It happened fast. Downloads of Argentina’s 15 leading crypto apps nearly doubled in 2024, rising 93% from the previous year.

Argentina’s preference for dollars predates crypto. Back in 2001–2002, the government froze bank deposits and forcibly converted dollar-denominated deposits and loans into pesos via Decree 214/2002. After the dollar peg ended, the exchange rate fell from one peso per dollar to nearly four, wiping out roughly three-quarters of the peso’s dollar value. The crisis deepened distrust of the peso and reinforced the habit of keeping savings in physical dollars outside the banking system — think bills under mattresses or in safe-deposit boxes.

Stablecoins started to pick up steam in Argentina after the government reintroduced currency controls in 2019. Within months, the government had limited Argentines to $200 in official dollar purchases each month, while additional eligibility rules excluded many people entirely. Dollar-pegged stablecoins became another way to save in dollars without relying on the official market.

More recently, stablecoins have become a larger part of contractor pay. Year-over-year inflation reached 289% in April 2024, and the share of Argentina-based contractors receiving pay in USDC rose over the same period.

The above data comes from a16z portfolio company Deel, which helps run payroll in more than 160 countries. Using this data as an indicator, we can see both the share of Argentina-based contractors paid in USDC each month and year-over-year inflation.

Since the two metrics are indexed to January 2024, we’re seeing how each metric changed from that point, rather than the raw values. For a while, the two metrics appeared to move together. Then inflation eased, and stablecoin usage appeared to fall with it. As of July 2026, both were holding at about a fifth of their respective peaks.

“Buying crypto” with pesos means “buying dollars” in Argentina. 94% of peso crypto trading goes to stablecoins — the highest stablecoin share of any major currency Artemis tracks.

What’s interesting is that for several years, the crypto dollar cost much more than a dollar bought at the official exchange rate.

By 2023, the country’s capital controls made official dollars inaccessible to many Argentines and pushed the gap between the official and parallel exchange rates above 100%. Stablecoins became an alternative because they were available around the clock and weren’t affected by the controls. After Argentina lifted most restrictions on individuals buying dollars in April 2025, the rates largely converged.

As of August 28, 2026, a digital dollar costs about 4% more than one bought through the official market.

The economic crisis in Argentina seems to be cooling for now. Inflation is down, dollars are legal to buy, and the pressures that originally drove many Argentines to stablecoins have eased. You’d expect usage to fade with them. But it hasn’t.

Paycheck usage leveled off instead of disappearing. And downloads of Lemon, one of Argentina’s largest crypto wallets, climbed every quarter, even as monthly inflation fell from 25.5% to 2.1%.

Stablecoins may no longer be just a hedge against inflation for Argentines — they could be becoming a habit.

Acknowledgments: Thanks to Ryan Holloway for data analysis and feedback.

is an editorial intern at .

Since many in crypto once marketed on “vibes”, we tried bringing rigor to the practice of marketing for builders. Here, we use social science research to navigate an otherwise subjective topic.

In the early 18th century, if you lived in London and you wanted to slander an opponent or rival, you could head to Grub Street, a slum just north of the city named for a garbage ditch.

Amid the narrow lanes teeming with hack writers and failed scribblers, you might find someone like Samuel Boyse — a drunken Irish poet who had fled Edinburgh because of his debts. His specialty, doggerel. He would happily compose you some for a shilling or two. If you had deeper pockets, he could even gin up a pamphlet that really laid into your target.

Boyse lived in a garret. He struggled with alcoholism. Once he found himself in such dire straits that he was forced to pawn the shirt off his back to buy the paper necessary for his craft. (Ah, the life of the writer!) Boyse’s situation was not unique — a writer as highly regarded as Samuel Johnson is alleged to have spent some time on Grub Street — although Boyse was one of the more prolific practitioners of his craft.

Grub Street produced mountains of what we today might call “slop”: single verses, pamphlets, periodicals, newspapers — all for sale in both its production and consumption. Inevitably, perhaps, this drew comparison of such writers to strumpets, and critics equated writing for pamphleteers to whoring. No respectable person would take part in such dreadful works … except that lots of respectable people did, even while decrying it to their equally titillated friends.

What I’m saying is, slop is hardly a new phenomenon. Neither is the moral panic around it. AI-generated content is the latest iteration of an age-old trend: Whenever technology dramatically lowers the cost of producing media, it unleashes a flood of cheap, derivative work — and a chorus of critics convinced that culture has reached a new low. After Grub Street came the penny press, then pulp fiction, television (a “vast wasteland” if you’re feeling fancy, or, more colloquially, the boob tube), followed by blogging, social media, and now AI.

Whenever technology lowers the cost of execution, the masses jump in. Each time, criticism comes hot and fast. And each time, the answer from the self-proclaimed cognoscenti is obvious: Claims to taste become their refuge, and taste achieves apotheosis. While AI may differ in degree and perhaps even in kind, the cultural response to falling production costs has recognizable historical predecessors.

But this immediately raises the question many are debating: What exactly is taste? If taste consists of stable criteria that experts consistently apply, then a modern AI can almost certainly parrot taste. A model trained on enough examples of Bauhaus design can infer the principles that make an object feel Bauhaus. A model trained on a literary agent’s acquisitions can approximate that agent’s editorial preferences. Taste as the consistent application of discoverable rules is precisely the kind of pattern-recognition problem modern AI excels at.

If, on the other hand, “taste” isn’t reducible to stable criteria, then we’ve been using the word far too casually. The idea that taste simply means knowing what’s “good” borders on the tautological, while reducing it to personal preference — I like this; I don’t like that — is too subjective to explain why some people repeatedly identify promising ideas, products, or creators before everyone else.

So what are we really talking about when we say someone has taste? Prediction? Judgment? Social position? Experience? Luck? “Taste” is sometimes all of these: it’s a folk category into which we’ve collapsed separate mechanisms. AI forces us to disaggregate them. Drawing on social science can help, not in giving us a single theory of taste but in teasing disparate ideas apart. Once we identify the separate strands, the question shifts from whether machines have taste to which mechanisms that produce good human judgment remain scarce when production becomes cheap.

Success is socially contingent

One body of research may help illuminate how songs become hits.

In the early 2000s, Columbia University sociologists Duncan Watts and Matt Salganik, together with a team of researchers, created an artificial online “music lab.” Thousands of participants were invited to listen to and download songs by unknown bands. The researchers divided listeners into eight separate groups, each with different conditions governing how the songs were presented. In some, participants could see how many times each song had already been downloaded; in others, they could not. The team also manipulated the information in some “worlds” (what they called their experimental conditions), showing, for instance, a particular song at #1 when it was not.

The larger lesson is that when social information was hidden, downloads were evenly distributed. But when people could see what others were choosing, outcomes diverged. In these manipulated conditions, a song that became a runaway hit in one world performed poorly in another.

Once quality clears a basic threshold, success is heavily shaped by social influence and path dependence. This makes it almost impossible, even for informed observers, to predict in advance which products will rise to the very top. Put another way, experts in a domain — TV executives, for example — are pretty good at filtering out obvious failures before they happen. But they cannot reliably predict breakout successes among the remaining strong candidates. Or as Salganik has said, the success of Harry Potter was a fluke.

Success is not necessarily dictated by quality alone. Therefore, taste cannot simply mean predicting success.

Taste is brokerage

Another possibility is that what we call taste isn’t prediction at all. Maybe it’s the function of a particular social position: standing somewhere that gives you a unique vantage point to see how the world could fit together, or how ideas and products could translate from one arena to another.

This is why some of the most innovative employees in companies tend to be the ones most connected to different groups — or that’s what Ron Burt, another sociologist, found. Burt calls this “brokerage.” Brokerage isn’t the clairvoyance necessary to pick winners but a structural position that generates novel recombinations.

Burt’s conclusions grew out of decades of research mapping social networks inside organizations. Rather than focusing on individual brilliance or expertise, he asked how people’s positions within those networks affected their performance. First, Burt reconstructed who communicated with whom. He then compared those patterns with measures like promotion, compensation, and managers’ evaluations. He found that the most innovative employees were rarely embedded in a single, tightly knit community. Instead, innovators (might we call them those with taste?) occupied “structural holes”: the gaps between otherwise disconnected groups.

Because they moved in different circles, brokers encountered information that others didn’t and, even more importantly, saw problems from multiple perspectives. Their position allowed them to recombine ideas that no one network could have produced on its own. Here, innovation isn’t a matter of exceptional intelligence but of extraordinary position.

This suggests that taste may not be an aesthetic faculty but rather a structural advantage. We often imagine taste as residing inside someone’s head — as a refined sensibility or intuitive aesthetic judgment. Burt suggests that what looks like taste may instead arise from where someone sits in a social network. The people who seem to have an uncanny feel for what’s interesting, original, or promising are often those who spend their lives moving between otherwise disconnected social worlds. Their “taste” emerges not from mysterious intuition but from repeated exposure to combinations that no one else is able to see.

An LLM trained on, well, everything has, in a sense, access to all the networks simultaneously — so if structural holes were purely about information access, AI might excel. But the broker’s advantage comes from living inside multiple social worlds, with the embodied, relational, contextual knowledge that comes from their network position. We might say the broker’s advantage is tacit. Brokers don’t merely possess information from multiple communities; they actively participate in multiple communities. They accumulate tacit knowledge, trust, and experience, and respond to novel incentives in ways that LLMs simply can’t. You can’t simulate being an outsider-insider from training data alone. That’s a defensible human edge — for now, at least.

Granted, Burt’s work is about organizational careers and competitive strategy, not cultural products. But perhaps what we call taste in other domains may be structurally equivalent to Burt’s brokerage — the advantage of the person who’s genuinely of multiple worlds.

Judgment is trained

So where can we find human judgment? We create it.

LLMs can perform the tasks often associated with entry-level roles: drawing up contracts like a junior law associate, performing basic financial analysis like a junior consultant, coding like an engineer, and so on. It’s been suggested that this may allow organizations to slash costs and increase output by replacing those jobs with an LLM. It also grants expertise to the common person: No longer do you have to rely on Big Law and its hourly rates for basic (or sometimes even complex) tasks.

The human role in all of this, argues economist Christian Catalini, is verification: making sure the LLM’s output didn’t drift too far. Humans check. Humans correct. Humans employ taste?

But this opens up the question of where humans learn taste. Good (human) evaluators develop their abilities with apprenticeship, feedback, and exposure.

Back in the day, when I was a junior editor at Princeton University Press, I used to have to photocopy the book manuscripts that my boss had edited with his green fountain pen before I mailed them to the author. We needed a copy for reference and also in case the original was lost. While the copy machine churned away, I took the time to review his edits, read his queries, understand both his green-inked penmanship (which was a nontransferable job-specific skill that hasn’t helped later in my career but was, at the time, crucial, especially because few others — including the authors — could decipher his writing) and, more importantly, his editorial style. This informed my own editing (which my mentor reviewed in turn) and created the foundation from which I still work. (This experience also points to another often underappreciated truism: Jobs can be messy.)

In other words: At least one element of what we call taste — judgment — is a kind of discrimination one accumulates and refines over time. It is not innate. If we are to continue to create humans with taste, we have to create spaces where they can develop it.

The obvious objection is that firms investing in apprenticeships may lose in the short term to competitors who aggressively automate. Maybe! But the tradeoff resembles overcutting a forest: Chopping down every sapling may improve one quarter’s balance sheet, but it also ensures that you’ll have no mature trees later on. And of course it’s incumbent on anyone looking for a job to make sure they train themselves in the absence of those roles.

Practice complicates things

Pushing each of these three findings toward some kind of practice, none of them break — but I don’t think any of them means quite what the obvious reading suggests, either.

Take the socially contingent hit first. Salganik’s conclusion doesn’t change: Success still isn’t dictated by quality alone, so taste still can’t simply mean predicting success. What AI adds is volume — more candidates clearing the “good enough” bar than any one curator can sort through. In other words, TV execs still filter the same way. It just means there are a lot more shots on goal, and quality alone determines even less of which shots land. Success depends on luck — or, more precisely, social or network position — in this environment more than ever. Whichever candidates already have an audience are the ones most likely to catch a wave before the field crowds them out.

Brokerage holds up better. An LLM can already do the cheap half of brokerage — surfacing what’s happening in an adjacent field to anyone who asks, which used to require actually knowing someone in that field. (Someone does still need to know to ask the right questions.) The structural hole that really matters isn’t “knows two worlds” in the informational sense. It’s inhabiting worlds that haven’t been written down somewhere an LLM could ingest them: subcultures, informal practice, arguments that happen in a room and never make it onto the record, and so on. If brokerage keeps paying off, it will be because someone seeks out exactly that kind of undocumented and undocumentable territory.

Apprenticeship is the one where the standard advice — protect it — holds up. The apprenticeship model assumed junior people learned by doing grunt work under a senior person’s mentorship, the way I learned editing by photocopying my mentor’s marked-up pages and then having him review my own work, once I was allowed to start editing on my own. If AI now does that grunt work, the easy conclusion is that the apprenticeship disappears along with the job that trained it.

This may be backward: The process that mattered was “someone catches what you got wrong, repeatedly, until you stop getting it wrong.” That can survive if the junior person’s job becomes checking and correcting the AI’s draft under supervision, rather than drafting something from scratch themselves (although I’d argue that can be invaluable). What changes isn’t whether firms keep junior humans around but rather what those humans spend their day doing. Firms that simply dispense with entry-level roles will tear out a lot of scaffolding with it. Firms that redirect entry-level roles toward supervised verification can maintain their scaffolding while still capturing some savings. (There is a Tragedy of the Commons possibility here, though, where all the firms dispense with junior jobs and simply lack senior judgment across the board later on, like the timber company that harvests all the young trees.)

***

Boyse pawned his shirt for paper and died on Grub Street. Samuel Johnson, who allegedly walked the same streets, didn’t stay. Not because he had better taste but perhaps because he had better scaffolding.

In the debate over AI, “taste” has become a kind of totem, invoked when someone wants to identify something humans can do but machines can’t. But taste doesn’t have to be mysterious. It is what good judgment looks like after it has been shaped by environments that reward experimentation, networks that expose us to novel combinations, and institutions that cultivate discrimination.

In the age of AI, the scarce resource isn’t taste. What threatens to become scarce is the social infrastructure that produces the people we describe as having it.

We writers can pontificate about what AI will do to the craft all we want. The reality is that most writing isn’t some artful monomyth. It just needs to work. Does it explain things clearly? Can readers trust it? Is someone — anyone — behind the wheel, making decisions here? In short, is the writing doing its job? The extent to which a human was involved will matter in some cases and not others. The nice thing about these questions is that they apply equally well to writing produced by a person, a model, or some increasingly entangled combination of the two. They’re also useful in industries like ours, where scams (often written by AI) and jargon (too often written by humans) can easily distract from real goals and values.

What is actually useful about the AI detection discourse is that it’s made some longstanding writing problems much easier to see. So instead of using some percentage of “Human Written” as the relevant heuristic, we take a look at the hallmarks of machine-generated prose — its rhetoric, voice, structure, punctuation — as a taxonomy of problems that have always existed. Our goal as editors is to help founders, and anyone else in the business of communicating ideas, figure out when these habits get in the way of effective writing, regardless of provenance.

Rhetorical hallmarks: Insight-shaped writing

One of the most infuriating (then rewarding) experiences in editing is finding a sentence that sounds good but just kind of feels off. You rearrange it, move a phrase here and there, and slowly realize it never meant anything to begin with. Then you delete that slippery little pretender and move on with your life.

A quick example straight out of asking Fable for its most recent punctuation meta:

“Human punctuation has a body; it fidgets. My punctuation is uniformly deliberate, and uniform deliberateness is itself the rhythm.”

I would describe this tone of voice as “person at an otherwise nice party talking about jazz improvisation,” but I’m not going to pick these sentences apart here — you get the point. People say things about this sort of writing, like “it could just never have been written by a human.” And I take their point, but as an editor, ghostwriter, and lifelong reader of fantasy novels, I can assure you that people do indeed write these sorts of sentences without AI all the time.

Spotting this kind of language, however, isn’t always as straightforward as in the fidgeting-punctuation example. Unlike other AI hallmarks (like three consecutive sentences full of lists or the familiar Morse code of too many em dashes), these sentences don’t actually look like a problem. They just kind of look like sentences. Some of them even sound better than the other sentences around them: “Something real is happening.” “This matters for crucial reasons.” “The implications are significant.”

AI writing produces these sorts of phrases in bulk, because they’re plausible (if semantically vacant) connective tissue. Before it became associated with AI writing, we used to call this category of bland, meaningless language corporate, because it’s your typical, dispassionate business-blog speak. Can you think of a more spiritless way to say you’re excited about something than to start with, “We’re excited to announce…”? “We’re at an inflection point” or “this is the next chapter of our journey” are two more examples. They’re phrases that sound weighty but generally lack the specificity to survive paraphrase.

I’ve heard other editors miscategorize this as lazy or thoughtless writing. That’s uncharitable. It can be a matter of taste, but mostly, it’s not easy to swat away the generic, instantaneously retrievable phrases and idioms buzzing at the front of our minds and find something better. It is first-draft language. And honestly, it’s a perfectly fine place to start. We just don’t want to end there.

The approach I recommend for avoiding meaningless language is the same, whether you’ve used AI or not. If you spot a sentence that feels off — if it uses any of the phrases or phrasings below, if you don’t immediately understand what it means, or if it’s just got a bad vibe to it — start rearranging and try to restate it.

If there’s a paraphrase, keep the better version. If it boils down to “stuff exists” or “things are changing,” skip it, and see if your piece can live without it. If it can, it’s time to embrace the illicit thrill of deleting words from your page.

Some tells and when to edit them

• Naturally essenced profundity (the La Croix of insight). “Something real is happening.” “The stakes couldn’t be higher.” Sounds important; is actually filler.

• Empty contrasts. “It’s not just about X — it’s about Y,” where Y is fuzzier than X. This format works sometimes when the contrast is very clear. But before leaving it as is, check whether you can delete that first “it’s not about” phrase. You may get to your point faster.

• Hedges. “In many ways.” “At some level.” “Arguably.” Each of these exists to make the sentence impossible to be wrong about. Note that in some industries, like finance or healthcare, some hedges are necessary to soften claims and comply with the law. I think AI would call these “load-bearing” hedges.

• Too much parallelism. Bullets and sentence structures that mirror each other too tidily. Every list item is the same grammatical shape and the same length. These are AI tells, but they also make for terrifically boring writing.

• In-summation phrases. “At the end of the day.” “When the dust settles.” It’s often possible to delete these phrases with no impact on your paragraph.

