2018, Tether publicized another self-proclaimed ‘verification’ of its cash reserve; this time at Deltec Bank & Trust Ltd. of the Bahamas. The announcement linked to a letter dated November 1, 2018, which stated that tethers were fully backed by cash, at one dollar for every one tether. However, the very next day, on November 2, 2018, Tether began to transfer funds out of its account, ultimately moving hundreds of millions of dollars from Tether’s bank accounts to Bitfinex’s accounts. And so, as of November 2, 2018 — one day after their latest ‘verification’ — tethers were again no longer backed one-to-one by U.S. dollars in a Tether bank account.

Due to the damning facts in the above settlements (resulting in nearly $60 million in fines to Tether’s parent company), as well as many other issues including Tether’s longstanding inability to complete an audit, skeptics have long considered Tether’s asset backing suspect at best and smoke-and-mirrors at worst.

It’s not just Tether’s individual investments that prompt head-scratching, but also their eerily static ‘shareholder capital cushion’ (assets minus liabilities):

What’s so strange about this is that, on a daily basis, both Tether’s assets and its liabilities change pretty frequently, and sometimes by significant amounts. Yet the difference between the two — the net assets or ‘shareholder capital cushion’ value shown above — is almost completely unchanging except for dates on which they release quarterly attestations (seen in the annotated vertical lines).

On those dates, the cushion changes abruptly overnight, almost as if it is being manually altered to match the values reported in the attestations.

All of which leaves a deeply unsatisfying status quo: if Tether’s self-reporting and quarterly attestations can’t be believed, how will we ever determine whether they’re insolvent?

This is where my Tether Insolvency Calculator fits in. The financial writer Patrick McKenzie has blogged extensively about Tether and has hit upon a fundamental insight that leads to a more straightforward way of evaluating Tether’s solvency. Rather than attempting to determine the truthfulness of any individual quarterly attestation, we can simply accept it at face value and, given its reported asset allocation, form reasonably solid conclusions about Tether’s solvency using Tether’s own self-reported data:

As McKenzie wrote in November:

The Consolidated Reserves Report alleges that Tether’s reserves included, as of September, $2,617,267,750 of “Other Investments (including digital tokens)” and $6,135,946,415 of Secured Loans.

These assets must have been impaired in the last six weeks. If they are not, Tether would have a legitimate claim to being the best risk managers. Not in crypto, no, in the history of the human race.

The risk-on assets are 12.86% of the reserves, via simple division. Their equity is 0.36% of assets. (No, I did not forget to multiply by a hundred when converting to percent.)

If their risk-on assets were impaired by more than about 2.86%, Tether must have (by their own numbers) needed recapitalization. Again.

It is not credible that they suffered no serious impairment. Bitcoin, the blue chip cryptocurrency, is down by more than 10%. I challenge anyone to find a portfolio construction for digital token investments that possibly survived this environment with only a 2.86% impairment.

In other words, even assuming Tether’s own recent attestation was accurate, they almost certainly slipped into insolvency (unless they obtained additional funding) due to the market performance of the assets they claimed to own.

Reading these pieces made me wish there were a live, interactive way to track the market performance of a hypothetical Tether portfolio over time. So I created it.

The Tether Insolvency Calculator is a good-faith attempt at reconstructing a hypothetical Tether asset portfolio based on their own attestations and statements, as well as public reporting on their investments. Of course, as I note on the site:

Constructing a hypothetical portfolio necessarily entails making some assumptions and arbitrary decisions and is, therefore, subject to potential errors of judgement (not to mention of calculation). Disclaimer: These errors may be significant.

(If you see any errors, please let me know!)

Because building this portfolio in the absence of more detailed asset reporting leaves much room for interpretation, the calculator allows users to modify the asset allocation to any other proportion they deem superior, and the chart automatically updates with new insolvency numbers. For example, here’s what Tether’s portfolio would look like if they’d invested entirely in Bitcoin since their most recent attestation on September 30th:

And here’s what it would look like if Tether were all-in on 4-week Treasuries instead:

Once you choose a different allocation, the Tweet and Share buttons allow you to post your version on social media as well.

Check out the site and let me know what you think!

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(Note: Within a day of this post being published, Twitter’s chief information security officer, chief privacy officer, chief compliance officer, and head of trust & safety all left the company.)

Yesterday afternoon (ET), Elon Musk hosted a Twitter Space, moderated by Twitter ad sales chief Robin Wheeler.1 The goal was to allay advertisers’ mounting concerns about Twitter’s direction under its new (ahem, mercurial) founder and his merry band of sycophants advisers.

Much digital ink has been spilled about the exodus of brands from Twitter since Musk took over the platform. Corporate giants General Mills, Pfizer, Audi, and others have all fled Twitter ads (at least temporarily) while they await more clarity on the company’s future. Ad agencies were no less twitchy:

Nearly all of this business anxiety can be directly traced to Elon Musk’s own actions. He:

• repeatedly implied that Twitter unjustifiably censored right-wing discourse and said he would reinstate Donald Trump

• denounced permanent user bans as “fundamentally undermin[ing] trust in Twitter,” before U-turning and announcing a new permanent user suspension policy for impersonation (only days after saying “no major content decisions…will happen” before a new moderation council could convene, which hadn’t yet happened)

• stated that he has a legal case to sue activist groups — whom he accused of “trying to destroy free speech in America” — for convincing advertisers to leave Twitter, issued a bizarre poll asking whether advertisers should support “freedom of speech” vs. “political ‘correctness’”, and then promised “a thermonuclear name & shame” of the advertisers themselves “if this [boycott] continues”

• tweeted (and then deleted) a loonie conspiracy theory about House Speaker Nancy Pelosi’s husband, engaged in spats with Democratic politicians, and encouraged his 115 million followers to vote Republican in the midterms2

On top of all this, he’s made what the British might call “a dog’s dinner” out of his newly announced Twitter Blue policy. To take a step back on this one, Twitter verification (the notorious blue checkmark) has long been a mess3, as Twitter’s own Head of Trust & Safety half-acknowledged:

But whatever it was before, it’s an order of magnitude worse and more confusing under Musk’s new $8-per-month Twitter Blue plan. He’s alternately presented this plan as:

A) light extortion: if you don’t pay the monthly $8, your tweets will be banished to the equivalent of the “probable spam” folder

B) a premium tier unlocking longform audio/video and fewer ads

C) a way to eliminate bots

He’s done all this while simultaneously allowing anyone to pay the $8 to get the blue checkmark, regardless of who they are and without undergoing a verification check. Per the Twitter Blue FAQs:

Accounts that receive the blue checkmark as part of a Twitter Blue subscription will not undergo review to confirm that they meet the active, notable and authentic criteria that was used in the previous process.

Which all sounds very weird, but at least corresponds to Yoel Roth’s idea of “getting rid of” notability labels. But just hours after Roth’s tweet, his colleague Esther Crawford announced that, instead of “getting rid” of notability, Twitter would just package verification into a separate label on some users’ profiles:

A feature that Elon summarily “killed” via tweet the next day, less than two hours before his Twitter Spaces event:

Prompting a very Trump-ian “Dear Leader” walkback from his team:

While all of this was happening, users were immediately pointing out countless examples of the obviously harmful consequences of allowing just anyone to pay $8 to get a blue checkmark that many people already associate with humanness / authenticity:

Worse yet, the Twitter Spaces call itself was not confidence-inspiring: one marketer told Digiday that “hearing the ineptitude live is shocking.” This was, perhaps, a reaction to a barrage of wobbly Musk statements like this one:

Robin Wheeler: Actually, you tweeted earlier today something about there's going to be dumb things coming in months. I assume that is... Well, because you're moving so fast, right?

Elon Musk: Obviously, the intent is not to do dumb things. We're not aspirationally dumb. We're aspirationally not dumb. But despite being aspirationally not dumb, we will still do dumb things.

Indeed, as Digiday further pointed out, he already has done dumb things:

Musk also welcomed feedback from advertisers and invited them to reply to his tweets if there’s something they don’t like. However, he’s already blocked some that have tried. Last week, he temporarily blocked Lou Paskalis, president and chief operating officer of the Mobile Marketing Association, after the prominent marketer tweeted him asking about brand safety concerns related to the content moderation team’s layoffs.

The backdrop against which this drama is playing out is that Twitter was already a sort of fringe ad platform for most brands, even before Elon Musk bought it. In terms of ad spend, there’s Facebook (~$115 billion in ad revenue last year), Google (~$150 billion), Amazon ($31 billion), and then everyone else.

Twitter is in the “everyone else” bucket, with about $5 billion of ad revenue last year. As one observer put it: “For the majority of agencies, Twitter is not a media plan necessity.” Or to borrow Columbia Business School professor Len Sherman’s pithier formulation: “In my humble opinion, to use a very technical term, their business sucks, and they need a radical transformation.”

Martin Sorrell, who founded (and, until a few years ago, ran) WPP, the largest advertising group in the world, was similarly blunt: “Has the uncertainty around Twitter caused advertisers to question whether they should be advertising? Yes, I think you'll see that in their numbers. But is that of material importance [to them]? No.”

As Sorrell’s righthand man, ex-WPP Chief Digital Officer Rob Norman, put it in a recent New York Times op-ed:

Unfortunately for Mr. Musk, Twitter is a bit player in comparison with other advertising-supported media platforms…Twitter lacks the mission-critical status of its competitors, and few advertisers would be significantly affected by a complete withdrawal.

Perhaps the biggest problem Mr. Musk faces with Twitter is Mr. Musk.

In short, Twitter desperately needs advertisers. But they don’t need Twitter much at all.

People who don’t understand this, like serial Twitter philosopher-kings Paul Graham and Balaji Srinivasan, see conspiracies around every corner, but the truth is far more banal: right now, Twitter just isn’t worth the headache for many brands.

Making matters worse is that the people widely seen as the “adults in the room” at Twitter have either resigned, been fired, or are starting to channel the strained comms strategy of a hostage undergoing a forced confession.

Take, for example, Yoel Roth’s claim yesterday that, contrary to popular perception of a spike in hateful language on Twitter in recent weeks, Search has actually seen a 95% drop in slurs:

This claim presents brands with a rather disquieting dilemma: either Yoel Roth is somehow gaming the metrics to achieve this abrupt (nearly overnight) 95% reduction in impressions of hateful tweets, or Twitter’s Head of Trust & Safety was entirely out to lunch beforehand. Neither option is a particularly appealing one if you’re deciding where to allocate millions of dollars of advertising budget.

I know this from painful firsthand experience: I spent the past four years or so leading Oracle Advertising’s brand safety products. In the wake of George Floyd’s murder and the resulting Black Lives Matter protests in 2020, much of my time was spent brainstorming ways to persuade terrified advertisers and agencies that running ads on major national outlets like The New York Times and The Wall Street Journal was brand-safe, relevant, and wouldn’t result in any significant customer pushback despite the traumatizing nature of the news itself.

As anyone who has worked in the brand safety or suitability space knows well, most large brands and agencies are extremely risk-averse. In some cases this is for very good reasons, as The Intercept’s Jon Schwarz wittily explained: “Proctor & Gamble can’t allow its ads for Charmin, targeted at the Upscale Panera Mom micro-demographic, to appear below frothing diatribes about annihilating all Muslims.”

But in other cases brands’ timidity is more knee-jerk and less considered. When presented in the abstract with the notion of supporting civic institutions like the press as they cover important racial justice issues, brands and agencies are generally all aboard. But when push comes to shove, many of them would rather compile a slapdash custom keyword block list that prevents their ads from showing up on some of the most trusted sites in the country than to run any risk of their CMO calling them up to ask why their ad appeared next to content about police brutality or mass protests.

Leave aside for a moment the fact that — as I have covered ad nauseum in this very newsletter (see here, here, and here) — brand safety itself is a nebulous concept whose usefulness is very much in question. And, indeed leave aside the fact that Twitter’s own research found that “divisive content” shown adjacent to advertising has no material impact on end users:

Results showed no evidence of any effect on brand favorability when ads were adjacent to any of the studied categories of divisive content, regardless of the distance of the adjacency.

Leave aside, moreover, the fact that Twitter’s text-heavy format renders it far more easily parse-able by automated brand safety and contextualization technology than its image- and video-based peers TikTok, Instagram, Snapchat, et al.

None of this changes the underlying calculation for brands. They are already worried about the volatility of user-generated content in general. They are especially worried about this content on Twitter, where the links between follower and followed are far weaker than on Facebook and Instagram; the usernames are frequently pseudonymous, rendering abuse less costly for the abuser; and the tone is often combative and vitriolic. (See “getting ratioed” for more on this.)

Twitter is a calamitous petri dish of snark and oneupmanship in search of a business model catering to the genteel business class. This chaos frequently makes the platform an intoxicating destination for its users4, perhaps performing a similar role that public executions once did for under-stimulated city dwellers. But Elon Musk, who famously penned an open letter to advertisers vowing that Twitter would not become “a free-for-all hellscape” on his watch, is quickly discovering that just because he spent $44 billion on his perfect shitstorm of a platform doesn’t mean Charmin will pay him a penny to sell its toilet paper there.

(Note: Brett Harrison has since deleted *all* of the misleading tweets I referenced below.)

On Thursday, the Federal Deposit Insurance Corporation (FDIC) sent a cease-and-desist letter to Brett Harrison, the president of the US arm of crypto exchange FTX, one of the largest in the world. The letter noted that Harrison had falsely tweeted that FTX US’s customer deposits were FDIC-insured. They were not.

As the FDIC letter noted:

In fact, FTX US is not FDIC-insured, the FDIC does not insure any brokerage accounts, and FDIC insurance does not cover stocks or cryptocurrency. The FDIC only insures deposits held in insured banks and savings associations (insured institutions), and FDIC insurance only protects against losses caused by the failure of insured institutions. Accordingly, these statements are likely to mislead, and potentially harm consumers….

You shall immediately remove any and all statements, representations, or references that suggest in any way, explicitly or implicitly, that: (a) FTX US is FDIC-insured; (b) any FTX US brokerage accounts are FDIC-insured; (c) any funds held in cryptocurrency or other financial products…are or can be protected by FDIC insurance; or (d) FDIC insurance provides protection or coverage in any manner or extent other than those set forth in the FDI Act…

Harrison explained himself on Twitter the next day:

Sam Bankman-Fried, the founder and CEO of FTX, followed up as well:

But neither of their tweets is true.

Harrison’s original tweet, the one the FDIC told him to delete, read:

This tweet is about as clear as it gets. It is virtually impossible to read it and conclude, as Harrison later described, that “we didn’t suggest that FTX US itself…benefit from FDIC insurance.” Indeed, the tweet very explicitly states that FDIC insurance applies to customers’ direct deposits on FTX US. That is, of course, false.

On the very same day, Harrison went even further:

This is also false, but even bolder: Harrison not only claimed the individual customer (as opposed to FTX itself) is the beneficiary of FDIC insurance, but also implied that the FTX T&Cs — which correctly noted that individual FTX customer accounts were not FDIC-insured — got this wrong. (They didn’t, and he did. Those unchanged T&Cs are still in effect today.)

It would be one thing — albeit a pretty staggering level of incompetence in its own right — if the president of FTX US made an honest mistake and somehow failed to understand that the financial entity he runs wasn’t FDIC-insured.

But that doesn’t appear to be the case, because he also tweeted about FDIC insurance a week later, in another tweet that (for whatever reason) the FDIC did not explicitly mention in its letter, and which is still live online:

It is in this thread that you can glimpse just how sordid and unethical FTX’s public communications are. Harrison tweets about a new product release, FTX Stocks, and is immediately asked by a user (@ath33) whether the product is FDIC-insured.

Keep in mind that there is only one possible way to reasonably interpret the user’s question: (s)he’s obviously asking whether FTX US customers’ funds are FDIC-insured. That is, if something were to happen to FTX US, would the FDIC reimburse users of the new FTX Stocks product for their cash holdings?

But Harrison responds with a deliberately misleading bait-and-switch: he writes that “cash associated with brokerage accounts is managed into FDIC-insured accounts at our partner bank.”

This is irrelevant. FDIC insurance exists to protect customers of a bank from the collapse of that bank. As the FDIC cease-and-desist letter states, “FDIC insurance only protects against losses caused by the failure of insured institutions.” That is, if I deposit up to $250,000 of my own money in an FDIC-insured bank, and that bank then goes bankrupt, I am entitled to receive my entire deposit back.

This is what the Twitter user was asking about. But Harrison’s response subtly conflated FTX US customers with FTX US itself. His tweet noted that cash held by FTX US on behalf of its customers was itself deposited in an FDIC-insured bank. That is, FTX US as a company is the customer of an FDIC-insured bank. But this has absolutely no bearing on the person’s question: all this means is that FTX US is entitled to FDIC reimbursement in the event that its own partner bank goes bankrupt.

Of course, this insurance does not apply to FTX US itself going under. (Imagine if the FDIC, or its member banks, were on the hook for reimbursing each of its customers’ customers.) It only comes into play if FTX US’s partner bank goes bankrupt. And anyone, most of all Harrison, would understand that the user was obviously asking about FTX US’s insurance, not its partner bank’s.

In other words, Harrison replied to a very clear query about whether FTX US users’ cash was protected from the failure of FTX US by answering a different question entirely, but in a way designed to look like he was responding to the original one. And in so doing, it becomes difficult to escape the conclusion that he does understand the difference between FTX US’s deposits in a partner bank being FDIC-insured (which they are) vs. an individual FTX US customer’s deposits being FDIC-insured (which they aren’t).

Who cares?

Or, why should a seemingly esoteric Twitter exchange on FDIC insurance matter to anyone?

First, it’s important to remember how the FDIC came about. It was established in 1933, in the depths of the Great Depression, following a slew of bank failures that had devastated ordinary citizens across the country. Bank runs (like the one pictured in the photo at the top of this post) were distressingly common: a rumor would spread that a bank was in trouble or going insolvent, and desperate crowds would form outside the bank doors as depositors rushed to withdraw their money before the bank ran out of cash.

This was a bad system, and the advent of FDIC insurance largely fixed it by insuring customers’ deposits at insured banks for up to a set amount of money (which is now $250,000). This government backing effectively ended bank runs for normal customer banking accounts, and the FDIC logo on a bank web site is akin to a stamp of approval declaring “Your funds are safe here.”

So when crypto companies mount a deliberate communications strategy of conflating their own funds at FDIC-insured banks with their customers’ non-FDIC-insured funds on their platforms, they are effectively committing fraud1 by appropriating the patina of security associated with FDIC insurance to a scenario where it doesn't apply. And the consequences of this scam -- end users thinking their funds are safe and risk-free when they very much aren't -- can be enormous.

In fact, this wasn’t the first time in recent days that the FDIC has had to issue a cease-and-desist notice to a crypto company over false or misleading claims. Late last month, the FDIC and the Federal Reserve Board issued a joint statement “demanding that the crypto brokerage firm Voyager Digital cease and desist from making false and misleading statements regarding its FDIC deposit insurance status and take immediate action to correct any such prior statements.”

