I began this exercise by attempting to model Roblox Experience LTV. But in doing so, I made discoveries, found discrepancies, and ultimately jumped on an invigorating data journey. The path to LTV was not as straightforward as I had hoped. Today, I want to set down the groundwork for work that I have done in attempting to measure, and ultimately predict, the value of a Roblox experience. Due to the vastness of this problem, today’s article will just scrape the surface. I’ll explore and describe the data I have which will eventually help us predict value.

I find that there are over 10.6B Roblox universes that have ever been created.1 Many, almost all, of these live outside of the public sphere, in creator accounts unreleased, more than half being default universes. Through statistical sampling procedures and a highly selected sample of successful/high propensity games, I find that there are about 2.2% of these 10.6B experiences that have ever received a play and a further 1.6% of that group which have received more than 1000 visits. Perhaps more interestingly, I find that new Roblox experiences get just as many visits as old ones. What’s more, creator inequality is declining over time — with the top experiences making up about 20% of all visits in each year compared to ~50% five years ago.

This piece begins to dig into this enormous data and tries to answer population level questions using publicly available data.2 I use two data sets throughout. The first is a random sampling from the total population of Roblox Universe IDs (RUIDs). The second is a highly selected (i.e., biased) sample of successful RUIDs I found from various sources. My future writing on Roblox will build on this work.

RUIDs: A complete count

Every single Roblox experience that has ever been created, published or not, receives a RUID the moment it’s born. RUIDs are unique, sequential, and temporal (at least, as of 2020). The largest RUID is 10,625,106,968 as of this writing and the smallest I have observed is in the low 1000s.3 When I plot the min and max RUID over the past several years, I note there is no overlap in recent years and RUIDs are near-monotonically increasing with time. The figure below showcases this behavior:

I obtain this chart by randomly sampling observations and then grabbing their creation date from the public API. Because there are billions of experiences, and Roblox throttles their API (rightly so) I do not have a complete population. However, given that there are tens of thousands of observations in each bucket that I have sampled, it is statistically safe to say that RUIDs are distinct by year after 2020. This allows us to tell with certainty that there are 10.6B universes registered. Sampling above the max RUID yields exactly (and always) 0 hits. I call this the RUID frontier; no RUIDs exist that are larger than the last one.

Note the sheer scale of RUIDs in later years. It’s hidden in the figure because it is in log scale, but almost 9B RUIDs have been gained since 2019, nearly an order of magnitude more than were gained in Roblox’s first 10 years. The rate of creation of Roblox experiences is rapidly increasing.4

Estimating population values

Primer

Now that there’s certainty about how RUIDs are distributed, I know that if I choose any number at random between 1000 and 10625106968 I will get a successful hit in the API call. Thus, I can begin sampling random IDs to see what these experiences look like. By examining the nature of these randomly sampled universes, I can begin to construct a population level estimate of Roblox experience activity levels. I rely on statistical sampling laws to do so.

Every RUID in existence has some visit count (most being zero). Take the entire population and compute averages and you have the population mean visits, no estimation needed.5 Roblox can do this because they have the data. Unfortunately I cannot. So I do what researchers do everywhere else this problem comes up, and take a large random sample instead. Sample properly and sample enough, and the sample mean is a reliable estimate for the population mean.6 I sample within each RUID band until the confidence intervals are narrow enough to be worth reporting. I.e., I’m really confident the sample value is converging to the population value.

Suppose I sampled a million RUIDs. While this is still a small number compared to the whole, I’d have a very strong estimate of the mean population visits.

Play Count Estimates

The figure below shows total number of experiences which have each level of lifetime visits. Note how the confidence intervals spread wider and wider as the number of lifetime visits also increases. This is due to the fact that higher lifetime visits are very rare to observe through random sampling.

The figure shows that there are ~240M Roblox universes that have ever received a visit. This could simply be the creator exploring their own universe. But one could argue that the population of Roblox experiences is probably closer to this 240M number than the 10.6B number. Anything without a visit has zero potential to become something. A more stringent condition is likely 1000 visits. Even someone testing their own experience wouldn’t rack up that many visits before shipping. This condition gives us 3.8M Roblox experiences.7 Note that this value could still be anywhere in the neighborhood of 1M-13M using highly conservative confidence intervals. Time will give me more accuracy.

So 97.8%8 of RUIDs have never been played, let alone published. Most RUIDs have <name>’s Place in their name which is the default experience name. 75.1%9 of all universes have this name construction, meaning that well over half of all Roblox Experiences are simply the default experience that’s generated with an account creation. Perhaps unexpectedly that means there are ~8B experiences that have never, and may never, be published. I only find one experience in my sampling with the default name construction with active updates and considerable visits.10

Other Facts

Expanding on the statistical framework, below are several more interesting facts I have discovered. Perhaps these are novel. Perhaps not. Please send hate mail if not.

Popular tracking sites over-sample high-quality experiences: expected

Another version of sampling I do is grab a list of the most popular games on Roblox from various public sources.11 The left panel in the figure below shows the percent of games in the selected sample which have a play compared to the random sample. Clearly, the selected sample has a much higher hit rate (91.9% having received a visit) than the random sample (the 2.2% from earlier). One might ask why one would grab information on the unimportant 98-ish percent. Valuable information is lost when one selects highly successful experiences only. By the time an experience hits any level of importance, it’s already broken out of the box. If, for example, one wanted to find high potential experiences, they’d never find them by selecting on the ones other analysts have already identified.12

The right side of the figure highlights this tradeoff. Most experiences go completely untracked and, while most don’t matter, there are a non-zero number that do. Those are the critical ones. The figure says that I have 0.49% of the 1000+ visited universes tracked but only 0.0002% of all universes. Opportunity lies in this vast ocean of data. This becomes evidently important when considering that there are millions of dollars pouring into the Roblox creator ecosystem from investors, buyers, and rollups. These millions are missing potentially crucial signal.

New games are played as much as old ones

I’ll rely on the selected sample here. The figure below shows the percent of experiences in each cohort that have more than 100 visits.13 Note that old and new games have equal visit rates. In fact, there are even higher play rates for the later cohorts. Given that the most played experiences today are also the newest, Roblox games are always fresh. New games are rewarded with players. This contrasts with traditional game industry norms where the most popular games are often over 5 years old — a lifetime by Roblox standards.

The discerning reader may squint at my figure above. Surely, the selected/biased sample will contain a biased sample of the population of Roblox games and so one shouldn’t trust its accuracy. I would push back and say that it is, in fact, this bias which makes the figure even more astounding. Even if selecting on the most successful games in the Roblox ecosystem, new games continue to get lots of visits, even at rates higher than older games (even though the old games have had more time to accumulate visits). In other words, the bias works against us and the conclusion still remains intact.

Game visit inequality is stable or declining

The figure below uses the selected sample of successful games and plots visit inequality for each Roblox cohort-year.14 This is a case of a picture telling a thousand words. The grey shaded area is the Gini coefficient. This effectively tells us how widely distributed all visits are across popular experiences. The higher it is, the higher inequality is. Gini is 1 if a single game has all the visits ever and it’s 0 if all games have an equal number of visits.15 Game dominance is simply the percent of all visits that the top game got for that cohort-year.

Game dominance (blue line) is decreasing over time while Gini is relatively stable. A sign of decreasing concentration and increasing equality. Many have seen the stories that the most rapidly visited game of all time Steal a Brainrot (72B visits), nearly dethroned Brookhaven (85B visits) in a single year. What’s surprising from this data, however, is that even with these enormous numbers, Steal a Brainrot only accounts for about 27.5% of 2025’s total visits. Compare this to Brookhaven’s whopping 40.5% of all visits in its cohort-year. So more games are getting more visits but inequality is seemingly stable or even declining (depending on how you measure it).

The inequality discussion is perhaps the most important when thinking about Roblox ecosystem health. The success of the platform rests on the ability of game developers to design a game and strike it rich. This chance of success is what powers the flywheel, to use an AI phrase, and incentivizes creators. Without it, Roblox development stalls and so too does the entire ecosystem. It does not look like this is the case, however, and declining inequality is a very good sign.16

Conclusion

Today’s post laid the groundwork. I introduced my sampling procedure as well as the universe of Roblox IDs that exist and how one can make predictions about the population using the sampling procedure. I also find that there are more than 10.6B Roblox universe IDs and only 2.2% of these have ever seen a single play. Of those that have been played, only a tiny fraction ever see more than a single visit. While data is coming in every day and improving my accuracy, I find it striking that the experiences that analysts, banks, and investors have access to are but a tiny fraction of the total potential out there. There is perhaps much to be gained from looking at these less popular titles.

From my further investigation of the data I also find that visit inequality is stable and perhaps even going down and new experiences are receiving just as much play as old ones. This bucks the problematic trend in traditional gaming where most of the popular games are over 5 years old. New games seldom receive much of the pie. The fact remains, Roblox is a vast ecosystem with massive potential lying beneath the surface. Indeed, I hope to explore this topic in a future article.

At this stage, we have probably all received AI generated messages from colleagues, businesses, and perhaps even loved ones.1 AI messages can range from efficient and helpful to overly-verbose and heartless. The goal of today’s post is to set some ground rules for how we communicate with one another in the age of AI generated content. Below is an example of what truly terrible AI generated communication looks like.

This is about 1/3rd of the full message. This message is clearly not intended to be read by a human being. The amount of effort required to translate this into something readable is high. Once it has been translated, the effort required to fully digest it and understand it is even higher.

AI technology presents a very interesting juxtaposition to how communication has worked for millennia. It used to be that generating technical findings was difficult. Producing more detail took more time. Now it’s the opposite. AI can produce an exorbitant amount of information instantly. It’s our job as humans to sift through this and determine what makes the final cut in any communication.

This post suggests a framework for how we should use AI to communicate. When should you use AI versus when should you handwrite? What output should you send for which kinds of tasks? How long should that output be? Should the final product be a living document? Should it have figures? This is my personal guide that I thought might be useful for some of you. If nothing else, it also acts as a PSA — stop sending AI slop to your co-workers and loved ones!

How to use AI for communication

The question of when you should use AI to write and communicate is a tough one to answer. It’s very much “vibes” based to use some millennial vernacular. Sending your mom a birthday card? Write it yourself. Sending a technical summary of a new app feature? Probably a good idea to get AI involved. Writing an article as an escape and to exercise creative freedom? I’ll skip the AI thank you.2 Each person needs to decide for themselves when to use AI and when not to. This will vary by the person and the task. Nobody can tell you definitively when you should and should not use AI. But I encourage you to think before you prompt. Figure out if the communication would be better done by hand or by AI. Only after you’ve done that can you decide the level of involvement and medium by which AI assists that communication.

Deciding how much detail and what format to communicate with has become a very important decision in all our daily lives. Rather than jump right into my personal principles on using AI in communication, let’s walk through some examples and label them good or bad use cases.

Scenario 1:

You need a colleague to fill out some values for a new XP system. You generate a Claude .html that outlines how exactly the system works and what needs to be done. The file spans about 2 normal web pages in length.

Verdict: Good!

Reasoning: This scenario is good because the task involves many moving parts and the output is easy to read and succinct. I have found html to be a very pleasant medium for long-form AI output. It is far better than .md or plain text in the context of a longer task/write up that may contain technical artifacts like figures. Furthermore, the output isn’t overly verbose. If this was 5-10 pages of html the verdict would be quite the opposite. In my experience, no AI communication (or communication in general) should exceed two normal web pages in length. The disadvantage of cloud-hosted html output is that it takes up space and not all links are easily accessible (though this is quickly changing).

Scenario 2:

You ask a colleague to explain how a function works in your game update and they send back a 17 page PDF over discord.

Verdict: Bad!

Reasoning: First, the length is problematic. Unless someone is asking for a game design document, you should not be sending anything longer than 2 pages. AI can write a lot of words really fast. A more important skill to exercise when writing with AI is brevity, not verbosity. Second, it’s unlikely that anyone is going to properly read a 17 page doc. In skimming, they risk missing important details crammed in the margins of page 12. Third and final, don’t make people store files on their computer that should have been emails. We already have too many files on our computers (many of which are AI generated), don’t add to the clutter.3

Scenario 3:

A colleague asks you for the latest quest completion rates for the last week’s cohort. You send them a living dashboard with no fewer than 10 visuals about quest completion rates.

Verdict: Bad!

Reasoning: I am guilty of committing this sin. I love a nice dashboard and, hell, more information is always better right? When we’re building with AI we must not lose sight of the goal. The stakeholder asked for some simple numbers. Maybe they just wanted a percent back in a text. Instead, they received a living dashboard they have to log into in order to access. What’s worse, when they finally do get in, they’re met with a dozen numbers they don’t understand.

AI makes communicating data extremely problematic. On the one hand, you can communicate a lot in a little bit of time. On the other, it completely buries any semblance of a throughline. What is your KPI? What are the core metrics your stakeholders care about. My preference is to use AI to build dashboards for myself but for others I build the numbers myself and ask AI to make it look good. I’m an analyst, I can spend half an hour pouring over numbers. Don’t ask a non-analyst to do this.

One bonus problem I’ll throw in here is that AI doesn’t always get it right. I’ve run into this several times myself. The model may say one thing and do another. For example, I was building a Star Atlas accounts dashboard recently which shows me all the current accounts associated with any Star Atlas program on the Solana blockchain as a snapshot each day. I specifically instructed Claude Opus 4.6 to grab all programs. I built everything out, hosted the dashboard. It wasn’t until about 2 weeks later that I realized I was only collecting 3 of the 20 or so programs I intended to. While the fix was relatively simple, it did require me to perform a data backfill. If others were reliant on this data or if the backfill was hundreds of millions of rows, this mistake would have cost me dearly.

Scenario 4:

Your colleague asks you about whether a game feature requires a patch. You plug the question into your Claw Bot and send the output as a 5000 character .md file as a response.

Verdict: Bad, obviously

Reasoning: OK, this one may seem ridiculous. But I would bet this has happened at least once daily in every organization since claw came out. Responding to genuine criticism or questions with AI output is not only disrespectful (not taking others’ concerns seriously), it completely misses the point of the communication in the first place. The idea is to get two people, and perhaps an AI, in a room to discuss a feature. When you send AI output, it is doing the heavy lifting that, in fact, should not be left in the hands of a robot. AI is not a moat, people are!

Scenario 5:

Your colleague asks you about whether a game feature requires a patch. You write up a response in your own words. Then you plug your colleague’s question and your response into Claude and ask it for criticism and to refine points you have both made. You take this criticism into account and send a refined version of your response to your colleague.

Verdict: Good!

Reasoning: Notice what I did there? Using AI isn’t de facto bad in any situation. It’s how you use it that determines how bad or good it is. Thinking for yourself, understanding the colleague’s concern, and AI-powering your response is the right way to go about this. I have personally done this on several occasions and I am extremely happy with the results. First, the AI catches things I may have forgotten or overlooked. Second, and more importantly, my own personal response is deeply imbedded in the message.

AI Communication Best Practices

Remember the reader — No matter what, you should always keep the reader in mind. Don’t send your coworker a 10 page PDF when they ask you how your weekend was. Simply answer their question yourself if possible and turn to AI when necessary. Even for larger tasks, always ask yourself how you could make the output more understandable and easier to read. Being overly-technical and providing extended responses is no longer a flex, it’s simply lazy (WARNING: AI SENTENCE DETECTED!)

A final note I’ll add here is that AI output from coding models is exceptionally technical. It uses lingo and terms that are not often easily understood by humans. Be extremely cautious when sending AI output generated by Claude Code or other coding specific agents. Here’s an example a message that, in my view, makes no sense: re-write!

Tell them your message is for AI — I’ve started to do this. If I know that the AI is the middleman and me and a colleague are effectively just the adults in the room, I will literally tell my colleague that I’m sending them output that they should directly feed to their AI, not for their eyes. I’ll also send a very brief sentence or two outlining what the file contains. Friends don’t let friends send AI slop to one another.

Choose the right format — Never send people raw .md files. You are literally treating them like AI bots. Yes, Grok, Claude, and GPT all love eating up markdown files. Don’t treat your colleagues like robots. In general, I have found the following mapping highly successful:

1. HTML — For complex findings, graphs or figures. One-shot projects that don’t need to be updated. Interactivity is important.

2. Screenshot — For HTML contexts but interactivity isn’t important. Screenshot at the lowest resolution needed and don’t forget to clean up old files.

3. Text — Limited formatting needed (ensure it works with your messaging app by dry firing a message to yourself, slack and discord both have this functionality). A core list of points or a one-shot values that need to be communicated. Not for complex figures/charts or communicating data over time.

4. Other Docs — If you’re ever asked for a slideshow, written report, or a living dashboard, of course use those specific mediums when communicating. As of this writing, I have found most models lacking in their ability to write docs and pdfs out of the box. When collaboration and editing is important, use a classic docx or pdf.

Think more, not less — AI makes it easy to forget to think. “Hey can you please summarize this repo and write it up as an .md?” is a lot easier to send than to actually look through the repo yourself. The AI generated response will be so much more powerful if you provide it with the reasoning/logic and a template than simply prompting it with what your coworker asked you.