Edit: Almost always. Meaningless, gobbledygook language is at the top of our stack-ranked list of writing pitfalls. It will never move your ideas forward, no matter how nice it sounds. It is a scourge. Eliminate accordingly.

Embrace: Almost never.

Tips: I often find myself circling two kinds of prompts: (1) those that make the writing more specific, and (2) those that make it more plainspoken.

My goal is always to drop unnecessary technical jargon, and work toward something that’s both information-dense and easier to read. If an LLM can help me do that, great.

More ideas for getting rid of filler language:

• Creating a rough style guide that defines what “good” writing looks like to you with examples and counterexamples. This will hold up better than a laundry list of AI tells.

• Using AI to run the paraphrase test I alluded to above. Just ask for the “boring” version of what you’ve written.

• Prompting the LLM to “write it at a 6th grade level” works decently as a blunt instrument to take to your prose.

Voice hallmarks: Alexa voice

There are roughly one million words in the English language. Default AI writing sounds like it’s working with about four hundred of them. But rather than trying to pinpoint which word of the day is an AI hallmark, the test here should be fungibility: could this sentence be taken word-for-word out of your writing and dropped into someone else’s essay on a different topic without anyone noticing?

For a while you could track this category of low-friction vocabulary empirically. Researchers famously noticed the word “delve” spiking in academic abstracts after ChatGPT launched in 2022, and an onslaught of banned-word lists followed: tapestry, testament, underscore; more recently, load-bearing, scaffolding, broader, etc.

In the meantime, thoughtful diction isn’t just a preoccupation of “literary” writers. It matters for anyone who starts or runs a company because people increasingly market on the strength of their personalities.

The best founder writing is individually expressive. Whether or not others would deem it “good,” it gives readers a sense of the person who believes it. It’s why we can so easily conjure what it means to write like Brian Armstrong (pithy, earnest, unironic); like Vitalik Buterin (technical, digressive, dense); or like Chris Dixon (austere, philosophical).

So, when using an LLM, please make sure you don’t cede your personality to it. Your own word choices and their imperfections add patina a machine could never.

For all of the writing available on finding the right words (two of my favorites are Theodore Bernstein’s The Careful Writer and George Saunders’ A Swim in a Pond in the Rain), there’s very little actionable advice on how to actually do it. Magic, alchemy, etc. It’s often an exercise in vibe curation, or emotional precision, rather than technical precision. Even quintessential writer of rulebooks E.B. White caveats that nobody can say for certain why some words “ignite” and others don’t.

Start by listing what’s good about the writing you like. Learn a new word, or use an old word in a new context. Match your word choices to the mood you’re trying to evoke — for example, use short, simple words to explain something opaque; or choose a word like “scheme” over “plan” when you want the reader to smell trouble. There’s a know-it-when-you-hear-it quality to diction that you can’t compensate for with rules.

Some tells and when to edit them

• Generic warmth. Friendly in a hold-message sort of way. “Great question!”

• Recyclable phrasing. Low-stakes phrases and sentences that could be transplanted into any other piece of writing with few consequences. “A useful way to think about it is,” “The key idea is,” “This can be understood as.”

• Low-friction vocabulary. Every word is precisely acceptable in an uncanny sort of way.

• Abstract nouns: The writing leans on words like “efficiency,” “complexity,” “society,” “communication,” and “innovation.” A timeless tactic for saying nothing in 3+ syllables that long predates ChatGPT.

• Insipid dynamism. “Navigate,” “leverage,” “unlock,” “foster,” both “power” and “empower,” “shape,” “elevate,” “streamline” — words that signal motion but fall flat.

• A beacon of something. “A testament to...” “Stands as a beacon of...” “Serves as a reminder that...”

• Gestures vaguely. “Landscape,” “space,” “journey,” “ecosystem,” “tapestry” — ambiguous, noncommittal words that point to the vicinity of things instead of identifying them directly.

• Vague intensifiers. Phrases like “very important,” “significant impact,” or “major role,” especially when there’s nothing concrete backing them up.

Edit: Most of the time. Sometimes there is simply no other word for “ecosystem,” and we’re all just going to have to live with that.

Embrace: When being an NPC is kind of the whole thing. Support docs, error messages, terms of service, safety instructions, apologies at massive scale, and anything else read by a million strangers in a million contexts. Here, personality is friction and Alexa voice is a mercy.

Tips: Many of these tips focus on using LLMs for detection, not for writing. Models are actually very good at identifying jargon, business-speak, and other words frequently used in AI writing.

• The Pangram “percent of AI in writing” debate aside, you can use an AI detector or any other LLM to flag the most generic words and phrases in your draft, and then edit accordingly. Prompt for hedging, fungible phrasing, etc.

• From there, try running the transplant test on any given sentence. If a stranger could claim it, consider reworking.

• Go through your laundry list of vague language and ask, “Is there a more specific word for this?” For example, “The Ethereum ecosystem is expanding” can become, with a little more specificity, “Developers are building more wallets, exchanges, and lending markets around Ethereum.” If you can clarify your meaning with more precise words, choose them every time.

• Finally, people are starting to feed voice notes into LLMs as a way to get words on paper. An underappreciated benefit is finding personal idiosyncrasies that set your voice apart. Note that you do have to edit the results; there is no such thing as a hole-in-one here.

Structural hallmarks: All form, no function

AI writing tends to feel a bit overstructured when left to its defaults — too many H2s and lists, enough paragraph breaks to resemble a William Carlos Williams poem. But it’s not all bad. People have always drawn on a bank of familiar structures that work well and feel good on the brain.

We group ideas into three because three feels complete. We add signposts (e.g., “First,...” or “In other words, …”) to instantly situate readers within our writing. We create neat little taxonomies because they’re easier to skim.

As students, most of us learn some version of “hamburger” logic, where we tell our audience what we’re going to say, say it in discrete and supported chunks, and then restate it to conclude. It’s useful while forming an argument, because it forces us to articulate a thesis, collect evidence, and arrange our thoughts into something legible — a good thing!

A good, clear structure helps readers immediately understand their surroundings, whether that’s an op-ed, an explainer, or even a piece of fiction. The trouble is when pre-set structures pressure writers to draw and quarter their ideas into formats that don’t necessarily make sense.

Structure is a set of decisions — which container to use, which information matters most, which ideas go together, and what to call them. The right decisions depend on the job to be done. A narrative essay needs discovery and tension to keep readers moving. A product announcement needs to be brutally efficient to catch readers mid-scroll. An explainer needs an order that builds on concepts sequentially learned.

Ask yourself — anywhere in the writing process, but hopefully at the beginning — “what is the best format for my idea?” Then, borrow from something that already works. If you’re working on an argument piece, take a moment to understand how other writers structure op-eds and similar articles. If you’re writing a technical explainer, pick out the best explainer you’ve ever read, and look at how its author organizes information. Once you have a template, you can apply it to your own work.

Some tells and when to edit them

• Over-organization. Lots of subheads, bullets, numbered sections, or mini-frameworks.

• Ideas and lists that come in threes. Though this remains a best practice; more below.

• Familiar essay shapes. Broad introduction, explanation, examples, caveat, conclusion.

• Formulaic openings. “In today’s rapidly changing world…”

• A little too much signposting. “First,” “Next,” “Finally,” “In conclusion,” “Here’s a breakdown,” “Let’s unpack this.”

• Hard-pivot transitions. “To understand why this matters, we first need to look at…”

• Section previews. “There are three key reasons…”

• Bulleted lists. AI detectors often flag these unfairly; bullets are useful! A signature move, however, is the bold lead-in to a bullet point. See, for example, these exact bullets (but they’re so much easier to read this way).

• Punchy fragments. Seen in many a dramatic LinkedIn post. Short. Punchy. Often in threes.

• Ending on a restatement. In other words, the conclusion paraphrases the piece instead of expanding and hinting at further directions.

• Moral-of-the-story endings. A vague final sentence about progress, the future, or “what we can learn.”

Edit:

• Subheadlines that don’t naturally fit your format (op-eds, personal narratives, most pieces argued through voice and momentum).

• When your sections aren’t solid or discrete (e.g., a “five takeaways” post with two very similar takeaways).

• When structure dilutes or changes your meaning (e.g., a numbered list of reasons a startup pivoted reads very differently than the story of how it happened, even when facts are the same).

• Signposts for self-evident structures (i.e., “three reasons why…”). Remove signposts that are just adding to your word count.

Embrace:

• When the structure of a piece complements what it’s saying. Op-eds, for example, all tend to have a built-in argument that’s easy to pick out and follow.

• Likewise, when writing listicles, explainers, how-tos, and other genres where we expect headers and subheadlines.

• When an idea actually comes in threes. This principle is only a problem when it contorts ideas into unnatural forms. Or when it’s obvious the writer is arbitrarily forcing a third thing.

• When optimizing for LLMs and search engines, which reward well-structured information.

• When creating reference content people will return to rather than read once — documentation, guides, FAQs, and anything else readers navigate and scan vs. follow from beginning to end.

• When creating content for skimmers. Headers can serve as a table of contents for readers deciding whether to stay or bounce within a tenth of a second. Headers taken together can even tell the whole argument, with the paragraphs underneath fleshing out the details.

Tips: Models are great at prompts like “organize this better,” but only if you actually want more organization. If not:

• Try telling the LLM what your structure needs to do for the reader — whether that’s creating suspense or making a piece easier to scan.

• Give the LLM a piece you like in the same genre and ask it to study how the argument works before it restructures yours.

• Paste a published piece in the same genre, and prompt it to create a “reverse-outline,” describing the function each paragraph serves. Then draft using this rough structure as a skeleton.

Even if the model fails to provide, the extra thought put into organizing and presenting an idea tends to pay off.

Punctuation hallmarks: The em dash panic

The em dash — now a canonical AI tell — has always been divisive (😏). Strunk and White advise restraint more generally: “Use a dash only when a more common mark of punctuation seems inadequate.” But there’s a very easy, conversational feel to em dashes, in particular, that no other punctuation mark really has.

Em dashes are often seen as the enemy of efficient writing — it’s admittedly kind of distracting to jam this whole other phrase into the middle of a sentence — but as soon as they started signaling slop writing, they were an easy target for a ban.

The thing about punctuation, though, is that it has extremely specific rules, based on whatever 1,000+ page style guide you’re following. Whether AI favors em dashes is beside the point, because there are certain situations where you should probably use them. At the very least, they’re the most appropriate choice for setting apart longer parenthetical information, or for illustrating some abrupt changes in thought and emotion (as rhapsodized by horror writer R.L. Stine).

My take: shift + option + dash (on macOS) is humanity’s own troubled, distracted, often off-topic child, and I, for one, will never abandon it. Sometimes nothing hits quite like an em dash, so I urge you to avoid letting the current AI writing meta influence your choice of punctuation marks, not least because all metas must, by definition, change.

Now that people are bending over backward to avoid em dashes, AI itself is routing around them with colons. Shall we start using interrobangs to look appropriately human?! Since no punctuation mark is safe, choose the one that’s best for your writing.

So how do you tell what’s best for your writing? The short answer is: whatever is most correct and least distracting. The particulars of the rules can be nuanced and vary across taste and styles (Oxford vs. no Oxford comma is a popular religious debate among people who’ve made grammar and usage part of their personalities), so getting them perfect isn’t as important as ensuring they support your meaning and don’t infuriate your readers.

Another punctuation-related hallmark to look out for is sameness; that is, are all of your sentences looking and sounding the same? LLMs often use consecutive lists starting with a colon, which makes for an exhausting read. Likewise, several sentences interrupted by em dash asides will distract from your point, no matter who or what wrote it.

A good test is reading your writing aloud, using punctuation like stage directions. Anything that sounds unnatural will probably stand out to readers too.

Some tells and when to edit them

• Colon-heavy phrasing. “The issue is:”, “The result is:”, “The key point is:”... but especially colons followed by grocery lists of things.

• Em dash clustering. Not the dash itself, but the density, especially paired with lists and asides. There’s not really a numerical limit on the number of em dashes you can use in a sentence, but don’t use more than two.

• Unserious parentheticals (like this one). These usually carry the self-aware or joking register, while dashes carry the qualifying one.

• Performative semicolons. Writer Kurt Vonnegut once said that the only reason to use a semicolon is to show that you’ve been to college. It’s actually the least mean thing he said about semicolons, but it’s still unnecessarily crotchety. They have many legitimate use cases; however, these don’t come up all that often, honestly.

Edit: When punctuation is repetitive, distracting, or otherwise fails the read-aloud test.

Embrace: When it works. Punctuation should support your point and suit your taste. It should aim to be grammatically correct and mostly invisible to readers.

Tips: Don’t overthink whether punctuation is making you look like AI. Just use the em dash or the colon, or whatever is presently on the ban list. If you must:

• Avoid the urge to tell a model to “remove all em dashes.” It will often just replace them with something else, while preserving the same underlying sentence structure.

• Instead, ask to default to periods and commas, and only use “special” punctuation when grammatically required.

• Again, giving an LLM examples and samples of your own writing (or writing you admire) helps build a “punctuation fingerprint” that estimates your use of a given mark.

As more people use these tools, interrogating whether something is machine-generated is becoming kind of pointless. The answer is almost always going to be “to some degree.” Of course, the egregious cases of AI writing still need outing, whether by our own standards or Proof of Person technologies: for example, when disclosure is required, when the human on the byline is the whole point, or when one person is pretending to be a thousand.

For almost everything else, we can ask the same question we’ve always asked: is the writing doing its job?

AI helps people articulate and publish ideas they’d otherwise never write down. It can save time on organization and research. It may even help us become better writers. It’s mean-spirited to dismiss the ability to express oneself as “cringe” or “slop.” And, if a piece works as intended, does it really matter which parts of the process show through?

Finally, with all of the hand-wringing over whether our writing will be exposed as AI-assisted, one question worth asking is: why are we showing LLMs this kind of deference at all? We would relinquish an entire punctuation mark — an innovation conceived at the dawn of printing! — all to avoid looking like a machine helped us. It’s a ridiculous concession, especially given how often people already use, and will continue to use, the machine.

Acknowledgments: Thanks to the editorial team — , , and — for feedback on this post, and for the many years of edits and debates that informed it.

People often tell the story of the first Bitcoin whitepaper as if it materialized in a vacuum and then led linearly to mining, smart contracts, tokens, DeFi, and every successive generation of blockchain design. But the intellectual history of these technologies is actually much older and more labyrinthine, complete with twists, turns, and false starts. And instead of a single inventor (or two), the foundational technologies behind blockchains pull on threads left by others.

For instance, answering questions like: How can machines agree when some fail or behave maliciously? How can strangers communicate and coordinate trustlessly? How do the rules of a market affect how people behave within them? Blockchains didn’t create these questions. But they have brought them together, helping propel them forward in new contexts.

This is why we created First Principles, a special series of conversations hosted by Institute for Advanced Study professor and a16z crypto Head of Research Tim Roughgarden. Throughout, Turing Award winners, Nobel laureates, and Gödel Prize winners share the origin stories behind their pioneering work — much of which underpins things we use every day — and explore how work done for one problem later became part of technologies its creators never anticipated.

You can watch all of the conversations here.

The conversations

Barbara Liskov (2008 Turing Award Winner, MIT) shares her path from programming languages and data abstraction to distributed systems, the development of viewstamped replication and Practical Byzantine Fault Tolerance, and the importance of specification and careful reasoning when building systems that have to survive failure.

Leslie Lamport (2013 Turing Award Winner, most recently at Microsoft Research) discusses his work on concurrency, logical clocks, Byzantine agreement, state machine replication, and Paxos, and how problems that began as theoretical questions became central to distributed computing.

Alvin Roth (2012 Nobel Prize in Economic Sciences, Stanford University) explains how market design moved from economic theory into institutions — including medical matching, school choice, and kidney exchange — and why the rules of a market often determine whether the market can exist at all.

Paul Milgrom (2020 Nobel Prize in Economic Sciences, Stanford University) discusses auction theory and the movement between theoretical economics and practical mechanism design — including the growing dialogue between economists and computer scientists.

Ron Rivest (2002 Turing Award Winner, MIT) revisits the invention of RSA and the early development of public-key cryptography, digital signatures, and hash functions, as well as the evolution of the security assumptions that underpin much of modern digital communication.

Shafi Goldwasser (2012 Turing Award Winner, MIT), co-inventor of zero-knowledge proofs, traces the path from early cryptographic puzzles to interactive proofs and zero knowledge, and from there to questions about efficient verification that eventually helped make modern SNARKs possible.

Noam Nisan (2012 Gödel Prize Co-Winner, Hebrew University of Jerusalem, also StarkWare) reflects on complexity theory, the sum-check protocol, algorithmic game theory, and the unpredictable path by which theoretical ideas can become part of practical systems decades after they first appear.

Crypto payment cards have gone from a novelty to more than $750 million in monthly spend. These cards let people pay with crypto anywhere traditional card networks are accepted. Behind the scenes, the crypto — stablecoins, overwhelmingly — gets converted to local currency at the point of sale, so the payments look like any other card transaction to merchants.

Crypto cardholders don’t require a traditional bank account. Depending on the program, users either deposit stablecoins with a card issuer, or hold them directly onchain through self-custody. Crypto cards expand people’s access to U.S. dollar accounts globally, and they offer a convenient way for stablecoin holders to transact.

Monthly crypto payment card volume reached $759 million in July, up (roughly 2.5x) from $306 million a year earlier — and up from less than $1 million when tracking began in October 2023. These figures reflect onchain activity attributable to card programs tracked by Paymentscan. (For RedotPay, the largest program by volume, spending is self-reported by the issuer rather than observed onchain.)

Growth in the number of purchases made with crypto payment cards is trending similarly to the growth in crypto card volume. Nearly 9 million purchases were made using crypto payment cards during July, up from about 5.2 million a year ago.

That puts the average amount spent per transaction at around $86.

In early 2024, crypto card spending was concentrated in a single chain: Gnosis, home of Gnosis Pay (the first Visa card connected directly to a self-custodial wallet). As new card programs have launched, the number of card settlement chains has expanded.

As of July, Optimism carries about 29% of crypto card spend volume, Solana about 19%, and Base about 19%, according to Paymentscan. Gnosis has dropped to about 2%.

Euro-backed stablecoins once dominated spending: In early 2024, around 88% of crypto card volume settled in EURe, much of it on Gnosis. As of July, EURe’s share has fallen to about 2%.

Dollar-backed stablecoins have since taken the lead. USDC handles about 58% of card spending and USDT about 26%, up from roughly 48% and 7% a year ago, respectively. Crypto payment card spending now happens overwhelmingly in digital dollars.