Here is how Voyager had described FDIC insurance on its web site’s home page:

And on its Twitter feed:

And on its Medium blog:

These lies had very real consequences. When Voyager filed for Chapter 11 bankruptcy, scores of its customers falsely believed their funds were insured when they in fact were not:

One customer wrote to the judge in Voyager’s bankruptcy case (per Yahoo! Finance): “I have been in shock since Voyager halted withdrawals. It is as if your bank is no longer allowing you to withdraw from your savings accounts. How would you feel? Would you not feel betrayed?”

In fact, Voyager’s incorrect FDIC language caused so many of its customers to reach out to its partner bank (Metropolitan Commercial Bank) asking about FDIC insurance2 that the bank had to issue a public notice on its web site explaining that its FDIC insurance didn’t cover lost funds due to Voyager’s failure:

Metropolitan Commercial Bank is a New York-chartered bank and member of the Federal Deposit Insurance Corporation.  Voyager customer funds held by Metropolitan Commercial Bank are insured by the FDIC up to the maximum amount of coverage per depositor under federal law. The standard FDIC insurance coverage amount is currently $250,000 per depositor for each account ownership category.

FDIC insurance coverage is available only to protect against the failure of Metropolitan Commercial Bank.

FDIC insurance does not protect against the failure of Voyager, any act or omission of Voyager or its employees, or the loss in value of cryptocurrency or other assets.

The crypto world is awash in these sorts of misrepresentations and scams. I have previously covered FTX’s sketchy handling of Tether on its platform: treating it as safe enough to list on FTX and defending its integrity on podcasts, etc., but not trusting it enough to promise 1:1 dollar redemptions for FTX customers:

networked

If Tether falls, FTX and Coinbase will let their users pay the price

If you’ve been following cryptocurrencies for awhile, you may have heard that the market is down: Above is the list of top 10 cryptocurrencies by “market capitalization.” The only three coins in that list whose price hasn’t substantially declined over the past 30 days are Tether (USDT), USD Coin (USDC), and Binance USD (BUSD…

Read more

4 years ago · 9 likes · Jay Pinho

But other examples abound. Do Kwon built a so-called algorithmic stablecoin (also listed on FTX) that was supposedly pegged to the U.S. dollar but instead collapsed spectacularly and cost its users $40 billion in direct value.3 Celsius famously urged its users to "unbank yourself"4 and repeatedly assured them that their deposits were safe and low-risk before filing for bankruptcy last month, upending countless customers’ lives:

It’s tempting to consider these bad actors as outliers, but this is an increasingly difficult proposition when the president of one of the world’s largest crypto exchanges persistently misleads his own customers and then, when ordered to stop by the FDIC, lies about what he has written.

I don’t want to pick on Nat. In fact, I have called his prior crypto writing “essential reading,” precisely because he has been instrumental in helping me understand crypto better by writing about key elements of it in admirably clear terms.

But his latest piece here is a good example of the sort of magical thinking that pervades the crypto ecosystem. I’m using his piece as an example because Nat is a smart guy and cannot be reasonably painted as someone who doesn’t understand crypto or hasn’t thought much about how it all works.

Indeed, one of the strangest aspects of crypto is the seemingly endless ability of many intelligent participants — and Nat is just one example among many here — to confidently state things that are straightforwardly incorrect and/or trivial to rebut with about 10 seconds of googling. Unfortunately, this piece is no exception.

The below commentary will be easier to follow if you read his post first.

Claim #1: “Crypto is EARLY. Painfully early.”

I’ve covered this in detail previously, so I won’t do it all over again here, but this claim is just empirically false. Satoshi Nakamoto’s Bitcoin whitepaper was published in 2008. Even if you’re being extremely generous and allow boosters to define the crypto era as beginning only with the release of the Ethereum whitepaper in 2014, that still means we’re closing in on a decade since the birth of the ecosystem, with nary a mainstream use case to show for it other than drugs and ransomware.

Weirdly, Eliason favorably compares the evolution of the crypto space to the mobile revolution. But as I have written previously, the pace of innovation on mobile — and specifically the explosion of apps following the debut of the iOS App Store in the same year as the Bitcoin whitepaper — puts crypto to shame.

Large swathes of activity in modern life — calling an Uber, booking a trip on Airbnb, using Google Maps to get around a city, messaging friends and family around the world on WhatsApp, listening to podcasts, checking your email, and a nearly infinite array of other tasks you do regularly — are made possible by the App Store ecosystem.

In crypto, on the other hand, you can speculate on shitcoins and make bets in DeFi, and that’s about it. To the extent that the crypto space intersects with the real world, it’s primarily via the endless parade of multimillion dollar hacks — here’s a $200 million one from just yesterday — that immediately get laundered out through mixers like Tornado Cash and then presumably cashed out into fiat currency so it can actually be used in the real world.

The App Store isn’t early. Neither is crypto.

Claim #2: Crypto prevents government censorship and property seizure

Eliason argues that Bitcoin was the first “global, digital, trustless, hard monetary asset” and writes that, while gold ETFs are somewhat similar, “you’re still trusting some other institution to redeem that ETF, and the government or bank could easily seize it or restrict your access to it.”

Along similar lines, he also compares property rights on the blockchain to what exists today:

Domain names can be taken away by ICANN. This blog could get taken down by Substack. Your Twitter account could be taken down. Your Instagram photos could get wiped out.

You can only rent on the pre-blockchain Internet. But with code that can live everywhere forever, without needing a trusted third party to support it, you can own things in a way that’s arguably even better than Title rights in a developed nation. You do still rent your real estate from the government, after all.

Right now, digital ownership is focused on art, pictures of monkeys, and ENS domains. But if you looked at the Lightsaber and Beer-pouring apps on the 2009 iPhone and said, “see, apps are all silly!” then you would have been missing the point. We don’t really know what all will get done with digital property rights, but the fact that we have them for the first time is pretty exciting.

Much of this section is either flat-out wrong or highly misleading.

First (and more related to Claim #1 above), no one would have looked at the 2009 iPhone and concluded that the Lightsaber and Beer-pouring apps demonstrated the device’s lack of utility. The Facebook iOS app was released in 2008! Indeed, among the top iOS apps for 2009 were The Sims 3, Madden NFL 10, and MLB.com At Bat. This is one of those maddening “please just use Google” moments that are so common to the “crypto is still early” crowd.

But another one of the prevailing implicit myths of the cryptocurrency space — and the centerpiece of Claim #2 — is that crypto somehow manages to avoid the external dependencies that dominate the rest of society.

Gold ETFs, as Eliason notes, are subject to government seizure, Twitter and Substack accounts can be banned, and so on.

That part is actually true. But many, many centralized choke points exist in crypto as well.

Signal founder Moxie Marlinspike’s January post “My first impressions of web3” memorably demonstrated two gaping holes in crypto’s decentralized armor. First, because no consumer-facing device — especially a mobile phone — has the digital storage capacity required to operate its own Ethereum node, the vast majority of consumer interactions with Ethereum are intermediated via centralized blockchain API providers like Infura, which query the state of the blockchain on apps’ behalf. “Imagine if every time you interacted with a website in Chrome, your request first went to Google before being routed to the destination and back,” Marlinspike writes. “That’s the situation with ethereum today.”

A separate but related centralization vulnerability applies to nonfungible tokens (NFTs) listed on platforms like OpenSea. Marlinspike created an NFT whose image changed based on the requester’s IP address and/or user agent, as a demonstration of the fact that owning an NFT doesn’t protect you from having the underlying asset changed or even removed at any time. It’s like buying the deed to a property and then showing up to check it out, only to discover that the entire house has been demolished and replaced with a giant 💩 emoji.

Inexplicably, OpenSea took down his NFT, implying (but not stating) he had violated its Terms of Service. But more strangely, the NFT didn’t just disappear from OpenSea: it also disappeared from his non-custodial crypto wallet itself. How is this possible, if NFTs live on the uncensorable blockchain? Well, it turns out MetaMask uses OpenSea’s APIs to find NFTs, so when OpenSea — a standard, centralized company — bans an NFT, that NFT also disappears from non-custodial, theoretically decentralized, and uncensorable wallets like MetaMask.

Marlinspike concludes:

Given the history of why web1 became web2, what seems strange to me about web3 is that technologies like ethereum have been built with many of the same implicit trappings as web1. To make these technologies usable, the space is consolidating around… platforms. Again. People who will run servers for you, and iterate on the new functionality that emerges. Infura, OpenSea, Coinbase, Etherscan.

There are other choke points as well. For example, the two largest stablecoins — USDC and Tether, with a collective capitalization of $120 billion — both maintain address block lists. That is, they can, and do, freeze assets denominated in their respective stablecoin tokens such that they cannot be traded or spent.

More to the point, both Tether and USDC have frozen these funds specifically at the request of law enforcement:

"Tether routinely assists law enforcement in their investigations... Through the freeze address feature, Tether has been able to help users and exchanges to save and recover tens of millions of dollars stolen from them by hackers," said Hoegner.

Tether's development came to light after The Block first reported that CENTRE, which issues the USDC stablecoin, blacklisted an Ethereum address holding 100,000 USDC for the first time.

A CENTRE spokesperson said that the consortium, managed by Circle and Coinbase, blacklisted the address "in response to a request from law enforcement."

This gets at a fundamental and inherently unsolvable weakness of the entire crypto ecosystem, which is that on- and off-ramps to fiat currencies are colossal choke points that governments routinely and effectively exploit to set boundaries on what is and isn’t allowed within the crypto space.

Take Binance, for example. The world’s largest crypto exchange, Binance has long enjoyed a reputation as a sort of regulatory wild child, facilitating billions of dollars of financial crimes, bouncing vaguely between various jurisdictions — its founder, CZ, is still cagey about where its headquarters is located — and acting as if it’s not subject to any particular country’s laws.

And yet late last month, as part of a Coinbase insider trading case, the Securities and Exchange Commission (SEC) fired a warning shot across the bow, listing nine crypto tokens that it considered to be unregistered (and therefore illegally listed) securities on Coinbase. Within days, Binance’s US arm (Binance.US) announced it would de-list the only one of those nine (the AMP token) it supported on its platform, explicitly naming the SEC document as the impetus:

We operate in a rapidly evolving industry and our listing and delisting processes are designed to be responsive to market and regulatory developments. Last week, the Securities and Exchange Commission (SEC) filed securities fraud charges against a former employee of Coinbase, among others. In its suit, the SEC named nine digital assets that it alleges are securities. Of those nine tokens, only Amp (AMP) is listed on the Binance.US platform.

Out of an abundance of caution, we have decided to delist the AMP token from Binance.US, effective August 15, 2022. While trading of AMP may resume at some point in the future on the Binance.US platform, we are taking this step now until more clarity exists around the classification of AMP.

It is important to note that this incredibly powerful governmental leverage applies to every crypto company that allows users to deposit U.S. dollars (or euros, or yen) and/or withdraw to those same currencies, because enabling these features puts those companies directly in the crosshairs of law enforcement and regulation authorities who are tasked with ensuring that Know Your Customer (KYC) and Anti-Money Laundering (AML) checks are being carried out by all financial platforms, whether they’re traditional financial entities like Bank of America and Charles Schwab or crypto exchanges like Coinbase and Binance.US.

And unfortunately for the crypto-is-uncensorable crowd, virtually no one actually wants to keep their financial assets in crypto in the long term. This is because:

• prices are both heavily manipulated and absurdly volatile

• even the “safe” crypto assets — the stablecoin tokens that comprise a large percentage of total crypto holdings — are suspiciously secretive about their finances despite operating a theoretically very simple business model

• you can’t use crypto to do just about anything useful and legal in the real world

Taken altogether, here’s what that means: end users always want a way to get money out of crypto and back into fiat currency. And this desire hands governments all the tools they need to starve the crypto ecosystem at will, by cutting off access to inflows and outflows if participants don’t follow the rules. When a reasonably competent and powerful government cares enough about stopping something from happening, it is very difficult, if not impossible, to continue doing business as usual.2 And this reality continues to take the hand-wavily libertarian crowd of crypto enthusiasts by surprise.

The persistent requirement for fiat on- and off-ramps is why virtually every anti-censorship technique that crypto comes up with instantly suffers from the same Achilles heel.

For example, cybercriminals use crypto mixer services like Tornado Cash, which blends together multiple transactions to prevent external observers from tying withdrawals to their associated deposits. But even leaving aside Tornado’s technical vulnerabilities that can compromise its users’ anonymity, the U.S. government could set rules prohibiting fiat-enabled exchanges from handling any funds that have interacted with a Tornado address.

(Note: Six days after this post was published, the U.S. Treasury Department did in fact sanction Tornado Cash. Needless to say, I had no advance or inside information about any of this.)

The cryptocurrency universe is like a privately run interstate highway system: the roads are wide and everything moves quickly3, but if all of the entrances and exits are guarded by cops, it doesn’t really matter how wide or efficient the highway is because the highway operator will still need to accommodate whatever the cops want in order to get any traffic in or out.

On that note, I really want to hone in again on Nat’s point below, which is a widely held crypto belief:

You can only rent on the pre-blockchain Internet. But with code that can live everywhere forever, without needing a trusted third party to support it, you can own things in a way that’s arguably even better than Title rights in a developed nation. You do still rent your real estate from the government, after all.

The persistent fallacy that blockchains and NFTs protect property rights in a more secure way than existing mechanisms demonstrates something important about many crypto boosters: they understand code4, but they are absolutely clueless about power.

Property rights are only abstract until they aren’t. If a homeowner arrives at his house at the end of the workday and finds someone squatting in his living room and claiming they own his property, he’s going to call the police. And if it’s not immediately clear to the police which person actually owns it, they can check the property records against each claimant’s identity in order to figure it out. At which point, cops — that is, armed agents of a very centralized state — will forcibly evict whoever’s lying.

At its most basic level, that’s what property rights are: the fact that a specific group of people with weapons will ensure you get to keep the thing that government records say you own. And this means that the most important question in this entire scenario is which property records these armed people consult.

This is why, whenever you read about a solution involving property rights — and especially physical property rights, such as real estate — being put on the blockchain, you should be immediately skeptical. If you turn your house into an NFT on the blockchain and sell it to someone else, and that person shows up to the house only to find someone else living there, the cops aren’t going to check the blockchain. They’re going to check the property records at the courthouse.

Those records could be memorialized in a physical logbook, stored in an MS Access database from the 1990s, or handwritten on a stack of Denny’s napkins. They could even be on a blockchain! But the chosen method of storage is simply a red herring: the salient point is that you have to rely on the centralized government to protect your rights.

There are no blockchain police. The mere presence or provenance of metadata on an open ledger has no bearing on whether armed agents of the state will actually ensure you get to keep your Bored Ape.

Claim #3: Single sign-on (SSO) using wallets is superior to existing solutions

Eliason writes:

Web2 companies like Facebook and Google have tried to make single-sign-on a thing for years. It started to get popular maybe five or six years ago, then everyone realized that if they shut down their Facebook or Google account, they would lose access to everything, and it started to wane.

But crypto applications are natively single-sign-on since you don’t typically create an account, you just connect your wallet. This is such a different experience that you’ll need to just try it to understand it, but it is a much nicer way of having an account on a website without having to give them a way to spam you.

There are some important differences between SSO using Google or Facebook and doing it via an Ethereum wallet.

One is that, in crypto, there is no easy backup plan if you lose your private key: once the key is gone, you’ve now lost your ability to log on to all of the services where you use that wallet. Think about how many times you’ve clicked on a “Forgot your password” link and now try to imagine not only not having that option, but also that instead of just forgetting your password to a single service, you’ve now been permanently and irreversibly locked out of every account you use online: email, social networking, your own banking and investment accounts, you name it. Gone. This is the promise of universal SSO using wallets.

Now wait a minute, some will say. This isn’t entirely true: you can set up a “multisig” wallet, which is a type of wallet that can be accessed using a combination of people rather than by a single person alone. For example, you can set up a 2-of-3 multisig wallet that allows access as long as any combination of 2 of 3 specifically chosen people can still access their private key. Or 3-of-5, or 4-of-6, or 5-of-10, or whatever. That way, even if someone forgets or loses their private key, access to their accounts isn’t lost forever, as long as they have a few family members or friends on the multisig wallet.

But now you have a new security problem to replace the old one: every time you want to do anything with your wallet you need to coordinate with the other members of the group to “sign” your transactions together. This is annoying and time-consuming, especially if you’re not all in the same time zone. So you’ll be tempted to relax the multisig ratio to something less like 4-of-6 and maybe more like 2-of-6.

(Edit: As Twitter user @joshisledbetter pointed out to me after I published this post, it is possible to set up multisigs that don’t require multiple signatures for every transaction.)

But now you’ve created another problem: if you get drunk at the Christmas party and manage to piss off two of your friends at once, you may lose your entire life savings overnight. Or, alternatively, maybe you and all of your multisig friends get drunk together at the Christmas party and lose the slips of paper containing your private keys, at which point the wallet is either forever inaccessible or immediately drained of all of its funds by whoever managed to steal all the private keys.5 Or maybe one of your friends dies and his partner has no idea where he kept his private key, transforming your 3-of-5 de jure multisig into a 3-of-4 de facto one. Or maybe it turns out that, instead of all your friends keeping their private keys secret, they’ve all sent their private keys to their single most trustworthy friend, and then that friend’s house gets burglarized and you lose all your funds. (This is effectively how Axie Infinity lost $600 million.)

And there are even more issues: if you use a single wallet to sign onto everything, this is a far creepier and more invasive form of identity resolution than anything dreamed up by the ad tech industry.6 Why? Because the blockchain is public and every transaction is visible to anyone. Today, privacy-conscious crypto users get around this frightening level of exposure by generating new Ethereum addresses for each separate use case (or even for each transaction), so as to fragment their visible activity and avoid de-anonymization. But in the world of crypto SSO, all of this protection goes right out the window.

In short: thanks, but no thanks. Also, if remembering passwords is such a problem, crypto people should really check out LastPass: it has a free plan! And it works just fine.

Claim #4: Crypto enables streaming payments

Here’s Eliason:

Being able to move very small amounts of money continuously using code is starting to create some interesting use cases. The idea of a biweekly paycheck can quickly become a thing of the past, with people instead getting their salary or hourly payments streamed to them while they’re working. 

This introduces quite a few other potential work setups, like being able to charge continuously for consulting work vs. needing to deal with invoicing before and after work is done.

It is true that you can enable streaming payments with crypto. But it is also true that you can do this without crypto. In fact, streaming payments really has nothing to do with crypto at all. Does crypto solve the problem of verifying that you’re actually doing real work for every hour that you’re being streamed your hourly wage? Does something about the blockchain ensure that you get paid promptly rather than thirty days after the fact?

The answer to both of these questions is no. The reason streaming payments isn’t mainstream isn’t because it was technically impossible until crypto came along. It’s because there’s not enough demand for it, or because employers don’t want to pay out contractors before validating the completeness of the work, or because of cashflow issues, or a million other causes. In other words, there are normal human reasons why streaming payments haven’t caught on, which the blockchain doesn’t solve. If the underlying conditions change, perhaps streaming payments will become a thing. But there’s no reason this has to be done using cryptocurrencies.