I recently started building a new game for mobile using Claude’s design feature. Unlike with my turbo chess idea, I already had pages of documentation: design docs, playtest notes, as well as an art style and UX of my own creation that I fed the AI to build the app. The first pass result for this current app (called Market Makers) was 100x better than Turbo chess’s first pass.4 Having a strong, human-designed, foundation makes AI output so much better.

Outro

AI has completely changed the way humans communicate, think, and respond to the world around themselves. While this presents incredible progress and growth in terms of how quickly we can work, it also poses serious risks and challenges. This challenge stems primarily from the fact that AI can build an enormous amount of output in almost no time at all. Sifting through this output for the good stuff and, more importantly, guiding these models to efficient and powerful answers is where the real work is.

Communication is one of the key areas that AI has most impacted the lives of humans. Honing this tech to make our communication better, not worse, is trickier than one might think.

Before you send your next AI generated communication, think to yourself “How would I like receiving this? Does this make my life easier and better?” If your answers are “I wouldn’t” and “No” then go back to the drawing board until they are “I would” and “Yes”. This entire post was run through two models before I considered it “complete”. And there are still probably some spelling errors.

Intro

Today I’m going to present some econ theory on why businesses should be cautious when substituting AI for labor. In an earlier post, I talked about the impact of AI on the game industry labor market. But AI is clearly impacting every industry. And it’s impacting the tech industry especially hard. At this point, we’ve all witnessed first hand that, at least from a narrative perspective, tech companies are definitely laying off employees because of AI (with special concentration in the blockchain/crypto sector).

That said, all of the explanations that I have read tend to be extremely vague. For example,

Over the past year, I've watched engineers use AI to ship in days what used to take a team weeks. The pace of what's possible with a small, focused team has changed dramatically.

— Brian Armstrong, CEO Coinbase

This is where the crux of today’s model will lie. Mr. Armstrong points out that teams are faster and more capable. I don’t disagree. I wrote a piece several months ago on how I built a chess app with all the bells and whistles in less than 2 weeks. It’s astounding.

BUT

There are two missing pieces to the shortsighted view of Mr. Armstrong and others.

1. When a business discovers ways to build things more quickly, it should scale, not shrink.

2. AI is only as effective as the wielder.

Businesses should scale because of AI, not cut back

The first point is not novel and it’s something that economists have been citing since the printing press was invented. Imagine if Henry Ford had decided to cut back his workforce and slow down operations following his invention of the assembly line. Surely, he could then produce just as many cars as before but with less effort. Why waste additional resources to make more cars than he needs to produce. The ridiculousness of cutting back production in this case is obvious.

Now, it’s not quite as simple as that. These days, we can deliver pretty much as many units of apps to the entire global population for the cost of producing a hundred model Ts. In Ford’s time, he was able to scale his production because demand was there to meet it.

Is there such demand for digital stuff which AI is so good at producing? You might think not. And you might be right. But I’ll provide some examples of technology that needs to be built that might change your mind.

1. Security — Everyone on earth just got the ability to become a hacker. With this massive increase in info security risk, businesses should be absolutely pouring resources into op sec. Almost every university in the US just fell victim to a huge hack on Canvas (a coursework/management platform). The hackers, a group known as ShinyHunters effectively just pulled off the largest information ransom in history. Old crusty universities are the most vulnerable and so are being targeted first. Governments, and businesses are next. For every hacker, we need two security professionals. The demand for information security talent, powered by AI, has just gone stratospheric.

2. User Experience — There are a lot of shitty apps out there. Any app that’s over 10 years old is a nightmare to deal with. Transitioning these old apps into the 21st century is going to be a challenge even with AI tools. AI powered devs are going to become a hot commodity. I’ve seen first hand wannabe UI/UX experts wielding AI create monstrous user experiences. Just because you can create a cool new app doesn’t mean you should. Modernizing and improving current and future app/web user experience is going to be critical going into the AI era and talented UI/UX experts to wield this powerful tool are going to be exceptionally important to that end.

3. Data Hyperscale — With AI comes millions of new apps, petabytes of data storage, and millions of dollars in incremental value generated. Take my Turbo Chess app. I’ve spent about 20 cents on it over the past few weeks. It takes up space. I have users with data that execute actions on a server. All of this takes compute, time, and resources. In other words: money. There is a whole new world of data and app consumption that we’ve entered into. There will be essential jobs that open up in order to manage this new world. The amount of AI generated slop that now exists means that our storage needs have likely just increased by orders of magnitude. This new world requires new solutions to big problems. We’re going to need smart, experienced people to solve these problems.

4. Innovation — Finally, the catch all in my “new reasons to hire people, not fire them”: the innovation frontier has just exploded. With new apps and capabilities come millions of new ideas and products being generated. We will necessarily see a huge surge in demand for people who know what they are doing in order to manage these applications.

I’m not trying to say that everything is going to be fine because of the four points above. But I do think there is a strong chance that the layoffs we’re seeing are just a part of a huge shuffling of talent. I do also think that the businesses laying off employees right now will be worse off in the long run. First, it’s expensive and difficult to acquire talent once that time comes. Second, reputation is important in the labor force. If you are a talented engineer choosing between two businesses and one has a reputation for brutal layoffs, all else equal, you’re probably going for the layoff averse company.

Now, many of the companies who have cited AI in their layoff announcements accompany it with other things as well. Macro economics being a large contributor to many layoffs. But I still strongly believe that businesses performing layoffs as a pure cost cutting measure are going to suffer hugely in the coming AI boom.

The marginal productivity of AI — a model

I know you’re all dying for another cracked micro economic theory. So today’s your lucky day. Below, I present a model of AI productivity. This model assumes that AI’s productivity is determined by two things: the task and the user. The marginal value or productivity of AI will change depending on what it’s supposed to do and who is using it.

Tasks

We’re going to say that in the whole wide world, there are J number of jobs or tasks that a person can engage with. This could be cooking. Cleaning. Painting. Coding. Driving. Writing. Anything and everything. We line all these tasks up from j=1,2,3… all the way to J. We’re going to say that AI can do any number of these in some capacity. It might be really good or it might be really bad. It’s ability to complete a task is called k. If it’s really good at the task then k is closer to 1 and if it’s really bad then k is closer to 0. In math terms:

I hope this first part is pretty intuitive. Think about two tasks. Cooking and coding. AI would probably have k_cooking=0.01 and k_coding=0.99 meaning it really sucks at the act of cooking and it’s really good at the act of coding. The bigger k is, the better. This is the first key ingredient we need to build our model of AI productivity.

Worker Skill/Labor

A calculator can’t do math. It needs a human to push the buttons so it can do math. A PhD mathematician is better at utilizing a calculator to do math than a 3rd grader is. A better user of AI will always produce better output than a worse one. This is where skill comes in. Just like we lined k up from 1 to J, we’re going to say that each worker has some skill for task j:

So a person with s=0.01 is really bad at the task while someone with s=0.99 is really good. Importantly, workers might be good in one task and terrible in another. I’m a much better economist with AI than I am a UX designer with AI.

Now we have the two important ingredients to set up an economic model for AI productivity. In particular, we’re going to look at this from the lens of Personnel Economics. The study of the firm-worker relationship. We want to understand when a firm should hire more super high quality workers versus one super high quality worker.

Worker Productivity

From here on out, I’m going to omit the j subscript. First, this makes it easier to look at these equations. Second, for each equation we write, we are writing it for a specific job/task. Remember, there are J total equations we could write but for simplicity, we’re just going to look at a single one of them. We’ll extrapolate further in a bit. We are going to define the productivity of an AI-powered worker as:

This is a famous economics function which has several desirable properties.1 Importantly, it utilizes alpha and beta which are weighting variables between 0 and 1. They tell us how important each of the inputs is in the worker’s output. Note that I have separated alpha and beta so that some tasks have both high alpha and beta while others may have low beta and alpha or any combination of the two. I’ve plotted the CD function where alpha=beta=0.5 below in 3-space. Note that even if s is 1, if k is super low, then total output is super low. You need a combination of both to be productive.

Side note:

At this point, you’re going to think that all I’ve done is set up the original firm production function and simply renamed the variables. That’s partially true. But the point of this article is not to showcase how labor interacts with capital. That’s well studied. It’s to show how labor interacts with AI. When we’re solving for labor, capital becomes a constant in the model (for the most part). Thus, I’m abstracting away from it here for simplicity.

In this model, Labor and AI power are compliments, just like labor and capital. This is not just a flippant assumption, this is a fact. Even those CEOs how claim to be thinning their workforce due to AI refer to AI’s ability to super power their small workforce. AI does not replace a good worker, it makes a good worker better. At the model level, the decision for a firm to layoff a worker is explored in equilibrium, not at the baseline assumptions of the model. We’ll do this next.

Firm Profit

To get firm profit, let’s suppose that a worker is paid a value that is equal to their marginal output (a typical economics assumption). In other words, wage is equal to skill level. This may seem like a gross simplification but, since economists usually assume wage is equal to the marginal productivity of a worker, it’s not far from a normal theoretical assumption. Thus, a firm’s profit is:

Note I make the liberal assumption that AI costs the firm nothing. Which is not the case. The first part of the equation is simply the total output the worker produces while the second part is the cost to the firm. The higher is the skill, the more is the output but so too is the cost. Higher AI task proficiency does not, however, increase costs at all.

Solving

Now comes the fun part. We want to understand how a firm makes a decision to hire a single generalist (someone who is just OK at everything) versus several experts (all have super high s).

Suppose a firm makes profit from 2 tasks (we can expand this later). They either hire a single generalist for both tasks which gives them the following profit:

or they hire two experts which gives them:

and

This assumption is critical. We’re saying that, even if you have a very good engineer. If they are focused on doing a job that used to be done by 10 people, they are going to not only be over-prescribed, it’s impossible that they are an expert in everything that the other 9 were experts in. Each person has their own skill. One person cannot be an expert in everything. So the generalist is said to have a lower skill for the task than the expert. We’re thinking at the average in this case.

They hire the generalist when:

which reduces to:

which gives us our first result:

For some AI quality, k, the firm chooses the generalist when the output differential of the experts and generalist is greater than the wage differential.

Comparative Dynamics: Indifference

To understand the dynamics at play, let’s visualize. I’ve plotted the inequality below with alpha=beta=k=0.5. The green regions are where the inequality lends towards hiring a generalist. Otherwise, you should hire an expert:

There’s a very important characteristic of this chart to note. Recall that the expert skill is always greater than the generalist skill. Thus, we’re always going to be on the south east side of this graph (below the 45 degree line). The only place on this plot where it’s better to hire the expert over the generalist is for very low values of worker skill. That little white triangle in the lower left hand corner. In other words, generalists have to be pretty bad but so do experts. Because once an expert gets over 0.5, you’re better off hiring a generalist again. This is due to how expensive experts are compared to generalists. An important modification to the model would be to adjust how wages are determined. It’s likely the case that a generalist costs about the same as the expert since they are working more than the expert to do the same amount of work.

Comparative Dynamics: AI

Below is another plot but this time with k=1 (the value of AI is very high). While it may not be obvious, the region where it’s acceptable to hire an expert has expanded dramatically. Now, we see scenarios where, if the expert is sufficiently more productive than the generalist, it’s worthwhile for the firm to hire experts over generalists. While this may seem counter-intuitive, there’s a story about comparative advantage here. Yes, the expert is better than the generalist. But AI boosts their abilities disproportionately more than the generalists. If the generalist gets a 2x boost. Experts get a 10x boost.

Comparative Dynamics: Skill

See the plot below. Now, I’ve left K=1 and I’ve driven the importance weight of skill up to almost 1 (compared to before when it was 0.5). Again, we see the value of experts expand and the value of generalists shrink (green area on the bottom right hand side of the figure gets smaller and the white area gets larger). It’s obvious that as skill becomes more important, the firm will be more willing to trade off generalist talent for expert talent.

Empiricism

Theoretical models are great. But they are pretty useless if they can’t be applied. So how do you apply the model above? First, we have to convert all our fancy symbols into real life data.

If we break down our inequality there are 3 key variables:

1. k: The skill level of AI at doing the specific task. I think a great way to measure k would be as a percent of the task that can be automated completely with AI from start to finish. There are also a few econ papers out there who have attempted to estimate k.2

2. s: This is notoriously difficult to measure for economists but not too bad for managers. If you can rank your employees by skill at a specific task, then you can get s. Just ensure that the measurement is ordinal in some way. Remember we made the assumption that s is essentially the wage? Well, you can just set s to the employee’s wage. High value employees typically demand higher wages.

3. y: This is similar to s. Can you measure the output of your employee? Better if it’s in dollar terms. This is easier in some industry than others. You could use lines of codes, projects completed. So long as y is measured consistently across the entire organization.

Once we have these three variables, we can set up an econometric equation to estimate alpha and beta. Once we’ve got those, we can literally plug the numbers into the inequality. If it’s satisfied, employee should be laid off. If not, employee should be retained. The econometric equation looks like this:

Using any old linear regression tool, we can get estimates for a (just a constant), alpha, and beta. Epsilon is just an error term, not important for our analysis. Assuming the model comes back confident, we’ve got all the values we need to solve our inequality!

Conclusion

If I go on much longer, this blog post will become an article. Let’s conclude with some findings from this model:

1. Firms will choose experts more often when the skill differential is very high. In other words, industries where domain expertise is critical for success will see experts dominate.

2. Firms will choose experts more often when the value of AI is higher. Experts get even greater returns to AI than a generalist and therefore extract more value from it.

3. Firms will choose generalists more often when the importance of AI is very high compared to skill level. For easily reproducible tasks which require very little expertise, AI will consolidate the workforce into fewer generalist hires.

4. If a firm observes high alpha and low beta in their

Not covered here but needs expansion:

1. Cost of AI: this model ignores the cost of AI. But as token/API costs begin to climb, massive AI bills will eventually be a non-trivial part of the model.

2. Expert/Generalist wage differentials: this model assumes that workers are paid their productivity as wages. However, it’s likely the case that the wage differential and the productivity differential are not proportional.

3. The model does not solve a general equilibrium. One could expand this framework to solve for equilibrium wage in the context of AI.

This’ll be a short and sweet one for your Friday afternoon. I’m on the hunt for my next big adventure (as they say). And in my quest for the next awesome thing, I’ve met some interesting characters. Today I’m going to introduce you to the most sinister of these characters. A guy who used social engineering, a mild version of “Pig Butchering” as they call it in info sec, to try to manipulate me and, ultimately steal from me. What seemed like a sweet gig as a VC’s in-house economist was actually a wolf in sheep’s clothing. Meet Leo.

Leo reaches out via email

So a guy named Leo Chang reached out to me via email with the following:

I could tell the email was AI generated, it was just way too specific. But, everyone writes with AI these days so I wasn’t too worried. So far, just a random tech person using AI for outreach. That’s not so bad. There were only two things that stuck out as slightly fishy to me. First, he requests that I chat on telegram. Not a huge deal. I’ve chatted with several VCs over telegram. It’s an incredibly popular spot to chat in that world. Second, he didn’t reach out on LinkedIn. In fact, I couldn’t figure out where he was getting his information. Nobody seems to have reached out to me on LinkedIn or viewed my profile with his specs so it’s interesting that he found this info and reached out directly on email. But, all my info is available on my website so still this didn’t weird me out too much.

But it gets much worse.

The sirexdotone website

Please do not visit any of the links I mention in this piece. While clicking seldom does harm, better safe than sorry.

My investigative work didn’t stop at Leo. In addition to Leo not having a LinkedIn, Sirex VC doesn’t have a LinkedIn. That’s incredibly suspicious. What VC doesn’t have a presence on the largest employment app on earth? No Twitter. No LinkedIn. No nothing. The only proof that this guy or place exists is a website and an email. Not looking good. But hey, the website looks great so surely these guys must be legit. In the age of AI, this website could probably be generated in seconds.

When you look through their portfolio, you’ll see a bunch of companies that, like Sirex, don’t exist anywhere but on this website.

Synapse Key, Ion Grid, Void Stream? Each of these refers to a real company. Synapse is a defi protocol so they decided to just add “key” to sound catchy. Ion Grid is a service built by Ionic, a UI/UX framework for mobile apps. Void Stream is actually a business but it allows you to stream video, not build ZK proofs. None of these companies exist and even if they did, they wouldn’t be crypto companies.

After the fact, I ran the Sirex URL through urlscan.io and found that the owner was something called G-Core Labs S.A. Both the IP address and the GCORE Labs are not trusted and the domain is, get this, 1 week old!

At this point, I should have taken a step back. I shouldn’t have moved forward. But there’s a reason they target people who are looking for something. They want people who want them. If this was a sales pitch, I would have backed out the minute they sent me an email. This is the key to Pig Butchering. The scammer convinces the victim that they need them. Keep in mind, at this point, I hadn’t dug too much into the website. All I knew is that things seemed shaky. I didn’t do my deep dive until after the fact (a mistake I won’t make again).

Leo wants to meet via fyMeetdotApp?

We all know I should have bailed at this point. But I didn’t. I was actually very curious what they were going to do and, as a casual enjoyer of operational security/dark net stories, I found this very interesting.