Crypto payment cards remain a small market next to traditional card networks, which process trillions of dollars per month.

But the trend is growing as stablecoins make greater inroads into the global financial system, including by piggybacking on existing major card network rails. For the tracked programs, this is happening almost entirely through Visa.

Crypto payment cards are one piece of a broader crypto acceleration post-GENIUS, which we’ve been charting here. That includes the rapid adoption of stablecoins and tokenized assets.

Robert Hackett is features editor and head of special projects at a16z crypto.

Ryan Holloway is a data consultant.

Robert: Congress is currently hashing through some legislation — once-in-a-generation market structure legislation — that could determine where the future of the financial system and internet gets built. It passed in the House last year with bipartisan support, and it has been making its way through the Senate ever since.

Now, there have been lots of fights and obstacles over it, which we’ll get into. But before we do, I wanna zoom out and go big picture and talk about why regulatory clarity matters, what the status quo costs us, and what’s at stake for the country and for anybody who may end up using this technology.

So Marc, I’d like to start with you. Back in January 2014, you wrote an op-ed for The New York Times called “Why Bitcoin Matters.” Things were a little bit different then. This was a pretty contrarian position to stake out. The crypto industry, which, if you could even call it that back then, looked very, very different.

How have things changed from then to now?

Marc: Yeah, sure. So 2014 was so long ago that The New York Times actually ran a positive piece on crypto. This was in the distant past. It might as well have been the 1500s.

I’m actually really proud of that piece. I think it holds up in a few respects. This piece is five years into the macro trend. And it was still big news even to believe that this technology mattered at all.

Like, there were still a lot of people who were going to have to learn about this, want to learn about this, and be part of it. And so I think that holds up. The thing that, in retrospect, needs to be changed about the piece is every time I say Bitcoin, swap in crypto, right?

And so the idea was that Bitcoin would be extended to basically enable both tokenization of real-world assets and also virtual assets like NFTs. Of course, that didn’t happen. And what happened instead was the creation of new blockchains and new crypto platforms, and then ultimately Ethereum and everything else that followed.

And so what started as a technology became an industry. Now, Bitcoin obviously has been very successful since then, but what we’ve really seen since then is this explosion of innovation.

Robert: It’s actually remarkable how well that piece holds up.

But there is one forecast in there that I want to cite in particular that stands out: “The coming years will be a period of great drama and excitement revolving around this new technology.” I think you were spot on there.

Chris, you’ve been there since the start as well. How have things changed from the early days to now?

Chris: So in the early days, it was mostly kind of hobbyists and super enthusiasts, and kind of a subculture, cultish thing.

The technology, which was Bitcoin, was sophisticated, but — a lot of the things Marc alluded to — the newer blockchains early on had performance issues, scaling issues, and other kinds of things. Fast-forward to today: Every day you’ll see a new announcement from a major bank or fintech company announcing a platform involving stablecoins or tokenized stocks or other kinds of digital assets.

So stablecoins — for those who don’t know — are basically what Marc described with Bitcoin, except they’re dollars on blockchains. They now rival the size of the Visa network, with trillions of dollars transacted. You can go into WhatsApp and send money anywhere in the world for almost free in the same way you’d send a text message, using stablecoins.

It’s the way money should work.

Marc could speak to this as one of the pioneers of the internet — but I think it was one of the ideas that early internet pioneers expected to happen much sooner. And for a variety of reasons, it took a lot longer, but we’re now finally seeing that dream come alive, with money moving as easily as bits.

So now it’s a very mature industry. The technology — the underlying infrastructure — has gotten much more sophisticated. Even three years ago, what I just described might cost a couple of dollars, if not tens of dollars, to do that transaction. Now, on most of the popular blockchains — Solana, Ethereum, and so forth — it’s sub-one second to settle the transaction, sub-one penny to send something.

Why crypto needs rules now

Robert: You cited that stablecoin volumes are now rivaling the Visa network. This is trillions of dollars per quarter, churning through this system. Lots of gigantic financial institutions have rushed in: BlackRock, JPMorgan, Visa itself, Fidelity, Mastercard.

I mean, you could rattle off just any number of names, and they’re all building on this technology right now. But we’ve gotten this far without rules. So there’s been very little in the way of regulatory clarity or guidance.

Why do we need rules now?

Chris: So for a variety of reasons, the regulation around crypto was broken into two components — stablecoins and the rest of the market.

Last year, a bill passed Congress — signed by the president — called the GENIUS Act, which provides a comprehensive regulatory framework for stablecoins. And it’s not a coincidence that stablecoins are also the area that’s taken off the most in the last year. Once you have regulatory clarity, builders have certainty. If you’re an American consumer, you know — if you use USDC or another GENIUS-compliant stablecoin — that if you have a dollar of that stablecoin, there is a dollar sitting in the bank.

That gives you, as a consumer, confidence and protection. If you’re an institution — if you’re a bank, if you’re Stripe, if you’re PayPal — and you want to go and enter this market, you want to know there are predictable rules. That there’s a framework around it. That what you build will be compliant, not just next year — but in the next 10 years. You want to know there’s certainty.

But the big, hanging problem right now is that you’ve got this other section of the market — including blockchains themselves, which stablecoins are built on — that doesn’t have a comprehensive federal regulatory framework. And that’s why the CLARITY Act, which is working its way through the Senate, is so important.

So you can imagine it’s as if we had a regulatory framework for cell phones, but not for cell towers or something. Half the technology is regulated, the other half has this sort of uncertainty around it. People are still building there. People are trying to do their best to understand what the rules are.

There’s this other lever of policymaking. Agencies such as the SEC and CFTC have provided some guidance. But ultimately what industries are built on — as you alluded to in the beginning with the internet — is legislation.

So we think that’s what is required, and why the CLARITY Act is so important.

Robert: So we do have some rules. They’re for stablecoins, as you mentioned. That covers a sliver of the market, but there’s this gigantic swath of the market that remains largely unregulated.

Marc, why do we need rules for all of this, and why do we need them now?

Marc: The US financial system has been through this many times before. The most famous case is the Securities Acts that created the SEC and regulated the stock market.

We’re not looking for a free lunch. We’re not looking for subsidies. We’re not looking for protectionism. We’re not looking for support in that way. We’re just looking for a permanent framework that lets people do business responsibly. And I think it’s — in a lot of ways — the most obvious thing in the world.

Chris: Right now, there is no federal regulator for crypto exchanges, as an example.

The New York Stock Exchange, Nasdaq — they have federal regulators.

A big chunk of the CLARITY Act is to provide that, so that the SEC and the CFTC have oversight. They put in disclosure regimes, anti-fraud regimes, insider trading rules, and all the things that we are used to — as Marc was saying — with other financial markets.

It’s just the same kinds of rules with the same agencies overseeing them. So for example, FTX wasn’t audited properly. This is what ultimately happened, right?

If you’re a federally registered crypto exchange, you’re being audited — there’s a whole suite of things that are in the act that would empower federal regulators to do their job.

And if you’re not compliant with these things, you couldn’t operate in the US.

There are a lot of hard details, and this has been a very bipartisan effort, which is why it’s been seven years or so that some of us have been working on the CLARITY Act. It’s now been over a year since the House passed its version of it, and the Senate has been working on it for a year.

But this has been done before: Financial industries have been regulated, and the principles that are used in other industries are applied here. There’s a very strict framework with strict definitions of who is subject to these requirements. It applies all the same money laundering and Treasury rules to crypto intermediaries that are applied in other markets.

The Fraternal Order of Police, the largest law enforcement organization in the country, just came out with its endorsement of the CLARITY Act. Opponents are saying it doesn’t have proper law enforcement clauses and rules, but this is simply not true.

Illicit finance and how ambiguity rewards bad actors

Robert: So let’s go over each of the debates going on. Illicit finance is one of the big ones.

Chris: Right now it’s extremely unclear which entities are subject to what rules. What I’ve learned is that when you have gray areas in regulation, you essentially race to the bottom, right?

I was on the board of Coinbase for a long time — Marc’s on the board. Coinbase is a US-based company. They take regulation and compliance extremely seriously. But for every Coinbase, it costs a lot of money, and it slows down your product development.

And so every year, a new offshore competitor will pop up that doesn’t do those things and becomes popular because they can offer lower fees or iterate on their product faster, because they’re not doing all the compliance things.

The ambiguity ends up favoring the bad actors. And so good regulation — I think the CLARITY Act does this — very clearly defines the regulatory perimeter.

So as an example, in the CLARITY Act, if you’re a financial intermediary, if you’re somebody like Coinbase or a company that’s holding customer funds, you’re subject to all of the same rules as a company like Stripe or PayPal or anyone else.

It’s black and white in the bill. And as I mentioned, there’s the endorsement of the various law enforcement agencies.

Does CLARITY enable sanctions evasion? Why privacy is not the same as secrecy

Robert: Marc, what do you think about this criticism that the bill enables sanctions evasion?

Marc: I would say the national security people I talk to typically don’t agree with that, because there’s a trail. And that is contrasted specifically with how a lot of, for example, terror financing happens today.

There have been people — even in industry — who have said things like, “Well, crypto is inherently anonymous. You can’t trace anything.” I think this stems from them not understanding the technology.

Chris: It’s actually a non-trivial research problem that’s going on today to build blockchains that do have privacy because they are so public.

We think that’s important — just not for nefarious reasons. Because if you want to pay for some service — maybe it’s a medical thing or a financial thing — you don’t want everyone to know about it, just like we do in the regular financial system. You want to have some privacy. It’s a natural thing.

Since the GENIUS Act passed and dollar-denominated stablecoins have gotten more popular, we’ve actually heard in DC now that people are saying, “Well, what about privacy? You need to have privacy.”

And like, yes, we agree. It actually is very reminiscent of the early internet when, Marc — wasn’t it you guys that invented SSL?

Marc: Yeah, we did.

Chris: And so for the kids who don’t know this — HTTPS was Netscape.

And at the time it was like, well, who would need that? Why would you need to send secret stuff? And at the time, probably some bad guys did use it, but of course, over time, we all realized that was important. And 99.9% of it is, like, good behavior. It’s just people interacting with their bank or something.

You guys had to go in front of Congress.

Marc: It was a four-year fight. It was actually quite analogous to today. And so when we started, Netscape was the first piece of consumer software that got widely used that incorporated public key encryption — modern encryption with public keys.

And at the time we shipped, we actually had to ship two different versions. We could ship a strong encryption version inside the US. Still, encryption was classified as a munition under what were called International Traffic in Arms Regulations (ITAR) export control rules at the time.

So encryption was classified in the same category as a Tomahawk missile, which meant that the Netscape browser was classified in the same category as a Tomahawk missile. So we had to ship deliberately insecure versions of the product overseas — and you can imagine how much our overseas customers enjoyed that.

And by the way, we clearly labeled it on the box, which was like “weak encryption — do not trust this.” They get this, and you can imagine how that went for sales. And of course, what happened was foreign competitors immediately stepped in, started cloning it, and started making strong encryption.

So people outside of the US didn’t use our product. They used products that were made outside the US. And so we went to Washington — as you do when you’re a kid, you go to Washington — and you explain things logically, and they tend to stare at you like you’ve grown a third eye.

And so you start to get into the specifics of it. But then you get into this fundamental dichotomy, which is like, okay, is encryption bad because bad guys are going to do bad things with it? Or is it good because it is the key to establishing trust, being able to do business, and for law-abiding citizens, both here and overseas, to cooperate and conduct business?

And so it’s like, do you want to eliminate the risk of any cybercriminality? Or do you want to make Amazon.com possible? And these questions are deeply intertwined. And so anyway, it was this very long education process.

It took four years. It took a very, very long time to unwind. They did ultimately make that change and, by the way, the world did not end for several reasons. One is, as you said, overwhelmingly the use cases were positive.

And the other incredibly positive thing that happened was American industry won. Not just Netscape, but the global internet economy is dominated by American companies.

How ethics and market structure are separate questions

Robert: So let’s talk about some of the other objections to the bill. Another big one is ethics reform. So the president and his family members have interests in crypto businesses. The critics say that this bill will enrich the people who are most vocally supportive of it. How do you address that?

Chris: It’s not my area of expertise, but I’ll say as a citizen, I think there should be ethics rules for government officials. I don’t think those should be exclusive to crypto. I think there should be ethics rules around trading stocks and, for sure, other financial assets. That seems reasonable to me.

Number two: the CLARITY Act, even without specific ethics provisions, will add significant restrictions to anyone in crypto, including requirements for disclosures around the risks and holdings of a crypto asset. It will add lockup requirements. So it’s only an increase in the restrictions that would apply to anybody, including government officials.

And then point number three: what’s been frustrating from a political point of view is that, normally, government ethics rules are done separately, and an industry is regulated separately. So this is very exceptional. This industry is being held to a completely different standard than other industries. That said, I think that a very reasonable debate for people to have and for politicians and policymakers to figure out is what the ethics rules should be for all financial assets, including crypto.

I hope they can figure something out so we can move forward, because we want to regulate this industry.

Marc: By the way, this bill puts controls on government officials’ use of crypto that are stronger than those that apply to government officials’ stock trading.

Chris: Yeah. And one other point I would add is that right now most people think of crypto as something you can trade.

I think what they’re missing is that increasingly, like with the stablecoins we’ve been discussing, it’s a thing you can use. And — if the industry’s successful — you’ll have more and more use cases and more and more ways in which it’s embedded into our financial lives.

And so I also think it’s important with these rules to make sure you separate that kind of trading behavior from the use behavior, because I think it’s important for government officials to have access to modern technology.

Robert: Okay. So there are government ethics rules in the bill, and also it’s a separate issue from creating market structure rules for crypto.

Chris: Look, the reality of where the politics are right now is that there are ethics provisions in the current bill.

They’re still being negotiated. I hope they come to a resolution. But I think from our perspective, the important thing is that — after many years of regulatory gray area and an environment that favors, as I was describing before, a race to the bottom — we get a regulatory framework passed. And we think the CLARITY Act, while not perfect, is significantly better than the status quo.

Stablecoins and the banks already building onchain

Robert: You mentioned stablecoins again just now. And one of the big fights has been over the treatment of stablecoins in this bill — in particular the banking lobby. Most notably, JPMorgan Chase has been very vocal about this.

They are unhappy with the idea that you could earn interest on stablecoin holdings. Now, the concern is that if consumers can earn interest on stablecoins, they’re going to pull their money out of banks and keep it in these other accounts — that’s going to cause deposit flight.

Chris: In the bill, essentially, I would say the banks got mostly what they wanted, which is that you cannot pay interest on balances.

I think the exact language is, anything that’s functionally or economically similar to a bank account, and there are further legal clauses. Now if somebody did multiple transactions using their stablecoin wallet twice a month at Walmart and you use a formula that’s not functionally or economically similar to paying out on a balance, what you can do is give them rewards.

Robert: So it’s more like a credit card or rewards program.

Chris: Yeah. Going any further, you start to ban things like Starbucks rewards points and things, right? There was a prolonged discussion — I think it’s gotten as far as it can go, and essentially it was a compromise that was somewhat painful for the crypto industry. But we’re still very supportive of the overall bill.

Robert: The thing that I find so interesting is Brian Armstrong has embodied this debate versus Jamie Dimon. They’re kind of the two leading voices on this. But JPMorgan — it has a big blockchain unit. They have tokenized deposits that are live onchain.

Chris: I mean, these are massive organizations. But yes, many big banks, including JPMorgan, have significant blockchain efforts.

And I think if CLARITY passes, you’ll see that a lot of those efforts will roll out in a significant way. We’re way past the point where enterprises adopting new technology will simply dip their toes in and do experimental stuff. We speak to a lot of these organizations regularly.

Some of them are already live, and they have significant deployments that are going to go live with greater regulatory clarity. I think they see significant opportunity. You have to remember, the banking system is the way it is for a bunch of reasons. And it’s not necessarily their fault. These systems are very complex and very intertwined, but a lot of the technology is very antiquated.

And so one of the things that blockchains have done for the financial industry is give them a unified framework where they can all kind of say, “Hey, together, let’s move into the 21st century.” And so it’s solved not just a technology problem, but a coordination problem.

And I think if you speak to them, they’re all very excited about that.

Blockchains remove layers of mediation — they’re disintermediation machines. When you send money through a wire, the bank has some piece of paper that gets moved somewhere else. There is no financial “network” — like a global network the way there is a global internet or a global WhatsApp network. It’s a patchwork of systems. And so one of the beautiful things with, say, stablecoins is you’re building something just the way you would build it on the internet.

And so the banks see that opportunity. They say, “Wow, we can save a lot of money. We can provide better products. We can modernize a whole bunch of things.” And so we see that — across the board — a lot of these organizations are very excited about this, including JPMorgan.

And don’t take it from me. Goldman Sachs CEO David Solomon just came out endorsing the CLARITY Act. Fidelity, BlackRock, a bunch of other major financial organizations. They all have major efforts. Much of this is public. The most innovative fintech companies like Stripe have gone in, in a big way.

The technology is on the verge of going very mainstream with the right regulation.

A “kill shot” to open source

Robert: Let’s shift over to another debate, which is over liability for developers. There’s a former White House cybersecurity official, Carole House, who has raised concerns that developers should be held more liable for the software that they write.

She argues that, if you don’t hold them liable, this could set a dangerous precedent for other areas of technology like AI.

Marc, I know you spend a lot of your time on AI. What do you think about this argument?

Marc: It’s a kill shot to the industry.

It makes software development impossible because how can any software developer anticipate how the software will be used down the road?

And you don’t even need to think about this in terms of software. If I run a hotel and a criminal stays at the hotel and they plan their operation — does that make me part of their conspiracy? If I’m an engineer who designed a car, and the car is used in a bank robbery, does that make me an accessory to bank robbery?

If you saddle people who build products that get used by many people for many reasons with downstream liability, it just kills the industry.

Chris: And just to add to that — if you build a piece of software and you’re like, “Hey, criminals, come use it, here’s how to do a crime,” that is a crime. No one’s debating that.

What we’re talking about is you’re building open source software for a constructive use case. You’re making an AI model; you’re making a blockchain; and then you make it open source.

If you add unlimited downstream liability, criminal liability, or civil liability — how could you ever decide to do that as an open source developer with that kind of risk? You couldn’t.

These people aren’t big companies that can afford to defend themselves. Five guys in a garage are going to take unlimited liability? It’s going to kill startups. It’s going to kill open source.

Marc: This is a very live issue in AI policy also. Right out of the gate, open source dies, because open source developers are not getting paid to start with. They certainly can’t take on enormous negative liability.

Because open source dies, academic research dies. Because without open source, there is no computer science research in any of these fields. It’s entirely dependent on open source. So it kills computer science as a field, and that’s just a start.