Claim #5: Crypto makes it easier to establish a reputation

Eliason writes:

If you build a reputation on Twitter, it’s very hard to migrate that reputation to YouTube or Instagram. Most social networks will reduce the spread of media from other social networks because they don’t want you taking your audience elsewhere.

Due to the single-sign-on mentioned earlier, social media in a Web3 world will likely center around following addresses instead of accounts siloed in one service. Since you’ll follow wallets, it won’t matter where they’re posting material. You’ll automatically be following them there…

The downside of a centralized account is that if you develop a bad reputation in one place, it will also be easy to punish you in other places. This could be good in general for weeding out bad actors, but you can imagine how it could be used maliciously too.

Of all the claims in this piece, this is the one that puzzles me the most, because it’s so blatantly incorrect. One of the biggest problems with crypto is that it makes it trivially easy to start over from scratch with a brand-new identity after you’ve nuclear-bombed your previous reputation into smithereens.

And guess who finds this feature particularly valuable? Serial grifters, that’s who. In case you haven’t noticed, the crypto world is disproportionately populated by serial grifters.7

Take “Sifu,” for example, most recently the pseudonymous co-founder of the crypto project Wonderland. In January, crypto investigator @ZachXBT uncovered Sifu’s true identity: he was actually Michael Patryn (previously Omar Dhanani), an ex-convict and repeated scam artist who had also co-founded the Canadian crypto exchange QuadrigaCX, which famously blew up in early 2019 when it turned out Patryn’s co-founder Gerald Cotten had disappeared apparently died only after siphoning about $170 million of his customers’ assets into personal accounts and luxury goods assets.

This is painfully obvious, but it’s worth spelling out anyway: any system that allows you to start over from scratch with a brand-new identity at any time is a godsend for scammers. I mentioned earlier that crypto is disproportionately populated by serial grifters, but it is also disproportionately populated by pseudonymous founders of projects that manage hundreds of millions or even billions of dollars of other people’s money.

The Venn diagram of these two outsized groups is not two separate circles.

In conclusion

These crypto defenses have been circulating for years. And so have, to at least some extent, the critiques. This inexorably leads to the somewhat depressing conclusion that Nothing Matters™️.

But if there’s anytime where perhaps the skeptics sound more credible, hopefully it’s now, following a particularly brutal crypto bear market spurred by extremely old-fashioned financial speculation and the creation of ill-advised unsecured loans.

Crypto is often cheekily described as having speed-run the last millennium of financial history in the space of just over a decade. If so, one silver lining is that the “This Time Is Different” illusions are being shattered much faster in the crypto iteration than they were in traditional finance.

Above is the list of top 10 cryptocurrencies by “market capitalization.” The only three coins in that list whose price hasn’t substantially declined over the past 30 days are Tether (USDT), USD Coin (USDC), and Binance USD (BUSD).

These are known as stablecoins, and they currently have a collective market cap of around $140 billion, representing at least 15%1 of all value in the entire cryptocurrency ecosystem. Their price is theoretically pegged 1:1 to the U.S. dollar, and thus is not supposed to fluctuate with the rest of the market. Within crypto, stablecoins act as a dollar stand-in. It’s where traders park their money, and also how they deploy leverage: speculators post, say, ether as collateral in order to borrow USDT (often in order to buy even more ether).

It may seem a little odd to have a dollar stand-in instead of just using actual dollars. There are a number of reasons for this, but one of the most important ones is the relatively limited supply of on- and off-ramps to fiat currency within the crypto ecosystem.

Much (all?) of crypto activity is speculative, and the traders engaging in it ultimately want to make a profit in real currency, i.e. USD. However, while there are plentiful options for crypto-to-crypto trading (say, exchanging UNI for SHIB), the number of exchanges that allow deposits from, and withdrawals to, actual sovereign-backed currencies like dollars, euros, yen, etc. is significantly smaller.

This is in part because, to enable withdrawals, exchanges need bank accounts. And banks are historically nervous to open accounts for crypto exchanges, because banks have to follow laws and regulations under the Know Your Customer (KYC) and Anti-Money Laundering (AML) umbrellas, while broad swathes of the crypto ecosystem are a loosely-held amalgamation of insider trading, fraud, pump-and-dump operations, and Ponzi schemes. (And, presumably, some legitimate trading too.) So banks and at least some exchanges are not always a great match.

There’s also a sort of market bifurcation that happens because of this. There are exchanges that operate purely within the crypto ecosystem — especially in “DeFi,” or decentralized finance — and don’t offer withdrawals or deposits to fiat currencies: because they don’t need bank accounts and thus don’t adhere to regulations, they can offer trading on the most absurd, obscure cryptocurrencies and provide wildly irresponsible products like double-digit yields on crypto loans.2 Meanwhile, the exchanges that offer withdrawals to real currencies must comply with KYC/AML regulations and thus offer a much more limited subset of features in the relevant jurisdictions.3

So let’s say I’m a trader that wants to speculate on BlatantPonziCoin (BPC). To get funds into the crypto ecosystem, I would first deposit real U.S. dollars into one of the more “responsible” banked exchanges like Coinbase, and then use that money to buy, say, USDT (the Tether stablecoin). At that point I can trade USDT directly on Coinbase. But if Coinbase doesn’t list BlatantPonziCoin, I’m likely to transfer my USDT to wilder, less restrictive DeFi exchanges so I can speculate to my heart’s content. For example, I might use USDT to buy BlatantPonziCoin and then post it as collateral to obtain a loan for more USDT in order to buy even more BPC.

So stablecoins are a way of greasing the skids for moving funds around within the crypto ecosystem and amping up leverage. They enable exchanges and protocols that don’t have fiat on- and off-ramps — that is, banking relationships — to act almost as if they do, by providing trading pairs for USDT and other stablecoins which traders treat as proxies for actual dollars. Stablecoins are also a good place to park funds when the market is crashing, without having to fully withdraw into a USD bank account and then re-deposit back into the exchange when the market recovers (each of which can take a few days of settlement times).

Now the key stipulation here — the assumption that underpins all fiat-backed stablecoins — is that every 1 unit of the stablecoin is backed by exactly $1 in real U.S. dollars. That is, every time a new Tether is created (or “minted,” in the lexicon), that should mean — by definition — that a corresponding U.S. dollar (or close equivalent, such as U.S. Treasuries) is sitting somewhere in a Tether bank account.

Otherwise, Tether could be minting USDT out of thin air and manufacturing demand for cryptocurrencies that doesn’t actually exist, thereby artificially propping up prices. This would also mean that, during a panic — when everyone’s trying to get out of cryptocurrencies and back into real ones — Tether could face a bank run where more people are trying to “redeem” their USDT for U.S. dollars than Tether actually has in the bank.4

So the single most important fact to establish, if you’re a stablecoin, is that you actually have U.S. dollar reserves (assets) that are equal to, or greater than, your total liabilities in issued stablecoins, which your customers can redeem for U.S. dollars at any time.

This is especially important for Tether, which — as the largest stablecoin in the world — has $67 billion of liabilities paired with an extremely concerning and well-documented history of not having fully backed reserves.

Tether shares a parent company (iFinex) and most of its executive team with Bitfinex, a cryptocurrency exchange:

This relationship is central to the nearly $20 million settlement Tether reached early last year, after it was sued in 2019 by the New York attorney general for “[engaging] in a series of conflicted corporate transactions whereby Bitfinex gave itself access to up to $900 million of Tether’s cash reserves, which Tether for years repeatedly told investors fully backed the tether virtual currency ‘1-to-1.’”

The 2021 settlement of this lawsuit required Bitfinex and Tether “to end all trading activity with New Yorkers,” and the AG’s statement announcing the outcome did not mince words:

In the case of Tether, the company represented that each of its stablecoins were backed one-to-one by U.S. dollars in reserve. However, an investigation by the Office of the Attorney General (OAG) found that iFinex — the operator of Bitfinex — and Tether made false statements about the backing of the “tether” stablecoin, and about the movement of hundreds of millions of dollars between the two companies to cover up the truth about massive losses by Bitfinex…

“Bitfinex and Tether recklessly and unlawfully covered-up massive financial losses to keep their scheme going and protect their bottom lines,” said Attorney General James. “Tether’s claims that its virtual currency was fully backed by U.S. dollars at all times was a lie.”

Seems bad! But things got even worse last fall, when the U.S. Commodity Futures Trading Commission settled its own separate case with Tether, this time for another $41 million. Perhaps inspired by the New York AG, the CFTC announcement didn’t shy away from its conclusions either (emphasis mine):

However, the Tether order finds that from at least June 1, 2016 to February 25, 2019, Tether misrepresented to customers and the market that Tether maintained sufficient U.S. dollar reserves to back every USDT in circulation with the “equivalent amount of corresponding fiat currency” held by Tether and “safely deposited” in Tether’s bank accounts. In fact Tether reserves were not “fully-backed” the majority of the time. The order further finds that Tether failed to disclose that it included unsecured receivables and non-fiat assets in its reserves, and that Tether falsely represented that it would undergo routine, professional audits to demonstrate that it maintained “100% reserves at all times” even though Tether reserves were not audited.

As found in the order, Tether held sufficient fiat reserves in its accounts to back USDT tether tokens in circulation for only 27.6% of the days in a 26-month sample time period from 2016 through 2018.

On top of all this, the Department of Justice is investigating Tether executives for bank fraud, which may stem from an earlier-reported DOJ investigation into “whether [2017’s] epic [Bitcoin] rally was fueled in part by manipulation, with traders driving it up with Tether.”5

Stranger still, given its role as a stablecoin, Tether’s own legal terms are remarkably clear that redemptions to fiat aren’t guaranteed:

Tether reserves the right to delay the redemption or withdrawal of Tether Tokens if such delay is necessitated by the illiquidity or unavailability or loss of any Reserves held by Tether to back the Tether Tokens, and Tether reserves the right to redeem Tether Tokens by in-kind redemptions of securities and other assets held in the Reserves.

As if this weren’t enough red flags, Tether’s CEO, JL van der Velde, and (ex-plastic surgeon) CFO, Giancarlo Devasini, are both almost entirely missing in action. They rarely speak publicly or grant interviews, leaving their CTO, Paolo Ardoino — who, incidentally, is married to their COO, Claudia Lagorio — to face the media. These interviews haven’t always gone that well:

Ah yes, this brings us to Tether’s audits. Well, lack of them. In the July 2021 CNBC interview referenced here, Tether general counsel Stuart Hoegner stated an audit was “months” away. Which would be great: given all of the sketchiness just recapped above, such an audit of Tether’s books is precisely the sort of thing that could help clear up its murky financing.6

The problem is, Tether has yet to deliver an audit despite promising one for years (note the tweet timestamps):

Bizarrely, simultaneously with making these promises that they will soon be audited, Tether has also stated that being audited is impossible (emphases mine):

"The bottom line is that an audit cannot be obtained," Hoegner told CoinDesk, claiming that this problem is not unique to his company but one faced by the entire cryptocurrency industry.

He went on:

"The barriers to getting audited are simply too big to overcome right now, and not just for us."…

Hoegner would not discuss the severing of ties with Friedman. However, he said Tether hasn't given up on the audit process. "We continue to be in discussions with a number of professionals and firms about what can be offered and when," he said.

🤷🏻‍♂️

Just last week, in fact, Ardoino was interviewed by Euromoney, where he provided this head-scratching update (emphasis mine):

Today, an accounting company called MHA gives quarterly attestations of its reserves, and Tether is working on a full audit – something other stablecoins also lack – but not with one of the big four auditors, according to Ardoino, because auditors are concerned about reputational risk, due to the lack of regulatory definitions around stablecoins[.]

“I think it’s one of the top 12, so not that bad,” he says. “The big four are a bit more cautious about providing a full audit when the rules are not clear.”

First, there’s an auditing Big Four: PwC, Deloitte, E&Y, and KPMG. There’s not actually a top 12 to speak of.

But even leaving that aside, why does the CTO of Tether not seem entirely sure who his own auditor is (“I think”), despite the fact that, nearly a year ago, his colleague stated its release was just “months” away?

In the glaring absence of these audits, Tether has instead provided third-party attestations, which bear caveats like this:

Our opinion is limited solely to the CRR and the corresponding consolidated total assets and consolidated total liabilities as of 31 March 2022, at 11:59 PM UTC. Activity prior to and after this time and date was not considered when testing the balances and information described above. In addition, we have not performed any procedures or provided any level of assurance on the financial or non-financial activity on dates or times other than that noted within this report.

This caveat is a giant loophole, especially considering that one of the CFTC’s key findings was that Tether moves funds into and out of their accounts as needed, specifically to pass attestations:

In addition, the order finds that Tether failed to complete routine, professional audits during the relevant time period. According to the order, Tether retained an accounting firm to perform a review of Tether reserves on a date Tether selected in advance, and Bitfinex transferred over $382 million to Tether’s bank account in advance of that review. The order recognizes that Tether has not completed an audit of the Tether reserves.

In fact, as Patrick McKenzie has pointed out, even if one generously accepts Tether’s attestations as gospel truth, one is inexorably led to the distinct likelihood that Tether is currently unbacked. Tether’s most recent attestation evaluates its assets as of March 31st, 2022. At that point, its net assets — assets minus liabilities — were $162,391,022. Meanwhile, its assets included approximately $5 billion worth of “other investments (including digital tokens).”

Since that date, the global aggregate market cap of cryptocurrencies has declined by 56%. Assuming Tether’s crypto assets fell at a rate broadly representative of the ecosystem as a whole, this means that even if Tether invested a mere 6% of that $5 billion “other investments” pool in crypto, these losses would more than wipe out its entire net assets cushion (presuming no other investments appreciated during that time), making Tether not fully backed.

Separately, just under a quarter of Tether’s total assets are comprised of “commercial paper & certificates of deposit,” where commercial paper is defined as “short-term debt issued by corporations.” This amount would make Tether one of the largest commercial paper holders in the world, and yet last year the Financial Times reported:

But this reported accumulation has largely gone unnoticed on Wall Street, according to several of the biggest players in the market including bank traders, analysts and money market funds.

“We’ve got lots of inquiries and heard lots of discussion, but have not seen any active participation,” said Deborah Cunningham at Federated Hermes.

“Until last week we hadn’t really heard of them,” said a trader at a large bank. “It was news to us.”

Indeed, despite numerous requests, Tether has steadfastly refused to name its counterparties, citing this information as Tether’s “secret sauce.” As Bloomberg’s Matt Levine notes:

That’s not a thing! That’s not a thing at all! Every money-market fund just lists all of its holdings, by size and issuer and CUSIP! Tether has broken new ground in the concept of commercial-paper privacy rights! But, why?

Or as Hindenburg Research short-seller Nate Anderson put it:

Now, what does this all of this mean? Does it prove that Tether is definitively unbacked? No. But as legendary short-seller Jim Chanos memorably detailed on a podcast last month:

The way I would describe Tether…is it’s as if you’re outside a door that’s red hot, where the doorknob is red hot to touch, the door itself is hot, there’s smoke coming out from underneath the door, and yet they’re telling you nothing is wrong inside the room.

That could be! But boy oh boy, there’s an asymmetric sort of situation here if that’s not the case. And the fact that they could just simply open the door and show you that there’s nothing wrong, by way of an audit, appears to be something that they are studiously avoiding.

In short, if the above was too long and circuitous to read through, the synopsis is this: Tether is profoundly unreliable, and there are very real and serious concerns about the extent to which their issued stablecoin is backed by dollar (or dollar-like) reserves. This uncertainty, given Tether’s size and dominance, necessarily transforms any investment anywhere within the entire cryptocurrency ecosystem into a harrowing proposition.

All of which makes it that much more bizarre that major exchanges like Coinbase and FTX continue to support USDT-based trading pairs on their platforms. Keep in mind that there are safer alternatives: for example, Centre and Coinbase jointly issue U.S. Dollar Coin (USDC), the second-largest stablecoin with a roughly $55 billion market cap. Centre publishes monthly reports on its reserves, which are executed by a third party accounting firm, Grant Thornton LLP, and — crucially — USDC does not have a long and sordid history of run-ins with law enforcement or regulators.

It is, therefore, little surprise that Coinbase’s web site explicitly states: “For customers with a US dollar bank account, 1 USDC can always be redeemed for US$1.00.” FTX’s site does the same: “You can deposit [TUSD, USDC, PAX, BUSD, or HUSD] and be credited 1:1 with USD, and you can withdraw USD as any of these 1:1.”

This is an important feature. You can’t log onto Fidelity and click a button to sell Microsoft stock to them at a fixed price at any time. But you can do that with USDC on Coinbase and FTX precisely because, from the exchanges’ point of view, USDC and the U.S. dollar are functionally interchangeable.

But just as importantly, neither exchange guarantees that Tether will be redeemable at par to the dollar. FTX’s help pages make this explicit: “Note that tether (USDT) is not part of the USD Stablecoins basket on FTX.  We do support USDT though.” Coinbase similarly lets it float as if it’s any other token.

Given the availability of USDC, why bother listing a potentially dangerous rival product like Tether while nevertheless not promising to redeem it at par? I wondered this aloud on Twitter and got a quick response from an FTX exec:

But this is a red herring. Tether charges a max 0.1% fee for redemptions, and even this small fee is only applied for conversions between Tether and fiat currencies. If FTX truly believed in Tether being fully solvent, they could simply “deposit” any excess Tethers with Tether when no longer needed, and withdraw them during periods of high demand, without incurring any fee whatsoever. The fee only comes into play if FTX wanted to trade in their Tethers for U.S. dollars. But why would FTX need to do that on a regular basis?

They wouldn’t. Unless, of course, they’re not sure Tether is fully backed. If a USDT token in FTX’s Tether account is just as good as a U.S. dollar, why bother paying a fee to jump back and forth between USDT and a U.S. dollar?

Even leaving this incongruity aside, Protos reported late last year that FTX’s parent company, Alameda Research, was the single largest destination of Tethers. You’d think that monopsony position would buy it some leverage over a 0.1% redemption fee.

Salame’s argument doesn’t make much sense from Tether’s perspective either. Why would Tether let a 0.1% fee get in the way of being treated as equivalent to their stablecoin competitors (to whom they’re rapidly losing market share at the moment) by being listed as redeemable 1:1 with U.S. dollars?

In short, it is difficult to escape the conclusion that FTX and Coinbase want to have it both ways: neither of them is willing to financially guarantee the USDT-USD 1:1 peg by promising to redeem USDT at par for their customers.

And yet both exchanges continue to list USDT on their platforms not simply despite the risk that Tether has manipulated crypto prices but, in some sense, because of it: if indeed the nightmare scenario is true and Tether is printing unbacked tokens at will, then the benefits of cryptocurrencies’ elevated prices and increased trading volume accrue to Coinbase and FTX (which, after all, accepts USDT as collateral for margin trading). But if the peg ever snaps, their customers will be left on the hook for all of the risk, holding USDT that’s worth less than a dollar each.