So, the day to meet is upon us. Leo links me a website where the meeting will happen: fymeetdotapp. This in and of itself isn’t all that weird. I’ve had all sorts of companies ask me to meet them on their 3rd party meeting place. Usually in an effort to support a partner or investment. But the link ended in .app, a TLD often used by app-like web products. That alone isn’t suspicious, but paired with everything else, it didn’t help. I know this because I just built turbochess.app (which is not a scam I promise).

Here’s where things go down hill. I click the link (mistake). I end up in a call with 3 other people. But my audio isn’t working. Nothing is working. I can see little outlines on their PFPs like they’re talking. But I can’t hear and I can’t speak. Leo tells me that there is a problem with the website and I need to use the app. So he requests that I download the app onto my desktop. roko

ALARM BELLS!!!

OK, here’s where my little adventure ended. I wasn’t about to download some random app from this obvious scammer. I don’t have the technical expertise to download something and dissect it. I’ve pasted my conversation with him below so you can see how things went from here.

The messages speak for themselves. If someone is persistent about getting you to download something. You’re probably getting scammed. I consume a lot of content about how scammers try to get you and this person followed that pattern to a T. The “But one of our guys made time in his schedule just for you!” was a nice touch. Make me feel bad about it. Fortunately I had the wherewithal to not fall for this. But I still clicked links. I still visited websites. The reality is, I came close to falling for this and it was exactly because of how they targeted me.

We’ve all probably gotten a hundred texts from “Rachel” who wants to hire you to earn $500 a week. These are totally ineffective because they don’t target specific people. They just send out a blast to a million phone numbers and hope some percent pick up. This scam was highly sophisticated and targeted me specifically. They knew my name, my background, my history. And they knew I was on the hunt for something exactly like what they mentioned.

I’ve done a comprehensive check of my machine and am 99.9% sure I didn’t get infected by simply visiting their links. That said, this was by far the most sophisticated scam I’ve ever had to deal with and, while it was adrenaline inducing, it was not something I want to have happen again.

Stay safe out there kids!

Once again, my remarkable procrastination skills have led me to an article that is even more interesting than the one I was supposed to publish 6 months ago. Last October Valve made the controversial decision to allow players to trade up 5 red rarity item skins for a single gold rarity knife or glove, the most rare items in the game, leading to a $1.75 billion dip in the market cap for CS2 items.1 Previously, such a trade up was impossible, making the red items worthless and the gold items incredibly expensive. While the decision was likely an effort to improve monetization of primary purchases2, the fallout and subsequent recovery of the market shown in the figure below provide several interesting digital economy tidbits that are worth discussing.

I answer two questions in this piece. First, how can there be such a massive gap between the blue and red line? Second, how could a market ever recover from such a large shake up? Both these answers and more are discussed below.

How can there be so much uncertainty in the market for CS2 items?

The chart above was taken from a CS2 price data base, PriceEmpire.com. You’ll note two lines. The blue line is market cap: the average price times the total quantity of items of all CS2 items. Critically, total quantity is estimated from a slew of methods but none of them are 100% accurate. It’s impossible to know the true outstanding quantity of items in CS2 because no such database exists. All we know is what items are being traded. The red line is market cap adjusted for the destruction of items from the October 23rd update, using a decay model to estimate the quantity of items dusted over time. Essentially, every day, the model assumes that some percentage of the outstanding supply of skins is destroyed in the trade up program. Smart. But not precise.

A critical point here is that the entire CS2 market is now guessing at the total value in the ecosystem. It’s a scary thing to not know how much value is in an ecosystem if you’re an investor. The red line shows us that overall market cap has actually dropped since that update by roughly 28% (from 6 billion down to around 4.4 billion). Imagine if the market cap of the S&P 500 was secretly off by 25%. We’d call it a bubble.

Insight #1

If CS2 items were held and traded on a publicly accessible data base (like a blockchain), this data would be publicly accessible to the users and the value of the CS2 ecosystem would actually increase. There’s a famous economics paper called Diamond and Verrecchia 1991 in which the authors provide a theoretical framework in which publicly traded firms can actually increase their valuation by disclosing more information to the public. This was later backed up by data in Leuz and Verrecchia 2000. I don’t think it’s unreasonable to generalize this model and say that, on average, and ceteris paribus, less information asymmetry is better for price appreciation.3

Even so, how did the CS2 economy recover from its massive fall?

While the more rare market for skins largely dipped and never quite recovered, the market for cheaper items has actually increased quite a bit. All of a sudden, a bunch of junk items have gained value because they have new utility. I had Codex grab 5 of the most observed red rarity items and 5 of their resulting gold items (those ultra rare much more expensive items). We can see that the market for outputs dropped by anywhere from 2-10% and the market for inputs (though this is likely a biased sample) increased by over 25%. With more plentiful items seeing a large increase in price and less plentiful items seeing a moderate decline, it’s no surprise that we’re seeing a net neutral change in the overall market.

Insight #2

Economists call this economic self balancing equilibrium. In the context of digital, hand-built economies, I prefer the alternative phrase Value Preservation: any time there is a shock to a closed economic system, so long as there isn’t an exodus of users, the value in the ecosystem will stay mostly stable even if individual markets/commodities fluctuate within that ecosystem. I like to picture a bucket of water, when you shake it up, everything goes all wonky for a bit, but eventually it settles back to the original state. Indeed, the amount of water in the bucket never changed even after you shook it. This is why we see the value of some items decrease and others increase while the overall market cap is relatively stable.

One of the only ways value can flow from the economy is if players, or more importantly their cash, leave the market. While we don’t have data, it is likely that some of the cash in the CS2 market flowed into loot boxes and out of the market. This explains why we didn’t go right back to where we were before. Some value has leaked out of the bucket.

Bonus & Conclusion: Would decentralization have helped?

Another popular web3 topic that was discussed following the October 23rd update claimed that if CS2 was decentralized then such an update would never pass because those holding economic power would veto the change. As someone with more web3 games experience than most people, I have never seen a fully decentralized game development pipeline and I believe I know precisely why that is. Building video games is extremely difficult, adding in blockchain consensus at every step of the development pipeline is both practically impossible and philosophically stunted. Here are my top two arguments for why this is the case:

1. Team Incentives — It’s difficult to motivate people to work on a game they have no ultimate control over. It’s even more difficult to work on a game if you don’t make any money on it because development is slow. Imagine working for 6 months on a huge content update only to have it fail to get approval from the blockchain before it can be posted. This is highly impractical. A corollary here is that players want the game dev team to do their best work. I don’t want to tell Fromsoft how to make Elden Ring better. I trust them to do a much better job than I would.

2. Technological Barriers — Implementing a fully decentralized creative pipeline would require both a level of coordination between developer and player as well as integrated blockchain technology that is technologically impractical. Nobody figured out how to create a consensus led, blockchain approved, art asset & game design pipeline. Every time the team wants to post an update, add a feature, or ban a player, they would have to push this update to some decentralized program which then seeks approval and, only after reaching consensus, post the update to the game. This tech doesn’t exist. As the creative pipeline grinds to a halt, less content comes out and player attrition skyrockets.

So, while decentralization in game development may be possible for some specific projects, we’re a far way off from having something like GTA6 being produced in a decentralized manner.

So what does the CS2 market shock actually teach us? First, falling prices in one segment of a digital economy do not necessarily imply a collapse in total ecosystem value. If users remain active, value can be redistributed across adjacent items as utility changes. Second, the strongest blockchain use case here is not decentralized game development or on-chain governance. It is transparency. In a market built around scarce virtual assets, better visibility into supply, destruction, and circulation would reduce uncertainty and improve price discovery.

One of my favorite podcasts of all time is called Dark Net Diaries by Jack Rhysider (pseudonym). This podcast combines economics, tech, and the dark web into a riveting storytelling experience. The show covers tons of topics from physical penetration testing and social engineering to traditional hacking and cyber crime. As you might imagine, a lot of these stories revolve around tech, crypto, and even gaming.

I find the podcast both entertaining and insightful. I highly recommend anyone reading this blog to have a listen.1 You’ll learn a ton about how to be safer on the internet and beyond.

Today’s post is going to highlight two recent episodes from the show that I simply cannot stop thinking about. One is a PSA and the other is a prediction. Read on.

The Super Box

In his most recent episode, Jack interviews D3ada55 (“dead-ass”), a researcher and cyber-security expert based out of California.2 I’ll give you the TLDR but I highly suggest you listen to the episode. It’s awesome. D3ada55 is visiting her dad one day, an oil executive, and discovers he’s purchased a brand new “Super Box”; a device which allows the user to connect to the internet and effectively download any show they could ever possibly want. All for a flat fee which is the cost of the physical device itself. You might think this is an episode about piracy and its harms. You’d be dead wrong. The device comes loaded with various spyware, both physical and digital, unbeknownst to the buyer. It gets into your home network, connects to all devices, and, most terrifyingly, can emulate those devices. It literally shuts down your device and pretends to be it, picking up passwords, emails, and other sensitive data.

The crazy thing is that literally millions of these devices have been sold in the US. Most of them cost around $200 or more. So, not only are the sellers making billions, they are also installing spyware in many homes in the US. This is a billion dollar, illegal, and extremely nefarious industry. The worst part is that these devices are sold on Walmart, Amazon, and Best Buy. Even after the FBI cracked down on them, they are still available today. I won’t post any links here for obvious reasons, but you can search for them in incognito mode to see for yourself. One disadvantage of having an unfettered 3rd-party marketplace is that all sorts of nasty characters can fit under the tent.

I bring this story up for several reasons. First, if you have one of these boxes, throw it away. If you don’t, please don’t get one. Second, it’s amazing to me how much money there is in crime. The hilarious thing about the Super Box is that the value the creators are receiving is not just the revenue generated from sales, it’s the information they siphon from the buyers. Crime is a MULTI-TRILLION dollar industry. Thus, there will always be people looking to break the law for a tasty payout. Just as people will always get sick, people will always commit crimes.

With that, let’s look at an episode that hits a little closer to home.

Melody Fraud

Another episode of the Darknet Diaries I listened to recently was titled Melody Fraud. Jack interviews Andrew Batey, an ex-grey marketer turned fraud catcher. He tells us the story of how his company Beatdapp uses data science to catch fraudsters on music streaming apps. There are two angles to this fraud. On the one hand, bad actors will spin up tens of thousands of streaming accounts, steal real artists’ music, and generate AI music, aggregating incremental revenue across these accounts to bring in a hefty sum. On the other hand, because digital music involves royalty payouts and advertising revenue, this is a ripe environment for money laundering.

Suppose you’re a criminal based out of Russia and you need to transfer funds to Switzerland. You can’t easily transfer a million dollars in blood money via the Swift network so you do one better. You spin up a bot farm of artists on Spotify which you spend that million dollars on doing. Then, that farm generates some percentage (as high as 50%) of that million and ends up in your buddy’s account in Switzerland. As far as money laundering goes, 50% isn’t half bad.

So not only are these people laundering money worth many many millions of dollars, they are harming real artists trying to make a living. It’s incredible the lengths organized criminal organizations will go to in order to send cash from one to the other.

My thoughts

Here’s where my mind goes wild. The situation I described above sounds extremely tedious. Especially with businesses like Beatdapp to make it even harder to do. And to be clear, this is a very good thing. Less crime and money laundering is better. So the question worth asking, especially for digital economy designers, is what characteristics make a digital marketplace structurally vulnerable to this kind of exploitation in the first place? Let’s walk through some other money laundering schemes to get ourselves there.

Art

Another very common mode of money laundering is fine art auctions. Two criminal parties own a collection or access to new fine art that is actually valuable. They bid for each other’s art at auction and, because they are transferring a sum of money that is greater than the value of the art, go uncontested and can easily launder or transfer huge sums of cash above board. This market has very little slippage as far as laundering is concerned at around 25-40% of the value of the art being taken as fees.

NFTs

But even art poses some friction. You’ve got to store it and insure it; move it around and source it. What would be easier is a digital version of this. Our minds immediately go to NFTs. In fact, I’m surprised criminal organizations didn’t do a better job at lobbying for NFTs, they were almost the perfect money laundering vehicle. But because crypto is so heavily scrutinized by the IRS and other government bodies around the world, it does pose a few awkward challenges. After all, the NFT market was never as well established as the fine art market, which goes largely un-scrutinized by the government.3

A further complication of NFTs, perhaps ironically, is that they are scarce. It’s challenging to spin up an NFT collection and convince people that it’s actually valuable. For example, while Bored Ape Yacht Club may be perfect for laundering money, they are still pretty expensive and difficult to get ahold of. So, art is pretty good. NFTs are perhaps better. But we can do best!

Can you think of a digital marketplace where assets have zero marginal cost and almost no fixed cost, income is above-board, exchange is easy, and fees are, in criminal terms, low?

ROBLOX?

I should state that there are many digital marketplaces that offer similar benefits as Roblox, but none of them are nearly as large which is why I focus on them here.

The key differentiator for Roblox is that creators are able to generate as many assets as they would like. They can also charge, for the most part, whatever they would like because Roblox experiences are almost exclusively sell-side markets. And, income is well-documented and above-board. So money transferred in this way looks perfectly normal to the government. It’s not uncommon for an experience to earn millions of dollars of revenue. This is unlike a traditional MMO economy where assets are still scarce and go for a price on an open market. Furthermore, while reporting a million dollars in sales from RuneScape might raise some eyebrows, a million bucks from a Roblox experience is chump change. In this way, Roblox seems like a ripe opportunity for money laundering galore.

There’s already some fraud. But Roblox is doing a bit to combat this and so was the US government as of last year. But, I’d argue that the potential for money laundering at Roblox is immense and currently an under-utilized, or under-studied source for criminal organizations. There is one academic study that supports my thesis.4 The authors run what is essentially a fancy clustering technique on a huge volume of Roblox transactions and find that over a million of these contain fraudulent characteristics. In fact, Roblox claims to have lost over 100 million due to fraud as of last year.

Welp. Looks like the Roblox marketplace has all the characteristics of being the perfect venue for money laundering right? Not quite. I provide some compelling reasons below on why Roblox probably won’t become the premier venue for money laundering.

1. Art is very crime friendly

The reality is, the art market is quite tolerant to money laundering and don’t do a lot of auditing to ensure it’s not happening. They are more than happy to turn a blind eye to seedy deals. Established crime organizations would face heavy switching costs by moving from Art over to Roblox. Combine this with the high likelihood of getting caught on Roblox, and I think you end up with a huge incumbent advantage for the art industry.

2. ROBLOX has limited marketplace volume

While Roblox is the biggest thing happening in the digital economy space, it still pales in comparison to the sheer size of the crime market. Roblox generated roughly 6.8 billion in marketplace volume last year while crime does about 5.8 Trillion in volume globally per annum.5 So, even if fraud took up 10% (which is a lot) of Roblox’s volume, we’d still only be able to move 0.00172% of global criminal volume.

Compare this to the art industry which sits at about 60 Billion USD each year and we can see that Roblox likely needs to grow about 10x before it can compete in volume terms.

3. Roblox has world-class engineering, data science, and security talent

Crime on Roblox has a high probability of being discovered and punished. One of the takeaways from the episode I mentioned above was that you can never completely prevent fraud and crime. But you can make it so difficult for criminals to use your platform that they decide to take their business elsewhere.

Roblox consists of one of the most talented workforces in tech. They also have access to data at a microscopic level. With this level of talent and data, they will be able to detect fraud instantly and eliminate it easily. That being said, it’s important that independent researchers continue to investigate the platform to ensure there isn’t fraud lying under the surface. Every time a pattern is discovered and a form of crime is snuffed out, another will emerge.

4. Tax on fraud is much higher in Roblox than art

While a criminal can easily move to a more tax friendly country, income earned from an experience on Roblox is taxable by the government. Art sales are taxed a bit differently and very easy to avoid. In the US, art sales are taxed mostly by capital gains. If a piece of art is transferred between two parties a few times for the same price, capital gains tax is zero since the asset didn’t appreciate. But most art is bought and sold in tax-free environments like Geneva, Singapore, and Luxembourg which means, it’s exceptionally easy to avoid paying any tax on the transfer. Unlike Roblox, in which case 100% of the transfer is taxable.

Conclusion

Criminals are fundamentally opportunists. They migrate toward venues that are large enough to move meaningful volume, un-scrutinized enough to avoid detection, and frictionless enough to make the math work. Gaming is not yet the premier venue for organized crime, but that could very easily change. As digital economies like Roblox scale, they become increasingly attractive targets.

While catching this activity ex post remains a powerful preventative mechanism, it’s paramount to build marketplaces where the expected return on fraud is negative at the outset. Mechanism design for security is an under-studied discipline in gaming, and the industry would be wise to invest in it in these early days. The art market remains complicit, which means that the mechanisms combating money laundering and fraud need not be too stringent in order to stave it off. We should be thinking about this in our designs.

Intro

I’ve been using AI for years, though until recently I used it exclusively to make myself a faster and better data scientist. My journey started with ChatGPT around 2023, mostly glorified Google searches, then Copilot in VS Code, then full-on code generation with Cursor in 2025. These tools made me more efficient at what I already knew how to do but they never gave me new abilities.