Then it kills venture investing, right? Because we obviously can’t invest in a company if they have a million times the level of exposure, because somebody does something that they didn’t even know about. And then it kills all the companies, right? All the startups. And then it kills the big companies because they also can’t take that on. So it rips right through.

What CLARITY actually does to securities law

Robert: Let’s move on to one of the last objections that I’ve heard most often, which is that this bill would punch a hole in securities laws. Marc, as you mentioned, securities laws have allowed US capital markets to thrive for the past 90 or so years.

So some people say that if this bill gets through, a company will just go, “I’m going to tokenize an asset, and put it on a blockchain, and this is going to exempt me from SEC oversight and securities law in general.” What’s your response?

Chris: Here’s what the bill says. If you take a stock and you tokenize it, that’s a security, and it’s regulated by the SEC like a security is. It’s very black and white.

The only thing that is different is that the CLARITY Act delineates that blockchains or tokens like Bitcoin or Ethereum will, in some cases, be regulated by the SEC and, in others, by the CFTC.

Let me walk you through it. So Bitcoin, when it started, was presumably one or some set of people — whoever Satoshi was — and by definition, when something starts, it’s centralized. There’s a person behind it. They have sort of inside information and control.

And the way the CLARITY Act works is, at that point, that token is regulated by the SEC. So someone creates a new blockchain. They have control over it, and they have rules, like lockups, for example. They have disclosure rules. They have all the kinds of things you expect from securities laws.

Over time, when you hit certain thresholds of decentralization and start to look more like a Bitcoin or Ethereum of today — where there is no central actor that controls it, has inside information, and so forth — the CLARITY Act designates that the CFTC oversees that token. So it’s regulated as a commodity and not a security. Because at that point, you still need regulation to make sure that people aren’t dumping on the market or cornering the market and all the other things that commodities laws regulate.

But it’s a different regulatory regime depending on the nature of the asset.

Robert: So it becomes more like gold or precious metals or oil or wheat.

Chris: That’s right. Even the last administration said at some point that Bitcoin and Ethereum were sufficiently decentralized to be regulated as commodities. So this has been implicit in court cases and in agency decisions from both parties for the last 10 years.

All CLARITY does is enshrine it in law and make the definitions specific, so that people know exactly where they are and you don’t have to go to court to figure it out. It’s taking what has been the consensus across courts and regulators, hard-coding it, and making it specific.

Today, if I create a token, there are no rules for disclosure. There are no rules for insider trading or lockups. If the CLARITY Act passes, our effective lockup period — meaning the period until we can sell — will increase significantly. We’ll invest in someone launching a product; it’ll have a token, and until these criteria are met for hitting sufficient decentralization, there are lockups on venture capitalists and founders and so forth — which is how it should be.

It’s a smart risk-based regulatory framework, and we think it will allow people to build long-term products and build trust within the market. Consumers, investors, and so forth — all participants will have trust in the market because it’s regulated and there’s a framework around it.

That’s how you build a real industry long term.

What happens if CLARITY fails

Robert: All right. So we’ve walked through a number of the live fights. All of these things are being debated right now. Any one of them could potentially tank this bill.

What happens if CLARITY does not pass?

Chris: We’ll keep working on it.

A lot of these things can and are being done at the agency level — meaning the SEC, CFTC, Treasury, and so forth. I think the big difference is that legislation is de facto permanent in a way that agency rulemaking is less so.

And that’s important both to make sure the rules are there and to protect consumers in the long term — but also for industry to have confidence. If you want to build something, it can take many years to build. If you have to try to build on shifting sands, you’re much less likely to make that investment of time and money.

You’ve got enough to worry about as an entrepreneur already without having a shifting maze of regulatory changes. So that would be the downside. It would just prolong that. But also, I’m optimistic it will get passed in the near future. If it doesn’t, we will keep working on it.

Continuing the long tradition of American technological leadership

Robert: Let’s zoom out and talk about what’s at stake for the country and for American leadership. Passing the CLARITY Act, why is it so important for the country to do this?

Marc: We would argue this is in the long tradition of American technological leadership, and I would say American technological supremacy, which is: do you want to be the country that leads the world in technology or not?

There are two parts of that, which are: do you want the technology to exist when some people argue that you’re better off without certain technologies? The other part is that technologies do tend to exist once they get invented — do you want that to happen inside your own country, or do you want to have that happen somewhere else?

And we always make a very strong argument on this, which is that every American citizen, regardless of their political position, should want America to be the world’s technology leader. That has many benefits. It has direct economic benefits in terms of the wealth of the country and the ability to pay for all the things that we want to pay for.

It has enormous benefits in terms of our security. For the crypto industry to be based in the US is overwhelmingly good for United States security. It’s overwhelmingly good for United States law enforcement. It’s overwhelmingly good for United States national security.

We’re so used to America being the technology leader that it almost feels embarrassing to make this argument because it’s such an obvious argument. America has so clearly benefited from this for the last hundred years. And in my view, we should all want that to continue for hundreds of years to come.

Robert: Well, time will tell whether policymakers heed your words, that this is urgent, and that the benefits will accrue to whomever sets the standards first.

Thank you both.

The corporation was the last great organizational leap, built for the industrial age and the coordination problems (and opportunities) it created. But software and internet-native protocols can now reduce the need for much of the previously unavoidable overhead — the many layers of centralized management, bureaucratic bloat, and intermediation — of the traditional firm.

Existing legal structures were not designed for this new world. Only one relatively new entrant is emerging as a serious contender for the next organizational leap: The DUNA, the sole legal entity explicitly recognized by once-in-a-generation market structure legislation now advancing through Congress. It’s arguably the only structure credibly built for internet-native organizations.

To understand why new organizational forms are emerging today, it helps to remember what problem the corporation solved in the first place — and where we’re going.

How merchants managed risk

Before corporations, business was personal: Imagine Marco Polo undertaking long-distance sales calls with his father and uncle. Family businesses like these quite literally took their lives in their hands. If a contract went south, their personal fortunes could be wiped out — or worse. Their very lives could be at stake.

Merchant ventures largely relied on two kinds of protection, but neither was guaranteed. The first was geopolitical: the relative peace of the Mongol Empire’s Pax Mongolica. If you attacked someone the Mongols liked, then you would be in trouble with the notoriously brutal Mongols. The second was social: If you cheated someone or defaulted on a contract and so violated the Lex Mercatoria — a self-enforced merchant honor code (ca. 1100–1600) — your reputation would be sullied, and you could be blackballed from the trade from Quanzhou to Timbuktu.

A merchant’s word really was, in the absence of strong institutions, worth more than gold. The Polo family had it somewhat easy, too, since they were bound by blood. Many other business partnerships have been less sanguine.

One of the most persistent issues businesses face is tensions between principals and agents; or, in this case, investors and merchants. One innovation was the medieval commenda, which provided some limited liability: Investors could only lose what they put in, and the same (theoretically) went for the merchants. The partners split the profits according to their original contributions. Commendas arose organically, long before there were any formal statutes. Yet every concern was still one small shock away from going under. And the model didn’t scale: Commendas dissolved at the end of a voyage, or in the event of bankruptcy. Or death.

A further innovation was the Florentine compagnia. Think: Medici bank. This form was a more durable and operationally sophisticated legal entity than the commenda. A compagnia could sustain ongoing multi-partner commercial relationships, but it continued to rest on the personal liability of all partners. This was the most advanced pre-corporate instrument yet devised — the high-water mark of medieval partnership — yet it still left its partners exposed. Unlike the Church and universities, which had long enjoyed legal personhood derived from Roman concepts of the universitas (a collective entity treated as a single legal person), commercial enterprises still lacked a fully separate legal identity.

These shortcomings wouldn’t be remedied until the 17th century, when early modern Europe came up with something new. This innovation, and its legal protections, allowed ventures to raise capital more easily, distribute ownership through stock offerings, and shield owners from liability; it was the corporation. Corporate powers, famously granted to the Dutch East India Company (or VOC: Vereenigde Oostindische Compagnie), arrived as a revelation and spread to the rest of Europe once it became clear what a good idea corporations were. (While the English East India Company preceded the VOC by a couple of years, it was far less sophisticated, raising capital only for specific voyages, and lacking a mechanism to raise public equity.)

By lowering the risk of doing business, and reducing coordination costs, corporations made large-scale, capital-intensive ventures — and much of the modern world — possible.

The price of scale

While solving a host of real problems, the corporation introduced new ones. Its first achievement was making participants care about one another’s outcomes: By tying shareholders, directors, and captains to the same legal entity and the same bottom line, the corporation forced each party to internalize costs it would otherwise impose on the others with impunity. But shared stakes are not the same as perfectly aligned incentives.

Let’s consider the VOC as an example. Its legal form is familiar but complicated: Shareholders included many Dutch citizens eager for a return on their investments, but who were too busy with their lives to be concerned with the VOC’s day-to-day operations or its grand strategy. The board of directors, the “Heeren XVII,” made plans for how everyone was going to make money. Ship captains and traders on the ground in Southeast Asia had to make the best choices for the company in the moment with limited information and resources.

In theory. In reality, these three parties’ interests were not perfectly aligned; they diverged slightly insofar as one party could make more money for themselves at the expense of others.

How do you make sure the captains, far removed from the oversight and control of the Heeren XVII, don’t ransack another ship and abscond with the loot? Prevent the traders from taking bribes, or cutting larger deals for themselves? Ensure the board is making the right calls? What if you’re a group of shareholders who also cleave to Protestant values and have concerns about the VOC’s sometimes rapacious practices? Questions like these led to all sorts of innovation in incentive design — options like bonuses, profit-sharing, audits, surveillance, and some might even say efficiency wages — and to new legal protections where the state would ensure fair play. It also led to untold abuses.

And yet! The corporation as it evolved over time remains the best thing we’ve got for aligning incentives, reducing coordination costs, generating profits, and protecting everyone involved in a venture.

Shortly after the United States was formed, it recognized corporations through special legislative charter, but these were rare. The First Bank of the United States, chartered by Congress in 1791, was both the first and most famous example. New York introduced the first general incorporation statutes in 1811. By the mid-19th century, more states allowed incorporation without special acts and the idea of “limited liability” became more standardized across states. An explosion of corporations followed during the phase of late 19th-century industrialization, culminating in the Delaware General Corporation Law of 1899.

Cooperatives were another option that gained momentum in the 19th century. They experimented with a different solution to the problem of coordination: member ownership and democratic governance. Farmers, consumers, workers, and credit unions used cooperatives to align the interests of participants more directly with the organization itself. While cooperatives succeeded in some settings, like agriculture (see for example Land O’Lakes), they generally remained more specialized. The corporation, meanwhile, grew more and more popular.11

Another solution was the limited liability company, or LLC. While the LLC had its precursors in older forms like the German GmbH or the United Kingdom’s limited company (Ltd.), the LLC itself is surprisingly modern: Wyoming introduced it into law only in 1977. Prior to this, corporations did offer limited liability but were rigid and subject to double taxation, while partnerships were flexible but exposed participants to personal risk. The LLC combined elements of each — limited liability with pass-through taxation — making it a better fit for a wide range of smaller ventures. Today, they’ve become a default form for many startups, small businesses, and investment vehicles.2

A succession of minor variations have followed: the LLP, or limited liability partnership (1991); the L3C, or low-profit limited liability company (2008); the benefit corporation (2010); and so on. These are certainly useful, refining the corporation for particular uses. But every now and then, technology changes what’s possible, and you get a form that is, in comparison, revolutionary.

DAOs and their discontents

Decentralization is one such revolutionary idea: the ability for large groups of people to coordinate without centralized management or trusted intermediaries.

Before crypto — and especially before Satoshi Nakamoto’s invention of the blockchain — that possibility was more philosophical than practical. One of crypto’s first great innovations was the DAO, the decentralized autonomous organization. A DAO is an organization governed by rules encoded in software and collectively managed by its participants rather than a central authority. There is no centralized management team or board of directors, no Heeren XVII.

But decentralized governance is hard. Getting token-holders to vote on important issues has proven even harder than getting individual shareholders to vote on board members, a process that already has abysmally low voting rates, on par with U.S. municipal elections. Ensuring that power isn’t concentrated in the hands of only a few token holders can also be challenging.

The recent legal environment only exacerbated these challenges. Regrettably, the previous administration’s Securities and Exchange Commission declined to provide clear rules for crypto projects and also weaponized that ambiguity through aggressive enforcement actions against the industry. Entrepreneurship doesn’t thrive under uncertainty; it’s hard enough even when you do know the rules.

At the heart of this question of legality is one of the three prongs of the so-called Howey test, which the SEC uses to evaluate whether an instrument is a security: (1) An investment of money; (2) in a common enterprise; (3) with profits to come solely from the efforts of others. Asking whether the value of the asset depends on the “efforts of others” in the case of a public company would include, for example, the managers who run the firm. In the case of crypto projects and their DAOs, the SEC noted that the ongoing development of the protocol, even if it were done by a bunch of unaffiliated people who may or may not hold the token, subjected a related token to securities laws — making widespread participation and onchain transactions impossible.

Equally importantly, since DAOs weren’t officially recognized by the state, the owners of a project wouldn’t have any of the protections mentioned earlier, like limited liability. In other words, members of a DAO could face unlimited personal liability, putting crypto governance on a footing that is, legally speaking, almost medieval.

So crypto projects did what their lawyers told them. They set up offshore foundations as separate entities that oversaw the ongoing development of protocols, severing the connection between those efforts and the U.S. businesses. Or they established themselves outside the United States. Either “solution” was to the detriment of innovation in the U.S., and to U.S. jobs and tax revenues.

Offshore crypto foundations were, to put it kindly, convoluted. These lawyer-concocted workarounds shifted authority and ongoing development efforts to an “independent” entity in hopes of avoiding securities regulation. The strategy was understandable at a time of regulatory hostility, but it also exposed deep flaws: Foundations have weak incentive alignment, limited ability to drive growth, and an inevitable tendency to entrench centralized control.

But what choice did projects have when stuck between the rock of “get sued by the SEC” and the hard place of “set up a weird organizational structure that’s going to create incentive alignment issues”?

This is why the DUNA — the decentralized unincorporated nonprofit association — is so significant. It draws on the long history of structuring ventures and governance to achieve the goals of every business ever: coordinating people efficiently around a common purpose. But it does so without centralized managerial control, reducing the principal-agent problems and information asymmetries that arise in traditional firms. In doing so, the DUNA departs from one of the core assumptions underlying the Howey test: that participants are relying on the managerial efforts of others to generate value.3

The crowd gets its own legal form

Before the DUNA, there were three options for organizing around and governing a crypto project: a DAO, which lacked legal recognition and exposed members to potentially ruinous liability; a traditional corporate entity, which forced projects into ill-fitting hierarchical structures and exposed them to action from the SEC; or an offshore foundation, which was legally and practically convoluted and pushed much of the industry abroad.

Until recently, there was no clean way for a group of users to govern a decentralized network — a form of organization that blockchain technologies have only recently made possible — while maintaining some of the protections enjoyed by companies. Now there is.

Put simply, the DUNA turns a crowd into a legal entity. Three states so far — Alabama, West Virginia, and Wyoming — have passed laws authorizing this new business structure, which combines the legal superpowers of existing forms with the ability to decentralize control, something very different from the traditional corporation and which no previous entities have ever fully achieved.

What protections does the DUNA provide more specifically? Its powers include legal personhood, limited liability, persistence over time, and state recognition — many of the same elements that make modern corporations possible. Recognition of the group’s “personhood” allows the entity to enter into contracts on behalf of its participants, while limited liability ensures that members aren’t held personally liable for organizational obligations. Together, these features make it possible for large, loosely knit groups of people to coordinate — raise capital, hold assets, hire administrators, pay taxes, make deals — without putting members at undue risk and exposing them to potentially ruinous liability.

Organizational forms don’t take hold all at once; they spread as jurisdictions compete, as lawyers grow comfortable with them, and as entrepreneurs begin to trust them. Before Delaware became the venue of choice for incorporation, New Jersey held sway;4 now, Texas and Nevada are gaining ground. The LLC first received approval in Wyoming and then migrated, once its tax treatment was clarified, to all fifty states by 1997. As for the DUNA, Wyoming was once again the pioneer, enacting it as law in March 2024. Already, crypto protocols and communities, including Uniswap Governance and Nouns DAO, have implemented it.

Much as the corporation gave large-scale enterprise its first native form, the DUNA is giving open, internet-scale, decentralized networks a legal form of their own.

A new era of organizational design

Think of the DUNA as a legal wrapper that allows the governance mechanism of a decentralized network to do business, but without introducing traditional centralized management. It’s built on the unincorporated nonprofit association (or UNA), a body of law adopted by 17 states and Washington, D.C., that helps groups like homeowners and civic associations, recreational sports leagues, religious congregations, and hobby clubs to organize under the law. UNAs provide lightweight governance without the overhead of a corporation or LLC, allowing these groups to own property, enter contracts, and sue (or be sued) as an entity.5

The DUNA is similar: It allows a group of tokenholders or contributors to govern through onchain rules or token-based voting rather than a board or management team. Members get the protections of limited liability, separating the entity’s obligations from their personal assets; and the organization can be understood by and interact with courts, regulators, and counterparties.

But the DUNA doesn’t solve every problem. It doesn’t eliminate governance challenges. It doesn’t guarantee decentralization (although to qualify as a DUNA, a DAO must have at least 100 active members). And it doesn’t magically bypass securities law. What it does do is close a specific gap, making it possible for a decentralized organization to be a legally recognized one.

• • •

From informal merchant networks, to partnerships, to corporations, to LLCs, to now, DAOs, each new organizational technology emerges when people need new modes of coordination. The DUNA may mark the beginning of a new era in this evolution of organizational design. But just as the corporation didn’t replace all partnerships, the DUNA doesn’t supplant everything that came before. It merely expands the menu. And for the first time, it makes possible decentralized networks that are represented by fully legible legal entities.

For most of human history, organizing at scale — even small scale — meant taking on enormous personal risk. Daring entrepreneurs, like the Polos, relied on family, reputation, and fragile norms to hold things together, always one shipwreck away from ruin.6 The corporation changed the calculus by separating the fortunes of a venture from the fortunes of the people behind it. The DUNA extends that separation into a new domain: communities governing decentralized networks based on blockchains.

Now, even a loosely organized network of strangers on the internet can act as a single entity — make deals, hold assets, take risks — without any one participant having to bet their livelihood on the outcome. In this way, it’s a new answer to one of commerce’s oldest problems.