And such a seismic event is not a particularly unthinkable outcome. Last month’s spectacular collapse of the $40 billion ecosystem built around the algorithmic stablecoin TerraUSD (UST) and its paired token LUNA — which was backed by little more than marketing and deliberately obfuscatory tokenomics — kickstarted a cascade of panic-selling, forced liquidations, and insolvencies in crypto that has yet to end.

Tether’s size is nearly four times that of TerraUSD’s. And while it is true that fiat-backed stablecoins are an entirely different animal from algorithmic ones, this only holds true if they’re actually backed.7 The whole point with Tether is that no one can be sure.

FTX CEO and founder Sam Bankman-Fried (or SBF, as he’s commonly known) has made a sort of cottage industry out of slyly winking at fraud-adjacent practices while simultaneously facilitating them on his platform and then pretending the gruesome endgame was obvious once they collapse.

For example, on a much-discussed Odd Lots podcast episode this April, SBF vividly and accurately described yield farming — a popular but highly risky (and often extremely-leveraged) crypto strategy involving staking collateral in order to receive improbably large, unsustainable yields — as a magical box into which investors indiscriminately throw their money:

And now all of a sudden everyone's like, wow, people just decide to put $200 million in the box. This is a pretty cool box, right? Like this is a valuable box as demonstrated by all the money that people have apparently decided should be in the box. And who are we to say that they're wrong about that? Like, you know, this is, I mean boxes can be great. Look, I love boxes as much as the next guy. And so what happens now? All of a sudden people are kind of recalibrating like, well, $20 million, that's it? Like that market cap for this box? And it's been like 48 hours and it already is $200 million, including from like sophisticated players in it.

To which Bloomberg’s Matt Levine astutely replied: “I think of myself as like a fairly cynical person. And that was so much more cynical than how I would've described [yield] farming. You're just like, well, I'm in the Ponzi business and it's pretty good.”

Or take the TerraUSD (UST) / LUNA fiasco last month. Immediately following its collapse, SBF tweeted:

And yet here was SBF’s own exchange, FTX, announcing it would list LUNA just three months earlier:

This nihilistic approach to (not) protecting their own clients from harm isn’t confined to SBF or FTX.

Binance, by far the world’s largest crypto exchange by volume, is run by Changpeng Zhao (more commonly referred to as CZ). Following Terra / LUNA’s collapse, CZ described Terra founder Do Kwon’s LUNA 2.0 plan as “wishful thinking:”

You’ll never guess what CZ’s exchange announced just two weeks later:

The straw man response to all this would be that CZ and SBF run agnostic exchanges, so why should they be making investment decisions on behalf of their clients? And the answer is that both Tether and the original TerraUSD (UST) token are stablecoins: they are unlike standard investments, which everyone understands can and will fluctuate in value over time.

People — and, crucially, the markets, based on their stable prices — treat stablecoins as a separate category from other investments and consider them fully interchangeable with actual U.S. dollars. But these stablecoins have none of the protections of U.S. dollars (for example, being insured for up to $250,000 in a bank deposit). Holding a stablecoin that could fall apart is a far more pernicious and dangerous risk than simply buying a random altcoin that fails to gain traction, because the baseline expectations are so wildly different.

And SBF knows this. We know he knows this because he and fellow FTX (and parent company Alameda Research) executives have deployed the same knowing wink in their defense of Tether as he has in the other areas mentioned above:

This is classic misdirection. FTX’s ability to redeem Tethers for U.S. dollars does not demonstrate that Tether is fully backed, any more than a wildcat-era bank’s ability to redeem a single customer’s IOUs demonstrates that it is fully backed. The proof comes when the entire customer base is banging down your door asking for their cash.

In an August 2021 appearance on Odd Lots, SBF was asked for his opinion on Tether and gave a long and rambling answer in which he acknowledged being perceived as “a Tether apologist,” repeatedly described Tether’s redemption process as “a messy process,” and yet ultimately concluded that “the funds are there” and that “everything sort of checks out in a messy way:”

And then you can sort of get to the question of: what's their business model? It’s probably getting yield on the dollars. How are they doing that? I dunno. You know, like some commercial paper-like stuff.

And you know, I think it's like one of these things where, if you want to try and argue about whether Tether’s worth 99 cents or a dollar and a penny, I think that's a pretty reasonable argument…

And I think the other thing is you get to know the people involved here. They really, really aren't scammers.

Presaging Jim Chanos’ later description of Tether, SBF summarized: “There’s sort of a case where there’s a lot of smoke, but I don’t think there’s really much fire. But there is smoke.”

So why is SBF smelling smoke on his exchange?

CORRECTION: This post originally, and incorrectly, stated that FTX had listed the revived LUNA token for trading on its exchange. In fact, FTX simply airdropped the new token to holders of the legacy LUNA and UST tokens. FTX does not appear to currently support active trading of the new LUNA token.

It is a cliche at this point to lament the ethical minefield that is TV news. But even in an era of media cynicism, CNN somehow manages to push the envelope with the audacity of its crimes against journalism.

Take this Friday Rolling Stone piece by Tatiana Siegel on recently fired CNN president Jeff Zucker. The entire article is worth a read, but here are a few highlights:

• CNN’s president, Jeff Zucker, was in an ongoing, long-term, undisclosed (to HR) affair with his subordinate, marketing head Allison Gollust.

• While employed by CNN, Gollust worked privately with her former boss, New York governor Andrew Cuomo, to set up favorable TV appearances for him. Not content with simply “relaying his preferred topics to CNN producers,” Gollust went so far as to text Cuomo: “I’m pretty sure I stopped being your publicist 8 years ago, but apparently I still am.” She also congratulated him after his TV appearances with text messages like, “Well done . . . Cuomo-W. Trump-L,” and he asked her for feedback on how he’d done. Their ongoing communication included sexual innuendo that at least implicitly suggests a potential affair, as well as her asking him for a business favor for a friend. (Cuomo: “Yes, u do ask me for favors, and that’s okay. It’s mutual.”)

• During the 2016 presidential campaign, “Zucker and Trump spoke directly about coverage” and, per leaked audio, Zucker allegedly “praised Trump’s campaigning, offered advice for that night’s Republican debate, and said he wanted to discuss giving Trump a weekly show.”

• Throughout the COVID-19 pandemic, CNN allowed its anchor Chris Cuomo to repeatedly interview his own brother Andrew, the governor of New York, on TV. Later, when Andrew Cuomo was under investigation for sexual misconduct, Chris Cuomo exploited his sources to assist Andrew in fighting his critics.

• CNN anchor Don Lemon allegedly advised the actor Jussie Smollett (who just this past week was sentenced to nearly half a year of jail time for reporting a fake hate crime) “not to hand over his phone records to Chicago police.” (Lemon has reported on the case on his show.)

(The Rolling Stone piece has many more details in addition to these above — including Zucker’s career-long pattern of supporting prominent men who would eventually be accused of serious sexual misconduct — so I recommend taking the time to read it in full.)

If this were the whole story, that would be bad enough: the idea that perhaps the most instantly recognizable TV news brand in America is full of blatant ethics violations, journalistic malpractice, and conflicts of interest — within and throughout the very top echelons of the company — is (or should be) shocking on its own.

What makes it worse, however, are two key additional problems, which I get into below. First, some of these unethical practices and conflicts of interest were widely known within the CNN newsroom for years. And second, not only did this knowledge apparently fail to raise any visible alarms during all of this time but, even worse, once Zucker was fired for them, CNN’s top talent angrily rallied to his defense.

Indeed, multiple reports, including the Rolling Stone one, have emphasized the open secret that was Zucker’s and Gollust’s relationship. Siegel writes:

All the while, sources say, Zucker was conducting his affair with his subordinate, Gollust, in plain sight, bringing her from one network to the other, promoting her — and approving her compensation — at every stage of his ascension.

Shawn McCreesh at New York Magazine reported on the same phenomenon. In a piece subtitled “His office romance has been an open secret for years. So why is he really leaving?” McCreesh reports:

For starters, Zucker and Gollust’s relationship was one of the biggest open secrets in media. CNN staffers awkwardly navigated the pairing, since every time they dealt with her, they were keenly aware that she was involved with the boss. They were rolling their eyes at Gollust’s own statement that said “recently, our relationship changed during Covid.” It had been going on for much longer: Page Six would wink at it from time to time, and the two have known each other since they worked together at NBC decades ago. As Katie Couric wrote in her dishy memoir: “I had to wonder why Jeff was angling so hard to bring Allison on board” at Couric’s talk show, Katie. “She and her husband and kids had moved into the apartment right above Jeff and Caryn’s — everyone who heard about the arrangement thought it was super strange.”

It is not yet clear how much, if any, insight CNN’s on-air talent had into all of the network’s non-sexual ethics-related violations — for example, Gollust’s PR work for Cuomo while employed at CNN or Don Lemon’s alleged private advice to Jussie Smollett. But we know for a fact they were aware of at least some of them well before Zucker’s firing and their subsequent defense of him.

For example, the fact that Zucker provided then-candidate Trump with debate advice in 2016 was covered in detail on FOX News’ Tucker Carlson Tonight show all the way back in September 2020:

And in The Washington Post the next day, where it was justifiably described as “ethical depravity:”

Carlson bore down on the part of the call where Zucker — who has openly acknowledged political ambitions — dispensed some debate advice.

COHEN: How many times do you think Cruz is going to call him a conman tonight?

ZUCKER: No, Rubio.

COHEN: I mean, Rubio. How many times do you think?

ZUCKER: A lot.

COHEN: I say a hundred.

ZUCKER: You know what you should do? Whoever is around him today should just be calling him a con man all day so that he gets so used to it, so that when he hears it from Rubio, it doesn’t matter. Hey, conman. Hey, conman. Hey, conman.

COHEN: Yeah, great — you want to get smacked in the head?

ZUCKER: He thinks that’s his name, you know?

That Zucker’s debate-prep tip was so dumb doesn’t diminish its ethical depravity. CNN makes a big deal of its journalistic purity, its standards and practices, its protection of sources. Here was a clear violation from a the man who supervises the network’s 3,000 employees at 36 “editorial operations” around the world. Any of those folks could have told Zucker not to pass along tips to a presidential campaign. (We’ve asked CNN for comment and will update this piece if we receive a response.)

And it goes without saying that everyone at CNN was well aware that Chris Cuomo was conducting frivolous interviews with his own New York governor brother on live television, like this hard-hitting exchange from the depths of the pandemic in June 2020:

Obviously, I love you as a brother; obviously, I’ll never be objective; obviously, I think you’re the best politician in the country. But I hope you feel good about what you did for your people because I know they appreciate it.

Beyond these specific incidents, Jeff Zucker’s deliberate conflation of news and entertainment has been well-covered for years, as in this 2017 New York Times Magazine profile:

Zucker is a big sports fan and from the early days of the campaign had spoken at editorial meetings about wanting to incorporate elements of ESPN’s programming into CNN’s election coverage. “The idea that politics is sport is undeniable, and we understood that and approached it that way,” he told me. Toward that end, the network built “pregame” sets outside debate halls with excited crowds in the background and created a temporary rooftop studio for the final weeks of the campaign with sweeping views of the White House and the Washington Monument. An on-screen countdown clock ticked down the days (then hours) to Nov. 8. Trump, the trash-talking (and trash-Tweeting) underdog who inspired raw, powerful feelings among supporters and detractors alike, was the ideal subject for this narrative framework…

As Zucker sees it, his pro-Trump panelists are not just spokespeople for a worldview; they are “characters in a drama,” members of CNN’s extended ensemble cast. “Everybody says, ‘Oh, I can’t believe you have Jeffrey Lord or Kayleigh McEnany,’ but you know what?” Zucker told me with some satisfaction. “They know who Jeffrey Lord and Kayleigh McEnany are.”

It defies belief that someone as keyed-in as, say, The Lead anchor Jake Tapper would have failed to notice all of this while everyone around him did. And yet when WarnerMedia CEO Jason Kilar fired Zucker, Tapper — whose perceived journalistic integrity inspired Trump-era headlines like “CNN’s Jake Tapper Is the Realest Man in ‘Fake News’” and “Why (Almost) Everyone Likes Jake Tapper” — grilled Kilar and implied Zucker’s firing was primarily a publicity problem (emphases below mine):

If you could address the perception…that Chris Cuomo gets fired by CNN, Chris Cuomo hires high-powered lawyer who has a scorched earth policy, who then makes it very clear to the world that unless Jeff [Zucker] gives Chris Cuomo his money, they’re going to blow the place up. Stuff starts getting leaked to gossip websites about Jeff [Zucker] and Allison [Gollust]…and then weeks later Jeff comes forward and disclosed this and resigns, not willingly. An outside observer might say, well it looks like Chris Cuomo succeeded. He threatened, Jeff [Zucker] said we don’t negotiate with terrorists, and Chris blew the place up. How do we get past that perception that this is the bad guy winning?

Even leaving aside his musings on internal CNN palace intrigue, Tapper’s summary dismissal of Zucker’s affair with Gollust suggests he viewed it as a PR risk rather than as the abuse of power and ethical conflict rolled into one that it actually was.

CNN’s chief White House correspondent Kaitlin Collins agreed with Tapper:

CNN’s chief political correspondent Dana Bash was in the same boat, telling Kilar that “the punishment didn't fit the crime.” (This might surprise the countless employees of corporations around the world who have been fired for violating company policies.)

As recently as last August, CNN’s chief media reporter and Reliable Sources host Brian Stelter used his own show to (incoherently) defend CNN’s decision to allow Chris Cuomo to interview his brother:

What I found is a more complicated story than you might think. This has been a conundrum for CNN that has no perfect answer, no perfect solution. Some think CNN made it worse by letting Chris interview his brother when covid-19 was ravaging New York. But that was an unprecedented time period.

Stelter also presaged Jake Tapper’s later concern with ending the bad publicity surrounding Chris Cuomo’s strategy calls with his scandal-ridden brother, rather than addressing the underlying ethical catastrophe:

I want you all to know, it’s not like Chris [Cuomo] is walking around the New York Bureau newsroom hanging out with the reporters who are covering the [Andrew Cuomo sexual misconduct] story. He works several floors away. He doesn’t have that kind of interaction. Still, there is an optics problem.

Two days later, Stelter followed this up with an interview on The Late Show with Stephen Colbert, in which he inexplicably claimed that “if we open up the journalism ethics book, there’s no page for this. It’s the craziest set of circumstances you can imagine. Right — a governor and a brother, both in these high-profile jobs.”

It is, perhaps, only fitting that a news network whose on-air personalities frequently depicted Trump as a carnival barker would so blatantly embrace a bread-and-circuses contempt for its own audience.

And while the occasional critic has successfully smuggled an ethics critique onto the air on CNN itself — such as Washington Post’s Erik Wemple’s admirable attempt last March — one gets the distinct sense that the political media ecosystem’s elevation of TV hits into a sort of social currency acts as a self-censor for any on-air guests who might otherwise find these ethical minefields disturbing enough to bring up on live TV.

So is CNN fixed, now that Zucker is out? Is it destined to take its journalistic mission more seriously? Perhaps the best way of answering that question is by noting who CNN announced as Zucker’s successor: the current producer of CBS’ The Late Show with Stephen Colbert, a late-night comedy program.

(Disclaimer: this newsletter represents my own views only, and not those of the advertising industry at large or my employer.)

What is brand safety?

When people in ad tech talk about it, they generally mean “efforts to keep brand messaging (that is, ads) from appearing next to content that's inappropriate and thus damaging to the brand's preferred image.”1

Why is this damaging, though? What are the main risks of a brand showing an ad next to something inappropriate online?

Based on numerous conversations with brand safety experts over the years, two of the most commonly perceived risks to digital ads showing up next to inappropriate content are:

1. The end user who sees the ad will be upset at its placement next to potentially unsafe content

2. Journalists may discover the inappropriate placement and write about it for news publications, damaging the brand’s public image

Both of these perceptions of risk are flawed in ways that have profound implications for the practice of keeping brands as safe as possible online.

Let’s take that first perceived risk. Keep in mind that digital content consumption — much more so than on TV — is entirely self-directed. As a consumer of linear TV, the only control you have over content is the ability to change the channel, and even on cable TV the number of channels is finite.

Online, you have a theoretically almost infinite number of choices about what to look at or do. This limitless buffet means that a user who has visited a web site or app almost certainly did so with a specific intent to view the content now on their screen. Which begs the question: why would they be upset that an ad appeared there?

Intuitively, in most cases the Morally Offended User theory doesn’t really add up: a user casting judgment on an advertiser for appearing next to a questionable web page necessarily implies self-judgment too. After all, the only reason he saw the ad appear in an inappropriate context is because he decided to look at that content in the first place. Now, there are still cases where — regardless of any end user’s state of mind — a brand may decide that’s not a place they want to show up anyway.2 But that’s a different argument than claiming that the user himself would perceive the brand negatively for appearing next to content he self-selected.

This is not to say there are no risks to a user’s brand perception based on subpar ad placement. But some of the research routinely bandied about in support of this risk profile doesn’t quite say what its boosters think it does. Even the 2017 CMO Council report “How Brands Annoy Fans,” which was primarily focused on brand safety risks, found that the negative ad experiences cited by the most consumers were “obnoxious or intrusive ads,” “discriminatory or hateful ads, “ “irritating or annoying ads,” and “ads that are racist or stereotype people.” That is, the problem was the ads, not the adjacent content.

The press release for a joint 2019 report by the Trustworthy Accountability Group (TAG) and the Brand Safety Institute (BSI) was headlined: “More than 80% of Consumers Say They Would Reduce/Stop Purchases of Products That Advertised Near Extreme or Dangerous Content.” But none of the eight actual survey questions directly asked whether an ad that the user herself viewed next to offensive content would negatively impact her views of the brand. This leaves open the possibility of conflating the first risk (users’ firsthand negative ad experiences) with the second (critical media coverage).

The severity of this first risk falls into further doubt based on the (limited) published research into this area over the past several years.

For example, in 2017 Australian research and analytics agency Nature published its conclusions regarding ads appearing next to undesirable content on YouTube:

Critically, there was no statistically significant difference in results between the three groups, providing evidence that an ad followed by undesirable content performs no less favourably than others. In fact, the ad was just as engaging and had the same brand impact (on positivity and consideration likelihood) for those who saw it when followed by the undesirable video clip, as it was for those who saw the ad on its own, or followed by a more innocuous video. Similarly, we saw no significant differences in brand perceptions between the three groups after exposure…

Our suspicion is that the brands that have pulled their advertising from YouTube have not only done so for ‘in principle’ reasons, but also to avoid potential negative washback on their brand. Our small experiment on this big issue suggests the latter concern is largely unfounded and could be a storm in a teacup.

Similarly, a 2018 study3 of brand safety effects on pre-roll video ads found…none:

There was no brand safety effect on the brand's reputation, measured by brand attitude. These results suggest that the content seen after a pre-roll ad does not interfere with processing that ad, even controversial—violent, sexually arousing, or extremist—content following the ad.

…This study suggests brand safety is not an issue for pre-roll ad effectiveness.

A 2020 Twitter study (see below chart) found that:

…on our platform, adjacency between ads and divisive content has not yet been found to affect brand favorability…

Results showed no evidence of any effect on brand favorability when ads were adjacent to any of the studied categories of divisive content, regardless of the distance of the adjacency.