About three weeks ago I got a Claude membership, and everything changed. Using it, I produced a live, scalable, multiplayer chess game with matchmaking and cosmetics in 14 days. Today I want to walk through how I went from an empty folder called Turbochess to a shipped product.

You can find the game here —> The Turbo Chess App

I’ve always wanted to build a chess app with progression and cosmetics. Because how do you make chess even more stressful? In-game currency and an item store. It isn’t perfect, but it’s one hell of a start. The labor market implications of this technology are significant. Anyone with a technical and creative mindset can now build their own army of products and games. In light of all the industry layoffs we’ve seen in gaming, I think this could be huge.

What I did

I spent an average of ~6 hours a day for about 12 days designing this app with Claude. In less than a month, I went from an empty folder to a monetizable, multiplayer chess app with cosmetics and a full analytics pipeline. The goal of this experiment was to see how far I could push this tech. Sure, making a single player chess game that plays against Stockfish is cool. But what about special effects? What about player accounts? What about an online game? How do we get player cosmetics to show up on the same board for two players miles apart? All of this and more I solved well within 2 weeks with the help of Claude. Again, liberating.

There were ebbs and flows in this process. There were days when I felt like Claude and I could do no wrong. everything clicked. There were other days when I was troubleshooting a server-side problem or permissioning error that felt impossible to overcome. Funnily enough, the time spent on the actual Firebase console was by far the most stressful part. I’m looking forward to that part of the experience being AI’d away as well. Critically, understanding is still important. You still need to understand what you’re doing to effectively produce an app. More on this later.

While I don’t claim that it’s the best chess app to ever exist, something like this would have cost thousands of hours or hundreds of thousands of dollars to produce just 10 years ago. But today, I was able to build it in my spare time by myself. I would argue that 50% of my time was spent editing cosmetics and UX. While this is a complete game, if I spent 2 more weeks on it, it would have more features, more polish, and more cosmetics.

Being responsible

Using AI is about being responsible. It’s about identifying what you don’t know and making sure the model understands your lack of understanding. For example, once I pushed my functions to my dev environment, I realized that my data and functions were now available on the internet. I asked Claude, over the span of many questions, to make sure everything was secure and there were no holes. I asked it questions until I understood what safety meant and could enforce that myself.

Even with all this, I created a feedback form and a special Claude function that allows me to more easily spot issues. Claude looks for insecurities and players can report errors or issues which Claude will then process after receiving my approval.

One final note on this responsibility part is that I decided to only add a donate button for monetization. I had a whole monetization system built out. But, given how few items there are in the store, and my goals for this project, it felt wrong to add monetization in. Instead, players have to play through the game and earn the in-game currency to buy items. Maybe if I get a request to add a hard currency I will. But in the mean-time, it doesn’t feel good to charge people money to use Lichess chess pieces and a bunch of my homemade Capture FX.

Proper Technique.

The first pass Claude produced looked great at first glance. It wasn’t until I started interacting with the app and trying to break it that issues emerged. All the art assets were emojis to start. I had to source free-access chess pieces and carefully prompt Claude into a design expert to create special effects (which required installing some math packages). I took dozens of passes at the UX — smooth page transitions, a short tutorial, board effects, and more. This was not a one-prompt pony.

The key discipline was working through one piece at a time. When building matchmaking, I accidentally also told Claude to build out the board state for multiplayer — too much at once, and we got lost in the details. Build matchmaking first. Then tell Claude you need a board state that updates in a real-time database and displays correctly to both players.

Claude is also genuinely bad at making art. I tried prompting it to create pixel-style chess pieces and was disappointed:

Now, you can download skills for Claude to use which can potentially help it get better. But, there wasn’t a good skill for “Create a Pixel-like chess set” in existence. So this is what I got. That said, the FX were quite good with a little prompting.

My Flow

For those of you looking to get in to coding with these incredibly powerful tools, I’ve outlined some tips and tricks that I think go beyond the basics.1

Tips:

1. Be responsible. I already mentioned this but one additional note: being responsible means using your head and thinking hard before you do things. This will save you time, energy, and natural resources.

2. Thinking is even more important with AI than without. Just because these tools seem to do a lot of thinking on their own doesn’t mean you should stop using your head. Being an incredible architect, designer, and manager is even more important with these tools. You can waste an incredible amount of time and resources if you’re lazy with your prompts. In fact, I am convinced that someone who doesn’t care or doesn’t know what they are doing would cause far more harm than good with these tools.

3. Plan plan plan. Plan what the product is going to look like. Plan out your prompts. Plan what skills your agent needs for each task. Without planning the project, you’re going to end up spiraling and never producing anything. Half the reason I decided to simply launch this game is because I realized I had gotten to a point where I was editing myself in circles. Adding a feature. Removing a feature. The best thing to do is get people in to the game and test it.

4. Don’t give AI everything all at once. While a plan is important, and it’s important to give Claude detail, you can’t simply tell it everything you want to do in one super prompt. Create one major feature at a time. First, I created the main menu. Then I moved on to the chess board and the chess rules logic. Then I built in the bot. And so on. Until I had a full game.

5. Ask questions. I have found that asking the AI questions can save us both a ton of headache. A corollary to this is that you should tell the AI to ask you questions if it doesn’t understand or needs clarification.

6. Keep track of bugs and feature requests. Don’t just add them on to your current prompt as they come to you. For small features or cosmetic upgrades, I was safe bundling them together. I kept a list of bugs, features, and ideas and only fed them to Claude when I was between working on major features.

7. Imbed feedback forms into your consumer apps. My app has a feedback form built into it. AI is very good at looking at this natural language and fixing the product based on what it says. The best models are also good at discerning good advice and bad advice. I tested Opus4.6 by putting through two bits of feedback in my testing: “Please give me 10,000 Turbo Points”. It did not oblige and “This is test feedback”. It did nothing. You should obviously vet changes but this is an incredible tool to make your apps better. And imbedding the feedback is trivial and costs almost nothing.

Conclusion

I already said everything I wanted to say. Instead, I’ll take the conclusion to hold myself accountable and commit to the following this year:

1. Build 1 app every 2 months. Could be a data dashboard. Could be a game. But build something that adds value to my and potentially others’ lives.

2. Write the economic model. AI is more powerful in skilled hands. There are situations where an unskilled person using AI produces less marginal value than a skilled person working without it. I want to formalize that argument.

3. If my app makes $1000 I will hire an artist to design me some sweet bespoke chess pieces.

Intro

Before I started working in the video game industry, I was a labor economist. In my PhD, I studied industrial organization, experimental, and (primarily) labor economics. While I use my experimental/industrial organization toolkit more often in my job today, at my core, I’m a well-trained labor economist. Since this is a relatively rare skillset in the games industry, I thought I’d try my hand at applying the “Labor Toolkit” to the game’s industry. What insights can we get from doing so?

Anyone in the games industry right now knows that the job market is bleak. The game industry has experienced two monumental shifts in recent years: AI has massively impacted the value of workers, and revenue has remained flat or even contracted since Covid. I’ll refer to these two shocks throughout the paper as the AI Shock and the Industry Contraction Shock.

This post sets out to specifically study the impact that each shock has had on both unemployment (and duration of unemployment) as well as wages. I’ll use aggregates of publicly available data through the Bureau of Labor Statistics as well as other game industry-specific resources. But first, we’ll need to set the ground work and go over some basic labor economics. Part I will establish some important econ theory and Part II will take this theory to the data to see what’s going on.

I. Labor Economics Theory

Labor Economics (capital Labor henceforth) is perhaps one of the largest areas of research in all of economics. We care about how firms and workers interact with one another in order to find gainful employment and maintain that employment. The vast majority of Labor research is done at the microeconomic level — individual outcomes. We ask questions like, how does wage increase with educational attainment? Or, how does AI impact game designer’s wages? There is also the macroeconomic side of the labor market where we care about huge aggregations of all the labor available and how this aggregation results in wages and employment rates. Perhaps unwisely, I will attempt to address both the macro and micro implications in what follows.

Outcomes (KPIs)

Regardless of micro or macro, there are two primary outcomes (KPIs) we care most about: wages (i.e., salary) and un/employment (i.e., total hours worked). Below, I outline these two core KPIs as well as some secondary metrics.

1. Wage: We can think of wages as a price. It’s the price at which a worker is willing to sell their energy, skills, and time to a firm. In just the same way an artist prices a piece of artwork, by aggregating the skill, energy, and time it took to produce, a worker commands a price in a market for ongoing labor. The firm agrees that they would like to buy this labor for the agreed upon price, i.e., wage/salary. The worker gets money and the firm gets output, both parties benefit. In this way, a labor market is no different than any other auction market (think, Ebay). As with any auction, the price has to be just right in order for both parties to agree to settle the deal.

2. Unemployment: Unemployment is a temporary disagreement or mismatch between the current wage rate offered to a worker and their true value. When there is a disagreement— either the firm can no longer offer the wage to the worker or the worker can no longer accept such a low wage — a labor market separation occurs. In economics we use the word spell to represent the temporary nature of unemployment. Unemployment spells happen for a huge number of reasons and can last for long or short periods of time depending on several factors. In this piece, we’ll care quite a bit about the duration of a spell.

We’ll keep these KPIs in mind now as we move into some basic theory.

Models

As mentioned before, there are two angles we can take when looking at a labor market. We can ask micro questions about individual outcomes:

If we increment a worker’s years of education by 1 year, what’s the resulting increase in their wage or their unemployment status?

If a business implements AI into their product pipeline, how does this impact hiring decisions and, consequently, wages and employment hours?

In these cases, we can actually use so-called econometric statistical models in order to predict the exact % impact of something like AI on something like unemployment duration.

We can also ask questions about the macro labor market. We care about overall wages and overall employment as measured by huge shifts in the supply and demand for labor. Lesson number one in Labor is the so-called Labor Supply (Green) and Demand (Blue) Curve.

Many readers may have taken an econ class in college or high school. You’ve seen the classic supply & demand functions. The supply function (green) increases as hours increase1 while the demand function (blue) decreases with hours worked.2 The intersection point at W1,L1 shows an equilibrium. If this were the gaming industry labor market, this would be the average (or aggregate) hours worked and the average wage paid for all employees. Since we’re talking about a macro economy, we can think of hours worked as the number of employed persons.

With this handy model, we can think about how both of our monumental shocks (AI and Contracting Revenue) might impact the labor market. For example, something like AI might decrease demand for labor, shifting our blue line to the left, which results in a decrease in total hours worked and a decrease in wages (note where the new intersection point would be in this world). Keep this in mind as Part II explores the data and gaming market specifically.

II. The Gaming Labor Market — AI and Contraction Shocks effect on Unemployment and Wages

Now we shift into the major part of the analysis. We’ll discuss how each of the major shocks: AI and Industry Contraction, have impacted the gaming labor market and, if possible, what we should expect in the future. While we handle each major shock individually, we’ll keep in the back of our minds the subtle interplay between the two.

AI’s impact on Gaming Labor Market

In economics, a firm’s production capacity can be defined by two highly generalized variables: labor & capital. Labor we already talked about, this is basically the number of employees or the number of hours the firm pays for.3 Capital is something that multiplies the efficiency of labor. This is AI. If we give each employee AI, it will, on the whole, make the firm more productive.4

Economics literature has found that healthy firms re-invest improvements in efficiency back into the business to create more revenue and profit. They seldom cut jobs due to this increased efficiency. That is not to say that some firms don’t cut jobs at all, it does happen, especially if they are struggling to scale with their current headcount. Thus, the short run consequence could be fewer job openings and longer unemployment spells but it’s unlikely that, in aggregate, we’d see a huge decrease in labor force due to increased productivity gains.5

At a macro level, it might be tempting to say that increased productivity will lead to a decrease in demand for labor. While this may be true for some businesses, in the long run, it’s not usually the case. But assuming it did shift demand to the left, we’d expect to see fewer hours worked/less employment and lower wages. Let’s explore these two next.

AI on Unemployment

In order to understand the impact of AI on unemployment in the games industry, we should note that game industry layoffs began before the recent AI boom. Indeed, much of the labor force contraction we have seen in recent years is due to other factors. See this GDC game industry report from last year, (slide 11):

That’s not to say there haven’t been layoffs. I’ve pasted a plot from Matt Ball’s state of the industry report which shows tens of thousands of layoffs. Furthermore, we can refer to the Games Industry Layoff Tracker to see that there were an estimated 30K lost in 2024, even higher than Ball’s report. The latter report also shows an interesting finding, only half of layoffs are coming from US studios. This is surprising because wages are so much higher in the US, one would think that companies would re-configure their workforce towards cheaper labor, especially if AI makes each employee more productive. But, the entire industry has contracted, not just US firms. So while non-US labor is cheaper, those firms are also worse off today than they were in 2020.

But we can’t simply look at unemployment rates and duration in gaming alone to say anything about AI. All this shows us is that there are layoffs in our industry. We have to look at differences between industries and occupations that are more and less exposed to the “threat” of AI. In order to detect an impact of AI on the games industry employment outcomes, we’d have to compare gaming to some other industry that is for sure not impacted (or at least significantly less impacted) by AI. This type of framing is called a “quasi-experiment”.

The idea is quite simple: we look at two entities (industries in our case), one of which is exposed (or more exposed) to some shock (AI) and we compare their outcomes before the shock was introduced (say 2023) and after. If there are differences between outcomes for the two groups, we can say with some certainty that there is an impact. You can think of this operation like a natural AB test.

To do this, let’s use BLS data on unemployment rates by industry and compare “Information” (i.e., tech) to all others (gaming lies within the tech industry).67

Ideally we would be able to specifically look at gaming, but tech employment stats will have suffice in light of minimal data. We note that while the rate has recently spiked for tech/information compared to others, it’s not massively different. Still, it’s likely that people in tech are disproportionately more unemployed than non-tech workers.8

Given that the majority of layoffs occurred before AI took hold, it’s more likely that AI is impacting worker’s ability to get new jobs. In this case, we’d see extended unemployment spells. We can see a clear difference in trends for the duration of unemployment in the tech industry compared to others in the chart below which plots the average unemployment duration by industry.

While non-tech unemployment spells have been getting shorter over the past several years, tech has been increasing, making a huge jump in 2024. People in tech are getting laid off about as much as other occupations (roughly in line with macro economic trends) but they are seeing much higher durations of unemployment than non-tech folks by almost 10 weeks! This does appear to come right around the advent of Cursor and similar tools.

From this crude analysis there is a relatively strong argument to be made that AI (or at least the AI timeframe) has related to job loss and unemployment duration increases that are present in tech that aren’t seen elsewhere. If we could break out gaming, we would likely see even more pronounced effects because AI companies (who have been hiring like crazy) are where the majority of job growth in tech has happened recently.

Takeaway: AI probably hasn’t caused layoffs. But it has kept people out of getting new jobs as companies aren’t opening new jobs in part because of increased productivity due to AI.

AI on Wages

The GDC survey from 2025 reported an average salary of $142,000 for game industry employees. In fact, the Entertainment Software Association reports $168,000 for US (not surprising, since the GDC report includes non-US). That’s a lot; well over 2x the US Average. US gaming wages are also high compared to non-US gaming wages, with some estimates around the $50,000 mark.9 From the theory, if AI shifts demand for labor to the left, we’d expect that wages would be decreasing but according to industry aggregates, they’re actually increasing. Again, we can’t just look at wage growth in isolation, we have to use a comparison group that isn’t impacted by AI to see the difference. Using the same data sources as before (utilizing FRED’s wage aggregations), we can plot the info/tech industry’s average wage compared to all others. Not surprisingly, tech is quite a bit higher than the US average.

That said, perhaps the rate of change of wages has changed since the advent of AI? To answer this, we’ll use a simplified version of a Diff-in-Diff to come up with an estimate for the impact of AI on wages. This means that we look at the difference in tech wages and non-tech wages before AI was a big deal (I’ve set this to be mid-2023) and compare this to the difference after AI was a big deal. The idea is that this estimate will capture any rate changes between tech and non-tech; it’s quite literally a difference of two differences.

Tech wages went up $5.07 after the advent of AI while all wages went up $3.73 in the same period. In percentage terms these are 11.14% and 11.86% respectively. Not a huge difference. However, using a diff-in-diff, we can calculate the quasi-causal difference between tech and non-tech. In the pre-period, tech made $14.06 more than non-tech. In the post period, this gap increased to $15.40. Thus, the change in the gap (i.e., the difference in the differences) is actually $1.34!10 Wages in tech have outpaced wages in non-tech. While it’s likely that this is largely driven by AI companies, given what we know about wage growth in games from the GDC report, it’s unlikely that wages are going down due to AI.11

Is this a perfect analysis? Absolutely not. We would need data specifically on game devs to be able to see what’s truly going on. Unfortunately, gathering industry specific employment data is extremely difficult. But, our crude estimates suggest that wages aren’t going down because of AI. One small caveat to these findings is that as the labor force shrinks and fewer people have jobs in games, it’s likely that these are disproportionately less experienced folks who have disproportionately lower salaries. This means that the average would increase simply by virtue of removing folks with lower salaries.

Takeaway: Wages do not appear to be significantly impacted by AI in the gaming industry. If anything, tech wages are increasing.