• • •

Acknowledgments: We’d like to thank Aiden Slavin, Alejandro Flores, Miles Jennings, Scott Duke Kominers, Sonal Chokshi, and Steph Zinn for their helpful comments and emendations. Any errors remain our own.

Notes

Unlike traditional equities, tokenized stocks can be self-custodied in digital wallets, moved permissionlessly, traded at any time, and used seamlessly as collateral across onchain finance. In just the past couple of months, Coinbase, DTCC, NYSE, Robinhood, and others have made moves here, ranging from processing trades onchain to forming new joint ventures to launching new chains.

The momentum shows up in the data. The market cap of tokenized stocks reached about $1.7 billion at the end of June, up from $329 million a year earlier — more than 5x growth. This makes tokenized stocks one of the fastest-growing categories of tokenized assets (which some refer to as real-world assets).

How much of that growth comes from new issuance versus price appreciation of the underlying stocks? Unlike stablecoins, whose circulating supply is a direct proxy for demand — one token, one dollar — tokenized stocks move with their underlying equities, so market cap does not cleanly separate the effects of new tokens minted and existing tokens repricing.

The evidence points to issuance, though. More than half of today’s market cap sits in assets that weren’t onchain a year ago. And most of the remaining balances arrived mid-year — after much of the period’s price movement in the underlying stocks had already occurred.

While the market for tokenized stocks is new, its composition has already changed greatly in the past year:

Crypto-linked products once dominated the market, but their share by market cap has dropped to 21% (as of June) from 79% a year earlier. They have lost the top spot to the “other” category — a long tail of hundreds of smaller listings — that now makes up 35% of the market, up from 15% a year earlier.

The rest of the market is climbing too. Megacap tech — tech companies with a market cap around $100 billion or more — now make up 10.6% of the market by market cap as of June, up from 0.6% a year ago. ETFs and indices grew to 17.3% of the market in that same period, up from 4.5% a year earlier.

The fastest riser has been, unsurprisingly, the AI and chips category. The category vaulted from less than $1 million in June 2025 — 0.3% of the market by market cap at the time — to 15.5% of the market as of a year later.

Transfer activity is on the rise too. Monthly transfer volume for tokenized stocks reached $9.22 billion in June, up from $53 million last June — a more than 170x increase. This metric measures any onchain movements, such as trading, transfers between wallets, or collateral deposits into DeFi protocols

.***

The institutional groundwork is now falling into place. In just the past month, DTCC processed its first live production trades of tokenized Treasuries and equities on Digital Asset’s Canton Network. (A full launch of the broader tokenization service is planned for October, giving Wall Street a direct on-ramp to the ~$114T in assets custodied at DTC.) At the start of the month, Robinhood launched its own chain on mainnet, bringing traditional markets, crypto, and real-world assets together on an open network. On June 22, the parent company of the NYSE announced a joint venture with OKX to make tokenized NYSE-listed stocks available to users, pending regulatory approval. And a week earlier, on June 16, Coinbase said it will offer 1:1-backed tokenized U.S. stocks to non-U.S. users, with dividends, full shareholder rights, and 24/7 trading. Binance shipped its own version days before.

The market for tokenized stocks is still a small one compared to traditional equities, which trade in the double-digit trillions per month. But the trend is clear: More issuers and platforms are bringing tokenized stocks online, and the category is growing.

You’re receiving this newsletter because you signed up for it on our websites, at an event, or elsewhere (you can opt out anytime using the ‘unsubscribe’ link below). This newsletter is provided for informational purposes only, and should NOT be relied upon as legal, business, investment, or tax advice. This newsletter may link to other websites or other information obtained from third-party sources — a16z has not independently verified nor makes any representations about the current or enduring accuracy of such information. Furthermore, the content is not directed at nor intended for use by any investors or prospective investors in any a16z funds. Please see a16z.com/disclosures for additional important details, including link to list of investments.

Every generation gets a chance to upgrade its infrastructure.

In the 1990s, it was the internet. Policymakers worried that it would disrupt existing industries and create new risks. But instead of cramming the technology into outdated regulatory boxes, lawmakers established rules that let innovation flourish while protecting consumers. The result was one of the greatest periods of economic growth in American history.

Today, we face a similar opportunity with blockchain networks. Stablecoins are making payments faster and cheaper. Tokenization is modernizing capital markets. Both depend on blockchain infrastructure.

These technologies will advance with or without Congress. It’s up to the United States whether it will set the standards, or cede that ground to others.

The GENIUS Act proved that clarity works

The GENIUS Act showed what well-considered policymaking can achieve. Stablecoins are digital dollars that move across the internet as easily as bits. Sending $200 from the U.S. to Colombia via traditional rails can cost more than $12 and take days. With stablecoins, the same transfer costs pennies and settles in seconds.

Regulatory uncertainty had for years held back stablecoin adoption. The GENIUS Act changed this. By establishing reserve requirements and a framework for issuers, it got rid of any confusion and unlocked growth.

The results speak for themselves. The stablecoin market amounts to roughly $315 billion, up more than 50% from a year ago, with dollar-backed tokens becoming one of the fastest-growing channels for U.S. currency abroad. By Visa’s count, stablecoins processed $100 trillion in transaction volume over the last twelve months. Major institutions — including JPMorgan, Citi, Visa, Mastercard, and BlackRock — are drawing deeper into blockchain infrastructure.

Crucially also, the dollar is winning. Millions of people worldwide who lack easy access to traditional dollar accounts can now hold and transact in digital dollars through software wallets. Clear rules have helped anchor this new system in U.S. standards and institutions rather than in competing alternatives.

GENIUS demonstrated that smart regulation expands markets rather than constricting them. But it addressed only part of the picture.

Blockchain networks still lack rules

Stablecoins account for less than 15% of the crypto market by market cap, yet they depend on the remaining 85% of foundational blockchain networks that still operate without a coherent federal framework. This is equivalent to regulating smartphones while ignoring cellular networks.

The CLARITY Act rectifies this.

At its core, CLARITY provides clear rules for blockchain networks and defines regulatory responsibilities across digital asset markets. It creates incentives for transparency, risk reduction, and competition under a common set of standards. It helps prevent the next FTX disaster by empowering regulators to supervise intermediaries with proven principles from traditional finance: proper custody, segregation of customer assets, and full disclosure. And it opens the next wave of institutional adoption.

Major firms are already moving. BlackRock has launched tokenized funds. JPMorgan is building blockchain-based payment systems. And DTCC, which holds $114 trillion in assets under custody, is preparing to scale tokenized securities with the Canton Network. CLARITY removes the barriers to entry, giving traditional finance a clear pathway to participate compliantly. This benefits not just the crypto industry, but consumers, investors, and the long-term competitiveness of U.S. capital markets.

The choice before us

History shows that open, neutral platforms governed by clear rules create the greatest value. The internet succeeded because entrepreneurs knew the rules of the road. They could build, attract capital, and compete on merit rather than on regulatory guesswork. Blockchain networks deserve the same chance.

Regulation versus innovation is a false choice. GENIUS already disproved that: During the second half of 2025 — after GENIUS was signed into law — more than $13 billion flowed into crypto startup investment, nearly double the $6.9B invested pre-GENIUS in the first half of that year. Meanwhile, forecasts project the market for tokenized assets to grow 100x growth over the next few years. The choice is between certainty and uncertainty, America can assert its leadership and build the future here at home or watch it be built elsewhere.

No law is perfect, and the CLARITY Act is no different. The reality is that there are no consumer protections on crypto currently. CLARITY puts those in place. As with any bill, no one will get everything they want, but the version released today will move the industry forward. It reflects months of bipartisan negotiations and significant compromises from industry. Whatever its imperfections, the CLARITY Act is significantly better than continuing with no safeguards at all.

The decisions Congress makes this year will determine where the next era of financial infrastructure will flourish and who writes its rules. If the CLARITY Act passes, it will let the U.S. lead once again, just as it did with the commercial internet. Failing to act means innovation will migrate elsewhere, under frameworks devised by others.

You’re receiving this newsletter because you signed up for it on our websites, at an event, or elsewhere (you can opt out anytime using the ‘unsubscribe’ link below). This newsletter is provided for informational purposes only, and should NOT be relied upon as legal, business, investment, or tax advice. This newsletter may link to other websites or other information obtained from third-party sources — a16z has not independently verified nor makes any representations about the current or enduring accuracy of such information. Furthermore, the content is not directed at nor intended for use by any investors or prospective investors in any a16z funds. Please see a16z.com/disclosures for additional important details, including link to list of investments

Spending more than two decades in Congress, I saw firsthand how legislative momentum can propel our country forward — and how legislative inertia can leave us scrambling to catch up. Today, Congress stands at a crossroads: the United States can remain the world’s technological and financial capital or give it all away, potentially to adversaries who are mounting an active challenge to U.S dominance.

The crypto market structure legislation currently moving through Congress, the Clarity Act, is unlike anything we’ve seen since the Telecommunications Act of 1996: a large-scale, forward-looking effort to embrace technological change and introduce consumer safeguards for a nascent technology. Like that earlier landmark law, this effort enjoys broad bipartisan support and requires coordination across multiple committees. And that only happens when Congress recognizes the high stakes.

For too long, American financial policy has been in a reactive crouch. Since the 2008 financial crisis, virtually every major piece of legislation – from Dodd-Frank to subsequent banking reforms – has been backward-looking, designed to address past crises rather than prepare for the next frontier. While these measures serve important purposes, they’ve left us with a regulatory framework built for yesterday’s risks, not tomorrow’s opportunities.

Crypto market structure legislation can break this pattern. For the first time since the Gramm-Leach-Bliley Act of nearly three decades ago, we have the chance to enact comprehensive financial policy measures that aren’t precipitated by crisis.

Some argue that nearly century-old securities laws already cover what crypto needs, or even claim that the industry would prefer no regulation at all. As someone who has spent his career cutting red tape and championing the free market, I can understand where those arguments come from. In this case, though, they are simply wrong

Those who claim the legal status quo for crypto is just fine have not listened to what businesses large and small are actually saying. They are asking for rules to operate. When entrepreneurs know boundaries, they build confidently within them. The internet and the thousands of startups that made America the world’s tech leader flourished not despite regulation, but because smart regulation created the infrastructure and protections users and investors required.

Over time, decentralized innovations can also prove their worth within a regulatory framework. But first, we need the framework itself.

The framework in the Clarity Act can ensure entrepreneurs feel confident in creating jobs and building businesses without fear of arbitrary or unexplained crackdowns. Upon becoming law, it will establish protections for consumers and investors, and give law enforcement agencies the tools they need to identify and stop criminals and bad actors seeking to hurt the American people. I’m proud of many bills passed during my tenure in Congress, including those I led as chair of the House Committee on Financial Services, that made important strides, but this effort dwarfs them in scope and significance.

This legislation is governance at its best: Congress identifying an emerging sector with enormous potential, and responding with the regulatory clarity needed for that sector to flourish within appropriate guardrails. While there is disagreement on precise details, almost everyone – from entrepreneurs and investors to academics and policymakers – agrees that this regulatory clarity is crucial for blockchain technologies and the innovation and economic growth they enable to thrive.

What gives me confidence that we are closer than ever to a breakthrough is the remarkably broad support this has garnered in Congress. The GENIUS Act, which regulates stablecoins, passed both chambers of Congress with strong bipartisan backing. And various crypto market-structure bills have attracted sponsors from across parties and the ideological spectrum. Members understand digital assets aren’t disappearing and recognize it’s time to act.

Other countries are moving aggressively, and global capital and innovation will flow to markets with clear rules. The Clarity Act is our best chance to maintain America’s role as home for global capital markets deployed in the world’s safest, most resilient regulatory environment, with rule of law and property rights clearly defined. This is about digital assets, but it’s also about deciding if America will lead the 21st-century economy and set the pace for the world or only react to crises.

I hope that decision is obvious.

You’re receiving this newsletter because you signed up for it on our websites, at an event, or elsewhere (you can opt out anytime using the ‘unsubscribe’ link below). This newsletter is provided for informational purposes only, and should NOT be relied upon as legal, business, investment, or tax advice. This newsletter may link to other websites or other information obtained from third-party sources — a16z has not independently verified nor makes any representations about the current or enduring accuracy of such information. Furthermore, the content is not directed at nor intended for use by any investors or prospective investors in any a16z funds. Please see a16z.com/disclosures for additional important details, including link to list of investments.

It’s a comforting story. It’s also mostly wrong.

Here’s the more honest version: where TradFi can use a blockchain to improve its existing business, it will. Not because it has embraced decentralization, but because it’s a compelling COGS story — the technology happens to cut costs, improve settlement, expand distribution, and tighten its grip on customer relationships.

What this means is that institutions aren’t somehow merging with DeFi. Instead, they’re selectively using the parts of DeFi that fit within their operating constraints and discarding the parts that do not; they’re reconfiguring DeFi around institutional requirements. The result is unlikely to look like either traditional finance or today’s DeFi. Instead, we’re beginning to see the emergence of a new category built on blockchain rails but optimized for institutional constraints: programmable financial infrastructure.

That dynamic may evolve as regulatory frameworks mature. Legislation such as the CLARITY Act could eventually make it easier for institutions to engage directly with permissionless systems. But regardless of what becomes legally possible, TradFi’s risk posture won’t reset overnight. Institutions still adopt technology through the lens of cost, risk, control, and operational fit — which is why this presents the industry with two opportunities, not one.

The first is helping institutions adopt the infrastructure they are ready for today. Every primitive an institution adopts — from atomic settlement to programmable money to tokenized collateral — validates the technology, builds out shared rails, and pulls real volume and capital onchain.

The second is to continue building the open, crypto-native financial system that institutions are not yet prepared to use.

These are not competing bets. They can and should exist in parallel, and done well, each reinforces the other. Open networks and ecosystems keep producing the primitives, markets, and innovations that institutions eventually adopt. If both succeed, convergence happens naturally — not because one system fully replaces the other, but because both increasingly come to rely on the same underlying infrastructure.

What TradFi is actually doing

TradFi adopts a primitive when it does two things at once: improves cost, risk, or distribution and stays compatible with control and accountability. The primitives that institutions discard — open access, pseudonymity, immutable execution — pass the first test but fail the second. That’s why the adoption pattern is predictable rather than arbitrary, and why builders can use it as a design test. That is, if a feature delivers value only by removing institutional control, it will almost certainly be reshaped or rejected, however elegant it may be.

Let’s put some primitives through the test. Atomic settlement collapses the gap between trade and finality, erasing counterparty risk and freeing the collateral institutions to park against unsettled trades. A shared ledger turns the highest hidden cost to the back office, reconciliation, into a non-event. Programmable money lets coupon payments, margin calls, and corporate actions run as code instead of a chain of manual instructions. AMM curve math, stripped of its permissionless wrapper, reappears as the pricing engine for onchain FX and tokenized money-market NAVs.

Each improves a number on a P&L or removes a line of operational risk and its associated cost, but none requires an institution to believe in decentralization. So let’s be precise about what’s happening with initiatives like JPM’s permissioned blockchain for institutional deposits, or the tokenized money market funds at BlackRock, and Franklin Templeton: These aren’t enterprises dipping a toe into DeFi. They’re using blockchain to do things they already do — settling interbank payments, managing fund subscriptions, and distributing yield-bearing instruments — but with better plumbing. These are deployments that use blockchains’ technical properties (programmability, transparency, atomic settlement) and deliberately discard the properties that make native DeFi work (open access, pseudonymity, and trustless execution).

That’s not a failure or a compromise. It’s a deliberate architectural choice, and one that tells us a great deal about where this is heading.

Different buyers, different rules

It would be a mistake to assume that institutional adoption is simply a larger distribution channel for existing DeFi infrastructure. Institutions are not evaluating protocols the way crypto-native users do. When institutions consider software vendors, infrastructure partners, operational risk, compliance controls, and long-term ownership of critical systems, they follow their standard operating procedure. The result is that success in DeFi does not automatically translate into success with institutions.

Enterprises rarely buy the “best” technology. They buy the technology that best fits existing workflows, risk models, and procurement processes, among other things.

Any technology that enters a heavily regulated, risk-managed, liability-averse institutional environment gets shaped by that environment. It happened with the internet (enterprise firewalls, private intranets). It happened with cloud computing (private cloud, VPCs, FedRAMP). It’s happening with AI (internal deployments, data residency requirements, model governance). Blockchain is no different.

The reconfiguration happens along two axes:

1. Compliance: KYC, AML, sanctions screening, investor accreditation, and regulatory reporting requirements aren’t negotiable for most institutions. Permissionless systems don’t natively accommodate these requirements. Institutions need the ability to freeze assets, reverse transactions, and identify counterparties. DeFi was not originally designed around those requirements, and accommodating them often requires meaningful architectural changes. This may evolve. For example, CLARITY could make it easier for institutions to access permissionless systems while meeting regulatory requirements. But today, most institutions must evaluate blockchain infrastructure through the lens of control, accountability, and operational risk.

2. Enterprise value delivery. This axis is often underappreciated. Institutions aren’t adopting blockchain because they believe in permissionlessness as a principle. They’re adopting it because it can compress costs, reduce reconciliation friction, create new distribution channels, or embed them more deeply in a customer relationship. The value proposition has to be expressed in those terms, or it doesn’t survive procurement.

Stablecoins may be the clearest example. Banks, payment providers, and fintechs increasingly view them as useful settlement infrastructure because they enable faster movement of dollars across networks and geographies. Yet few are embracing the broader philosophy of permissionless finance. They’re adopting programmable dollars because they’re useful, not because they’re trying to recreate the financial system around DeFi’s principles.

Circle’s evolution is an apt illustration. Arc reflects how blockchain infrastructure is increasingly being packaged for institutional buyers: emphasizing compliance, operational controls, trusted counterparties, and integration into existing workflows rather than permissionless access and composability. The value proposition isn’t permissionlessness for its own sake. It’s faster settlement, global reach, and improved capital efficiency delivered in a form institutions can actually adopt.

Even organizations like SWIFT increasingly frame blockchain through this lens. Their efforts around tokenized asset interoperability are not attempts to replace existing financial institutions. They’re attempts to improve how existing institutions coordinate with one another using the SWIFT network. The pattern appears repeatedly: Blockchain adoption that strengthens established financial networks rather than displacing them.

This is simply how powerful technologies evolve when they encounter large, established markets.

Two opportunities for builders

At the industry level, it would be a mistake for everyone to abandon one opportunity for the other. At the company level, it would be a mistake to try to pursue both at once.