And in what appears to be a reference to more recent data from this same research project, three months ago Twitter’s head of global brand safety strategy reiterated one of its central findings in an AdAge piece:

Even when brands appeared adjacent to content in the categories of political or sensitive news, EyeSee saw no impact on brand favorability or consideration. [A]nd while this research may go against some conventional wisdom, it is encouraging.

Of course, one cannot simply take the public statements of a platform like Twitter (or research funded by other ad tech giants or news publishers) at face value about the safety of their own user-generated content: they are, after all, financially invested in proving it. Nevertheless, even outside of platform-sponsored research, consumer-facing surveys have revealed user preferences to be almost charmingly inscrutable and idiosyncratic, rather than didactically moralizing: as just one example, an AdColony EMEA report found that more users viewed an advertiser positively than negatively when their ads appeared next to coronavirus-related content.

In short, while consumers generally respond to questionnaires with conventional viewpoints on how advertisers should avoid brand risk, there is vanishingly little hard evidence that these stances necessarily equate to reduced value for the advertisers attempting to reach them.

This leads us to the second perceived risk: the threat of negative news coverage of the inappropriate ad placements. The logic underpinning this one is no less dubious than that of the first risk, although for a different reason. The canonical example — cited in countless articles — of a brand safety breach leading to bad press is when advertisers like Procter & Gamble, Toyota, and Anheuser-Busch inadvertently ran YouTube ads directly in front of ISIS videos.

It’s important to note that this did lead to a flood of negative press coverage — which, in turn, led to major brands temporarily fleeing YouTube. However, much of the negative tenor of media coverage was directed at Google (YouTube), rather than the advertisers.

But that caveat isn’t the main reason to be skeptical about how dangerous this risk is. It’s that the type of brand safety breach most likely to trigger a negative reaction from the end user, or critical news coverage from journalists, is when an ad appears (spatially or chronologically) adjacent to actual unsafe content — just like those ads in front of ISIS videos. This is precisely the type of scenario brand safety technology should be designed to avoid.

The map is not the territory

And yet much of the brand safety conversation today rests on the unspoken assumption that journalistic coverage of an unsafe entity or event — say, a newspaper covering a murder trial (and thus using flagged words like “murder” and “death”) — is equivalent, from a brand risk standpoint, to the underlying event itself: a video of the murder, for example. This verdict owes more to the limitations of current brand safety technology (which far too often relies on simplistic keyword matching) than it does to actual evidence of safety breaches. It’s also a textbook case of the questionable cause fallacy: conflating the symptom for the disease. It reminds me of this classic Donald Trump tweet:

The underlying problem was the large number of people infected with COVID-19. Reducing the number of positive reported cases by testing fewer people would do nothing to alter that.

Brand safety concerns fall prey to this same fallacy (albeit with less catastrophic consequences). For the most part, the risk isn’t that a brand’s image may become associated with news organizations covering crime, or terrorism, or hate speech. Rather, a risk occurs if a brand becomes associated with underlying crime, terrorism, or hate speech itself — like those ads that appeared directly before ISIS videos. But many brand safety technologies treat these two vastly different threat models as if they are interchangeable.

While high-profile mishaps like the YouTube ISIS calamity certainly generate negative press attention, I struggle to recall a single prominent example of a brand getting pilloried for appearing on a high-quality news publisher’s site next to an article about something broadly controversial. (You’re far likelier to see this as a topic of amused discussion in r/adops than you are among ‘normies.’) Even the activist group Sleeping Giants, the scourge of the ad tech industry whose work largely centers on public-shaming brands for advertising on Breitbart.com, does so because Breitbart itself — which famously had a story tag on its site labeled “Black Crime” — engages in hateful rhetoric, not because it reports on it.

Moreover, even when an unsafe ad placement does occur, the financial consequences are often negligible. A 2018 GumGum report on “The New Brand Safety Crisis” acknowledged that just six percent of ad tech industry respondents reported revenue loss of over $10,000 due to a brand safety incident.

Despite all of this evidence, existing brand safety solutions seem to have a limited ability to keep advertisers away from actual unsafe content, even while they routinely block news coverage of it. And the consequences for journalistic organizations are deeply concerning.

This is all precisely backwards.

For a variety of reasons I’ve covered earlier, third-party brand safety products on the “walled garden” social platforms — Facebook, YouTube, Twitter, and so on — have, for most of these platforms’ existence, ranged somewhere between highly limited and nonexistent. This is unfortunate, since user-generated content (UGC) giants like these — contra the highly curated environment of, say, The New York Times — are precisely where actual brand reputational risks are the highest:

Meanwhile, the decreasingly lucrative open web is far easier for brand safety companies to crawl, measure, and potentially block ad placements on. This results in a perverse scenario where high-quality news publishers that invest heavily in covering important social, political, and cultural stories — like the coronavirus, police brutality, women’s health, LGBTQ issues, and so on — are penalized by brand safety vendors, while ad-bloated puff piece content farms4 thrive (so long as they don’t mention the word “Trump”). As Mantis general manager Benjamin Pheloung explained:

“Almost everything about the Black Lives Matter movement is going to fall afoul of some kind of blocking. George Floyd's murder will be caught by segments blocking violence. Concerns about gatherings spreading coronavirus will be blocked by segments looking for Covid-19. Perhaps most perniciously, segments blocking hate speech will also be triggered when discussing racism..."

“News publishers create the densest pages out there. Sites with lists, carousels of images and the like become the default home for open marketplace programmatic spend.”

Again, this is completely backwards. Leaving aside the potential deluge of free PR that could accrue to any brand taking a vocal stance in favor of supporting journalism, it’s also just smart business to do so.

As more and more high-quality news organizations migrate to a subscription-first business model, news is becoming a highly lucrative self-selected advertising audience: unlike the internet’s early years, when most news sites offered some or all content for free online, a nytimes.com reader today is likelier than ever to be paying for the content. This signifies relative affluence (newspaper subscriptions are effectively a luxury good) and high educational attainment, a perfect duo for big-spending brand advertisers interested in associating themselves with journalistic titles favored by the moneyed set:

Indeed, many of these brands are already deep-pocketed benefactors of the newspaper’s lower-class cousin, TV news. It defies reason that top brands like Verizon, BMW, and Wayfair that run ads on CNN — a news channel that once devoted weeks of (often absurd) wall-to-wall coverage to the grim disappearance of Malaysia Airlines Flight 370 and its 239 passengers — should balk at, say, The Washington Post’s careful, meticulous online coverage of the El Chapo trial in Brooklyn.

Or to use an even more extreme example, brands like Pfizer, Procter & Gamble, Amazon, and Kraft Heinz regularly advertise on FOX News, a TV channel that routinely broadcasts disinformation and conspiracy theories on everything from the COVID-19 vaccines to election fraud. In what universe would it make sense to keep these same brands “safe” from coverage of the Black Lives Matter movement in The New York Times?

As News UK’s Bedir Aydemir wryly noted:

“The fact is no marketer or agency has got sacked for blocking too many words, but they certainly could if their ad is seen on dodgy content, so they just load on more,” said Bedir Aydemir, head of audience and data, commercial at News UK. “It basically means they are blocking ‘news.’ Where on earth do you think your ads are appearing if you are blocking thousands of words? I can only imagine it must be recipe sites.”5

Indeed, one senior advertising executive for a major national news publisher once told me: “Our site is brand safe almost by definition.” And he was basically right. As he then pointed out, there is a yawning chasm between, on the one hand, contextual classification technology designed to ascertain the broad, directional topic of a news article — say, tagging a piece as relating to COVID-19 or basketball — versus, on the other, repurposing that methodology to render a far more difficult and subjective decision about its safety for a brand.

The reductionist use of technology that commoditizes a storied institution like The Wall Street Journal into the same (or worse!) risk tier as “sites with lists, carousels of images and the like” is a worrying trend.

Part of the problem here stems from foundational methodological beliefs — canonicalized by industry trade groups — that underpin digital brand safety conventional wisdom. One of them is that brand safety is best measured at the page or content level, rather than as part of a holistic assessment of a site or domain overall.

This is sensible for generalized contextual classification: as a brand, I might want to know what percentage of my impressions ran on articles about healthcare, and I may even be OK with a 5-10% error in either direction. But it’s considerably less obvious how well this works for determining safety, a concept that’s as much about the publisher’s credentials and long-term trust-building than it is about the ephemerality of a single ad impression appearing on an individual page.

Indeed, a 2018 Edelman study on brand messaging asked respondents which attributes they considered most important when deciding whether to trust information they saw on social media:

That list looks a lot like…a newspaper. And it aligns perfectly with other research that reached similar conclusions, like the 2017 CMO Council report which found that “64% [of consumers] respond better to ads delivered from a trusted news site than those that appear on social media, search or fake news channels.”

In other words, it matters less whether an advertiser’s message runs in the sports section or in real estate than whether it appeared in The New York Times versus, say, The Daily Mail. It’s the institutional gravitas of the Times as a brand that lends instant credibility to any marketer’s messaging associated with it. Atomizing this trust down to the article level misses the forest for the trees, and it’s not how risk actually works in the real world.

So, how does risk work?

First, it’s important to recognize that high-quality journalism doesn’t scale (that’s another shameless link to my prior writing on this): there’s not an infinite supply of trustworthy publishers out there to run ads against. When it comes to buying ads online, there’s huge scale, brand-safe inventory, and low cost: pick two.

Second, moving away from a model that equates terrorist content to coverage of terrorism because they share keywords, and towards one that incorporates publisher-level analysis as part of a broader safety solution, will help reorient brand advertisers towards substantive brand safety. The question immediately shifts from “Which news articles are safe to run on?” to “Which sites are (high-quality) news sites?”

That is, we need to start identifying which sites count as news — including, importantly, local news! — and which sites don’t. To do this we may be able to (at least partially) rely on membership lists of news industry trade groups that have ethics and/or standards requirements for joining. Alternatively, the advertising trade group Interactive Advertising Bureau (IAB) launched a project to curate an allow list of news sites (based on IAB Canada’s earlier efforts). The Local Media Consortium hosts the “Local News Advertising Inclusion List” on its site.

Whatever form it eventually takes, and regardless of the dissatisfaction it may cause avowed proponents of relying solely on page-level brand safety tech, lists like these can help stem the tide of journalism demonetization, by replacing the myopic brand safety controls that preceded them. It can even be coupled with measures of quality that analyze attributes, such as page loading time and ad clutter, in order to restrict brand messaging to inventory that boasts the best user experience.

That still leaves the question of what to do everywhere else. Page-level brand safety classification can continue to play a dominant role for the programmatic long tail, where publisher branding is virtually nonexistent, managing allow lists is infeasible, and adhering to brand safety standards is just about the only differentiating factor for much of the content. And page-level filtering can still be useful on high-end publisher sites when used to avoid custom topics specific to a brand’s sensitivities (e.g. an airline wishing to avoid articles about plane crashes). But as a general rule, a hybrid approach — with publisher quality signals dominating the top tier and page-level classification handling the remainder — is promising.

As to the social platforms, major challenges remain. The platform whose early brand safety crises set the stage for the last few years is YouTube, an environment that current brand safety methodologies are almost singularly ill-designed to address. Brand safety technology applied to YouTube, unless it incorporates accurate, sophisticated object- and scene-level recognition that even today’s most vaunted technology struggles to achieve at scale, relies in part on sparsely populated metadata like the video title, description, tags, and (in some cases) the audio transcription.

Solutions built on such foundations are blunt indeed: for example, they will over-block music videos since the lyrics are often posted in the description, increasing the odds of a brand safety flag. And it will fail to block just about any unsafe videos that don’t loudly proclaim themselves as such in a keyword-friendly way (and why would they?). Just as with news sites, this technology is unequipped to understand the larger controversy around a publisher like PewDiePie, whom brands may justifiably want to avoid entirely even if many of his individual videos lack any offensive content.

Worthwhile brand safety measurement and blocking solutions on the social platforms must be highly customizable and likely platform-specific. Ad adjacency on YouTube videos raises few of the privacy concerns that apply to Facebook’s News Feed. Risk detection on text-heavy Twitter will necessarily operate using different models than ones that would work for the image-centric Pinterest. Ads on TikTok and Snapchat conspicuously don’t share screen space with organic content the way ads and editorial do on the pages of any news site. In other words, there’s a lot of work left to do here.

This all may sound like a radical departure from the brand safety zeitgeist. But the evidence in favor of the status quo is worryingly thin, and it’s incumbent on brand safety and verification vendors to follow the data. One need not be a cynic to observe that a cookie-obsessed ad tech industry that spent a decade arguing that environment doesn’t matter — “why pay a $15 CPM on ESPN.com when you can target the same users on the long tail for $1 or $2?” — has abruptly reversed course in the post-GDPR/-CCPA era and now warns of contextual dangers around every corner.

The question for marketers, then, is whether these dangers primarily threaten their brand reputations or, rather, the business models of the ad tech providers selling to them.

It seems I have developed a sporadic January tradition of skewering Adam McKay movies. Three years ago it was Vice, an angry biopic about Dick Cheney, which I called “an unequivocal catastrophe of a movie.”

I won’t bury the lede: Don’t Look Up is even worse.

The principal culprit is once again McKay’s knee-jerk contempt for his own audience. Back in January 2019, I wrote this about Vice:

His condescension towards the American public is rendered in brief but telling snippets. There’s a slow-mo close-up of young adults in the throes of ecstasy (and, probably, Ecstasy) at a nondescript party, a scene presumably meant to illustrate the apathy of the millennial set in the face of their government’s horrific actions, but which mostly reminded me that there are more enjoyable ways to spend one’s evening than shelling out thirteen bucks to watch a bad movie.

Similarly, a post-credits scene depicts a focus group session that devolves into politically-motivated fisticuffs, at which point one woman turns to another (the American dunces in Vice are all women) and says, “I can’t wait to see the new Fast & Furious movie. It’s going to be so lit.” This is an odd series of objections from the guy who spent the Bush years making movies about mustachioed ’70s-era news anchors, gay French racecar drivers, and a pair of adult step-brothers living with their geriatric parents.

Since then, McKay’s worst instincts have only worsened. Somehow, the more horrifying the subject — Dick Cheney in Vice, the extinction of the human species in Don’t Look Up — the more ham-handed and farcical his portrayal.

For those who haven’t seen it yet, Don’t Look Up tells the story of two astronomers, Randall Mindy (played by Leonardo DiCaprio) and Kate Dibiasky (Jennifer Lawrence), who discover that a massive comet is on a trajectory to collide with Earth in six months, extinguishing all life. Faced with this terrifying countdown, the two proceed to do what any reasonable person would: call the authorities, alert the media, raise awareness.

But their strategy is blunted at every turn by apathy and cynicism. The American president, Janie Orlean (played by Meryl Streep), is more concerned about shepherding her increasingly controversial Supreme Court nominee through the confirmation process than in confronting the imminent obliteration of the planet — the news of which, she worries, may hurt her party in the midterms.

Meanwhile, the hosts (Cate Blanchett and Tyler Perry) of The Daily Rip, a slightly-too-on-the-nose stand-in for MSNBC’s real-life Morning Joe, are (respectively) too sexually enthralled or viscerally repulsed by their astronomer guests to take seriously the looming threat they were invited on the show to discuss.

Despite these setbacks, Mindy and Dibiasky eventually persuade the White House to pursue a credible mission to prevent total devastation. A plan is developed to launch nuclear explosives to “blow Comet Dibiasky off her course.”

But just after the mission gets underway, it is unceremoniously canceled by the president, at which point it is revealed that one of her political benefactors, tech industry CEO Peter Isherwell1 — a disconcerting fusion of Elon Musk’s persona with Christopher Walken’s vocal cadence, in a deft portrayal by Mark Rylance — has discovered the presence of trillions of dollars’ worth of valuable minerals on the comet, whose mining would financially benefit his company, BASH.

And so the sensible, safer mission is replaced with an absurdly risky one tailored towards benefitting corporate interests rather than ensuring a maximal chance of success: use a fleet of BASH space drones to attach themselves to the comet, deploy “microexplosives” to splinter it into 30 smaller meteoroids, and guide them to the Pacific Ocean, where they will be picked up by the U.S. Navy.

Meanwhile, across TV and social media, the population begins to retrench into familiarly polarized camps. Chastened by a brief dalliance in the halls of power, Mindy rejoins Dibiasky to publicly castigate the White House for its duplicity and corruption, urging the public to “just look up into the sky” and see the comet for themselves. But as political campaign season heats up, “Don’t look up” predictably becomes a countering rallying cry for President Orlean’s rabid fans.

McKay’s portrayal of this aspect of the American cultural milieu manages to rank among Don’t Look Up’s less preachy and most affecting plot points, not least because its cultural doppelgänger has been playing out in realtime every day for nearly the past two years. As New York’s Eric Levitz (in an otherwise harsh review) noted:

Although written before the pandemic, many of its social criticisms feel sharper in 2022 than they would have in 2019. The notion that a threat as immediate and universally menacing as a descending comet could become culture-war fodder — thereby turning the mere act of “looking up” into a litmus test for partisan allegiance — is a bit too plausible at a time when anti-vaxx identity politics has pushed U.S. COVID deaths over the 800,000 mark.

As you’ve probably gathered by now, Don’t Look Up is an allegory for our collectively impotent response to the slow-motion catastrophe that is climate change.

And in case it wasn’t perfectly clear, Adam McKay thinks we’re a bunch of miserable failures. (Hard to argue with that.) We’re so engrossed in our inane morning-show nothingburger controversies — in the film, Ariana Grande and Kid Cudi enthusiastically embrace vapid, Extras-style celebrity alter egos embroiled in a made-for-TV relationship spat — that we can’t be bothered to mobilize to save our own lives.

More importantly, though, Adam McKay is concerned that his audiences won’t grasp how smart he is. So he repeatedly hits us over the head with visual metaphors to ensure we can’t miss it.

Here, for example, is a still from a Don’t Look Up scene depicting the New York headquarters of a certain newspaper2, whose font, I’m sure, is not at all meant to evoke its real-world inspiration:

A similarly heavy-handed treatment engulfs the president and her family. The gauche “Don’t Look Up” baseball cap she wears, the storyline about her scandal-ridden Supreme Court nominee, as well as the entire character of her son and chief of staff Jason (played by Jonah Hill), are designed primarily to remind you of a certain recent occupant of the White House.

As if to underscore the parallels, at a political rally Jason exclaims to the crowd: “Is that a rock-solid ten smokeshow of a president or what? If she wasn’t my mother…" he trails off, in an homage of sorts to Donald Trump’s infamous 2006 interview where he made an almost identical comment about his daughter Ivanka.

What does any of this have to do with climate change? Who knows, really?

Between Vice and Don’t Look Up, it is clear that Adam McKay has lost faith in the American public. This is understandable! There’s a lot to be depressed about. And McKay — whose political passions appear considerably more personal and deeply held than those of most of his show-business peers — is not wrong to pinpoint villains: the struggle to save our planet is among the clearest cases for moral certainty of our (or any future) generation.