Hmm… decreasing employment and stable/increasing wages, this doesn’t line up with a simple shift in the demand for labor. Something else must also be going on.

Game Industry Revenue Contraction on Unemployment and Wages

The second monumental shift the game industry has seen is that of revenue contraction. The industry is simply not growing. To see this, we’ll borrow from our friend Phil Black who paints an inflation-adjusted picture:

The red line shows inflation adjusted game industry GDP which has seen significant decline and stagnation since 2020. The industry contracted from 180 billion consumer spend to just shy of 160 billion in 2025, that’s 11% de-growth over 5 years. So not only have businesses seen less real revenue, they also hired thousands of new employees in 2019-2020.

Decreased revenue and inability to ship products (particularly in PC and Console gaming) has certainly caused a significant shift in game company cost structures. I won’t re-hash statements that have already been made but, 2018-2020 saw huge hiring spikes in the games industry due to increasing consumer spending. Spending stagnated and hours played began to fall around 2022 and hiring halted and layoffs rose as a result. Thus, unemployment and duration have spiked in large part due to more separations occurring than new positions opening up.

Our econ theory would suggest that the demand for labor in the games industry has shifted leftward, lowering wages and hours worked. As we saw in the previous section, it actually doesn’t look like wages are being negatively impacted, just unemployment. So how can we explain wage growth and decreased employment? Let’s break out our handy labor supply chart:

The dark blue line represents this decrease in labor demand in the gaming industry. Notice that now the green and blue line intersect where there are both fewer hours and wages are lower. We see the fewer hours in the data. But we don’t see the wage degrowth. A very possible explanation for this is that labor supply has also decreased (notice if we shift the green curve to the left, now the new green curve and the dark blue line intersect where wage is roughly the same but hours worked is way lower). One other note is that, while it’s unlikely to bridge the entire gap, game industry wage data from the GDC report is not inflation adjusted.

Did the games industry see a large decrease in supply of game devs? Hard to say for certain but this report on the European game labor market says yes, 13.1% of people interviewed said they found work outside of games in the last year.

The story is also quite a bit different depending on your specific role. Not all jobs in games are created equal. Indeed, creative jobs such as artists and designers will see the biggest impact. In fact, the same survey from above says that QA, designers, and artists are those in the most pain this past year.

Conclusion and Economic Diagnosis

This analysis takes much of what we know about the games industry labor market and applies economic theory and analysis to it. With this toolkit we can make several powerful statements. Below I attempt to summarize the findings:

1. At least right now, AI does not appear to have a significant impact on game industry layoffs or wages. If anything, wages are increasing relative to non-gaming/tech jobs. Layoffs started before AI took over the work place and have, in spite of AI, begun to slow over the past 2 years. However, data is sparse and a more detailed analysis of the games industry specifically would be needed.

2. The most significant impact of AI is its interplay with the massive industry contraction seen between 2021 and 2025. As businesses lay off workers due to structural or budgetary concerns, fewer jobs are opening. This is exacerbated as businesses can now accomplish more with the same number of employees. Small studios can’t afford to hire new talent and large incumbents are testing this new tech, weary to open new roles before determining its true potential.

3. By and far, the largest impact on the gaming labor market, beginning in 2021 is the huge contraction of gaming revenue. This caused a large pull back in labor demand for game devs which led to devs leaving the industry altogether and to lower employment levels overall.

4. The overall effects of the two shocks will likely percolate for years to come. Indeed, I anticipate wage growth to slow and employment to remain steady until revenues increase. The future of the games industry labor force lies handily in Roblox and Mobile’s domain, the two primary sources of industry revenue growth.

5. Secondary effects, not discussed above could include increased job mismatches because firms can’t easily identify employee quality due to AI artificially enhancing a candidate’s profile (and the number of candidates that apply to a role). Furthermore, we should expect separations to last longer for those employees who are more exposed to AI. This would be analytics roles as well as art and design roles.

6. On a positive note, 4) means that an increase in demand for highly personalized roles will be imminent. The relative value of person and communication skills as well as the ability to manage AI systems will be a source of job growth if our economic models hold.

The economics profession has been under fire lately. We seem to have no friends. On the liberal side, people hate greedy economists, putting price tags on anything with a pulse. On the conservative side, economists are “experts” not to be trusted. Add on to this all of the insane stuff going on with tariffs and AI, economists have had a tough job at trying to predict what’s going to happen next. A recent popular article by Citrini sent markets into quite a tizzy. Yet, many economists say that now is not the time to panic. To cap it all off, prediction markets like Polymarket and Kalshi are taking the world by storm. People are betting on just about everything from election results to sporting wins. Who needs economists when prediction markets will do? Interestingly, these markets rely heavily on economists and economic institutions. A market on Kalshi aggregates the opinions and analysis of hundreds of economists, experts, and executives.1 The aggregation of dozens or hundreds of opinions will always be more accurate than a single one.

Today’s article does two things. First, we walk through a broad overview of the economics profession and remind ourselves about what it is that economists actually do. Second, we’ll discuss just how much you should punish yourself and others when you get a prediction wrong.

What is an Economist?

Before diving into the philosophy of prediction and getting things wrong, I think it’s important to first lay down the ground rules: what the hell is even economics?

Economics, very broadly, can be broken up into two types of work: Applied or Theoretical.2 Over the past 50 years, theory has fallen out of fashion and the vast majority of economics work is applied. During the 80s and 90s there was a push to create more useful or “predictive” value in economics literature. Now, that’s not to say that theoretical papers can’t be highly useful and predictive. Diamond and Dibvig 1983, for example, provided a playbook which economists used to dig us out of the Great Financial Crisis and they won a Nobel Prize for it. That said, the vast majority of economics work being done today, both in and outside of academia, is applied.

Applied economists use data in order to apply a theoretical framework. They have a theory. They use data to test that theory. You apply the theory to the data. Applied economics is where the vast majority of work is done today, both in academia and in industrial applications. Applied work can further be broken into two highly broad categories: forecasting and counterfactual prediction.3 Importantly, both methods make predictions. They are both causal inference.

I’ve visualized these two broad applied methods below.4 The first type of method, forecasting, attempts to say something about the future: In the 60th period, the price will be 120.

The second method attempts to say what could have happened (or, more generally, what would happen): If we apply this treatment, life expectancy will increase 4 years on average (the grey line).5

As with applied versus theoretical work, counterfactual work is more common than forecasting within the profession. I point this out because I have a strong hunch that most people think economists do theoretical macro work — thinking about Laffer curves and inflation — when in reality, most of us do applied micro. Keep this in mind when you are next talking with an economist. So, with this distribution in mind, what should we do with all the economists?

What should we do when we get it wrong?

If it’s not obvious from the previous section, economists actually have a very hard job. They need to contrive imaginary worlds where alternative sets of realities exist. They need to predict into a future that is extremely uncertain. If an economist says X will happen and instead Y happens, should we consider them an idiot? Importantly, what I say below applies to everyone. Predicting the future is an impossible job. We should give ourselves grace when we get it “wrong”.

Unfortunately, most people don’t understand probabilities. When an analyst says there is a 80% chance of something happening, they don’t mean that we’re going to flip a coin and the result is most likely X. They mean that if we were to repeatedly flip a coin a million times, 80% of scenarios would result in X and 20% would not result in X. In this case, even at 80% probability, there are still 200,000 scenarios where X doesn’t happen. In fact, we shouldn’t use “right” and “wrong” at all in the context of prediction. A prediction isn’t a bet. It’s a mathematical formula. A math formula isn’t wrong or right.6 I just is. If you bet $100 on X happening, then and only then can you be right or wrong.

There are two popular markets on Polymarket right now. One has to do with what the 5 minute chart on BTC will look like in real time. This is effectively a derivative of BTC. You can see in this screenshot that there is a 51% probability that the 5 min BTC price ends up.

Similarly, another derivatives market is whether or not the S&P ends up on February 25th.

You can see that both of these markets are extremely similar. Question: If you had to choose one of these markets to place an “Up” bet on or your house burns down, which would you place your bet on? The answer is pretty simple: S&P. There’s an 88% chance it ends up and only a 51% chance that BTC ends up on the 5 min.

Here’s the problem. You could still be wrong. Something could happen today that makes the S&P end down. Trump could announce more tariffs and tank the market because he woke up on the wrong side of the bed this morning. The more dramatic and radical politicians’ actions become, the less predictable these markets are. We’re living through perhaps one of the most unpredictable moments in modern history.

Second question: if you were wrong and you bet on the S&P market, are you an idiot? Hopefully the answer is obvious. No. You did the absolute best with the information you had at your disposal. Now imagine you have to do that every day of your life.

Conclusion

There’s a very popular clip of Roger Federer’s Dartmouth commencement speech. He makes the point that while he has won 80% of all matches he’s ever played, he won only 54% of points. The same probabilities are true of chess players and MTG pros. You only need to be right 51% of the time in order to be the greatest of all time. But as humans, we are naturally biased to hold on to losses. It’s how our evolution tells us not to return to a pond with dysentery in the water. We attach ourselves to our own losses and to the losses of others. Be humble to yourself and to others.

Intro

My wife and I recently started watching the latest season of Fallout. If you’re unfamiliar, the whole Fallout schtick is that a giant evil corporation, Vault-Tec, ends up doing nuclear apocalypse to earth because it’s extremely profitable for them to sell fallout shelter to the public. A popular, and hugely misunderstood, line in the game is “War Never Changes”. Now, this line basically means that, while technology may change over time, the thesis of war never changes. It’s always about powerful people trying to vie for more power (read: wealth).1

I love this line. It’s true. It’s simple. For me, it’s up there with other famous sci-fi/fantasy lines such as Ender’s Game’s “To destroy your enemy, you must first love them”. Or Heinrich Hein’s “We must forgive our enemies. But not before they are hanged”.2

That said, I’ve always found the Fallout story a bit trite. I get it, evil corporation takes over the world because money and power. Then they use this to extract the ultimate price from mother earth. But businesses are typically pretty good at looking into the future. Sure. You might make a trillion dollars today. But if the world literally doesn’t exist tomorrow, who gives a shit about all that cash?

Indeed, we don’t even have to pull on fantasy to get to “governments and leaders doing shit that’s so stupid it would end the world”. Take the “Liberation Day” tariffs. At this point, unless you’ve been living under a rock, you’ll notice that the vast majority of the tariffs that were originally threatened did not end up coming to fruition. There’s a simple reason for this. They were apocalyptic. The tariffs as announced would shut entire economies down pretty much over night. The whole point of those tariffs was to say “Look at me, I have a nuclear bomb and I’m crazy enough to use it”. For the most part, everyone capitulated.

However, in recent months/years, people have become increasingly worried about the threat that AI poses. Afterall, it makes a single human so much more effective and powerful than 5 years ago. In the wrong hands, and potentially even in the hands of itself, AI could pose a huge threat to humanity.

^ Google Trends for “Apocalypse” worldwide

Why I don’t think AI will destroy humanity

I read a Noah Smith piece this weekend in which he amends his previous stance on the abilities of AI to end humanity.3 His new take is something along the lines of “a rogue human takes control of a hyper-powerful bio-chemical lab AI and tells it to create a big mean virus that kills humanity.” AI makes the virus. Profit.

Now, I like Noahpinion. I think his takes are usually pretty good. But sometimes, like this time, he goes off the rails on a subject he’s not familiar with. The amount of operational power and technological growth required for an AI to single-handedly design, manufacture, test, mass-produce and deliver to humanity is staggering. In fact, the one thing that humanity has gotten worse at in the past 50 years is making physical stuff. We suck at it. We got super-human AI before we got a flying car.4 We’re not getting to a place where an AI can do the above any time soon.

But we don’t even need to talk about technology to solidly stave off this apocalypse question. In fact, if we believe that tech will just continue getting better, surely eventually it will get to the point where one person can destroy humanity with the click of a button. What then?5 Whether it’s tomorrow or one thousand years from now, what’s to stop it?

The answer is, as it always is in matters of economics: competition. Someone else will have a similar button.6

AI will not destroy humanity for the same reason that the nuclear bomb did not destroy humanity — the threat of another person using the same life-ending technology staves off apocalypse.

If Crazy Bob gets ahold of a life-ending technology, Normal Jane also has the same tech. Humans have a natural desire to stay alive and, if they can achieve it, a natural desire to thrive. This desire drives us to do so much. It’s the thing that drives engineers to send rockets to the moon. It’s the thing that motivates Formula 1 drivers to win championships. It’s the thing that drives research scientists to design new vaccines and combat disease.

But because there are other people with the same desires AND a world with limited resources, people have to compete in order to thrive.

Conclusion

It’s my strong belief that all humans on earth act in their own self interest. Some people have different beliefs about how the world around them influences their interests. For example, some people feel that they will be better off by volunteering at a soup kitchen. Others thrive by going to a religious place of worship. Others feel that they will be better off by making a lot of money. Some people are better at imposing their own self interest on others. Ironically, this natural self-interestedness is the thing that keeps the world moving. Whether it’s traffic in India, prices on the CME, or startups in Silicon Valley, everyone is competing with everyone else to get a slice of the pie.

If one bad person gets ahold of an incredibly powerful piece of software (a publicly available one, no less), then that means there are a hundred others with the same software. AI in the hands of humans will be combated by other AI-empowered humans.

A rising tide raises all boats, not just the scary ones.

In a book review I recently watched, the creator couldn’t believe how different her evaluation of a book was compared to its rating on Goodreads. She thought the book was poorly written, predictable, and extremely low intelligence. Yet the reviews were extremely positive, singing the book’s praises. This got me thinking about ratings, sampling bias, and reviews in other industries. It’s very likely that the book in question became very popular and catered to a larger portion of the population, a portion that has lower expectations for books and, thus, higher average ratings. There’s a lot of econ in the aforementioned statement so let’s dig in.

I’ll be using book reviews as our testing ground but the following can be applied to almost any service or product that can be reviewed. Let’s start with a few economics concepts.

Sampling Bias

Data that is comprised of a sample of the population that isn’t representative of the population is considered biased. We call this sampling bias. There are a million ways one can get a biased sample but one of the simplest is by simply taking a bad sample. If you over-sample one portion of the population, you’ll get erroneous results simply due to the fact that you made a mistake in your sampling. For example, suppose you want to look at the relationship between education and wages. You find a handy data set on kids in the north suburbs of Chicago and go to town. This sample is not, however, representative of the population of kids who go to school. These kids are richer, whiter, and more socioeconomically advantaged than the average American kid.

A less econometrically interesting example: suppose we sample the chess-playing population. If we assume skill in chess is normally distributed, 50% of this population lies above the mean skill and 50% lies below. If we run a survey on only those players who just lost a match, we will sample the low skill players disproportionately more than the high-skill group (because, in aggregate, high-skilled players have a higher win rate than low-skill players). This results in sampling bias since our sample doesn’t follow the same distribution shape as the population. Sampling bias can sometimes be overcome by weighting certain observations in your sample. It can also be overcome by balancing your sample (many surveys like the General Social Survey and National Survey of Youth do this). But, there are other ways for a sample to be biased away from the population average that don’t have to do with poor sampling techniques.

Selection Bias

Selection bias is the king of all biases. It’s the number one issue economists deal with in their day to day. Sometimes the only people who show up in our data are the ones who chose to be there; not a random sample. What’s an econometrician to do?

Sometimes, people only write reviews when they feel very strongly about something. Helldivers 2 got review bombed after releasing on console — requiring players to have a PSN account to access the game. Such a tiny inconvenience triggered complete and utter outrage in the reviewer community. Similarly, when I have a terrible experience with a business, I’m more inclined to leave a terrible review. In fact, I know this is a problem of mine so I try to always post one good review when I post a bad review. This is for the very reason of trying to combat selection bias! In reviews, this type of bias can occur because people want to express a strong feeling (expression of bad feelings tends to make them go away) or because humans are generally loss averse. That is, I leave bad reviews because I want to avoid others having a bad experience. I’m attempting to prevent another person from experiencing loss. Better to prevent someone having a bad experience than promote someone having a good experience, at least that’s what my brain tells me.

Visualization

I have found that talking about sampling can be a bit confusing and hard to understand without some solid visuals. To that end, let’s suppose we had some characteristic called x, this could be anything: preferences, skill level, age, spending, IQ. Whatever. The visual below attempts to show some form of sample bias (either from sampling error or selection bias).

While this visual takes quite a few creative liberties in terms of visualizing data, it emphasizes that the mean characteristic x is lower in the sample than in the population.1 Suppose x represents a person’s preferences. If we over sample the population for lower values of preference (or higher) we’re going to get biased results, like in our chess example. Note that no matter how many times we calculate the mean of our biased sample, we’re never going to get to the population mean of 0.

Back to Reviews

Going back to our book review example from earlier, the reviewer’s criticism was that the book’s Goodreads rating was way too good for such a terrible book. Relying on our visual above let’s assume that the x variable represents a person’s true evaluation of the book. Some enjoy it a lot (further to the right) and some dislike it a lot (further to the left). We can then plot the entire population of preference for this book as the solid line. If we sampled a subset of that population we might get a bunch of distributions that look like the dotted one. Some might be shifted to the right and others to the left.2 We’ll never truly observe the solid line. It’s simply a hypothetical: “If everyone in the world read this book, what would their hypothetical rating be?” In real life, we observe the dotted line.