Institutional adoption and open networks can reinforce one another at the ecosystem level. But for most teams, they remain fundamentally different businesses. Building for institutions requires understanding procurement, compliance, controls, channel partners, and long sales cycles. Building for open networks requires optimizing for developers, liquidity, composability, and network effects. The customer, distribution model, product requirements, and success metrics are often entirely different.

This doesn’t mean one opportunity is better than the other. It simply means founders should be clear-eyed about which market they’re serving, and be aware that what unites them are the rails underneath: public blockchains as neutral settlement.

Partnering with institutions and building an adjacent financial system aren’t in tension. When done right, each makes the other more valuable. The permissioned layer brings volume, legitimacy, and capital; the open layer keeps producing primitives the permissioned layer adopts next. Convergence, when it comes, happens at the rails — not by one system surrendering to the other.

Public blockchains may become increasingly important settlement rails, even as the applications built on top of them become progressively more permissioned.

Building for programmable financial infrastructure

There are two approaches to consider when building this new programmable financial infrastructure: building from scratch or adapting existing products.

Consider networks like Canton. Rather than adapting existing DeFi infrastructure, they are designed specifically around institutional requirements for privacy, compliance, and controlled interoperability. The goal isn’t to bring banks into DeFi. It’s to use blockchain-based coordination while preserving the governance, confidentiality, and operational controls that institutions require.

Not every successful institutional strategy requires rebuilding from scratch. Morpho, for example, is taking the opposite approach. Rather than abandoning its DeFi primitives, Morpho has focused on making them easier for institutions and asset issuers to consume. Apollo’s ACRED fund, for example, uses Morpho as part of its onchain lending strategy, pairing a DeFi- native lending primitive with institutional-grade distribution, compliance, and fund structures. The result is neither pure DeFi nor a fully isolated institutional stack. It is a model where institutions selectively adopt existing crypto infrastructure while packaging it in a way that aligns with their own requirements for control, compliance, and distribution.

This new category is purpose-built for institutional constraints. It draws from DeFi, but operates in a more permissioned, compliant manner and is therefore necessarily different from what exists today.

Some teams, like Morpho, have successfully adapted crypto-native infrastructure for institutional use cases. But builders shouldn’t mistake this for the default playbook. Institutions are a distinct customer segment with distinct requirements. In many cases, designing for those requirements from the outset will prove more effective than adapting products originally built for open networks.

The opportunity to keep building in DeFi

The innovations institutions are adopting today did not originate inside banks, asset managers, or existing financial infrastructure. They emerged from open networks where builders were free to experiment with new market structures, coordination mechanisms, and financial primitives.

That distinction matters. Institutions are not the industry’s primary source of innovation: The permissioned layer is often downstream from the open one.

This brings us to the more important strategic point: If our industry becomes too focused on selling to banks and asset managers, we risk mistaking a large buyer category for the entire opportunity. TradFi is an important customer. But it’s not the only one.

Designing for institutional requirements is a legitimate and valuable pursuit, but it is only one lane, not the whole road. The companies that endure will be the ones that remain clear-eyed about who they are building for. Institutional adoption may be a large opportunity, but it is not simply an extension of DeFi. Success in one market does not guarantee success in the other.

***

If you’re building for institutions, embrace it fully. Don’t assume crypto-native traction automatically translates into enterprise adoption. Learn the customer, understand the buying process, and build intentionally around institutional requirements.

If you’re building for open networks, keep doing so. Don’t abandon your vision simply because institutions are the loudest buyers in the market today.

Remember: These are complementary, not competitive. One adapts, commercializes, and scales proven innovations. The other discovers them. A version of this technology will almost certainly become part of the financial plumbing of the existing TradFi system. But that is not the only future being built. Open networks remain the industry’s most important source of experimentation and innovation, and many of the primitives that shape tomorrow’s institutional infrastructure will likely emerge there first.

TradFi isn’t adopting DeFi. It’s selectively adopting parts that fit its model. The opportunity for builders is not to chase every market at once; it’s to understand which one they’re building for. And to execute accordingly. The future may indeed run on institutional infrastructure, but many of its most important innovations will continue to emerge from open networks.

You’re receiving this newsletter because you signed up for it on our websites, at an event, or elsewhere (you can opt out anytime using the ‘unsubscribe’ link below). This newsletter is provided for informational purposes only, and should NOT be relied upon as legal, business, investment, or tax advice. This newsletter may link to other websites or other information obtained from third-party sources — a16z has not independently verified nor makes any representations about the current or enduring accuracy of such information. Furthermore, the content is not directed at nor intended for use by any investors or prospective investors in any a16z funds. Please see a16z.com/disclosures for additional important details, including link to list of investments.

Fundamentally, arcade tokens are the blockchain-based equivalent of assets people are already familiar with: airline miles, credit card points, in-game digital gold, and so on. What all these have in common is that they are currencies that are internal to, and support the functioning of, a marketplace economy: Frequent flyer miles and rewards points, for example, can encourage brand loyalty and be used to make flight and upgrade purchases; digital gold can let you buy or sell items in a video game.

While companies have used assets like these for decades, almost all previous instances have existed on centralized databases, limiting ownership, transferability, and user choice. Arcade tokens on public blockchains differ in that they are open, interoperable, and composable, which offers a range of new market-design benefits.

This post aims to answer the most common questions we’ve received about arcade tokens: what they are, what they do, why they have value, how builders can use them, the design tradeoffs they entail, and the opportunities they present.

What is an arcade token?

At a technical level, arcade tokens are just digital currencies intended for spending within their associated application ecosystems — with supply and demand flexibly managed to maintain price stability. Think of them foremost as currencies within a digital economy.

So, where does the term “arcade token” come from? Irrespective of whether you’ve visited an arcade, you’re probably familiar with the concept: You go inside; you trade cash for tokens, typically physical ones; then you use those tokens to play a few rounds of Galaga, Gator Panic, or whatever else you’re into. The tokens let you participate in the arcade’s economy.

The metaphor of an arcade makes it clear how these tokens work: Arcade tokens have a relatively stable value within the context of the economic system of which they are a part — whether that’s within a single service or across a variety of services. The relative stability of arcade tokens’ value sets them apart from other types of tokens, such as those whose value derives from underlying assets (as in asset-backed or collectible tokens); from the operation of decentralized network marketplaces (as in network tokens); or from speculative investments in a specific entity (as in company-backed or security tokens).

Despite the playful name, arcade tokens are a powerful, programmable economic primitive — and they are the key to unlocking a new area of the crypto design space.

What isn’t an arcade token?

Again, the most substantive distinction between arcade tokens and other types of tokens is that arcade tokens aren’t for investment or speculation. Unlike network or security tokens, which people often acquire in hopes of generating investment returns, arcade tokens are for spending.

People sometimes refer to arcade tokens as “utility tokens” because they’re designed to provide, well, utility. We avoid this label because it suggests that other token types lack utility, which is absolutely not the case. (See our “Defining tokens” article for more on this.) Alternative names for arcade tokens can include “points” (although in common parlance, this often implies that the associated records are kept on private ledgers rather than on public blockchains) and “loyalty tokens” (which describes just one specific application).

This doesn’t mean the value of an arcade token can never change — as we describe below, the price of an arcade token could change by small amounts over time. But arcade tokens typically have an unlimited supply at the prevailing price and do not offer, promise, or imply financial returns. This means they are generally unsuitable as investment products, and are thus typically outside the scope of U.S. securities laws.

What are arcade tokens good for? And why should builders consider using them?

Arcade tokens enable builders to print and provision value within a digital economy. Crucially, that ability both to create and allocate value can incentivize user behavior, bootstrap early growth, and generate network effects — without relying on external capital or speculative demand.

The intuition is simple and, again, lines up with the arcade analogy: If you’re running an arcade, you will likely want to control the supply of tokens to keep their issuance in line with customer demand. For example, on a day when twice as many customers show up, it may help to have roughly twice as many tokens in circulation so that all visitors can play as many games as they want (barring any capacity constraints). Why turn people away when you can mint enough tokens to go around?

You’ll also probably want to be able to adjust prices: If you make big improvements to the arcade itself — such as doubling the number of games, bringing in more elaborate full-featured machines, or offering fancier prizes — you might bump up the price per token. In short, you want flexible control over your economy so that you can better manage the trade-offs between supply and demand (and, furthermore, signal the value of your arcade to customers).

Beyond smoothing out day-to-day operations, this kind of economic control also helps build lasting relationships with your most loyal customers. You could, for instance, hand out bonus tokens to your most committed players. Significantly, when people go home at the end of the day with a few leftover tokens in their pockets, they’re carrying an incentive for them to come back to your arcade, since that’s where the tokens can be used.

Stated more formally, arcade tokens support:

• Dynamic pricing and promotion: Arcade token issuers can adjust token prices, adjust the token-denominated price of making purchases, or both. This allows them to discount goods or services during low-demand periods or reward spending sprees during peak moments.

• Network effects: As with frequent flyer miles and credit card points, users who receive or hold tokens are more likely to remain attached to the brand. And the value of this established user base drives increased participation and collaboration from merchants, developers, and other service providers, which in turn raises the value for users — a classic platform network effect.

• Incentives and loyalty rewards: Arcade token issuers can give bonuses and other perks to customers who perform desired actions. They can also use their issuance powers to reward network participants for accepting or redeeming the token. All of this reinforces the network effects just described.

• Economic control: Arcade token issuers can burn tokens upon redemption, track liabilities onchain, and implement monetary policy akin to central banking — all while keeping supply and pricing within a predictable band.

How do arcade tokens work?

The economic dynamics, explained

The economic dynamics of arcade tokens differentiate them from other kinds of tokens. Instead of granting holders ownership rights in the underlying ecosystem, arcade tokens grant holders access to or use of certain applications or services; crucially, their market value is designed to be programmatically bounded. This doesn’t require that arcade tokens be pegged to the price of a fiat currency like a stablecoin; it just means that an issuer can use certain mechanisms to effect a price floor and (often more importantly) a price ceiling.

Arcade tokens are typically freely available at a pre-specified price. Again, think of a token vending machine in an arcade on the boardwalk: You walk up, put in a dollar, and the machine gives you, say, four tokens at 25 cents each. Such token dispensers, commonly referred to as “faucets” or “spigots,” in effect set a price ceiling that the market value should never exceed. Hence, arcade tokens are non-investible: They are for spending, not speculation.

The value of the token can then be assessed based on what it is redeemable for, via any “sinks,” or mechanisms for removing tokens from circulation. In the case of an arcade, the sinks are games’ “slots” — as in, insert coin to play. If playing one game costs one token, then it must be worth 25 cents. Alternatively, or in addition, the arcade could make the tokens redeemable at a buyback price slightly below the faucet price, so an issuer can guarantee it will always pay, say, 20 cents apiece to buy back those 25-cent tokens. This establishes a floor under which the price should not drop.

Consider the implications of these parameters for the market: Would you ever buy a 25-cent token from a speculator for a dollar when you know you can buy that same token from a faucet (or vending machine) at any time for a quarter of the speculator’s price? Not a chance — that wouldn’t make sense! (Or cents, for that matter.) A person who’s moving to a different town might stand in front of the arcade and try to sell their leftover 25-cent tokens for 22 cents apiece, but it wouldn’t make sense for someone to buy them for more than 25 cents. So while some people may choose to sell their arcade tokens for a discount (for example, if they’re leaving the ecosystem for good), the price should remain relatively stable at any given point in time.

All these non-speculative factors make arcade tokens especially well-suited as foundations for controlled marketplace economies. And note that this doesn’t depend on whether the arcade token is used narrowly, within a single application or service, or more broadly — they are simply consequences of the arcade token’s faucet/redemption design. (Continuing the arcade metaphor: even if the local grocer is a huge video game fan and thus opts to accept the local arcade’s tokens in lieu of cash for payments, there’s still no reason for anyone to pay more than 25 cents per token if you can always get them at that price just by walking over to the arcade.)

Why not just accept stablecoins as payments?

Arcade tokens have some conceptual overlap with stablecoins — both are designed to facilitate economic transactions while holding a relatively stable value. But arcade tokens can provide much greater flexibility to builders. An issuer can print arcade tokens on demand (although issuers must still track these tokens’ “shadow” value — accounting for what happens when they are redeemed — on their balance sheets). Issuers can then use these tokens to provide grants and subsidies to users, developers, and other network participants.

Moreover, the tokens encourage participants to remain within the given economy rather than spend their funds elsewhere. (There’s a reason airlines give out “miles” that must be redeemed for future flights, rather than simply giving cash rebates to frequent flyers.)

Arcade tokens can also give builders more options for making money. Issuers can sell tokens directly to users (at fixed or dynamic prices), bundle them into subscription packages, or distribute them through promotional campaigns. When a network of partners agrees to accept a certain arcade token, it enables them to set up cross-promotion and affiliate models — tactics that can broaden each partner’s reach without requiring external capital.

Crucially, arcade tokens can also allow issuers to exercise fine-grained control over how value flows within their economy by

• restricting transferability (e.g., only within the app or among whitelisted addresses),

• imposing depreciation or expiration dates (encouraging timely usage and reducing hoarding), and

• tying redemptions to specific goods or services (aligning utility with economic intent).

These features help reinforce the token’s value as a medium of exchange — not a speculative asset — and can be encoded programmatically onchain. Put simply: Arcade tokens can help bootstrap growth, encourage engagement, and manage the operations of internal economies, all while affording their maintainers a level of control.

The power of interoperability

As we’ve already described, arcade tokens issued on public blockchains are similar to loyalty points or airline miles — but they have one major difference: They’re onchain, which means they can be open, interoperable, and composable.

Unlike traditional loyalty systems, which trap value within closed ecosystems, blockchain-based arcade tokens can be permissionlessly shared, accepted, and redeemed by multiple players — even, in principle, among competitors. Portability is a benefit: In this model, users can carry loyalty across services, and status can be transferred easily (unlike today’s convoluted airline “status match” processes). This portability encourages market participants to compete on the quality of their products and services — rather than on pure lock-in — and can turn fragmented loyalty programs into public goods.

One of the best onchain examples of an arcade token is $FLY, issued by Blackbird. This token creates a loyalty program for restaurants, similar to Starbucks Stars or MyMcDonald’s Rewards. This may sound familiar, but it has a twist: the same token can be used across many different restaurants. Patrons receive tokens when they make a purchase at a Blackbird network establishment, and can redeem them for discounts and other perks at any participating restaurant. Because the underlying protocol is on a blockchain, all this can happen without those restaurants having to interact with one another. And just as an individual restaurant’s rewards program reinforces customer loyalty, here $FLY strengthens loyalty across the entire restaurant network at once.

Consumers benefit from wider utility; businesses benefit from shared network effects.

The result is coopetition (as opposed to classic competition): Your local coffee shop and Starbucks could both gain from accepting the same token, for instance. While at first it might sound like neither coffee business would want that to happen, a shared loyalty program mediated by an arcade token could actually benefit both. The arcade token could enable the Starbucks and local coffee shop experiences to complement each other, so that going to either one generates perks that enhance the value of both. If one of them gives out free mochas in exchange for the arcade token, for example, then this boosts the value customers get from purchasing coffee at either place. Deals like this can reinforce customers’ loyalty to the network and encourage them to spend a larger share of their budget on coffee overall.

Such coopetition results in more total surplus for the network, which can be shared among all the providers in proportion to the sales they generate. Put differently, instead of competing over slices of the pie, you grow the pie overall.

Design tradeoffs (and opportunities)

Arcade tokens aren’t right for every project. They don’t make sense in situations that could require a speculative asset. For example, layer one blockchain networks that have their own network tokens generally do not need arcade tokens to function.

But for many projects — especially those with spend-centric economies or physical-world integrations — arcade tokens can be a compelling option. They offer:

• Price stability: Through ceiling and floor mechanisms and controlled issuance.

• Usability: Intuitive and stable value that helps users understand what they’re spending.

• Accounting clarity: Their cost on your balance sheet is the opportunity cost of what they’re redeemable for — no more, no less.

• Control: Issuers can manage them in a way that resembles what central banks do.

We’re also seeing an emerging role for arcade tokens as complements or precursors to network tokens. Blackbird’s $FLY token allows users to redeem it in any participating restaurant, and that redemption behavior is managed on a purpose-built blockchain layer powered by a network token. A decentralized computing network might, for example, use a network token for security and incentives among compute providers, while using an arcade token to establish network effects among the customer base. Alternatively, a marketplace might bootstrap participation with an arcade token and later introduce a network token as it decentralizes its operating protocol. In these cases, arcade tokens can serve as an on-ramp that catalyzes early demand and helps build initial traction before a network shifts to a more decentralized system in the long term.

Regulatory outlook

An early example of an arcade token is Quarters, from the blockchain-based gaming platform Pocketful of Quarters. Players can use Quarters tokens to access features and rewards in participating games. Relevant to the idea that arcade tokens are not investable assets, Pocketful of Quarters received a no-action letter in July 2019 from the Securities and Exchange Commission, in which the agency stated that it recognized that people were using Quarters solely to participate in gaming, not for speculation or investment.

Despite this positive precedent, the Quarters no-action letter and many state regulatory regimes have shortcomings. They take a skeptical view of interoperability, for one thing, viewing it as a bug rather than a feature. Their reasoning stems from the mistaken view that where interoperability exists, a priori, assets may become more readily tradable, taking on the characteristics of financial instruments. This view overlooks the fact that trading demand still depends on whether an asset has speculative upside — which, as we’ve already explained, is generally not the case for arcade tokens. Meanwhile, interoperability is one of the most exciting promises of onchain arcade tokens, offering substantial consumer benefits, including reduced friction and more choice.

Smart design can mitigate regulatory concerns. Arcade tokens don’t need to be kept within closed networks. Mechanisms such as capped pricing, faucet-and-sink models, and usage-linked redemption allow issuers to dampen speculative activity programmatically. Consumers also benefit from interoperability, as it enhances usability, fosters competition, and creates broader network effects — ultimately promoting innovation and delivering greater value to users without relying on financial speculation.

***

Though arcade tokens aren’t for every use case, they represent a critical building block in the evolution of crypto networks. Just as stablecoins unlock new forms of commerce and network tokens enable decentralized value sharing and governance, arcade tokens can power digital economies at scale.

With greater regulatory clarity, we expect more builders and users to recognize the benefits of arcade tokens as more projects — including those not native to crypto — explore their utility.

You’re receiving this newsletter because you signed up for it on our websites, at an event, or elsewhere (you can opt out any time using the ‘unsubscribe’ link below). This newsletter is provided for informational purposes only, and should NOT be relied upon as legal, business, investment, or tax advice. This newsletter may link to other websites or other information obtained from third-party sources — a16z has not independently verified nor makes any representations about the current or enduring accuracy of such information. Furthermore, the content is not directed at nor intended for use by any investors or prospective investors in any a16z funds. Please see a16z.com/disclosures for additional important details, including link to list of investments.