The trick is picking the right villains. In one of Don’t Look Up’s cheekier cameos, Chris Evans appears as a movie star dispensing classic political punditry during a promotional tour for his comet-inspired film, Total Devastation: “Yeah, this pin points both up and down. Because I think as a country we need to stop arguing and virtue signaling. Just get along.” False equivalence couldn’t have been expressed any better if Ryan Lizza had written the damn thing in Politico Playbook.

But what’s less understandable is why McKay has determined that mocking his audiences for their cumulative frivolousness represents his best chance at turning things around. As The Guardian’s Charles Bramesco wrote: “McKay is so un-shy about expressing his blanket contempt that one starts to wonder who this could possibly be for.”

In this sense Don’t Look Up is Hollywood’s Mueller Report: either a total catastrophe or the second coming of Christ. (Or, just maybe, neither one managed to make the best possible case without muddying its thesis.) Rolling Stone concluded: “While McKay may believe that we’re long past subtlety, it doesn’t mean that one man’s wake-up-sheeple howl into the abyss is funny, or insightful, or even watchable. It’s a disaster movie in more ways than one.” A different Guardian piece called the film “an A-list apocalyptic mess.” And New York’s Vulture site observed: “McKay…just doesn’t care enough about popular culture or social media to effectively skewer it.”

Even its fans are forced to make strategic concessions. Current Affairs’ Nathan Robinson’s lengthy review concludes: “I have not commented on the quality of Don’t Look Up as a film. But as I said, I think that’s somewhat beside the point.” He continues:

We can imagine, in the world of the film, those concerned about the comet making a film that satirized the lack of national action to stop the comet. And in the world of the film, reviewers simply respond by calling it “heavy-handed,” the director’s “worst film yet,” saying it “misses its targets,” that its humor is too “broad.” Instead of discussing the issues the film raises, they discuss whether the film is good or bad and whether it is successful in the way that it approaches the issues…

A central point made by Don’t Look Up is that when things are matters of life and death, we need to treat them as such. Giving such a film a thumbs-up or thumbs-down and assessing the quality of its humor shows that one has missed the point entirely.

This preemptively defensive conflation — the notion that disliking the film renders you no better than one of the many lemmings portrayed in it — has been parroted elsewhere, most notably by Adam McKay’s Don’t Look Up co-writer, journalist David Sirota:

But this line of reasoning only underscores the critics’ point: if the chief response to viewers rubbishing a movie about climate change is to condemn them as morally equivalent to those apathetic about climate change, then perhaps the real problem was…the movie?

There is ample reason to suspect this. While waiting for Don’t Look Up’s comet to make touchdown and put me out of my misery, I repeatedly thought back to Melancholia, a 2011 Lars von Trier film about a planet headed towards a devastating collision with Earth. Contra McKay’s portrayal of the planet’s final moments — which alternate between harried depictions of grotesque ignorance and the protagonists’ righteous indignation — Von Trier’s ethereal treatment of humanity’s fleeting last breaths continues to haunt me with its realism a full decade later. It’s not always about how loud you shout.

2

Incidentally, Don’t Look Up’s cinematic reincarnation of the Times is head-scratching in so many ways, starting with the scope of its employees’ duties. One editor asks his colleagues to set up the astronomers with a pro bono attorney, while another says, in front of Randall Mindy: “Make sure this one gets media training before he hits the shows. He seems a step slow.” The Herald then proceeds to set up the astronomers — who, again, are sources, not journalists! — with an interview on a TV morning news show, The Daily Rip.

Immediately after their appearance on the show, Mindy and Dibiasky are inexplicably invited back to a conference room at The New York Herald’s offices to review a full presentation of web traffic analytics and social media reactions to their TV appearance (“clicks overall were below basic weather and traffic stories”) as well as a collage of notable memes (“Meow! Me likey hunky Star Man”).

I realize I’m just ranting now, but how many weather and traffic stories have you ever read in The New York Times?! It’s enough to make you wonder if Adam McKay has ever picked up an actual newspaper. (Or, to borrow Eric Levitz’s more concise appraisal: “McKay’s conception of the news media’s role, meanwhile, seems slightly deranged.”)

I’m about as vociferous a critic of the Times as they come, but somehow I doubt Dean Baquet has nothing better to do than spend his workday reviewing memes. (Most likely because he’s too busy both-sides-ing the word “lie” at a panel event.)

But what if instead of paying $99.98, I would rather pay $5,000 for the exact same bond, and still receive only the same $100 back three months later?

OlympusDAO solves this.

OlympusDAO calls itself “a decentralized reserve currency protocol,” but you can think of it as a sort of crypto central bank and treasury rolled into one. It issues currency — OHM tokens — just as the U.S. Treasury issues dollars. And just as the U.S. dollar is backed by the full faith and credit of the United States government, OHM is backed by its pseudonymous founder, a suspected teenager who calls himself “Zeus.”1

The Olympus project has gotten a lot of attention lately, not all of it positive. Bloomberg’s Money Stuff columnist Matt Levine describes it as “crypto Ponzi economics,” and his colleague Joe Weisenthal is no more enthusiastic:

It was also cited last week under the subheading “Some in the Crypto Industry have Warned that Olympus DAO may be a Ponzi Scheme” by a witness before the Senate banking committee, which is almost never a sentence you want to hear about your project. Even the first line in a much-discussed and otherwise laudatory CoinDesk piece on Olympus from early December began: “Yes, it’s a Ponzi scheme. But who cares? So are the dollars in your pocket.” (Equating fiat currency to a scam is a common theme in the crypto world.)

And yet Olympus, whose self-reported “market cap”2 exceeds $3 billion, has its fans — and even converts. Nat Eliason, whose “DeFi Fridays” newsletter series is essential reading for anyone trying to understand the basic mechanics of the crypto ecosystem, penned a mea culpa in October titled “I Was Wrong About Olympus:”

It’s hard to admit when you’re wrong. But I was very wrong about Olympus. 

When a crypto project has a cult-like following and super high APRs, I usually take that as a sign it’s some kind of scam…

But I was wrong. After months of writing it off, I finally dug into what was going on, and god damn it is a brilliant product. It might be one of the most important DeFi protocols ever built.

And Andrew Thurman, who wrote that CoinDesk piece, concludes:

But my heart is with the frogs. [Olympus’] money isn’t very different from the shoddy, debasing stuff the state forces into our bank accounts (and it’s certainly not dumber).

The problem Olympus solves, according to its announcement post, is “that we still do not have an independently valued digital currency.” It describes the “perfect currency” as one that “holds the same purchasing power today as in 50 years.”

“In the long term,” its web site states, “we believe this system can be used to optimize for stability and consistency so that OHM can function as a global unit-of-account and medium-of-exchange currency.”

This is a lofty, dare-I-say Olympian goal. But what’s actually gotten crypto folks exercised about the promise of OlympusDAO is specifically its innovative approach to maintaining liquidity within the decentralized finance (DeFi) ecosystem. To understand how Olympus is new and different, you first have to dig into how liquidity generally works (or doesn’t) in DeFi today.

Say I’ve just generated a new cryptocurrency, JayCoin, and minted one million tokens of it. Following in the footsteps of previous cryptocurrency projects, I “airdrop”3 — or gift — a bunch of them to friends, family, and influencers in the hopes of incentivizing them to drum up publicity and convince other people to buy some, thereby driving up the price and making themselves a neat little profit.

But how can anyone actually buy, sell, and trade my new JayCoins for other cryptocurrencies? Well, before decentralized exchanges (DEXes) came along, this wouldn’t have been very easy:

Before DEXes, we would have had to email someone at a centralized exchange like Coinbase and ask them to list us. Then there’d probably be months of discussions, paperwork, debates, and we might not even get listed. It would heavily favor companies with huge bank accounts and money to burn. And it would have been slow.

Enter DEXes like Uniswap and SushiSwap. What’s new about these entities is that they’re fully decentralized and noncustodial. Unlike Coinbase, they never hold onto your token balance on your behalf, so you’re always in control of your private keys. You don’t need permission to list your new cryptocurrencies on them. And they also don’t use centralized order books to line up large institutional “market-making” buyers and sellers to settle on a market-clearing price.

So how do DEXes come up with a market-clearing price? They have an automated market maker (AMM), which utilizes a “constant product formula” to generate a currency exchange rate. At its most basic implementation, a constant product formula simply mandates that x*y=k (a constant).

x and y are references to a pair of tokens in a “liquidity pool,” or a currency trading pair. A real-world equivalent might be something like British pounds and U.S. dollars (GBP/USD). In a DEX, however, anyone can create a new liquidity pool from scratch by depositing pairs of tokens.

For example, if I wanted to allow the public to trade JayCoin against ether, I might deposit 1,000 JayCoin and 10 ether into a liquidity pool. 1,000 * 10 = 10,000, which is the “constant product.” Anyone who wants to buy JayCoin must deposit ether into the liquidity pool and withdraw the corresponding amount of JayCoin such that the product of the two tokens — 10,000 — remains constant. (Hence “constant product formula.”)

So if someone wanted to buy 700 JayCoin, thereby leaving 300 JayCoin remaining in the liquidity pool, they would need to deposit about 23.3 ether in exchange, such that the liquidity pool would now consist of 300 JayCoin and 33.3 ether in total, for an unchanged constant product of 10,000. This comes out to an approximate ETH/JayCoin exchange rate of 700 / 23.3 = 30.

One of the key differences from centralized exchanges is that the liquidity, or currency pair, in DEX liquidity pools can be provided by regular crypto users, not just large, institutional market-makers. In order to incentivize those users to lock up, or “stake,” their cryptocurrencies for this purpose and thereby increase liquidity in the crypto ecosystem, the DEX protocol automatically gives these users a cut — usually 0.3% or less — of the value of all trades occurring in that currency pair (proportionate to their share of the total liquidity pool). They do this by issuing liquidity provider (LP) tokens that represent the liquidity provider’s share of profits from trading on the DEX. Additionally, token issuers themselves often incentivize liquidity providers as well, by rewarding them with additional tokens.

This is a genuine innovation. But it has a major vulnerability: the only thing liquidity providers care about is yield, the rate at which their staked tokens earn additional revenue. If and when another opportunity arises to earn higher yield, they’ll redeem their LP tokens for the underlying currency pair and decamp immediately to another exchange or protocol, leaving the original liquidity pool less…liquid.

The net effect of this is that, for any given token such as JayCoin, the amount of liquidity — and thus the ability of users to buy and sell the token easily — is highly fickle and unpredictable. Imagine if tomorrow you tried to exchange your dollars for euros but there were no one around to make you an offer. This is similar to the position many tokens find themselves in today: the market isn’t “liquid” enough for buyers and sellers to easily enter and exit at will.

This problem is exacerbated by the absurd levels of volatility in the crypto ecosystem: since LP tokens are effectively claims on an underlying currency pair, their “profits” are relative to those currencies. That is, if I provide liquidity for an ETH/SHIB currency pair and my LP tokens earn a 10% profit, I may still end up losing real-world money if ETH and SHIB themselves have depreciated against the U.S. dollar in that time.

Olympus believes it’s solved these problems by owning its own liquidity. Before we get into how it works, though…

A brief digression

Olympus claims both that A) “1 OHM is backed by 1 USD” and B) “each OHM is backed by 1 DAI.” In reality, however, Olympus’ “treasury” contains DAI, not U.S. dollars. DAI is a crypto “stablecoin” theoretically backed by U.S. dollars such that 1 DAI = $1, so Olympus is effectively claiming that each OHM token will always be worth a minimum of $1, because every new OHM it mints will have 1 or more DAI corresponding to it in the Olympus treasury.

It is tempting to condense all of the above into a simpler sentence: “1 OHM token is backed by $1.” But that is not strictly true. DAI is itself comprised of a lot of ether and other cryptocurrencies which, in combination, attempt to maintain approximately $1 of backing per DAI token. DAI’s whitepaper calls itself (emphasis mine) “a decentralized, unbiased, collateral-backed cryptocurrency soft-pegged to the US Dollar.”

This complex reality is widely transformed into the less accurate shorthand claim that 1 DAI is equivalent to $1. And that settled conventional wisdom is largely the result of DAI’s parent organization, MakerDAO, repeating it in prominent places like its home page:

And its Twitter feed:

But the reliability of that principle is extremely doubtful in the case of a significant crypto downturn.

It is so doubtful, in fact, that MakerDAO contradicts itself in its own public-facing documents as to DAI’s interchangeability with U.S. dollars:

On the Maker Protocol page: “In extreme events, 1 Dai can be redeemed for \$1 worth of collateral through a process known as Emergency Shutdown.”

On the Emergency Shutdown page: “It is, therefore, possible that Dai holders will receive less or more than 1 USD worth of Collateral for 1 Dai.”

🤷‍♂️

DAI’s effective equivalence with U.S. dollars is its most important feature — its raison d’être. So the fact that its parent organization isn’t sure if it’s strictly true is…concerning.4 If you visited your bank’s web site and discovered one page claiming you can withdraw your balance at any time and another page saying meh, actually, you might not be able to, this is presumably how fast you’d sprint to your closest local branch to demand all of your cash immediately:

And remember that DAI is the principal currency backing Olympus’ OHM token.

OK, back to our regularly scheduled programming

Right now 1 OHM token is trading at approximately $450. Since Olympus only needs 1 DAI’s worth of assets in its treasury for every new OHM it issues, it has spent most of its existence minting OHM tokens left and right and selling them into the market, currently at close to 450x its underlying “value.” This increases its treasury’s over-collateralization — or “runway,” as Olympus calls it.

OHM is so expensive right now that it’s not just selling at about 450x its minimum collateral backing: it’s currently selling at over 14x the Treasury’s per-token “risk-free value” (that is, its holdings of all dollar-pegged currencies like DAI, FRAX, and others) and almost 6x the per-token market value of all (non-LP OHM) assets in Olympus’ Treasury.

Given all of this, why would anyone in their right mind buy an OHM token for $450? After all, how many successful currencies have you heard of whose primary function is as an investment vehicle? There are two reasons: one short and one a bit longer.

The short reason is that Olympus actually sells OHM tokens directly to users at a slight discount to its market value — the cost of OHM if you were to just buy it directly on a DEX. (Think of this like an employee stock purchase plan, where you can purchase your employer’s shares at a slight discount.) Olympus calls its direct sales “bonding,” as in selling bonds to the market à la the Federal Reserve, because you can buy OHM at a discounted price but must wait five days for it to fully vest.5 So as a short-term strategy, as long as you think OHM’s price is unlikely to decrease much over the next five days6, you can make a profit simply by buying OHM from Olympus and selling it when it vests.

Now, the truly innovative element of this transaction is that the payment Olympus accepts in exchange for minting these bonds is…liquidity pool tokens. In other words, in exchange for you obtaining OHM tokens at a discount to their current market value, Olympus receives a share of its own currency’s liquidity pools, or trading pairs, on decentralized exchanges.

In so doing, it theoretically reduces, or eliminates, the problem faced by so many other tokens: mercenary capital hopping from one investment vehicle to another at the first sign of risk or better opportunities elsewhere. “Owning” its own liquidity ensures that OHM tokens can be traded easily and at large volume in the open market regardless of its price trajectory, just like, say, a large-cap company trading on the New York Stock Exchange.

Indeed, as long as OHM’s price stays above 1 DAI, Olympus can continue minting new OHM tokens, selling them for a profit, and owning more of its own liquidity. If the price dips below 1 DAI, Olympus will buy back OHM tokens at market value indefinitely, again at a profit, and then “burn” them (i.e. remove them from the supply), thereby pushing the price back up to 1 DAI per OHM.

OK, so bonding is the short (and short-term) reason to buy OHM. But it doesn’t really explain why anyone would buy into OHM at current prices and hold onto it. The reason for that is “staking.” That is, once the OHM in your “bond” has vested, you can “stake” it — effectively locking it away, sort of like a certificate of deposit (CD) at a bank.

If you do, Olympus will mint additional OHM — unlike bonding, these new OHM are backed by depleting Olympus’ existing excess treasury reserves, meaning it has a dilutive effect — and subsequently grant them to you over time as a reward for staking. If you don’t stake — that is, if you buy OHM and then just hold onto it — your share of total OHM outstanding will be diluted over time as new OHM are minted and distributed to stakers. So staking OHM effectively just allows you to tread water in relation to the overall supply: your OHM holdings increase along with the total supply of OHM.

Now at this point, you may be thinking to yourself: Waiiiiittttt a minute. So first I have to buy in at an inflated price, and then — in order to maintain (or possibly increase) the value of my holdings over time — I have to lock up my funds in a crypto equivalent of a bank CD.

Yes.

To which your second thought is likely: How is this not just multilevel marketing (MLM) or a Ponzi scheme?7

To which the response is usually some version of: “But look at the annual percentage yield (APY) rewards on this bad boy!”

No, the APY on that chart is not a typo, and no, it’s not using the European convention of a comma as a decimal separator. But anyway, don’t worry: as you can see below, following its initial speculative phase, as of mid-December the APY for staking rewards has cooled off to an extremely sensible 5,226%.

So staking a single OHM token now could net you over 50 more by next Christmas.

In short, the appeal of staking takes longer to explain not because its fundamentals are so complicated but because they’re so simple. If you happen to have purchased some OHM tokens from Olympus, you have the following options:

1. Sell them and pocket the profit (assuming OHM hasn’t depreciated within the five-day vesting period)

2. Hold them and watch the value get diluted over time as new OHMs are minted but not distributed to you

3. Stake your OHM and keep pace with “inflation” via ludicrously gargantuan APY rewards

So far, the vast majority of “OHMies” (yes, it’s a thing) have chosen door #3:

In other words, throughout most of Olympus’ existence, about 90% of the OHM in circulation is being staked, or locked up. I have many questions, but for now I’ll just note that a currency for which 90% of its value is untouchable is…generally not a very good medium of exchange.

But even leaving that aside, the basic math is ominous. In order for you to make money — real-world money, as in the kind you use to buy food — via staking OHM, the reward rate for staking needs to be greater than the rate of decline in the value of OHM itself. To take a basic example, if you stake 1,000 OHM for one year and end up with 10,000 at the end of it, but the price of OHM has dropped from $450 to $22.50 in that same timespan, you’ve lost half your money.

And the key point is: some version of this scenario is absolutely going to happen. Olympus itself states that 1 DAI is the “intrinsic value” of OHM, and it’s not committing to backing each OHM with anything more than that.8 To its credit, its announcement post explicitly states:

The best way [to participate] is to buy as close to or below 1 DAI as you can. The distance from 1 is the risk you take on.

And on podcast interviews and in the Olympus Discord chat, Zeus has also made clear that he doesn’t see OHM’s high prices as sustainable.

Olympus’ web site even acknowledges, however obliquely, the absurdity of its token’s current market price. One of the questions on its FAQ page is, helpfully: “What is the point of buying it now when OHM trades at a very high premium?” I will quote the answer in its entirety here:

When you buy and stake OHM, you capture a percentage of the supply (market cap) which will remain close to a constant. This is because your staked OHM balance also increases along with the circulating supply. The implication is that if you buy OHM when the market cap is low, you would be capturing a larger percentage of the market cap.