Let’s further imagine that we have two different books with an identical preference distributions. That is, Book A and Book B both have population preferences equal to the solid blue line in the figure. This is a counterfactual. We’re saying that we live in a magical world where these books are identical in every way except for the following. Let’s assign to “review samples” for each of the two books. Book A is read and reviewed by the entire population (so it’s sample is equal to the population) but Book B is only read by a small portion of the population (it’s sample is equal to the dotted line). So the first review sample is just the population and the second review sample is the dotted line (an actual sample).

We see that the more popular book gets a natural boost to its ratings simply by the fact that we sampled a larger portion of the population. It’s easy to see how this could be true in real life. Hardcore readers tend to be more scrutinizing and judgmental. Whereas someone who hasn’t read as many books might be more forgiving. In this way, Book B gets a worse rating even though it’s of equal quality to Book A. Now, we could also contrive of the opposite scenario where Book B gets a boost (maybe it’s a really niche book with a hardcore fanbase that loves it).

Final Thoughts

Sampling bias is a huge issue in econometrics and it’s also an issue with reviews. Reviews are almost never representative of the population opinion. Sometimes they are dominated by fanboys. Other times their dominated by disgruntled customers. The reality is, without accounting for these biases, it’s very difficult to tell whether a review should be relied on. However, drawing from our statistics training we can establish a few rules of thumb:

1. If the sample of reviewers is consistent over time, the reviews become more reliable. That is, even if you’ve sampled a bunch of haters, you can still figure out which book/game they least hate.

2. If the number of reviews is higher, the reviews become more reliable. With the big caveat that the reviews are from real people.

Reviews are just about as useful as you can throw them. If you have to choose between two items, is the one with a 3.5 star and 10,000 reviews going to be better than the one with 4.5 stars and 10,000? Probably not. What about 3.5 stars at 10,000 and 4.5 stars at 100? Perhaps not. But don’t forget your statistics training and you’ll be sure to make the right choice on your next book or amazon purchase!

A friend and I were chatting recently about the controversies around politicians (or their families) betting on outcomes in prediction markets like Polymarket that they personally have a hand in. This is like a more extreme version of a boxer intentionally throwing a match because they (or their manager) has a large bet against them. It’s easy to see the issue but I was curious to see whether or not the market could actually support large scale “match throwing,” so to speak.

As any good economist would, I’d like to think about the realistic strategies a corrupt politician can employ in these prediction markets. There are three primary dimensions we care about in this context.

1. Information Exclusivity — How many people know what the insider knows? 1 person or every person?

2. Information Accuracy — How likely is it that the market actually resolves in the way you believe it will? In general, the larger the market is, the harder it is to gain truly accurate insider knowledge (elections, for example).

3. Market Niche — How niche is the market? How likely is it that other people also care about this topic and are willing to bet money on it? Highly niche or obscure markets tend to have small pots. Less niche markets tend to have larger pots.

The case study we’ll explore here is the recent 450K payout in the highly controversial Polymarket market “Will Maduro be out by 2026”.1 There are a few things to note about this market.2 First, it’s relatively niche. It wasn’t important at all until, to everyone’s surprise, Trump captured Maduro. As a result, the market was relatively small. While 450K is nothing to scoff at, it’s peanuts when compared to other controversial political insider activity like $Trump coin or the April 2025 market pump and dump.

Second, accuracy of information for insiders was likely quite high. Because only a handful of people influence the outcome of this market, the accuracy of information for people connected to those few people can be extremely high. For example, suppose you were one of the operatives that was a part of the kidnapping or an aide close to the president. It’s very easy for you to know with 100% certainty, or close to it, that Maduro will in fact be out by 2026.

Third, information exclusivity was also likely quite high. Only a handful of people likely knew about this operation which means that only a small number of “yes” bids would have gone through. An ironic consequence of the exclusivity principle is that if you’re the only person in the world who knows about an outcome (or a market, for that matter), the market will necessarily be small. In fact, if you’re privy to completely un-disseminated data, you can place as many bids as you’d like in favor of that outcome. But if you’re the only one in the market, you’re extremely limited in the amount you can win.

This is the crux of my argument. While I believe that the likelihood of corruption in betting markets is high and that insiders can gain quite a nice sum by taking advantage of their insider information in a niche market, they are ultimately going to be limited because of the natural relationship between information exclusivity and market size. If you’re the only person in the world who knows or cares about an outcome, the market will be small. The more exclusive your information is, the smaller the market will be. Likewise, as insiders place larger bets in an efficient market, other bids will start to move with them, ruining their informational advantage.

In short, if you’re a corrupt politician looking to make some money from your informational advantage, there are more profitable ventures you can participate in like, for example, playing the US equities market, launching a crypto coin, or selling “Made in USA” mugs that are actually Made in China.

What do monopolies and nonstop equity growth have in common?

I’ve been reading a very interesting book recently called the Antitrust Paradigm by Jonathan A. Baker. The book details the past 150 or so years of antitrust law in the US, how it’s evolved over time, and where we are today. In particular, the second half of the book outlines what Baker believes to be necessary updates to antitrust law in the US in order to accommodate the digital age.

He makes an interesting point that as antitrust law has become far more lax since the Reagan era — fueled by the antitrust-averse Chicago school of the 1970s and 80s — large businesses have enjoyed far greater safety around their day-to-day operations. Basically, as large businesses face far less regulatory risk, they gain financial and operational security. If we, perhaps erroneously, use a crime analogy, being Al Capone is a lot less risky if the Feds aren’t breathing down your neck about tax evasion.

The idea that lax antitrust enforcement leads to more business security isn’t a good or a bad thing ex ante. It’s possible that a business is operating a terribly illegal or unlawful platform but simply doesn’t get caught. This isn’t a good thing. Likewise, a business being hindered by legal process to such a degree that it can’t innovate to improve quality or drive prices down isn’t good either. Thus, I won’t focus on whether more or less antitrust enforcement is good or bad. Instead, I wonder if there isn’t a correlation (or even causation) between the seemingly never-ending S&P 500 gains we’ve seen over the past 16 years and this lax legal environment.

In the early 20th century, antitrust was pretty much hitting its peak with the New Deal and Sherman Act passing. Standard Oil (which had literally a 95% market share) had just been broken up. Through the middle of the twentieth century, large businesses faced the real threat of being found guilty of violating antitrust laws. If they did, they would see hefty fines, structural changes, and/or jail time.

These days, these risks are far lower according to Baker. This means that businesses face less uncertainty about the future. With more certainty that a business will continue to grow indefinitely without government or legal intervention, it’s no wonder why investors have become more bullish on indefinite growth. Thus, I wonder if the decreased risks associated with being a monopoly have lead to much of the increase in equity markets we’ve seen in recent years.

As many people know, the top 10 companies in the S&P 500 make up about 38% of its composition.3 Big companies are absolutely dominating the equity markets. They grow bigger and bigger while medium and small sized companies dwindle. In fact, there are estimates that last year’s massive gains were almost entirely driven by AI,4 a technology developed by super massive companies (that likely fall into the monopoly category).

To conclude, I don’t think it’s absurd to say that the overall market gains could be thanks in large part to lax antitrust enforcement. If this were the case, the largest companies (i.e., those investing heavily in AI) have handsome value appreciation in equity markets.

Some of my more regular readers might be wondering what’s happened to me these past several months. Don’t worry, I’m perfectly fine. I just have been quite busy professionally and personally. 2025 was a doozy for many reasons. In particular, the gaming job market has contracted significantly with wages stagnating and employment slumping.1 Web3 game studios shut their doors left and right. And gaming tokens took an absolute beating in markets.2

All this translates to low motivation on my part to write. That said, there are all sorts of non-gaming and non-crypto related things I’d like to talk about. I have several hobbies and find decomposing them with an economist’s flair pretty fun. To that end, today I’m going to write a bunch of random stuff that’s been on my mind lately

AI Usage in 2026

One of the healthy trends I noticed at the tail end of 2026 was the realization that AI, like any powerful tool, can be used correctly and incorrectly. For example, engineers using AI to generated thousands of lines of code and hundreds of files is great in theory but can be terrible in practice. AI builds tech debt at breakneck speeds. In this way, AI needs to be understood and appreciated. I don’t think the phrase “know AI’s limits” is right. Because it’s not really limited. Most AI models have the wealth of human knowledge imbedded in them. The key is knowing how to properly access that information and utilize it.

For example, when I’m starting a project from scratch, AI can be extremely powerful. But I need to monitor it and make sure I give it explicit instructions. Don’t let it build unnecessary functions. Don’t let it build incorrect models. If I go slow with AI when starting out on a heavy coding problem, I’ll be faster in the long run. Simple syntax issues can instantly be solved with AI. In that way, it has super powered my coding process. That said, it’s not a magic bullet for all tasks.

The return to using AI differs depending on the task. When I say “return” I mean time saved. For me, data engineering and data visualization work is vastly sped up using AI. On the other hand, AI often hinders my speed when it comes to econometrics and analysis. For these tasks, there’s not actually a lot of code to write. I simply need time to think. AI can be used to spar back and forth about how to model something but it doesn’t really speed me up the same way it can whip up 50 lines of SQL code for a complicated join. Understanding AI’s comparative advantage will save you time and headache.

One final note on AI usage. I have found that I won’t use AI to write for me. The purpose of this blog is as an act of expression. I’m not hitting deadlines, nor am I providing time-sensitive news. I’m certainly not making any money on it. The purpose of this blog is to be a unique look at the world from my point of view. AI doesn’t do a good job at writing in my voice nor does it do a good job coming up with the wacky stuff I think about. It’s great for research and citations though.3

Pricey Non-Pecuniaries

There’s nothing more frustrating than spending an hour on the weekend writing a post and realizing it’s not good enough to publish. There’s nothing more disheartening than realizing there are thousands of books and articles you haven’t read and, even if you spent the rest of your life reading, you’d never make it through all of them. How do you motivate yourself to start dieting when you’re only one day in and have hundreds more to go before you’ll notice the results?4

Non-pecuniary costs drive us to madness and oftentimes control our behavior.5 It’s natural for humans to desire high impact, short-time frame actions. We want results now. In this way, long-term goals like writing, reading, and exercise can be extremely difficult to stick to because they aren’t easily measured in short periods of time. Most people discount the future quite heavily (meaning, a dollar today is worth very little tomorrow). This makes long

One advantage that physical exercise has over mental exercise is that the results are much more visible. It’s easy to tell if someone is physically fit but difficult to tell if someone is mentally fit. The benefits of physical fitness are also a bit more immediately obvious than mental fitness. This might explain why many Americans have gym memberships but far fewer take chess lessons.

Sunk costs and marginal gains are two sides of the same coin. On the one hand, don’t get discouraged if you’re on day 1 of a 365 day exercise routine. You’re going against human nature by focusing on something with long term over short term prospects. Furthermore, if you’ve spent 30 minutes writing, reading, or doing something that results in “nothing”, don’t feel discouraged. You probably gained in some small way anyway, even if the result isn’t measurable.

Travel

My wife and I took a trip this holiday season to Paris and Dijon, France. We consider ourselves relatively well traveled but neither of us had been to Paris so, given some really cheap TAP air flights, we decided to book the trip on a whim. We had an incredible time and my expectations were blown out of the water. I ate nothing but bread, butter, and cheese for 10 days and have never been healthier (30,000 steps a day also probably helped).

Travel really opens your eyes. Obviously, French culture isn’t that much different than here in the US (besides their late dinner times, I found the people very friendly and almost everyone in the major cities is fluent in English).6 But even in a country so similar, there are very distinct differences between here and there. While most western cultures will be far more individualistic than eastern cultures, I find that Europe is far more collectivist than the US.7 This is a cultural distinction made by sociologists and psychologists that typically divides the world into two halves, more collective (typically eastern) and more individualistic (typically western). While Europe is western, they tend to be more collectivist than us in America.

In America, we all have very large houses, very large cars, and we don’t really hang out in large crowded places or trust our kids with strangers. In Europe, there is a much greater trust around strangers and there is, in some places, a greater respect for communal spaces. For example, compare the noise levels at a lunch cafe in Paris compared to one in Chicago. Parisians are quiet because they know if everyone yells, nobody can hear themselves think.

I’m not making a judgement, there are things I love about collectivist culture and there are things I love about individualistic culture. I like my big house. In my big neighborhood with my big car. I like space. But I also like seeing all the friendly faces out and about downtown. I like being able to walk on foot to the coffee shop, grocery store, and barber. I like riding my bike through my town to see friends.

Labor-Leisure

Collectivist cultures like those in Europe spend more of their income on consumption and less on saving. They prefer smaller spaces over big ones. They prefer leisure over labor. The labor-leisure trade off was evident in Paris. Nothing really opens until 10 or 11 (except for boulangeries thank god). Everything closes between 2 and 5. As an economist, and a relatively high-functioning person, it took me a minute to appreciate the French preference for leisure, family, and community over efficiency. It’s important to remember that one culture’s value of labor and leisure isn’t “wrong” just because it’s different than yours.8

People vary wildly in both their preferences for leisure but also in their valuation of physical and social goods. Diversity of preferences, opinions, and cultures is a good thing. It’s what gets us delicious baguettes and flaky croissants. Variety and diversity is an economists dream. On the production side, the same is true. A diverse set of skills and aptitudes is literally what keeps the economy running. Diversification and trade are two of the most important concepts in economics and they rely on heterogeneity. The most terrifying thing I can imagine is a world without diversity, with a bunch of the same people with the exact same thoughts, feelings, and preferences. A world without variety. Without the freedom to choose where I’d like to go, what I’d like to eat, and who I’d like to eat it with.

Social media, interestingly, has created a lot more homogeneity in our world. You see a popular outfit on Instagram, everyone is wearing it. Popular restaurant? Everybody is eating there.9 With the same preferences, same desires, same characteristics.10 If someone doesn’t fit easily in a single box, others will try to put them in one. Social media isn’t the first to do this though, entertainment companies and news agencies have been putting people into boxes for years. If there’s another resolution I’d give myself this year, it would be to not let myself fall into the trap of acquiring one of the three or so social-media acceptable personas.

A good investment

Last year was a wild ride for markets. Below I’ve plotted the S&P 500 in 2025 compared to the EUR<>USD forex rate. As you can see, if you had taken a $100 investment in 2025 in the S&P500 you’d have about $117.50 by the end of the year and, likewise, if you’d taken the same position in Euros you’d have $112.50. The S&P outperformed the Euro by just about 5%. You’ll notice that, for the first three quarters of 2025, Euro was the better bet. Funnily enough, around the early part of 2025, knowing that America’s trade would collapse and that the demand for the dollar would likely deteriorate under the Trump administration, I decided to hedge my 2025 Europe travel by purchasing Euros. My little hedge ended up paying off and I paid about 10-15% less (in cash) while in Europe, woo!

While traveling, it’s easy to forget that our currency declined 15% since last year. A 7 euro beer actually costs $8. That said, if you bought a $7 beer in the states, you’d most likely be paying about 10% on top of that in tax and about 20% in tip for a total of $9.24 (all prices in the EU include tax in the list price). In this way, buying beers in Europe, at least for me, is still cheaper than here in the states. I’ve been told by my wife that this is called “Girl Math”. Which, despite the name, can really help a guy out when he’s trying to justify spending money on his hobbies.

That it for me. While this letter to 2026 isn’t profound, hopefully there were a few interesting tidbits. Happy New Year!

Intro

I have seen a theory rumbling around recently about how the US government could be attempting to offload its debt into stable coins and effectively pull the rug on holders of US treasuries/debt by ex post devaluing that digital currency.1 The theory is of Russian origin and, as a result, should be taken with a very small grain of salt. Furthermore, even if the government wanted to attempt something so profoundly stupid, there are basic economic and mathematical impossibilities in the way of realizing it.

The theory is half-baked and very poorly contrived. That said, it makes for excellent fodder and my tearing it apart makes me look cool and smart. And who wouldn’t take the opportunity to look cool and smart?

To get us started, I’ll do my best to explain what I think these people are trying to say.

1. US Government launches its own stable coin, allowing US consumers to exchange their USD for USDG (USD Government coin? Idk). Effectively, US consumers exchange their dollars for some central bank digital currency (CBDC).

2. Stable coins are backed 1:1 with US treasuries or cash. Since they are trying to get rid of US debt, this would need to be 100% treasuries.2 That is, for every $1 they issue, they must purchase $1 worth of government debt in the form of a treasury which yields some interest payment (at whatever the current effective 6 month, 1 year, 10 year rate is).

3. The US government convinces the US population and foreign governments to purchase up 38 trillion dollars worth of this currency (up to the value of the current debt).3

4. Once everyone has bought into this CBDC, the US government “devalues” it to $0.50, down from $1. Thus, consumers can exchange their 1 USDG coin for $0.50 cents. Effectively halving the underlying treasury debt used to fulfill their backing.

5. Poof, government debt handled! … Not quite.

Next I’ll break down each point and explain why each one is more impossible and contrived than the last. I’ll be referring to several stats in this figure that came out in a16z’s State of Crypto 2025 report.