The first part of our list focused on how things work: technologies, markets, institutions, organizations, energy, biology, and the systems behind change.

This second part turns to people: how they adapt, grieve, improvise, and generally carry on. In this list, you’ll find fiction, memoir, history, philosophy, and science fiction — books about what people do when things get complicated.

How people keep going

We Few: U.S. Special Forces in Vietnam by Nick Brokhausen

“Brokhausen’s memoir of MACV-SOG, the unit that ran the most dangerous missions of the Vietnam War behind enemy lines in Laos and Cambodia, is the rawest portrait of high-performing operators I’ve read. What stays with you is the psychology of doing something unprecedented and lethal, the macabre humor, and the resilience it takes to keep walking into the jungle, knowing each step might be the last. For anyone who works in adversarial, high-stakes environments, it’s a bracing look at what real edge cases demand.” – Conner Lyons Brown, tech ops

Higher Love: Climbing and Skiing the Seven Summits by Kit DesLauriers

“Inspiring first-hand account of a fearless skier and mountaineer conquering a massive feat — climbing and skiing the world’s seven highest peaks. It gives a peek into what drives her to complete this impressive feat but also the very real fears and circumstances she has to overcome. The book also introduces you to a colorful cast of characters that make up the climbing community around the world, who band together to help Kit reach her dream.” – Kim Milosevich, marketing

The Cold Cold Ground by Adrian McKinty

“What do you do if you’re a Catholic copper in Protestant Belfast at the height of The Troubles? You check under your car for a bomb every morning before going to work. Adrian McKinty’s The Cold Cold Ground combines the kind of detective story I like with a vividly realized setting. Belfast in 1981 is a place where every institution is contested, and every identity carries consequences. Sean Duffy solves crimes while navigating that landscape with intelligence, wit, and a healthy appreciation for the possibility that someone may be trying to kill him. The result is both gripping and funny — just the kind of police procedural I love.” – , editorial

How people improvise

The Man Who Saw Seconds by Alexander Boldizar

“A fascinating speculative-fiction framework: what happens if one can see just a tiny bit into the future, and pick the preferred passage through time? While you might hope that you could just spend those precious seconds winning at casinos, such a metaphysical gift — at least seen through a certain lens — might put pressure on the very foundations of civil society.” – , research

The Warrior’s Apprentice by Lois McMaster Bujold

“Lois McMaster Bujold’s The Warrior’s Apprentice follows Miles Vorkosigan, a brilliant, physically fragile young noble who fails his military entrance exam and then, through bluff, charisma, and improvisation, accidentally assembles a mercenary fleet. It is a great space opera, full of tactics, politics, and action, but what makes it memorable is how much it leans into character development, relationships, and comedy. Miles’s superpower is not technology or force; it’s his understanding people well enough to turn chaos into coordination.” – Jason Rosenthal, operations

The Will of the Many by James Islington

“I loved how The Will of the Many blends an elite academy setting, Roman-inspired politics, and a genuinely propulsive mystery. The book is smart and immersive without feeling slow and it has novel world-building.” – Liz Harkavy, deal

How people carry grief

Think You’ll Be Happy: Moving Through Grief with Grit, Grace, and Gratitude by Nicole Avant

“A book about grief, purpose, and the legacy a mother leaves behind — woven with stories from music, culture, and politics.” – Hoang Nguyen, marketing

Speaker for the Dead by Orson Scott Card

“When I was younger, I tried reading Speaker for the Dead many times. It was tucked into the back half of my well-worn copy of Ender’s Game, which I had devoured. I could never get past the first chapter.

Speaker was unlike everything I loved about the Ender saga. No pressure cooker for child prodigies. No eerie, video game-mediated, psychoanalytic dream sequences. No fate of humanity hanging in the balance. And where the Formics were terrifyingly badass, these aliens were… ‘piggies’? C’mon.

I’ve always felt a kinship with Ender, as I suspect most kids who become engrossed in the story do. By some cosmic coincidence, I was surprised to discover that I’m the same age as he is in Speaker, just as I was his age when I first read Ender’s Game decades ago. Then as now, it has always fascinated me that Card considered Speaker to be the book he had really set out to write, and that he rushed the first book merely to tee it up. How could that possibly be the case?

I think I understand better now. Speaker lacks the narrative perfection — and kinetic energy — of its prequel, but I can appreciate its slower, contemplative pace far more now that I’m older. It deals with themes like family, memory, death, otherness, forgiveness, guilt, and redemption — subjects I cared, apparently, less about in my youth. I still find Speaker challenging to love, but I respect the way it complicates the story I loved so much growing up. I still prefer Ender’s Game, but I have a much better sense of why Card saw Speaker as his true purpose.” – , editorial

Ghost Forest by Pik-Shuen Fung

“A slim, soft, and plainspoken piece of autofiction on loss, grief, and the complexity of parent/child relationships. The narrator grows up in Canada with an ‘astronaut father’ who lives in Hong Kong for work — so the relationship she’s mourning was mostly conducted across an ocean, in short visits and longer silences.

As a bonus, the book includes some lovely depictions of death rituals and mourning practices, particularly in the sections on Hong Kong and the family’s Chinese cultural background. It’s short enough to finish in one sitting, but it stuck with me long after.” – , editorial

How people become institutions

Fahrenheit-182: A Memoir by Mark Hoppus and Dan Ozzi

“Punk rejects mass culture. Pop is mass culture. So how does a pop-punk band like Blink happen?

In his memoir, bassist and the only constant member, Mark Hoppus, recounts playing a show in Bologna where the crowd hurled rocks and bottles at them. A year earlier, Blink had released a viral video mocking boy bands, and had become so successful that some audiences — a contingent of hostile Italian metalheads, in this case — could not tell the parody from the thing being parodied. Or didn’t care to. As Hoppus puts it: ‘They didn’t want some bullshit American pop act fucking up their heavy show.’

I love this anecdote — not just because it’s darkly comic, guitarist Tom DeLonge reportedly swore off pasta bolognese afterward, and boy bands are dumb, but because it illuminates one of modernity’s supreme ironies: What happens when countercultures go mainstream. A similar arc can be traced across jazz, abstract art, hip-hop, skateboarding, streetwear, open source software, and, relevantly for this audience, crypto.

Fahrenheit-182 is also a founder story. Hoppus and DeLonge are a volatile creative duo — like Lennon and McCartney with fart jokes. The same forces that allowed them to pump out hits eventually tore them apart. Fortunately, unlike most founder breakups, this one had a reunion tour. I caught them at Madison Square Garden a few years ago, and it was a glorious, stupid, perfect blast of nostalgia.

So is the book.” – , editorial

Catherine the Great & Potemkin: The Imperial Love Affair by Simon Sebag Montefiore

Just your standard romance, but with more urban planning and foreign-policy memos. Catherine the Great & Potemkin is part imperial history, part power-couple biography, and part extremely high-stakes situationship. The story of this Antony-and-Cleopatra-level romantic and political partnership is told with all the drama and detail you’d hope. – , editorial

Augustus by John Williams

“Everyone always talks about Stoner, but not enough people talk about John Williams’s other incredible work: Augustus. Told in epistolary form, this portrait of Augustus as an idealist and pragmatist feels deeply relevant.” – Drew Coffman, editorial

How people search for meaning

The Education of Henry Adams by Henry Adams

An extraordinary reflection of living through a period of intense technological change. Adams reflects on how the educational system failed to anticipate the radical transformation he experienced, and describes where he learned life’s true lessons. A classic and timeless book that’s perfect for today’s moment. – , research

I See Satan Fall Like Lightning by René Girard

“One of the most thought-provoking frameworks that reveals why we desire the things we desire and what this implies for how society is organized. If you love philosophy, anthropology, history and theology — great read.” – Robbie Petersen, deal

Thrown by Kerry Howley

“In this work of literary nonfiction, maybe, the narrator, Kerry Howley — who may be the same person as the author, but may not be — follows a pair of mixed martial artists around Iowa, where author Howley had done her MFA. Both Howleys explore obsession, uncertainty, and the search for meaning with unusual intelligence and empathy — and with a prose style that both surprises and delights. It’s nominally a book about fighting, but it’s really a book about commitment, and maybe a book about a book about fighting.” – , editorial

Sátántangó by László Krasznahorkai

“This is one of the unique books I liked mainly for its narrative style, characterized by lengthy sentences and complex paragraphs. Very Kafkaesque undertones as well.” – Ertem Nusret Tas, research

Leisure: The Basis of Culture by Josef Pieper

“Pieper wrote this in 1948, but it reads as if it were written for the age of AI. He diagnoses the two roads most traveled by any professional: the total world of work, where a person’s worth collapses into output, and a hollow pursuit of pleasure with no center. Pieper refuses both. Leisure, he argues, is not idleness or time off but a contemplative, receptive stance toward reality, the stillness to actually perceive the world rather than just operate on it. As AI absorbs more of the doing, the open question becomes what the doing was ever for, and Pieper’s answer, that the highest human activities are the ones done for their own sake rather than for compensation, lands harder now than when he wrote it.” – Conner Lyons Brown, tech ops

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This first part of our list is organized by curiosity: how does the world work, why do systems change, and what makes certain ideas, technologies, markets, and institutions take hold?

In this list, you’ll find books on manufacturing, biology, energy, market design, financial models, management, and more — books that trace how things work from first principles, unearthing the hidden structures behind progress and reinvention.

What powers life — and everything else

How Life Works by Philip Ball

“We all learn in basic biology that the cell’s DNA is a blueprint for life: genes are copied into mRNA, the mRNA is sent to the ribosomes, and the ribosomes make proteins that drive the cell. It is a simple story that is appealing to engineers. This book explains that the story of life is far more complicated and, in some sense, that the story we are taught is wrong. The book gives a more accurate, yet still incomplete, picture of how the billion molecules in a single cell interact to regulate the cell and keep it alive.” – Dan Boneh, research

The Art and Craft of Problem Solving by Paul Zeitz

“While I’ve been ‘Lean’-ing into formal mathematics this spring, I’ve also been revisiting classic works to fine-tune my mathematical thinking and problem-solving. And there’s no better book for that than Paul Zeitz’s magnum opus, now in its third edition.” – Scott Duke Kominers, research

Energy by Richard Rhodes

“Rhodes is the author of one of my favorite series of all time, the four-volume nuclear history that begins with the Pulitzer-winning The Making of the Atomic Bomb, plus several very good novels, if less heralded. Energy is his five-century study of how humanity moved from wood to coal to oil to electricity and on toward nuclear and renewables, told the way Rhodes tells everything: through people, and through false starts, bad bets, and inventors who accidentally solved the right problem.

Reading his work, it’s hard not to notice how often progress comes down to scaling capacity as much as fundamental breakthroughs — exactly the question the AI buildout is now forcing, as compute demand revives interest in nuclear and reshapes grids. The constraint on this next computing era may be joules rather than algorithms, a challenge we’ve navigated before.” – Aiden Slavin, policy

How technology becomes an industry

Apple in China by Patrick McGee

“How did China become so good at tech manufacturing? This book tells the story of how Apple spent over a decade and significant resources training firms in China to build Apple products to stringent requirements and with high precision. Along the way, it tells the history of many Apple products and how they came to be. It is an interesting story of how we ended up where we are.” – Dan Boneh, research

Apple: The First 50 Years by David Pogue

“An exhaustive march through 50 years of Apple’s history. Pogue has been chronicling Apple for so long (starting at Macworld in the 80s) that he’s one of maybe three people alive who’d get this kind of access, which is what makes it great. Dropped weeks before Tim Cook announced his exit, this text works as a bookend, catching Apple right as one chapter closes and another cracks open.“ – Drew Coffman, editorial

A World Beneath the Sands: Adventurers and Archaeologists in the Golden Age of Egyptology by Toby Wilkinson

A sweeping exploration of the history of Egyptology and how humanity deciphered hieroglyphics, developed the science of archaeology, and competed for prestige through scientific discovery. You’ll find interesting and strange connections to today’s tech race. – Andy Hall, editorial

How Music Got Free by Stephen Witt

“Did you know that one CD manufacturing plant employee leaked thousands of albums before release, becoming an inadvertent kingpin of the warez scene? This book chronicles how technology repeatedly kneecapped the music industry over 20 years: MP3s begat piracy, which decimated CD sales; iTunes changed the album; YouTube opened up a new listening stream; VEVO made the music industry money again; and then streaming cannibalized everything. Reading this book makes it clearer that the music industry’s current model is just another temporary equilibrium waiting to be disrupted. A great example of how tech obliterates business models and forces reinvention.” – Drew Coffman, editorial

How markets are designed

Moral Economics: From Prostitution to Organ Sales, What Controversial Transactions Reveal About How Markets Work by Alvin E. Roth

“My doctoral advisor, Al Roth, has been thinking for decades about ‘repugnance’ – why some people prefer that some markets should not exist. In Moral Economics, he tackles the motivation for these prohibitions and the trade-offs they force, head-on. And he explores, in particular, how such non-market norms emerge and sometimes later collapse — limitations on alcohol and drugs, and, in a completely different category, same-sex marriage have all been relaxed in recent years — and what this means for making markets in the future.” – Scott Duke Kominers, research

Why Nations Fail by Daron Acemoglu and James A. Robinson

“Acemoglu and Robinson’s central thesis, that inclusive institutions drive prosperity while extractive ones cause stagnation, maps surprisingly well onto crypto. Decentralized protocols are essentially an attempt to hard-code inclusive institutions: open access, no gatekeepers, rules enforced by code rather than by whoever’s in power. The book is a great lens for understanding why certain blockchain ecosystems thrive while others are captured by insiders. A must-read for anyone thinking seriously about governance, whether at the nation-state or protocol level.” – Kira Song, finance

An Engine, Not a Camera: How Financial Models Shape Markets by Donald MacKenzie

“Sociologist Donald MacKenzie’s An Engine, Not a Camera is nominally a book about financial economics, but its real subject is more provocative: What happens when theories stop describing the world and start changing it? MacKenzie traces how academic models of markets escaped the university and became embedded in the markets themselves. First published in 2006, it remains relevant today in a number of contexts — not least because of the recent interest from TradFi in crypto. But I wanted to read it again because of the book I’m working on with colleague Robert Hackett, about how computer science theories acted on the world.” – Tim Sullivan, editorial

A Man for All Markets: From Las Vegas to Wall Street, How I Beat the Dealer and the Market by Edward O. Thorp

“Growing up, I was obsessed with the movie 21 and the idea that you could actually use math to beat the house. This book is the autobiography of the man who essentially started it all. Edward Thorp went on to apply those same principles to Wall Street, becoming the father of quantitative investing. It’s an incredible story about using pure logic to disrupt rigged legacy systems, which perfectly captures the same builder mindset we see in crypto today.” – Ben Wu, talent

How organizations are designed

The Super Gap (초격차, or Cho-gyeok-cha) by Oh-Hyun Kwon

“This book is written by the former chairman of Samsung Electronics, who led the company to become the world’s number one semiconductor company. ‘The Super Gap’ means a gap so wide that competitors cannot even think about challenging you. Based on his 33 years at Samsung Electronics, he distills his leadership experience into four pillars: leader, organization, strategy, and talent. The author explains that this ‘Super Gap’ applies not only to technology but also to systems, culture, people, and organization. It’s especially compelling because these insights come from someone who experienced both success and failure firsthand and ultimately walked away from the peak to practice the ‘sustainability’ he preaches.” – SungMo Park, go-to-market

Scaling People: Tactics for Management and Company Building by Claire Hughes Johnson

“My friend and co-author Jesse Shapiro recently recommended this book to me, and it’s fantastic: easily the best book I’ve ever read on both managing people and managing people’s interactions with you. Hughes Johnson, who served as COO at Stripe through their period of massive growth, gives a vivid and very, very practicable guide to understanding individuals’ and teams’ motivations and operating modes. The book comes with detailed principles and strategies for everything from hiring and onboarding to day-to-day management, communication, evaluation, and feedback. But the biggest payoff is the way the book trains awareness and recognition of people as they are, and how to build on that to help them maximize themselves.” – Scott Duke Kominers, research

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Total volume across all platforms is now more than 10x what it was a year ago.

Open interest hit $1.6B last week — the third consecutive weekly all-time high.

Unlike volume, open interest can only grow when new positions are opened faster than existing ones resolve, so the growth seen here reflects how more money is being put at stake in these markets.

The chart has grown steadily since last fall. Over that stretch, open interest has increased roughly 8x, from under $200M to $1.6B.

The World Cup is attracting lots of interest and activity, and other areas are growing alongside. Non-sports volume — across categories like politics, economics, geopolitics, and current events — hit $3.6B last week on Kalshi and Polymarket combined. That’s larger than total prediction market volume (sports included) was just last year.

In July 2025, non-sports weekly volume was around $200M. It has grown roughly 18x since then, with the trajectory steepening sharply this month.

Acknowledgments: Data and charts by Ryan Holloway and (a16z crypto features editor and head of special projects).

You’re receiving this newsletter because you signed up for it on our websites, at an event, or elsewhere (you can opt out anytime using the ‘unsubscribe’ link below). This newsletter is provided for informational purposes only, and should NOT be relied upon as legal, business, investment, or tax advice. This newsletter may link to other websites or other information obtained from third-party sources — a16z has not independently verified nor makes any representations about the current or enduring accuracy of such information. Furthermore, the content is not directed at nor intended for use by any investors or prospective investors in any a16z funds. Please see a16z.com/disclosures for additional important details, including link to list of investments.

But over this last decade, and especially in recent years, there has been a tremendous sea change in tech: MVPs, great ideas, and incredible teams no longer cut it for an external audience. Since the crypto industry in particular has been hit the hardest — with the combination of regulatory questions and headline-grabbing bad actors — people’s BS meters have been heightened as they have been increasingly inundated with noise and have begun to filter accordingly.

And when traditional finance (TradFi) players take crypto seriously — like BlackRock launching a tokenized money market fund, Fidelity filing for a crypto ETF, JP Morgan settling transactions on a blockchain it built in-house — the conversation changes. Not just about what crypto is, but about what it takes to be taken seriously in the industry.

That’s the moment we’re in right now. This moment has quietly rewritten the rules for everyone building in this space.

Welcome to the “show me” era.

What changed, and why now?

For most of the industry’s history, it operated on a kind of promissory logic: The vision was the product. You could launch with just a whitepaper and a token, and the press and crypto community would meet you there. The bet was always on what something could become, not what it had already proven. That dynamic has shifted.