This is an extremely strange answer. The unstated logic goes something like this:

1. Yes, you are probably overpaying by an absurd — like, actually genuinely ridiculous — amount of money.

2. However, others who come after you will continue to do the same thing.

3. But by then, you’ll have already been staking, and they won’t.

4. So you’ll own more OHM than they do.

(This photo does not appear on Olympus’ FAQ page.)

The problem, of course, is that there is currently no logical reason why the price should go up indefinitely, or even maintain its current level. It’s supposed to be a currency, after all. Olympus has launched new products — notably Olympus Pro, which gives it a cut of other tokens’ upside — that may hold some revenue potential, but nothing that supports a price 450x its treasury backing.

So, as the price of OHM declines towards its self-described “inherent value,” the APY rewarded to stakers — which, per the above chart, is also declining over time — will begin to look like a very risky proposition. After all, it’s the excess treasury reserves (via the sales of overpriced bonds) that are funding the high APY in the first place.9 (Remember that, while OHM minted by bonding can generate excess treasury reserves, OHM minted by staking rewards can only deplete them.) In such a scenario, stakers will be incentivized to un-stake and sell their positions for literally any other asset, to lock in whatever value they’ve accrued to date before OHM falls further still.

This leaves us with one possible way for Olympus to wriggle its way out of this morass. It could just say, “Actually, the reference point you’re using is all wrong. Stop denominating your value in dollars. Real value is denominated in OHM.”

As it turns out, this is exactly what Olympus is saying. “You can't trust the FED but you can trust the code” is a real sentence that appears on its FAQ page. Olympus sees itself as the heir apparent to sovereign-backed central banks like the Federal Reserve:

This is unfortunate, because Olympus appears to have very little idea how central banking and currency exchange actually work. Let’s head back to its FAQ page:

Is OHM a stable coin?

No, OHM is not a stable coin. Rather, OHM aspires to become an algorithmic reserve currency backed by other decentralized assets. Similar to the idea of the gold standard, OHM provides free floating value its users can always fall back on, simply because of the fractional treasury reserves OHM draws its intrinsic value from.

Zooming in on this part:

Similar to the idea of the gold standard, OHM provides free floating value…

Free-floating value is quite literally the opposite of the idea of the gold standard. The Bretton Woods system, agreed to in 1944, codified the establishment of fixed exchange rates between the U.S. and other countries, as well as convertibility between the U.S. dollar and gold at $35/ounce.

This is not Zeus’ first run-in with the historical record. In a podcast interview in June, he said:

No one really complains about the dollar pre-1970, back when it was backed by gold. Things actually worked pretty well — probably the largest economic expansion in human history. But then you had this departure from the gold standard, you had just now full fiat. This is backed by nothing, and it went pretty poorly after that.

This is an odd thing to say. Even leaving aside the overly simplistic economic history, how can all of the following things be true at once:

A) The perfect currency is one that retains constant purchasing power over a 50-year period

B) No one complained about the pre-1970 dollar

C) In the five-year period from 1965 to 1970 alone, cumulative inflation in the U.S. totaled 23.2%

Or stated in simpler terms: if “no one really [complained] about the dollar pre-1970,” why did Richard Nixon get rid of it in 1971?

The answer, while complicated, was partly inflation — that crypto-world bugbear that supposedly can only happen in a world of free-floating currency. As Zachary Carter explains in his masterful book The Price of Peace, “The United States had been leaking gold for years thanks to inflationary pressures and the new phenomenon of an American trade deficit.” As you can see clearly from this chart, elevated inflation is not solely a post-Bretton Woods phenomenon:

In fact, the current annual inflation rate in the U.S. — which has prompted howls of criticism from the libertarian-leaning crypto community — is roughly in line with where it was in the summer of 1970, before the U.S. abandoned the gold-backed Bretton Woods system.

This is not the only aspect of banking Zeus misunderstands. In the same June podcast interview, he was asked what a crypto reserve currency is supposed to accomplish:

In my mind, it really comes down to liquidity…The real goal in my mind is to have something where, when there’s excess demand, there’s people to sell to it. When there’s excess supply, there’s people to buy from it. Whenever there’s a trade being made, there’s someone on the other side of it.

This is actually what central banks primarily do, is they provide liquidity.

This is not true. The primary purpose of the central bank, at least in the U.S., is the famed “dual mandate” of maximum employment and stable prices (i.e. low inflation), which the Fed achieves by setting the short-term interest rate via open market operations.

Yes, the Fed also provides liquidity to its member banks, but in normal (non-emergency) times this is an almost negligible aspect of its function, and is certainly not what it “primarily” does or is intended to do. A Congressional Research Service report from February underscores this point:

The Federal Reserve (Fed), as the nation’s central bank, was created as a “lender of last resort” to the banking system when private liquidity becomes unavailable. This role is minimal in normal conditions but has been important in periods of financial instability, such as the 2007-2009 financial crisis.

As then-Vice Chairman of the Federal Reserve Stanley Fischer cautiously celebrated in 2016: “While we have likely reduced the probability that lender of last resort loans will be needed in the future, we have not reduced that probability to zero.”

Needless to say, this is not the rhetoric of an institution whose primary purpose is providing liquidity. When a typical consumer buys bonds for their investment portfolio, they generally do it via a brokerage like Fidelity or Schwab precisely because there’s so much liquidity in the bond market already. “Lender of last resort” is the last resort for a reason. (This is also, as it happens, why not “owning its own liquidity” is not a problem for the U.S. government: unlike OHM, U.S. dollars are widely valued, so the government doesn’t need to become the dominant participant in retail foreign exchange markets simply to prevent a complete loss of confidence in the dollar.)

Given these inaccuracies about central banking as it exists today, it’s worth asking what Olympus thinks central banking should do.

There’s a distinct dog-that-caught-the-bus quality to the Olympus project: its implicit first principle is that sovereign central banks are bad, but given the chance to define itself in opposition, it falls into near-monastic silence. Price stability is bandied about, but stability relative to what? It’s not that Olympus has a bad answer to this question. It has no answer to this question.

I mean that literally. In a Discord thread earlier this year, Zeus was asked what would be a good metric to measure OHM’s price stability against. It’s worth reposting the entire conversation:

This is not a sentence I ever thought I’d write, but: “cryptofreebies” is spot-on here. “Price today being similar to price yesterday” needs to be denominated in something to have meaning. It also needs to be measured against a relatively stable basket of goods in order to be comparable over time. If only we had a way to do that.

In addition to the gaping vacuum of information on how the OHM currency will achieve price stability, Olympus also appears to have no strategy on how to become a currency anyone actually uses in the long run. The steps seem to be:

1. Rake in cash from overeager buyers.

2. ….?

3. Become the world’s reserve currency.

This is a Benjamin Button-esque vision of currency creation. Generally, the best way to bootstrap currency adoption is to force citizens to pay their taxes in it, backed by a credible threat of force. Olympus intends instead to reverse-engineer this process: first, it generates billions of dollars of assets and then, through some automagical but undefined process, OHM becomes used everywhere.

And the problems don’t end there. If the U.S. dollar is such a problematic foundation for the global economic ecosystem, how is OHM planning on independently maintaining price stability while simultaneously guaranteeing a 1 DAI minimum backing? This would be like the British government allowing the pound sterling to float in currency markets while also promising it will always be worth at least $1.

This cognitive dissonance extends to Olympus’ adherents. Near the end of Nat Eliason’s “I Was Wrong About Olympus” essay, he enthuses about the backstop provided by stablecoin assets in Olympus’ treasury:

So if the community lost 100% of their faith in OHM and it had no speculative value, there would still be ~$30 per OHM token in existence. That’s quite a bit lower than the current price of $866, but considering most coins don’t have any backing like this, that’s pretty nice.

But this only serves to underscore the project’s absurdity: in what universe is it reassuring that a token you could once buy for $866 will always be worth at least $30? If the answer to that question is “one in which Olympus is a global reserve currency,” then OlympusDAO is perhaps one utopia we can all live without.

Appendix: Reading List

Decentralized Exchanges: Why Crypto’s First Killer Apps are So Powerful

150% APR? How Are DeFi Yields So High?

I Was Wrong About Olympus

OlympusDAO’s decentral bank — what might stop DeFi’s “liquidity black hole?”

A 7,000% APY Crypto Token | $OHM Investment Memo

Talking Crypto #72 - Zeus from Olympus DAO

Up OHMly featuring Zeus & JaLa

No One Understands Olympus DAO

Additionally, I would like to thank my buddy Ariel Stulberg for being a constant sounding board and debating partner on all things finance and crypto.

CORRECTION: This post originally stated that OHM is “currently selling at over 25x the Treasury’s per-token ‘risk-free value’…and over 60x the per-token market value of all (non-LP OHM) assets in Olympus’ Treasury.” This has been corrected to “over 14x” and “almost 6x.”

6

To paraphrase Matt Levine, this is definitely not investment advice. As seen below:

David Letterman: I can remember a couple of months ago there was a big breakthrough announcement that on the internet, or on some computer deal, they were going to broadcast a baseball game. You could listen to a baseball game on your computer. And I just thought to myself, does radio ring a bell?

(laughter)

Bill Gates: There’s a difference. It’s not a huge difference.

David Letterman: What is the difference?

Bill Gates: You can listen to the baseball game whenever you want, too.

David Letterman: Oh, I see. So it’s stored in one of your memory deals and then you can come back and a year later —

Bill Gates: Exactly. That’s the RAM that we talked about earlier.

(pause)

David Letterman: Do tape recorders ring a bell?

- Bill Gates on Late Show with David Letterman, November 27, 1995

No one wants to sound like David Letterman.

The terror of missing the next big thing - a sentiment so universal it has its own acronym: FOMO - is reason enough to think twice before dismissing the latest tech obsession.

Which brings me to the phenomenon that’s taken over my Twitter feed recently: web3.

What is web3?

Good question. Let’s toss it over to the folks at Ethereum.org:

Web3, in the context of Ethereum, refers to decentralized apps that run on the blockchain. These are apps that allow anyone to participate without monetising their personal data.

They also helpfully include this table:

Slate published what I think is roughly the most commonly accepted (and comprehensive) definition of web3:

Web3 refers to a potential new iteration of the internet that runs on public blockchains, the record-keeping technology best known for facilitating cryptocurrency transactions. The appeal of Web3 is that it is decentralized, so that instead of users accessing the internet through services mediated by the likes of Google, Apple, or Facebook, it’s the individuals themselves who own and control pieces of the internet. Web3 does not require “permission,” meaning that central authorities don’t dictate who uses what services, nor is there a need for “trust,” referring to the idea that an intermediary does not need to facilitate virtual transactions between two or more parties. Web3 theoretically protects user privacy better as well, because it’s these authorities and intermediaries that are doing most of the data collection.

So web3 is primarily about using blockchains to build apps that are independent, permission-less, and decentralized, and where the value produced in the economy is largely captured by content creators themselves. This is primarily made possible via “smart contracts,” pieces of code that run on the blockchain and execute actions automatically based on specific conditions (e.g. “send 10 ether from X to Y if X has >= 10 ether in its wallet”). Smart contracts are often compared to vending machines: if you insert the right amount of money, the machine gives you a candy bar.

This decentralized and deterministic schema stands in contrast to the status quo ante (web 2.0), where much of our tech infrastructure and culture is trapped inside highly centralized corporate walled gardens that retain most of the monetary value produced by their network of users.

I was a child when that David Letterman interview aired, but to the extent that I’m loosely familiar with the era, his views were rather in line with conventional wisdom. As another example, here’s future Nobel Prize winner Paul Krugman writing three years after the Letterman/Gates interview, in 1998:

The growth of the Internet will slow drastically, as the flaw in ‘Metcalfe’s law’ — which states that the number of potential connections in a network is proportional to the square of the number of participants — becomes apparent: most people have nothing to say to each other! By 2005 or so, it will become clear that the Internet’s impact on the economy has been no greater than the fax machine’s.

I’ve been thinking about these catastrophically bad predictions lately, because much of the recent online discourse around web3 — not unlike the internet enthusiast discourse circa 1995 or even 1998 — has the disquieting air of a cult.

And like a cult, the urge to dismiss or mock it (“covered wagon!?”) is nearly overpowering.

So is the hype real?

I write all of the above as a sort of throat-clearing exercise. That is, I’m sufficiently wary of casting my lot with the likes of David Letterman and Paul Krugman that I want to disabuse myself of my skepticism if there’s actually a web3 there there.

As Will Wilkinson agonized in his own piece — spoiler: he entered the web3 rabbit hole and emerged as a convert — part of the difficulty with evaluating web3 is the intensity of opinion by intelligent people on both sides:

However, a few of my best and smartest friends are deeply into crypto stuff and know more about it than I do. One of my personal epistemic precepts is that if I disagree with somebody who is (a) at least smart as I am, (b) has generally sound judgment and (c) is better informed on a subject, then I ought to concede that I’m probably wrong and revise my opinion in their direction. The trouble is, I also know plenty of people who satisfy all these conditions but think crypto is unmitigated bullshit.

As for me, the more I read about web3, crypto, DAOs, NFTs, etc., the more dubious the whole ecosystem appears. So while this is unlikely to be my final viewpoint on all things web3, below represents my top 7 reasons — some of which overlap — to be highly skeptical about its usefulness.

#1: The technical details don’t support the grandiose claims of trustlessness and decentralization

The web3 fandom discourse is densely populated by a cohort of people that are extremely excited about the future possibilities enabled by decentralized and trustless networks and can (sort of) explain, in broad, hand-wavy terms, to an uninformed public why this might matter.

These are people like former Planet Money founder Adam Davidson: “I believe that [web3] will be enormously consequential for nearly all forms of human interaction.”

Or, perhaps more absurdly, podcaster and crypto enthusiast Aaron Lammer1:

I do think there are ways where crypto comes out of a reckoning of the 2.0 internet. I think I’m stealing this line from somebody, but you remember the Google slogan “Don’t be evil,” right? Well, that was sort of a recommendation; it wasn’t, like, law. But what if it were?

The problem is that this cohort’s claims, while perhaps immediately graspable, often collapse under light scrutiny. Take the Ethereum Name Service, for example. Designed as a sort of wholly decentralized version of DNS, the architecture that underpins the internet, ENS’s own FAQ page acknowledges (emphasis mine):

The root node [under which all ENS domains are registered] is presently owned by a multisig contract, with keys held by trustworthy individuals in the Ethereum community. We expect that this will be hands-off, with the root ownership only used to effect administrative changes, such as the introduction of a new TLD, or to recover from an emergency such as a critical vulnerability in a TLD registrar.

Or look at The Render Network, the self-described “leading provider of decentralized GPU based rendering solutions, revolutionizing the digital creation process.” Its whitepaper’s section on fraud prevention states (emphasis mine):

Additionally, other features are built into the network to verify render jobs - these features will not be made public in order to not divulge information to those looking to take advantage of it.

Or take a peek at NBA Top Shot, the subject of an earlier post of mine on NFTs, which are a key component of web3:

networked

NFTs and Nomar

Everyone these days is talking about non-fungible tokens, or NFTs. Fred Wilson is talking about it. Alex Danco is talking about it. Chris Dixon at Andreessen Horowitz is talking about it. Reuters is talking about it. Li Jin and Nathan Baschez are talking about it. Benedict Evans is talking about it (at least I assume so: he blocked me on Twitter…

Read more

5 years ago · 2 comments · Jay Pinho

Top Shot’s help page says that “NBA Top Shot is the home for NBA fans to collect Moment™ collectibles of their favorite stars and team” and continues (emphasis mine):

An NFT is a one-of-a-kind, non-fungible, cryptographic token representing a unique digital asset for which there is no copy or substitute…

Each moment is secured by the blockchain, meaning your Moment is Unique and Licensed by the NBA and NBPA.

And yet the very same site’s Terms of Use reads (in part, and emphasis mine):

For the sake of clarity, you understand and agree: (a) that your purchase of a Moment, whether via the App or otherwise, does not give you any rights or licenses in or to the App Materials (including, without limitation, our copyright in and to the associated Art) other than those expressly contained in these Terms; (b) that you do not have the right, except as otherwise set forth in these Terms, to reproduce, distribute, or otherwise commercialize any elements of the App Materials (including, without limitation, any Art) without our prior written consent in each case, which consent we may withhold in our sole and absolute discretion…

In each of these cases, either decentralization or trustlessness or ownership (or some combination) turns out to be a mirage.

Which leads me to…

#2: NFTs prove neither ownership nor uniqueness

Fans of non-fungible tokens, or NFTs, say they have the potential to upend internet-era economics of infinite supply and restore artificial scarcity. Here’s a fairly typical description:

That means that, for the first time, it's possible to prove ownership of a digital artwork, and for digital art to have provable scarcity.

By encoding ownership of a unit of digital content — say, a piece of artwork — directly and immutably on a public blockchain, NFT boosters claim that scarcity can be created out of cryptographic thin air. As a digital artist, for example, I can decree that only ten copies of my JPEG art exist, and then auction them off one by one, with each owner obtaining one of only ten “unique” copies.

But this isn’t true. NFTs don’t prove you own anything:

In other words, someone copied and pasted this guy’s real-life artwork, created an NFT out of it — which is usually nothing more than a reference to a piece of art already existing either online or in the real world — and sold it for $170,000. And there’s nothing stopping anyone else from doing this, either.

We know this because it keeps happening:

Thieves have stolen bored ape NFTs. They’ve stolen from dead people. They’ve created a performance-art parody of The Pirate Bay called The NFT Bay that literally consists of thousands of “stolen” JPEGs:

NFT theft is so common, in fact, that there’s an entire Twitter account dedicated to NFT theft. It’s called @NFTtheft.

And that’s not the worst of it: for a variety of reasons too boring to go into here, the content of the NFTs — the artwork itself — often isn’t stored on the blockchain. Only a link to it is:

Traditional URLs pose real problems for NFTs. The owner of the domain could redirect the URL to point to something else (leaving you with, perhaps, a million-dollar Rickroll), or the owner of the domain could just forget to pay their hosting bill, and the whole thing disappears.

In other words, NFTs are less like a digital version of a rare LeBron James basketball card and more like a piece of paper with written instructions for where to find the card in a specific apartment located in a building that may or may not have been razed several years ago.

#3: Most product ideas for web3 have preexisting counterparts that already work pretty well

This most obviously applies to financial transactions themselves, which are the entire point of the OG web3 project: Bitcoin. (The Bitcoin whitepaper, published by the pseudonymous Satoshi Nakamoto in 2008, is literally titled “Bitcoin: A Peer-to-Peer Electronic Cash System.”)

Bitcoin currently has the capacity to process about 4 transactions per second. Ethereum can process about 15.

Visa processes 1,700.

The dissonance extends beyond mere transaction throughput to the availability of financial products generally. Web3 is often inaccurately credited for feature sets that long predate it:

Or take “composability.” Web3 enthusiasts are particularly excited about the stackable LEGO effect made possible by smart contracts:

Smart contracts go beyond simply allowing for instant and verifiable transactions for various applications though — they can also be programmed to interact with each other. In other words, they make crypto programs composable, like building blocks…Because composability allows anyone in a network to take existing programs and adapt or build on top of them, it unlocks completely new use cases that don’t exist in our world.