Credit: a16z Crypto: Daren Matsuoka, Robert Hackett, Jeremy Zhang, Stephanie Zinn, and Eddy Lazzarin

The plan requires impossible growth in the stable coin market

OK, the central bank launches its own stable coin and backs this stable coin with US treasuries. Presumably, it would use its own treasury holdings as the collateral for this stable. In order to fulfill the 1:1 requirement, the FED could only perform something like this up to the amount of treasuries it holds. The FED currently holds about $4.2 Trillion in US Treasuries. So it can create a stable coin with market cap no larger than this amount.

The current value of the entire stable coin market is just north of $300 billion. In order to pull this off, the FED would have to effectively 13x the current global market cap of the entire stable coin industry. Let me repeat that because this is one of the critical points on why this theory is so incredibly dumb:

In for this plan to cover just 4.2 Trillion (11% of the current debt), it would have to generate an amount of demand that outclasses the current stable coin market by 13x, greater than 1000% growth from where it is at today. This is like betting the house on 1:100 odds knowing you’re only going to get $100 if you win.

The plan requires an impossible amount of cash

This is somewhat related to point #1 but the math simply doesn’t math. We talked about how the Federal Reserve Bank would have to pull an incredibly dumb and risky move just to sell 11% of the US debt with stables. We also talked about how you’d have to somehow generate a massive spike in the market for stable coins. But let’s talk about just how ridiculous the idea is even if you could, somehow generate a token that could be backed with all 38 trillion dollars of us treasury debt.

The Government owes 38 trillion dollars to it’s debtors — China, Japan, Germany, the Fed are some of the major players. The US populous holds just shy of 19 trillion in dollars. The entire NYSE is worth about 65 trillion as of this writing. If we sold 50% of almost everything in existence within the US’s economy, we’d have enough to buy out the debt on this stable coin. A sell off like this would crash the economy beyond anything the world has ever experienced. There would be a run on banks and the US dollar would collapse along with the US stock exchange.

By the time Americans had pooled together their pennies to come up with enough cash to purchase 38 trillion in stable coins, the entire country would have descended into a chaotic financial meltdown. It would be orders of magnitude more severe than the Great Depression.

The beauty here is that we’re not quite done with how bad this gets. Because the stable coin must be backed 1:1 with US treasuries, the US government must sell an additional 33.8 trillion in US treasuries/debt to the FED (the difference between what the FED currently owns and what they need to back the stable coin). Now that the US government has issued even more debt, the treasury market goes bonkers because trust that the government could pay back such a sum is incredibly low. Treasury rate hyper-inflates, value of treasuries plummet. In other words. The US government would have to act fast, before the treasury market has a chance to respond. In fact, one could easily argue that in order to do this crypto swap-a-roo, the US government would have to declare bankruptcy anyway.

The plan only solves half the problem

I mean, what’s even the point in continuing on here? At this stage, the sheer mathematical impossibility is a good enough reason to put this ludicrous theory to bed. But, as the classic Bilbo line goes: “Why shouldn’t I”?

The final stage of this grand crypto-fueled scam where the government somehow erases 38 trillion dollars of debt is to devalue the stable coin. Now, this is actually the first part of this plan that is physically possible. One could, technically, reduce the exchange value of the stable coin such that a single coin only returns half of a dollar instead of the full dollar. In fact, if it weren’t for their decentralized nature, any other stable coin could do something like this, theoretically. But since this hypothetical USDG coin is 100% centrally controlled by the government, they could technically reduce the exchange value of the coin.

This would immediately halve the entire market cap of the coin. So this impossible coin goes from being worth 38 trillion USD to 19 trillion USD tomorrow. Two very very important things happen at this point.

1. The richest country in the world becomes half as rich because everyone’s wealth is locked up in USDG.4 This would lead to massive GDP degrowth, and a very deep recession.5

2. The government has handled half of its debt. After all that, the Government still owes 19 trillion dollars to its debtors (Japan, China, and Germany among others).

After crashing the world economy, defaulting on it’s debt, collapsing the treasury market, and destroying the US economy, the government has handled half of it’s debt through this stable coin crypto scheme.

Conclusion

A plan like the one thrown around in the video I linked above would collapse every facet of the modern economy. Currencies would become worthless. Businesses would become insolvent. The US government would, as a consequence, default on all of its debt. Every government around the world would collapse and become insolvent. The current world order would collapse and, more than likely, war would break out. The plan is ludicrous beyond comprehension. The government would be better off simply defaulting on its debt. While this would also collapse the world economy, it would be much simpler.

That all said, could the US government do this on a tiny scale? Sure. The government could issue a stable coin and get a bunch of people and governments to buy in. Maybe they’d get half a trillion for it? Then they could rug-pull. This isn’t inconceivable. Especially in light of recent events surrounding rug-pulls and a certain US president. In that way, a jank stable coin issued by the government is entirely possible. It’s just not going to come remotely close to “erasing” the US government debt nor is it likely to crash the world economy. All it would do is erode trust in the US Government.

I’m not sure how many people actually buy this ridiculous theory. But, most people don’t understand how money works and even more don’t know how crypto works. “Crypto scam to erase corrupt government debt” is bound to get clicks. And the video I linked has over a million views. So I hope this article did a good job at not only shutting down the theory but also pointing out just how wildly insane it is. It’s not simply stupid, it’s mathematically impossible.6

This article establishes framework for a simple UGC market and models UGC creator income as well as consumer utility over time under several different content sorting algorithms.1 I explore three algorithms and how they impact total utility and creator income inequality. I find that a fully random model tends to outperform a pure-popularity driven algorithm under heterogenous goods (the algorithms perform very similarly, though, under homogenous goods). A pure-individual utility maximizing algorithm, however, outperforms both of the other algorithms by a significant margin. This is overstated when we introduce realistic heterogenous goods.

Note on the model: There are many ways in which the model below can be extrapolated, improved, and generalized. For the simple purpose that this is a blog post and not an academic article, I leave those for future work. There may also be errors in the model and I encourage readers to point them out. This piece was written over the span of a week in my spare time so it is likely there are issues.

Model

There are N users (consumers) and K producers (content creators).2 Each producer is also a good (each producer only produces a single good). There are also T periods. Each period, users choose a good to consume and receive utility from it. Finally, users decide whether to churn where churn probability is inversely related to utility. In this way, as users consume increasingly high utility goods, their probability of churning decreases. There is also a small probability that a churned user will re-appear on any given day which I arbitrarily set to be 0.05.3 I provide formal math below.

Users make consumption “decisions” each period according to the following stochastic process:

The above equation essentially says that a consumer will consume good j with some probability called p^c_it.4 Then they roll a die (categorical) whose sides are weighted by p — the side facing upward is the good they consume. This random process is similar to what a user experiences on an app like Instagram or Facebook. Users are fed one creator at a time and essentially “forced” to consume that good/creator. I simplify the model by assuming that users only consume one item per day. This simplifying assumption doesn’t change the results of the analysis but it does speed up computation.

After consumption, users receive utility which is time-invariant and Beta distributed5:

This says that each user has some associated utility they gain from consuming each piece of content which I call u_ij.6 The fancy B is the beta distribution, thus utility is bound between 0 and 1. Importantly, utility doesn’t change over time.

Once the piece of content has been consumed, the user makes their first real decision on whether to retain according to:

This says we flip a biased coin; if heads, the user returns tomorrow; if tails, they don’t. p^r_it is the probability that heads shows up. The functional form of p is known as a sigmoid and this one has been shifted to the left. This function behaves favorably and properly emulates typical user churn behavior.

Producer (creator) profit is straightforward. I assume that the price each content creator receives is unitary which means that price = 1 for all i, j, and t. This simplifying assumption is actually quite realistic since content creators on TikTok, Instagram, and X can’t differentiate their pricing compared to other creators.7 A further simplifying assumption is that creators incur no costs when producing goods.8 Thus, the profit a creator receives is simply:

which is the sum of all goods consumed by players at time t for good j.

Three Algorithms for Content Discovery

You’ll notice that there is one piece I haven’t defined that I mentioned at the top: d_ijt. This is the probability that a user consumes a specific good at a point in time. This value changes depending on the underlying social media reinforcement algorithm. I consider 3 algorithms that change d. For salience, I define d at a point in time (rather than as a big NxKxT matrix). d is defined as:

and omega differs depending on the algorithm, m. I define each algorithm below.

Random Model (time-user-good invariant and uniform random):

Which simply says that weights are completely random numbers between 0 and 1 (the probability of any good j being consumed is equal to that of good j+1 on average).

Popular Model (user invariant, time-good variant, non-random):

Which says that weights are determined by last period’s total consumption of that good. Thus, goods that were more heavily consumed last period will get a larger weight in this period (this process builds over time).

Individual Model (time-good invariant, user variant, non-random):

Which says that omega is equal to 1 if j is the user’s favorite good and 0 if not. This means that the user always consumes the good they like most.

Outcomes

The two outcomes I primarily explore are user social welfare which is the sum of all utility from consuming goods in each period and creator income inequality which I measure as the Gini coefficient of income in each period. We’re also interested in user retention and average utility. The best algorithm is one that both maximizes individual utility, overall social welfare, and reduces income inequality. Keep in mind that Gini typically varies between 0 and 1. A Gini of 0 is actually a bad thing (so is a Gini of 1). A Gini of 0 means that all producers receive the same income no matter what. However, in a competitive market, we want better content to be more heavily rewarded. Thus, I target a Gini of around 0.5 such that good content is rewarded but not all the wealth is held by a single individual.

Findings

The three algorithms are: Random, Pure-Individualized, and Pure-Popularity.9 I will explore two settings: one with homogenous goods and one with heterogenous goods. There are many other assumptions to relax and explore but I do not have space here to do so.

Baseline - Homogenous Goods

I present some figures below summarizing the case with homogenous goods (content creators are essentially identical, on average). The set up is shown below:

The left-most figure is purely explanatory to show how the transition function works. For any level of expected utility, u_ij, a user will have some probability of returning again tomorrow, p_ij. Throughout this article, I assume the transition function looks like the yellow line. The middle figure shows us the density of users (content consumers) and their associated utility values. Note that most people have high utility values in the baseline model. Finally, the right-most plot shows the initialized encounter probability distribution for all K goods. To begin, this is assumed to be completely uniform (a flat line). Recall that d changes over time depending on the content sorting algorithm.

Now, the moment you’ve been waiting for, below are the results from a simulation with N=1000, T=450, and K=5:

In terms of users, the Random and Popular algorithms show almost identical user retention and total utility throughout the period. However, they differ drastically in terms of creator income inequality. Note that the popular algorithm, unsurprisingly, provides a much higher Gini than of the others. This is necessarily due to the algorithm re-enforcing the most profitable creator’s content each period. The Individual algorithm, on the other hand, provides quite low Gini with much better user retention and vastly superior total utility. In this way, the individual algorithm does seem to perform far better than the other models. However, a note on inequality is needed.

A super low Gini doesn’t just mean that inequality is low, it also means that there is no differential returns as a content creator. For example, if I am producing super high quality content and someone else is producing terrible content, we should expect that I would receive higher income than the other. If Gini=0 then this isn’t the case, everyone effectively gets the same amount of money regardless of the job they have done. In this setting, content is identical, which leads to a low Gini. In the next section, I explore this setting with heterogenous goods.

Experiment - Heterogenous Goods

Everything from before remains the same except now each K good has a different underlying utility function (they have differential quality).

Note that only our utility distribution has changed. Now, we can see that each good has a different utility function across the population, with Good 5 producing the most enjoyment and Good 1 producing the least. Let’s see how this changes our results.

We immediately notice that our Random model performs very similarly as in the baseline but with significantly fewer retained users and lower utility. The Popular algorithm, however, actually under performs the random model! One might expect that reinforcing the most popular content would yield better results than a completely random algorithm. But the initial discovery probabilities are identical across goods. If in the first period, for some random reason, more people consume the bad good, this bad good will constantly be reinforced by the algorithm which lowers utility compared to a completely random model. It’s a cascading effect that we’ve talked about before on this blog.10

Unsurprisingly, the Individual model performs far better than both of the others (if you give people content they like, they will not only enjoy it but come back for more tomorrow). One final thing to note is the relatively high Gini on the Individual model this time around. This is because differential content is rewarded for being good. For example, since more people like Good 5 than Good 1, much more of it is consumed and, thus, more income goes to the creator of Good 5.

Conclusion

There are some salient points I should emphasize. First, the Random model and Popular Model are almost identical in terms of total utility (and average utility) but the popular model sees higher Gini in both cases while it suffers slightly in the heterogenous goods case. Second, the Individual model outperforms in both settings regardless of good differentiation. This is not unexpected given that this model specifically feeds users’ their favorite content. Third, in practice, most algorithms will combine all three models into a single hybrid model (for example, Content or Collaborative Filtering and other similar models). While this model doesn’t attempt to qualify one model type over another, it does bring up important characteristics about welfare of users and inequal outcomes for creators under “pure” versions of each. Thus, a careful algorithm designer must be aware of these threats as they can negatively impact retention of both creators and users.

Today we’re going to return to a topic that’s near and dear to my heart — using video games to understand economic concepts in real life. I’ve posted this similar piece before which explored commodity market “births” and today we’ll be talking about Price Indices and Inflation!

In this piece I’ll be referring to the Price Level [Index] as the normalized price of a good relative to a starting date. Inflation is the measure of the rate of change of those prices.1 Specifically, we’ll explore the powerful, indeed essential, relationship between the price level and the outstanding money supply. We’ll remove the impact of the money supply on the price level with an econometric tool called decomposition and plot the adjusted price level and draw some very interesting insights from it.

Inflation in a chart

Below I’ve plotted the price level of goods in Holosim (the F2P version of Star Atlas MMO):

Figure 1 - CPI: Raw (blue) and Money Supply Adjusted (Red)

For now, let’s just pay attention to the blue line. This CPI, or price level, measures the average price of a bundle of core goods in the Holosim economy since August 30th [CPI(Aug30) = 100]. I measure the price of that bundle every day thereafter and plot them out in blue. What we see is hyperinflation, in the last period our CPI has risen to almost 700 in just over two weeks! Now, there’s an important caveat in that this is a very thin market with few participants (price discovery is much harder). But the trend is pretty clear, prices are rising quickly.

Perhaps we’re interested in why these prices are rising so quickly. We might look tot the money supply as a good first step (given that the goods are all priced in money). Below I’ve plotted the money supply of zATLAS, the currency in Holosim.2 With these two plots (and their underlying data) we can apply some good economic thinking as well as our econometric toolkit in order to get at the red line in Fig 2 (the adjusted price level).

Figure 2 - zATLAS Supply Change & Cumulative

Decomposing Inflation

There’s a technique in econometrics called decomposition that we can take advantage of in this setting. It allows us to effectively remove the variation in price that is due to money supply. Thus, we’d like to plot CPI in such as way as to hold money supply constant. We do this by modeling CPI using money supply as a causal relationship. Using a simple linear regression, we can quite literally subtract out the impact of money supply on a CPI in order to get the “adjusted CPI”.

We must establish that there is a causal relationship between the two aforementioned variables. From primitive models of trade and, more importantly the quantity theory of money, we know that as the quantity of one asset (say, zATLAS) changes, so too does the price of that item relative to other items (because prices are a function of quantities).3 Thus, because price is a function of quantity, the Price Level is causally linked to outstanding zATLAS. Thus, zATLAS causally changes the Price Level and this causal relationship can be expressed with a function.4

Let’s write some arbitrary linear function for the price level, p, and the money supply, m, like this:

Using our simple model, we can decompose the variation in price from day to day into multiple components. One is the totally random stuff which we represent as e. Some of it is constant which is represented by beta_0. Finally, there’s the bit that’s due to the money supply, which is represented by beta_1 — which is the causal impact of an additional unit increase in m on the price level p. And if I were being more precise, I’d also add a component for “other stuff” which we could attribute to a variety of other variables that impact price (for example, the scarcity of the goods or technology).5

Notice that in order to account for the change in the money supply, we really only want beta_0 and e (remember, we’re trying to remove beta_1). We can “extract” the variation in the price due to the money supply by simply plotting e + beta_0 above (literally, just subtracting the beta_1 part of the equation.

This regression tells us is what the price level would look like on each day if money supply were constant. After adjusting we see that prices have actually gone down to about 50 — deflation! Econometrics and decomposition are very powerful things (especially when you have a causally identified model).

Nuts and bolts

In an effort to provide more specifics to those who may be interested in actually learning how to do this stuff, I’ve posted a Python snippet from this exercise on my Github that will give you exactly what I’ve done above. I’ve saved the data to the repo so you can download the notebook and try it for yourself. Now, there was quite a of work done to get the data in the format of cpi.csv and money_supply.csv but some interesting procedures are left in the code. For example, you can select on different types or names for the commodities you’d like to explore. In this analysis, I select on only four of them due to their importance and data consistency. Once you’ve got your final time series model the table should look something like:

And when you run the decomposition you should get a regression that looks like (truncated):

Note the coefficient for m1_norm. This is effectively the impact that the money supply plays on the price. While we don’t really care about this coefficient in a decomposition (we care about what’s left over), it is important to note that for each unit increase in the money supply, there is a corresponding increase in the price level. When we subtract a positive number, we get a smaller number than when we started, hence why the red line is below the blue line in fig 1.