Why? Simply put, I believe that this communications shift is happening due to the combination of a lingering and growing skepticism of the technology, now a couple of decades in; the entrance en masse of traditional financial institutions into the crypto arena, not just in name but in product; and the AI industry (with its overnight success that was really decades in the making) shipping tangibly consumer-ready products.

Massive institutions have stopped just watching the space or siloing efforts in “innovation arms” and started building for scale: BlackRock and Larry Fink’s full embrace of tokenization. Fidelity’s custody and ETF infrastructure. JP Morgan’s Onyx network. Franklin Templeton’s onchain money market fund.

These aren’t experiments anymore — they’re real products, with the corresponding TradFi compliance frameworks, institutional clients, and balance sheets behind them.

The entry of TradFi at scale raised the baseline for what “serious” looks like in crypto. When the largest asset manager in the world is tokenizing treasuries, the bar for what a credible project needs to demonstrate — to media, to partners, to the market — rises with it.

The industry has entered the mainstream from a policy perspective as well. With stablecoin legislation (GENIUS Act) passing last year, and now comprehensive market structure legislation (CLARITY Act) now ready for a full Senate vote, expect product communications to change further. If CLARITY passes, it will allow founders to publicly talk about what they’re building with a level of specificity that previously was not possible.

Altogether, the industry has matured, whether it was ready for it or not.

The result is a communications environment where the starting point is no longer ”What are you building?” but:

“What have you built, and who is using it?”

In practice, this means that a compelling story alone will no longer move the needle. We need proof.

The new proof stack

The pitch that used to work — “we’re building X for Y, and here’s why it matters” — now needs a second act. I call it the proof stack: the layer of evidence that transforms a hypothetical, abstract narrative into a credible, concrete one.

So what does that proof stack look like?

Partnerships with teeth — not “in conversations with.” Actual integrations, deployed contracts, and partners willing to go on record about why they chose you. The partnership announcement used to be a lazy substitute for traction. Now it only works if the partnership itself is evidence of traction. That is, a major institution, protocol, or platform chose you over a dozen alternatives, and you can explain why.

This also means sharing more hard data and transaction volumes on mainnet, not just testnet, active wallets, revenue, and retention curves. Not “growing fast” — but percentages, timeframes, baselines. Journalists covering this space are increasingly sophisticated; they’re doing their own onchain verification. If your numbers don’t hold up to a Dune, CoinMarketCap, or other analytics dashboard, your story won’t hold up either.

The proof stack also involves sharing real signals of product-market fit. Who is using your product, and why would they (including other market customers) keep using it?

I believe the clearest proof of “fit” isn’t a launch announcement — it’s an organic and growing community that existed before the PR push.

If your most enthusiastic users are your investors, or people with skin in the game, that’s a yellow flag, given they’re financially incentivized. But if they’re people who found you through word of mouth, that’s a story worth telling.

This is all about coverage that came before the press push, not because of it — third-party validation, audits, independent research. The most credible kind of proof is the kind you didn’t manufacture. It’s the kind where someone else is telling the world there’s something there.

What this means for startup comms

The temptation when you’re early — when the product is still taking shape but the vision is more vivid — is to lead with the vision, a manifesto. It feels honest. It is honest.

But in the current environment, it reads as a risk.

The better approach is to sequence your story around what you can prove. Lead with the data point you’re most confident in, even if it’s small: 1,000 active daily users who didn’t know the founders is more compelling than 1, also carries a thesis of a million dollars in strategic backing. A protocol that processed $50M in volume in its first 90 days is more interesting than a protocol that “will” process volume once it scales.

This also means being more precise about what you’re claiming. “We’re building the future of payments” is a thesis, not a proof point. “We’ve reduced cross-border settlement time from three days to four minutes, and here are three businesses using it today” is a proof point that happens to carry a thesis inside it.

For comms teams and founders doing their own communications, the practical implication is that the story should emerge from the facts, not the other way around. That’s a different kind of writing — harder in some ways, more disciplined — but it’s what lands. Especially right now.

The long game

None of this means vision is irrelevant. The best crypto communications still operate simultaneously on two tracks: here’s what we’ve built, and here’s why it’s the beginning of something much bigger. The difference is in the sequencing and messaging proportion.

By “proportion”, I mean that you could get away with 80% vision and 20% substance back in 2021. That ratio has flipped today.

You can still publish a whitepaper, a manifesto… but it’s not enough. The vision still matters — it’s what makes the proof points more meaningful, and what gives journalists and analysts something to write toward — but that vision has to be earned by the substance underneath it.

* * *

The Show Me Era isn’t a temporary industry correction. The growing sophistication of the crypto audience — media, institutional, retail — has permanently ratcheted up.

The best builders in this space have figured out that this is actually good news. If you have real traction, real data, and real partners, the higher bar works in your favor; it filters out the noise and makes your signal much louder by comparison.

The question is whether your communications strategy is built to show that proof, or is still built to promise it.

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Crypto is the first modern technology built natively for this. If your startup hasn’t architected your product and business to benefit from these principles, you’re missing out. And thanks to stablecoins, money and value now move at the speed of the internet — settling globally, 24/7, with end-to-end programmability. The rails are open, the unit economics are public, and the total addressable money flow is every dollar that moves anywhere in the world.

The pattern

Blockchains are network businesses by design. Every transaction settles on a shared ledger. Every new participant strengthens the same substrate that the next builder uses. The network becomes more valuable to all users as more people use and build on it.

Most companies spend years manufacturing network effects on top of legacy infrastructure. Crypto founders inherit them as a starting condition.

Network tokens compound this. A well-designed token aligns users, developers, suppliers, validators, and the protocol around a single outcome — growing the network — and pays out in proportion to each participant's contribution. But the protocol’s revenue belongs to the people who use it. No partnership rebates, no side deals. Just a feedback loop between value moving through the system, and value accruing to the people who built and grew it.

This isn’t a new pattern. Crypto has just made it more accessible and scalable for startups for the first time.

Railroads didn’t make money on locomotives. They made money on every ton of grain, coal, and steel that crossed their track. Standard Oil, U.S. Steel, and AT&T were all companies that were in the money flow. Google and Meta replaced print and television not because the ads were better, but because they sat at the chokepoint where attention turned into commerce, allowing them to take a clip of trillions in commercial intent. AWS sits in the flow of compute.

The pattern is consistent: Find where value moves and put yourself in the middle of it.

Financial markets make this pattern even clearer. Visa moved $15.7 trillion in payment volume in fiscal 2024 and reported $35.9 billion in net revenue. Jane Street posted $20.5 billion in net trading revenue last year — more than Citigroup or Bank of America. The top five U.S. market makers handle 87% of payment for order flow: They don’t predict markets; they sit in the flow of every order and earn more as volume grows.

The other thing these businesses have in common is network effects. Visa is more useful to more merchants as more cards exist and more useful to more cardholders as more merchants accept it. Order flow works the same way — every additional broker tightens spreads, attracts more brokers, and attracts more flow.

Money flow plus network effects is one of the most durable business structures ever built.

Your margin is my opportunity

Bezos called it when he said, “Your margin is my opportunity.” He was talking about retail, but it’s even more applicable to traditional financial services — the largest pool of margin extraction in the world. Payments, custody, lending, foreign exchange, securitization, settlement, market making. Visa and Mastercard charge 2-3% interchange on networks designed in the 1960s. Remittance corridors charge 6-9%. Prime brokers and custodians take a clip on every securities transaction. Even after the U.S. moved to T+1 settlement in 2024, capital still sits idle overnight as a structural tax on every participant.

Each of those margins is a target. Compress costs, increase velocity, and potentially expand the entire market. Stripe and Square proved it works in payments.

Crypto founders have the opportunity to build the next version — programmable, instant, global, and natively in the money flow.

And the frontier extends well beyond financial services. Compute and GPU marketplaces. Memory chips. AI training data. Energy. Robotics. Space. Rare earth metals. Each is a category where global value can start moving in volumes that the existing rails were never designed to carry.

Each is open ground for a money-flow business built on programmable infrastructure from day one. Markets with no incumbent rails, no entrenched intermediaries, and nothing to defend.

As a founder, ask yourself:

1. Are you in the money flow today?

2. When the value of the activity on your product grows 10x, does your revenue grow with it?

3. If you’re building a new product, where in your target market is margin extraction the highest relative to the value being created?

That’s where the opportunity lives. Compress it, sit in the new flow, and let the network compound from there.

You’re receiving this newsletter because you signed up for it on our websites, at an event, or elsewhere (you can opt out anytime using the ‘unsubscribe’ link below). This newsletter is provided for informational purposes only, and should NOT be relied upon as legal, business, investment, or tax advice. This newsletter may link to other websites or other information obtained from third-party sources — a16z has not independently verified nor makes any representations about the current or enduring accuracy of such information. Furthermore, the content is not directed at nor intended for use by any investors or prospective investors in any a16z funds. Please see a16z.com/disclosures for additional important details, including link to list of investments.

You’re receiving this newsletter because you signed up for it on our websites, at an event, or elsewhere (you can opt out anytime using the ‘unsubscribe’ link below). This newsletter is provided for informational purposes only, and should NOT be relied upon as legal, business, investment, or tax advice. This newsletter may link to other websites or other information obtained from third-party sources — a16z has not independently verified nor makes any representations about the current or enduring accuracy of such information. Furthermore, the content is not directed at nor intended for use by any investors or prospective investors in any a16z funds. Please see a16z.com/disclosures for additional important details, including link to list of investments.

More capital just extends the runway to a bad outcome. Growth hacks and continuous airdrops disconnected from real strategy look less like a path to PMF and more like a substitute for admitting you haven’t found it. Some of the very things that make crypto so powerful — tokens and network effects — can even mislead companies on their path to product-market fit.

The good news is that strong teams across crypto are finding PMF faster now, thanks to killer apps like stablecoins and more widespread adoption among TradFi and other consumers.

Here are three patterns that are working — pay attention, especially if you’re pre-PMF or in the middle of a pivot.

1. Partner with elite customers to build what they need

Find the most sophisticated potential customers in your space and build with them. Their requirements become your spec.

It’s slower than shipping a generic product and iterating in public, but when your first customer touches trillions in daily volume, their adoption is worth more than any amount of press, TVL, or retail attention. The very definition of product-market fit is when your product resonates with a broad set of customers, and those flagship customers are the best indicators of adoption.

As we’ve seen with several high-profile announcements of partnerships and product releases between crypto startups and TradFi companies, the product roadmap is currently being written by institutions. Blockchains are beginning to run the global financial infrastructure.

2. Find an exponential curve and get in front of it

Sometimes PMF comes from serving an existing market better. Sometimes it comes from seeing where the market is going before the market fully understands it, and positioning early enough to matter.

The curve right now is AI agents becoming economic actors: Autonomous participants calling APIs, deploying capital, and executing transactions at machine speed. The human-in-the-loop assumption is breaking down faster than most people expected.

Take the example of agentic commerce — and Ryan Sproule at Merit Systems saw this early and are building AgentCash on top of x402. AgentCash lets AI agents pay for API access using crypto — the infrastructure the world needs where agents transact programmatically without human-managed billing.

Payments are how agents become actors rather than assistants. Whoever builds those rails now owns a foundational piece of the agentic economy when it arrives.

3. Be your own first best customer

The most durable infrastructure companies don’t wait for external developers to validate their tech. They build the application themselves first — on top of their own rails — and use it to prove out the capabilities they’re asking others to build on.

Amazon pioneered this approach and played it to perfection with Amazon Web Services (AWS). They didn’t pitch AWS to startups first. They built the infrastructure they needed for their own e-commerce business, got it working at scale, then externalized it piece by piece for everyone else.

Alex Gluchowski at Matter Labs is running this same play.

Rather than selling Prividium as an abstract enterprise product, he anchored it to a concrete application: tokenized deposits. The result is Cari Network — U.S. regional banks, including Huntington Bancshares, First Horizon, M&T Bank, KeyCorp, and Old National Bancorp, moving customer deposits instantly across institutions on blockchain rails, without those funds ever leaving the regulated banking system. ZKsync didn’t just build the rails. It found the killer app for them.

* * *

Three patterns, one underlying truth: The fastest path to PMF isn’t iterating in the dark. It’s choosing the right game and playing it with conviction before everyone else jumps on board.

Co-build with the customer whose validation compounds. Get in front of a curve before consensus. Be your own first best customer.

Pick the one that fits your product. Then move.

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As an economist who has long studied marketplaces and incentive mechanisms, my answer is simple: Prediction markets are simply markets. Markets are a fundamental tool for allocating resources — ensuring that goods and services get to those who value them. Along the way, markets also aggregate information: the market-clearing process takes everything participants know and distills it into signals like price.

Prediction market platforms and products directly harness that information‑aggregation power to try to forecast specific future events: They introduce an event-specific asset that pays off if a given outcome occurs, and people then trade that asset based on their beliefs about whether it will happen.

Companies have long embraced prediction markets to elicit tacit information from their employees for forecasting whether an important product will launch on time. We’ve also seen scientists use prediction markets to assess which experiments are likely to replicate. And we are now seeing multiple media outlets partner with prediction markets for “wisdom-of-the-crowds” information to complement reporting from their sources and traditional journalists.

By gathering information directly from market participants — their individual beliefs about the future — and aggregating that information into a marketplace, prediction markets seek to answer questions about the likelihood of various events. People can “bet” on these events in the same way they can “bet” on the future value of a company in the stock market, or on the future value of a commodity like oil. But instead of keying off an asset like oil, whose demand depends on many different factors at once, prediction markets introduce an asset that pays off if and only if a given event occurs.

If we see the price of oil go up, then we know demand has increased relative to supply, but we don’t necessarily know, for example, whether that’s because people expect escalating conflict in the Middle East, or because someone’s come up with a new use for petroleum. With a prediction market, by contrast, you can isolate predictions for each individual possibility. A prediction market for, say, “will the Strait of Hormuz be open at a particular date and time,” could center around a contract that pays one dollar per unit if that event happens. With people trading the asset back and forth, the market price can be interpreted as a probability sensor: an estimate of traders' aggregate beliefs about the likelihood that the event will occur.

Here’s how it works: Let’s say the market price per unit for the outcome is $0.50 — probability 50/50. If you think it’s more likely than a 50% chance that the Strait will be open — say, 67% — then you would buy. If you’re right, you’d gross $0.67 for a price of $0.50. That purchase will, in turn, push the market price and associated probability estimate upward, reflecting the idea that someone thought the market was underestimating. This works in reverse, too: when someone believes the market is overpriced, they would sell for less (or short), which lowers the market’s overall probability estimate.

When prediction markets work well, they can offer significant benefits over other forecasting methods. First of all, the simple fact that they provide a probability estimate is a superpower. Polls and surveys, by contrast, just give an opinion share — and to convert that into a probability, you have to reason statistically about how the share you measured relates to the overall population. Polls also typically reflect just a snapshot in time, whereas prediction markets can update in real time as new participants and/or new information arrive.

And crucially, prediction markets are incentivized: Buyers and sellers have “skin in the game” because they stand to lose if they bet wrong. This provides an incentive for prospective participants to think carefully about what information they have, and to bring their capital to the issues where they believe they are most informed. And quasi-conversely, the opportunity to leverage information and expertise in prediction markets can also create incentives for people to conduct their own research to learn more about the issue at hand. (Famously, leading up to the 2024 U.S. presidential election, one prediction market participant even conducted his own opinion surveys, using an atypical method to try to elicit information standard pollsters didn’t have.)

Finally, prediction markets have a big advantage in the breadth of coverage they offer. While someone with knowledge about events that may affect petroleum demand can, in principle, go short or long on oil, there are plenty of outcomes we might want to predict that aren’t well supported by large-scale commodities or equities. For these, prediction markets can be ideal. For example, prediction markets have recently sprung up to try and aggregate estimates of which AI models will perform best at various tasks — something too micro to be reflected in a traditional commodities market. Anyone can establish and fund prediction markets to answer these sorts of niche questions.

These ideas aren’t new: They’ve been around in some form since at least 16th-century Europe, when they were used for predicting the next Pope. Contemporary prediction markets have their roots in economics, statistics, market design, and computer science: Charles Plott and Shyam Sunder introduced the first formal academic frameworks for them in the 1980s. The first modern prediction market — the Iowa Electronic Markets — launched soon afterward. Thanks to the internet, the model has grown to draw on dispersed, decentralized information worldwide.

At the same time, there’s still more required for prediction markets to fulfill their promise. There are infrastructure questions, like how to validate and reach consensus on whether a given event has occurred, as well as how to ensure the market’s operations are transparent and auditable. Or how to determine contract resolution, which may be disputed or manipulated, at scale.

On top of that, there are market design challenges: First, the participants with the relevant information have to show up. If everyone is uninformed, then the prediction market’s price signal doesn’t really tell us anything. Conversely, people with all different types of relevant information have to decide whether they want to participate, or else the prediction market estimate will be biased: I argued back in 2016 that prediction markets may have underestimated the likelihood of Brexit and the first Trump election because the people who were participating in prediction markets back then were insufficiently plugged into the rise of populism.

At the same time, if someone with “perfect” information shows up — like someone who knows in advance what the true outcome is going to be — that can be a problem, especially if they have the ability to affect what happens. Imagine, for instance, if someone from inside the papal conclave had bet in the “next Pope” prediction market, front-running the public announcement of Pope Leo — or even trying to tilt the papal election in support of the candidate they had bet on! If prospective participants expect insiders to be trading in the market, the rational decision would be to stay away, leading the market to unravel.

Finally, there’s the possibility that people might try to skew prediction market prices to impact public perception about the likelihood of a given outcome — turning prediction markets from tools for aggregating beliefs into tools for manipulating them. If an election candidate’s comms team wants the world to think they’re winning, they could use part of their war chest to try to sway the associated prediction market. That said, prediction markets are somewhat self-correcting in this regard because people can always take the other side of a contract that seems to push the probability estimate beyond what’s reasonable.

All of this speaks to a need for prediction markets to ensure greater transparency and clarity around how they manage participation, contract design, and operations. But if designers of prediction markets successfully solve these puzzles, they could become a core part of how we navigate the future.

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You’re receiving this newsletter because you signed up for it on our websites, at an event, or elsewhere (you can opt out anytime using the ‘unsubscribe’ link below). This newsletter is provided for informational purposes only, and should NOT be relied upon as legal, business, investment, or tax advice. This newsletter may link to other websites or other information obtained from third-party sources — a16z has not independently verified nor makes any representations about the current or enduring accuracy of such information. Furthermore, the content is not directed at nor intended for use by any investors or prospective investors in any a16z funds. Please see a16z.com/disclosures for additional important details, including link to list of investments.