But composability is hardly new. APIs — which have been around for decades — are atomic units of composability. Anyone who’s used Zapier, IFTTT, or Workato intuitively comprehends that composability is not an innovation introduced by web3.

These examples go on and on. Adam Davidson published a piece last month titled “Real world problems that web3 could solve—at least for me.” In it, he cited numerous features as new or groundbreaking possibilities opened up by web3: the ability to record proof of meeting attendance, display projects and courses and communities on a personal digital profile page, send “tips” to people who’ve been helpful to you, or empower workers to “have a portfolio of projects, rather than a single job.”

If these things sound like Zoom, LinkedIn, Buy Me a Coffee, and…um, freelancing, congratulations: you’ve been alive and online for more than five minutes.

Do tape recorders ring a bell?

#4: Core features of crypto and web3 aren’t actually desirable to most people

It’s not just that some of the analog, or web2, iterations of products work as well as, or better than, their web3 counterparts in some abstract or theoretical sense. It’s that real people prefer them as well.

Think about Bitcoin. If you live in the US, and you’ve ever bought Bitcoin, chances are you probably did so by signing up for an account with Coinbase or Robinhood (or a similar centralized exchange or trading platform).2 You connected your USD bank account and deposited cash into your Coinbase account.

The key thing here is that, by doing this, you’ve discarded a core advantage of cryptocurrencies. Crypto enthusiasts like to repeat the maxim “not your keys, not your coin” — meaning that unless you have sole control of the private key to your cryptocurrency wallet address, you don’t actually own the coins. (They say this because, just as with NFTs, cryptocurrency theft is a constant threat.)

But by holding your cryptocurrency on Coinbase (a “custodial” wallet, so called because Coinbase holds the private keys, and thus your cryptocurrency, on your behalf), you have to trust Coinbase to take care of your crypto for you, to protect against hacking, and to withstand subpoenas or orders to freeze your account from the US government. (This is both legally and historically not a good bet.) You’re also implicitly signing off on the benefit of (or at least you’re not particularly bothered by) Coinbase’s adherence to “know your customer” and anti-money laundering regulations.

All of which suggests that maybe what’s attracting so many people to crypto isn’t the libertarian philosophical underpinnings or the technological breakthroughs in decentralization so much as it’s:

As Robin Sloan writes:

A large fraction of Web3’s magnetism comes from the value of the underlying cryptocurrencies. Therefore, a good diagnostic question to ask might be: would you still be curious about Web3 if those currencies were worthless, in dollar terms? For some people, the answer is “yes, absolutely”, because they find the foundational puzzles so compelling. For others, if they’re honest, the answer is “nnnot reallyyy”.

But it’s not just trustlessness that’s unpopular. The immutability of a distributed, public blockchain is one of its primary differentiators from traditional databases. The problem is, people often don’t want immutability.

If you’ve ever been the victim of identity theft and had funds fraudulently removed from your bank account or charges added to your credit card balance, the reversibility of transactions is something you’ve almost definitely benefited from. It’s not a bug; it’s a feature.

Speaking of bugs, they appear routinely in smart contracts, with catastrophic results:

As Stephen Diehl writes, “These disasters happen about two or three times a week now because coding at that level of correctness required in a Javascript-like language with loose and ill-defined semantics is near impossible.” More to the point, on a truly decentralized and trustless network, those losses are also irreversible — because no one has the authority to do so.

And that’s before we even get to the countless stories of people and companies losing cryptocurrency because:

• their private keys to $280 million in Bitcoin were stored on a hard drive they threw away

• they accidentally transferred crypto to the wrong address

• $115 million was stolen via a hack of a DeFi (decentralized finance) web UI

• they got duped by one of the endless scams in the crypto ecosystem

Oh, and this thing, which happened to crypto exchange BitMart while I was writing this newsletter:

Imagine if personal bank accounts were this rife with fraud!

Of course, as covered above, there are ways to mitigate some of the worst risks, such as by storing your coins with one of the more reliable firms like Coinbase. In his post on web3, Mischa Spiegelmock explains:

Just as we have banks to relieve us of the need to stash money under our mattresses, trusted (and scammer) establishments with customer support phone numbers and backups will pop up to service the general populace and hold on to their private keys.

But therein lies the rub. If you’re prepared to discard trustlessness and decentralization, there needs to be something else blockchains are better at than the established alternatives. And it’s not at all clear what that is. (See points #5, #6, and #7 below.)

Many of the spectacular blowups mentioned here are the inevitable consequence of deliberate design choices — specifically, the philosophy that blockchain activity is immutable and irreversible.

The design choice is just that — a choice. And in an ironic twist, no one understands the chosen design’s undesirability better than web3’s founding fathers, because — in one of the most (in)famous episodes in crypto history — the premier layer-1 blockchain, Ethereum, reversed a fraudulent transaction entirely, to the tune of approximately $60 million.

#5: Inscribing governance in code doesn’t resolve real-world conflicts over competing values

Quick glossary check: a DAO is a decentralized autonomous organization, whose governance rules are set by smart contracts executed on a blockchain. You can think of it as a corporate-like entity or venture fund with programmatically enforced bylaws. Generally, the more money and/or time you invest in a DAO, the more control you gain over its governance, via the distribution of crypto tokens. (Now, is a securitized popularity contest actually a good way to run an organization? See point #4.) DAOs are often cited as one of the more genuine innovations enabled by web3, especially in comparison to the wild-eyed speculative gold rush often associated with cryptocurrencies.

The first DAO to rise to prominence was “The DAO,” founded in 2016 on Ethereum. It raised $150 million worth of ether in order to crowdfund new projects on the blockchain. But it didn’t last even three months before a hacker exploited a vulnerability and stole about $60 million worth of ether.

A debate quickly broke out among developers and victims of the hack: what should be done? On the one side were those advocating the purist crypto stance: the smart contract is a sort of digital law, and by the rules of that law as written — buggily, as software often is — the hacker was able to withdraw about $60 million of ether. Ergo, by definition the hacker was playing by the rules as they existed and nothing “wrong” had occurred.

On the other side3 were advocates of a “hard fork,” a proposal to reverse all the hackers’ transactions and lop them off the blockchain as if they had never happened, thus restoring the victims’ funds.

Ernesto Frontera describes the thorny dilemma:

The “code is law” advocates had a point: how would a hard fork be any different than standard central banking procedures like “bail-outs”? The intervention of central developers into the monetary policy of Ethereum worried many.

You can probably guess where this is going. Common sense prevailed, and the hard fork proposal won, returning the “stolen” funds to their victims. This modified, not-at-all-irreversible blockchain still powers Ethereum today.

In fairness, the losers didn’t exactly walk away with their tails between their legs. They continued the original “pure” blockchain — complete with hacked funds — as “Ethereum Classic,” which also still exists. But it’s considerably less popular than its “sellout” sibling: the total value of ether outstanding is currently just below $500 billion, while Ethereum Classic’s total value is just 1 percent of that.

The episode of The DAO demonstrates that, when push truly came to shove, participants in the world’s dominant smart contract platform simply decided that immutability was…mutable.

More importantly, the hard fork exposed the stark limits of smart contracts’ power: code is totally deterministic, but if the broader (one might say “societal”) expectations of what that code should do turned out to differ from the reality of what it did, then smart contracts aren’t really all that smart. (See point #6 below.) They’re just code. And like all code, the outcomes they produce represent specific social values. If those values come into conflict with those of the broader community, either the community’s views must prevail or you end up with authoritarianism.4

To his credit, the creator of Ethereum, Vitalik Buterin, came close to acknowledging this himself in the wake of The DAO hack:

All instances of smart contract theft or loss - in fact, the very definition of smart contract theft or loss, is fundamentally about differences between implementation and intent…This leads to the next challenge: intent is fundamentally complex.

#6: Smart contracts aren’t smart

This point initially looks the same as #5, but it’s not. #5 is specifically about the inability of code to referee among competing values systems. Point #6 is that the limitations of smart contracts innately prevent them from producing much real-world value at all.

To understand this, you have to remember why web3 backers are so excited about smart contracts. Their key advantage is that they are deterministically-executed code packages representing an agreement between counterparties: the code executes once the blockchain has validated that certain underlying conditions have been satisfied. There’s no human or manual process required: when a smart contract’s conditions are achieved, the outcome is predetermined.

But in order for this to work, all (or at least most) of the nodes on the blockchain - the thousands of servers all running the same Ethereum Virtual Machine (EVM) software that powers the network — have to agree that the underlying conditions have been satisfied in the first place.

Therein lies the first problem. These nodes cannot independently evaluate the truthiness of almost any underlying conditions whatsoever, except for the very limited set of conditions directly relating to activity that has occurred on the blockchain itself (such as the balance of specific wallets). In other words, to ensure deterministic outcomes based on a condition, the condition has to be deterministically evaluable as well: otherwise the EVM nodes may disagree about whether the smart contract should be executed or not.

For this reason, some of the top use cases you often see written about for smart contracts are in lending (for example, via Aave) and margin trading: a creditor can use a smart contract to programmatically lend crypto-denominated funds to someone and automatically be repaid, with interest, on a set schedule. (Similarly, a brokerage can enable margin accounts for their clients.)

But how does one programmatically assess creditworthiness, you ask? Answer: you don’t have to, because these loans are over-collateralized. For every, say, Shiba Inu coin you borrow, you have to put up — in digital escrow, effectively — a greater value of ether (or perhaps Tether5) as collateral. If the value of your borrowed asset falls relative to your collateral6, your creditor automatically takes possession of the collateral.

You can see how this is necessarily limiting. Unlike what I can do with my credit card right now — rack up thousands of dollars of charges without putting up any cash in advance, because I have previously established myself as creditworthy — a loan like this can only be made to someone with more funds in cryptocurrency than they are trying to borrow, which also must be a cryptocurrency.

This limiting scenario obviously lends itself to one primary use case: wild speculation on various cryptocurrencies. How wild does this financial speculation get, you ask? Well, for comparison, the maximum initial leverage that U.S.-based traditional brokerages can legally offer their clients is 2x. That is, “an investor can not borrow more than $1,000 to buy $2,000 worth of stocks within a brokerage account.” In practice, brokers often offer ratios even more conservative than that.

Until this summer, one of the leading crypto exchanges, FTX, allowed traders to leverage at a 100x ratio. That’s right: a 1% drop in the value of your leveraged bet would wipe out your entire position. When FTX finally announced in July they were going to reduce this ratio, they changed it…to 20x.

Now I’m trying to be sympathetic here: like many web3 denizens, FTX CEO Sam Bankman-Fried wasn’t old enough to legally smoke a cigarette during the Great Financial Crisis. So this grumpy elder millennial would just like to gently remind web3 folks who are enthusiastic about financial product innovation that — when it comes to devising highly leveraged financial instruments — web3 is not, ahem, our first rodeo.

As a side note, enabling frictionless financial speculation is a particularly odd triumph to hang web3’s hat on in an era where a 30-year fixed mortgage rate is currently 3.11%. It would be like announcing a major breakthrough in how to become unemployed in 1931. Money is practically falling from the sky: neither a smart contract nor even a particularly smart person is required to find funding for just about anything. Hell, four years ago a startup selling a $400 fruit juicer boasted $120 million in venture capital funding in a higher interest-rate environment than the one we have now.

The reason I’ve taken you down such a lengthy rhetorical detour is this: to achieve anything more interesting than over-collateralized lending or absurdly leveraged margin trading with smart contracts, you pretty much have to use an oracle. (Not, like, Oracle. Just an oracle.) An oracle is a method for obtaining off-chain, real-world data — like today’s weather in New York, or the current Dow Jones Industrial Average, or the winner of the last presidential election — and making it available on the blockchain.

Chainlink, a leading oracle provider, reiterates the blockchain’s inherent limitations I walked through above, in explaining why oracles are so central to doing anything useful on the blockchain:

The oracle problem revolves around a very simple limitation—blockchains cannot pull in data from or push data out to any external system as built-in functionality. As such, blockchains are isolated networks, akin to a computer with no Internet connection. The isolation of a blockchain is the precise property that makes it extremely secure and reliable, as the network only needs to form consensus on a very basic set of binary (true/false) questions using data already stored inside of its ledger.

But guess what happens when you use oracles to pull in useful data? There goes trustlessness, right out the window.

Oracle providers rely on a marketplace of data sources — for example, the Associated Press can be queried for election results via API (there’s composability again!) — to bring external data into the blockchain. That means the data has not been subjected to the same consensus validation methodology central to web3.

In short, it means you have to trust someone. And like anything that involves trusting other people, sometimes that trust turns out to be misplaced. That is, when the oracle itself is compromised, so is your transaction that relies on it.

A smart contract, therefore, isn’t very smart at all. The universe of data types that can be validated trustlessly on the blockchain is infinitesimally small, but to expand that universe requires relying on external institutions like those dirty hippies over at the Associated Press.

#7: Smart contracts aren’t contracts

The advantage of smart contracts is their determinism: given the presence of certain inputs, a given output is assured.

This epistemological certainty does not survive first contact with humans. Although they are called “smart contracts,” there is nothing inherently legally enforceable about anything that happens on a blockchain.7 The term “contract” is really a misnomer for "code execution.”

And just as validator nodes are only able to independently verify data that is already on the blockchain, the execution side of a smart contract is similarly limited in scope: nothing can be “executed” on the blockchain that deterministically affects the real world with a specific outcome.

This is because the real world is complex and ever-changing. For example, imagine a theoretical smart contract that could e-sign a deed of property from its owner, Person A, to a wallet belonging to Person B, provided that Person B has sufficient funds to pay for the property.

While the underlying condition could potentially be validated with certainty using blockchain data alone (does Person B have sufficient funds?), there is nothing preventing Person A from, for example, already having sold his property to someone else beforehand. What is the enforcement mechanism to prevent this from happening?

Well, you say, a smart contract can be designed such that both the buyer and seller have to put in collateral funds, and in the case of a breach the victim automatically receives the breacher’s collateral. But then how is the existence of a breach determined?

No matter how far down the road you punt the problem, eventually you must confront the fact that the outcomes of human interaction are inherently and necessarily nondeterministic, and thus cannot be resolved using only a blockchain or any other programmatic resolution mechanism. This is not a bug, but a feature, of being human.

It’s also why the court system exists. If we could simply programmatically determine outcomes for every real-life situation without the rather messy and arbitrary juridical process this often entails, we would be no better than robots ourselves.

The unreality of the “contract” half of smart contracts is also why NFTs can’t prove ownership or uniqueness or, really, much of anything at all. Smart contracts are inherently self-referential. It’s why an NFT only proves that a specific address says it owns something, rather than proving it actually does. It’s why virtually the entire DeFi ecosystem is an M.C. Escher universe of self-referencing financial speculation that either never touches anything tangible or requires oracles — that is, trust — to do so. (If you want to feel dizzy for free, try reading up on “flash loans.”)

So where is web3’s killer app?

It’s been 13 years since Satoshi Nakamoto published his Bitcoin whitepaper. In all that time, the closest the crypto ecosystem has come to creating mainstream “killer apps” are ransomware and online markets for illegal drugs.

Proponents of web3 often respond with a version of the argument that the emergence of web3 — which, to be fair, is more attributable to Vitalik Buterin’s Ethereum whitepaper in 2013 than Satoshi’s in 2008 — is akin to that of the internet itself: so vast and immeasurable in its potential future implications that it will take time to actually work backwards from the theoretical genius of the protocol itself into an array of practical applications that sit atop it.

And yet contrast this with the App Store on the iPhone, which was launched in July 2008, just 3.5 months before Satoshi’s whitepaper. One could write (and many have) entire books on the ways in which the robust app ecosystem that has blossomed from the App Store “platform” has changed the world. Not all of these changes are for the better, certainly, but virtually no one would question whether they exist in the first place.

Web3’s first years, it must be said, look quite different than the iOS App Store’s public journey since its initial introduction. Like a 17th-century Dutch tulip, web3 captures the imagination not for what it does but for how much money has been made and lost with it.

This befits a technology whose rhetoric is ever-soaring, whose potential remains open-ended, but whose lingua franca is borrowed directly from Wall Street: the buying, selling, swapping, and lending of tokens in DeFi; the forced reintroduction of scarcity via NFTs; and the securitization of governance via DAOs.

It was perhaps not web3’s original objective to serve as a signpost of human existence’s increasing hyper-financialization (see the linked tweet above for a non-web3 example), but to paraphrase Vitalik Buterin: there can be very big differences between implementation and intent.

For that same reason, I think people can genuinely believe they're obsessed with web3 because of its inherent interestingness when in fact it’s because web3 has made them a lot of money very quickly.

And yet…

You may have noticed that in this post I’ve focused on web3’s usefulness, not its financial value.

This is for two reasons.

First, debates about the future of web3 as an investment vehicle are inherently unfalsifiable (at least in the present). There’s no way to know: a single ether could be worth $1 million a decade from now, or it might not exist. It’s not just that I have no way of predicting which outcome is likelier, but I’m also somewhat bored by the conversation.

The second reason for focusing on web3’s functionality rather than its investment potential is that the answers to those two questions will not necessarily correlate with each other in the medium or even long term.

So to be clear, I am not saying that A) web3 is not particularly useful and B) therefore you should not invest in cryptocurrencies.8 I’m just saying A. A general dearth of functionality is hardly a guarantee of imminent, or even eventual, collapse. (If you’ve ever been a customer of an American internet service provider, you already know this well.) As the axiom goes, the market can remain irrational longer than you can remain solvent.

My caution stems partially from what I perceive as similar divergences in other sectors. As I wrote almost exactly a year ago about the ad tech industry, the status quo in any given market can feel unsustainable and yet continue to exist in seeming perpetuity:

In short, no, it is not clear that online advertising is a bubble. But one can forgive Tim Hwang for thinking it is. His fault lies not in being overly pessimistic, but rather the reverse: in order to pop a bubble, you first need a market that’s curious enough to poke around.

Are web3 participants curious enough to poke around?

Appendix: Reading List

While researching for this newsletter I ended up spiralling down a web3 rabbit hole myself. Below are a few of the resources I found particularly insightful in explaining and/or challenging various elements of the web3 ecosystem.

From contemptuous to indifferent to curious to pretty damn excited: my journey to web3

Real world problems that web3 could solve—at least for me

A Crypto True Believer Makes His Case

How Digital Art and NFTs Are Changing the Nature of Ownership

Is Crypto Bullshit?

Is web3 bullshit?

Web3 is Bullshit

The Handwavy Technobabble Nothingburger

The Token Disconnect

The Eightfold Path to DAOism

What Was The DAO?

A History of ‘The DAO’ Hack

What is Web3? Should You Care?

Notes on Web3

Why NFTs are bad: the long version

‘Buy the Constitution’ Aftermath: Everyone Very Mad, Confused, Losing Lots of Money, Fighting, Crying, Etc.

So you want to use a price oracle