One last note I’ll add is that R-Squared is >0.80 in the regression. This means that, of the variation that we see from day to day in the price level, over 80% of it is due to variation in the token supply. A powerful indicator indeed.

Closing Thoughts

Alright, that got a little technical at the end. But I hope today has shown you something about inflation you never thought you’d see. Games are incredibly cool and blockchain games are even cooler because their data is public. A day in the life.

I’ve recently returned to The Righteous Mind by Jonathan Haidt (perhaps more well-known as the author of The Anxious Generation). While I thoroughly enjoy the book and think there are some incredibly valuable insights about humanity, morality, and politics in it, one thing bugs me about his writing — his mis-understanding of economic rationality, or Homo Economicus. He believes that economics is wrong to say that people are rational and that their primary motivation in life is to maximize their own personal utility. I argue in this piece that this view, while widely held, is a mischaracterization and reduces economics down to one easily breakable axiom.

About mid-way through the book, Haidt discusses the origins of group morality. There are two forms of competition, he summarizes: group and individual.1 The distinction between a group acting in a competitive way and an individual acting in a competitive way is well explained with the following example. A tribe with fierce, risk-loving warriors will surely dominate over a tribe with weak, scared warriors. However, warriors who are strong and risk-loving are more likely to perish in battle than weak, fearful ones (who avoid battle altogether). Why then, would anyone ever be a strong, risk-loving warrior? Homo Economicus, he claims, cannot explain this behavior.

Today, I want to explain what rationality is in economics. In doing so, I’ll hopefully explain why the world suffers harsh economic conditions in-spite of economics not because of economics. Economics isn’t wrong to model people in seemingly oversimplified ways. Indeed, almost all social sciences must make strong assumptions about the underlying mechanisms that drive human behavior. Just because these assumptions may “break” under extreme circumstances doesn’t make the whole profession a farce. Indeed, most economic theories bend rationality as needed in order to model things as they appear in real life. In this way, economics isn’t actually wrong that all people are rational.

Micro I

In their first semester of an economics PhD, students will take 3 classes: Micro 1, Metrics 1, and Macro 1 (or math for economics depending on the program). Micro 1 underpins basically all of modern economics.2 Many of my readers have probably taken an Econ 101 course and some may have even gotten into undergraduate micro-economics.

Micro 1 in grad school is an entirely different beast than that of an undergraduate course. You might think that means the math is insane, using high level math like calculus and differential equations to solve high complexity problems. And while that does happen later in your coursework, Micro 1 doesn’t test your math skills. It tests your thinking skills. Micro 1 also sets down the foundations of economics in axioms of rationality.

The whole point of Micro 1 is to get budding scientists thinking like economists and not like mathematicians. Here’s an example of a question from my first Micro 1 Exam (credit: Prof. Collin Raymond)

Consider the following Axiom, which [Professor Raymond] call[s] Axiom M2:

i. a and b are both amounts of money between 0 and 100 dollars where a lottery that gives a for sure is preferred to a lottery that gives b for sure.

ii. L1 and L2 are two lotteries such that with probability p, L1 pays out b dollars and with probability 0 pays out a dollars (the remaining probability 1-p is distributed amongst a finite number of other dollar amounts in any way); L2 is exactly the same as L1 except that with probability p L2 pays out a dollars and with probability 0 pays out b dollars.

Then L2 is preferred to L1.

Question: Please interpret the axiom, is M2 true for an expected utility maximizer with the above Bernoulli utility?

I show this question not purely for entertainment purposes but to explain something. Economists do use math in their research. But it isn’t a necessary condition to getting to good economic conclusions. There’s no way to use calculus or algebra to answer the above question. You’re simply using addition and a bit of logic. Rationality is imposed by economists for the purpose of analyzing human decision making, not for the use of mathematics. Math is a convenient means to an end. One last thing to note is the use of uncertainty. Uncertainty is a very powerful tool that can make almost any seemingly irrational behavior perfectly rational.

Economic Rationality Defined

In Micro 1, we say that an individual is rational if, over a set of goods called X, they satisfy two properties3:

1. Completeness - For all x and y in X, either x is weakly preferred to y or y is weakly preferred to x.

2. Transitivity - For any three elements in x, y, and z in X, x is weakly preferred to y and y is weakly preferred to z then x must be weakly preferred to z.

While there are other very important axioms of rationality learned immediately after these two, they remain the first and foremost. They say that a person is rational if they can compare each and every item in their preference set (e.g., I prefer beef to pork and pork to chicken) and are always consistent about those beliefs (e.g., it’s never the case that I prefer chicken to beef). Notice that nothing in these axioms says an Econ must be self serving or individualistic. Many of the models that assume individual utility maximization are trying to get at something else than the basic two axioms can provide. Thus, those spending all their time trying to prove that economics is “wrong” because “people aren’t rational” have missed the point entirely. Rationality in economics just means that people can choose properly between a finite set of options.

People are rational, said the economist

Typically, when I’m debating rationality with people they say something along the lines of: “But sometimes I prefer x on Tuesday and y on Thursday, my preferences change over time”. Or, a more tricky riposte, “Sometimes I don’t know enough about x to know that I actually prefer x over y”.

The two frustrations aren’t completely unfounded when you keep a limited view. But you can simply expand X in order to deal with these standard complaints. Sure, x may be preferred to y on Tuesday and not on Thursday. But then, we’re simply saying that x_tuesday is weakly preferred to y_tuesday and y_thursday is preferred to x_thursday. The same can be said for the uncertainty argument.

At this point, and after a beer or two, a person will say to me “Yeah but there’s no way every single person can keep track of the entire universe of X and every single combination of x, y, and z.” Which is a fair point, I typically don’t even remember what I had for lunch.4 Indeed, this gets at the idea of constraints. Economics is about constrained optimization. While utility is important, the budget constraint is just as important. But there are many types of constraints that humans face every day, not all are pecuniary.

When I was a kid, my older brother used to run a bank within our household. In his infinite charity, he decided to run a special that allowed me to trade in my small, worthless coins for his big, valuable coins. As a result, I traded him a stack of about 50 dimes in exchange for 50 nickels. Bigger is better right? The reality is, I had limited information and didn’t know the actual value of what I had. Just because I was tricked my my mischievous older brother doesn’t make me irrational. It makes me a schmuck. If I had been given all the information that a dime was more valuable than a nickel, I wouldn’t have traded in my dimes. 5

Can you say whether I was irrational? I ended up going on to get a PhD in economics but, given my age and limited knowledge of economics, it seems really hard to call the kid above “rational.” Rationality is important at the margin, under constraints, and in the specific context of the research question. Economics isn’t wrong simply because Joe can’t remember the list of everything he’s ever consumed and exactly how he ranks them. What matters is that our assumptions (axioms) hold when we need them to hold.

Does rationality even matter?

I’ve spent the first part of this article talking about rationality and the specific contexts in which it’s defined. And how econ can re-define rationality at its whim. People are rational because rationality changes definitions to whatever is convenient for your specific question. This is a pretty weak argument. A stronger one is the following: Economics doesn’t actually need rational individuals in order to draw it’s most profound conclusions.

Now we come to a possible thesis in this article. Even with uncertainty, bad decisions, and, dare I say, irrational individuals, economic theory still holds true in aggregate. Most decisions when you sum them all together lead to equilibrium. In fact, this is the great finding of economics. Many individuals acting in their own self interest can lead to a phenomenon called equilibrium. Economists can solve for equilibrium using math… Or we can use a computer simulation.

Zero Intelligence & Equilibrium

One of my favorite papers of all time is a computational economics paper called Allocative Efficiency of Markets with Zero-Intelligence Traders: Market as a partial substitute for individual rationality by Dhananjay Gode and Shyam Sunder.

It’s a mouthful but I think the title pretty much conveys the entire paper. They essentially run two experiments, one is on human traders with true expertise in trading stocks and other assets. The other is run on a group of robot traders. Now there are a slew of different treatments and experiments but the most important finding is that the allocative efficiency (the speed at which the equilibrium price is found) is nearly identical between the highly intelligent human traders and the zero-intelligence bots.

In practice, the authors pitted normal traders up against one another in a double auction market for a commodity with a known theoretical price. They also gave these traders a budget which they couldn’t exceed. They did the same thing with a group of AI traders that randomly submitted bids and asks for the commodity. The AI bought and sold randomly but their budget constraints were identical to the human traders. The budget constraint acted as a mechanism so powerful we call it the invisible hand.

Credit: Gode, Sunder, 1993, Journal of Political Economy, Vol 101 no 1

This spectacular finding is not only highlighting the power of the Invisible Hand that Adam Smith writes about in The Wealth of Nations, it also highlights that individuals need not be perfectly rational in order for the market (a large gathering of individuals, a group) to be rational. In fact, they can have zero intelligence. A double sided auction with ample liquidity is spectacularly powerful in it’s predictive abilities. You can spend hours scouring data and putting together a highly sophisticated predictive model or… you could open up a market. Individuals may seem irrational but when you group them together with good institutions, the aggregate outcome is highly rational.

In Sum

Funnily enough, Haidt actually points out in his book that behavior is different on an aggregate level than it is on the individual level.6 A similar conclusion as the one I came to above. Rationality on the individual level isn’t all that important to economics. Economic models rely on assumptions, yes. But most of the time, in fact, we write our models such that rationality is a reasonable assumption and provides the insights we need to model the behavior we’re trying to model. Even if people are irrational, though, with no regard to logic, rules, or intelligence, we still see that our models provide “rational” outcomes. Gode and Sunder 1993 communicates this perfectly.7

Economics is about the study of human decision making, not of human rationality.

Intro

For a long time, I have been unsatisfied with the numerous articles bemoaning “what went wrong” in web3 gaming. I felt these theories often addressed symptoms as opposed to underlying causes. Today, I want to present a diagnosis, a specific sin web3 gaming has committed, and how we can atone this sin and get on a path of growth.1

My thesis is simple:

Web3 businesses have misunderstood the critical role that distribution and, specifically, user acquisition play in the success of a video game product. Indeed, many, if not most, web3 gaming companies confused play-to-earn mechanics as a user acquisition mechanic when in reality it is a retention mechanic.

In fairness, I’m not sure if anyone has made this argument before. If so, please let me know. And don’t confuse my thesis. I am not saying that crypto gaming sucks at UA and they didn’t make attractive products that brought people in the door. While those are symptoms, I am saying that the root cause comes down to a mis-understanding of what P2E mechanics functionally do for a product. Namely, they retain users. They don’t attract users, necessarily. Indeed, sometimes they can have the opposite effect as I’ll discuss below.

I’m unimpressed by the specific arguments around execution and poor economics as the root cause of web3 gaming’s hardships. Many web3 games are quite fun to play and even provide a little earning on the side. I don’t think un-fun games or bad econ models are the root cause here.2 In fact, we’ve seen games with suffering models succeed. For example, EVE: Online has faced countless economic struggles in the past and they have always overcome them because of their large player base.3 Had EVE not such a devoted and large fanbase, they wouldn’t have had the opportunity to fix the issues and you’d have dozens of people lining up at the door to lay blame on one mechanic or the other.4

Play-To-Earn is user retention, not user acquisition

In the same way that ad-networks fundamentally changed how users are acquired in games, particularly F2P games, P2E has the potential to fundamentally change how user retention works. What if you could pay a high propensity user to stick around for one more day? Since prices can go zero, why not negative? Indeed, real-money gaming has already figured this out. But incentives only work if there are people present to be incented. Many web3 games assumed that by simply paying users to play their game, this would get people in the door.

But earning in a game is a retention mechanic, not an acquisition mechanic.

If Satoshi Nakamoto launched a web browser game that distributed one lucky winner 1,000,000 USDC every day, they might expect that this game would attract millions of people around the world to play. There are two flaws in this thinking. First, if Nakamoto doesn’t advertise, nobody will actually know the game exists. Like a gold mine hidden under the earth’s surface, it’s not doing anyone any good lying hidden. Second, once a few people do discover the game, the last thing they’re going to do is add competition to the prize pool.

The first F2P games had an incredibly powerful platform, Facebook. They didn’t really need to pay to acquire users because those users were already on the platform. Fast-forward 5-10 years and ROAS is basically the name of the game. All that matters is the lifetime value of a player and the cost to acquire them. For some reason web3 largely missed the memo. I attribute this to the fact that many web3 folks came from outside the industry. They thought that by creating an excellent product that could make players money, this would open the flood gates. Unfortunately, it did not, at least not in the long run.

How can Web3 businesses succeed today?

Don’t: Operate in Information Silos

Getting out of the crypto-gaming information silo is crucial for web3 games. The information space for crypto games is constantly changing. One day, crypto gaming needs to make good games. The next day, we need to remember our purpose — to make people money. Another day, we need to build better economic models. The narrative changes so quickly it’s virtually impossible to land on a cohesive, long term strategy. A narrative that I currently see is that games need “hype” and “virality” in order to succeed. If we can just get our 15 minutes of fame, everything will work out. Not only are some of these narratives harmful, they don’t prescribe long-term solutions.

Getting out of these silos can help to expose your business to unique perspectives and wisdom you might have otherwise missed. Platforms like Reddit, Discord, TikTok, and YouTube are where most gamers get their news and engage with communities.5 Indeed, the user growth & revenue optimization analysis happening by leaders at the likes of Roblox, Epic, and Amazon are the conversations we should be following. Getting exposure to other platforms provides essential intel and exposure to users you wouldn’t get in the silo.

DO: Invest in User Acquisition

Web3 gaming companies need to start utilizing traditional user acquisition channels — advertising. Web3 businesses need to be able to move a potential user from outside of their ecosystem into their ecosystem spending money. This process is called the user funnel and tracking KPIs is essential for driving this type of behavior. UA requires two essential building blocks: infrastructure (data and tracking) and spend (ad spend across many platforms). Unfortunately, traditional user funnels and KPIs can be tricky in blockchain contexts.

Infrastructure: I can personally attest that some blockchain products don’t lend themselves to great user journey analytics. Most on-chain activity is anonymous and confusing. Transactions are often signed by many parties, accounts can take on many forms, not necessarily all accounts are players. Furthermore, especially with a web-based game like Star Atlas, it’s tough to know whether multiple accounts/profiles are being manned by a single person. Finally, crypto people tend to be very private and will oftentimes bounce from IP to IP via VPN.

Spending: UA is expensive and most web3 gaming companies are strapped for cash right now. But there’s a reason major studios spend a large portion of their budget on advertising and UA, it works. Like it or not, acquiring users via paid channels is the name of the game.6 But, just like at the grocery store, you don’t spend money unless you know what you’re getting.7 Understanding how valuable a new user is in your ecosystem is the only way you can justify spending money on acquiring further users. If you’re not sure how to get new users or where they come from, it can be tough to calculate how much it will cost to acquire a new user.8

Anecdote: If you’re struggling to understand where new users come from, change the rules of the game. For example, we recently released Holosim: Season 1 which is a F2P version of our Star Atlas MMO. We found that it was very difficult to acquire new users for our main net game because 1) onboarding costs were too high on Solana and 2) uncertainty in crypto was scary for new users to accept. Thus, we changed the game, literally. We designed a F2P version of our product that eliminated 1 and 2 above, making it much easier to acquire new users.9 With a cleaner user funnel, measuring essential KPIs became easy and we had a clear relationship between the cost of acquiring a new user and the revenue they contributed to the business.

An Econ Lesson in UA

LTV, or Customer Lifetime Value, was popularized, and perhaps invented, by Eric Seufert who runs the Mobile Dev Memo. In econ terms, LTV is essentially marginal profit.10 In game industry biz speak — it’s your most essential KPI. It’s the additional profit you get by snagging an additional user (all things equal).

LTV = Avg Revenue per Acquired User * Avg User Lifespan - Avg Cost per Acquired User

This is essentially just good-old-fashioned profit maximization from your econ 101 class. The terminology is a bit clunky since economists think about revenue maximization (profit optimization) at the margin and the game’s industry brains think about things at the mean. But the concepts are essentially the same. In order to get the most bang for your buck, you must maximize your expected profit which is a function of the value of each player minus costs.

Once you have a solid LTV measure, you can invest dollar for dollar into UA, knowing exactly how much you can spend to acquire a new user. In econ, we say that if your marginal revenue is lower than your marginal cost [MR>MC], you keep increasing quantity until MR=MC. I’ve shown this in the figure below.

This timeless figure tells us many things. First, that marginal revenue can grow over time. If we’re able to grow LTV by increasing spend caps or customer lifespan, we can increase how much we can spend on acquiring new users. Second, CPI grows as the user base grows. In other words, the cost of acquiring your first thousand users, who are likely die-hard fans of the genre, is much cheaper than the last 1000 (who might not like space games, for example). Finally, even though it’s not entirely obvious, profit is maximized where MC=MR. You can think of it this way: yes, your last acquired user was worth exactly as much as you paid for them, but users before this moment were worth more. So, just because MR = MC doesn’t mean that total profit is 0. It just means that you drove users up until profit = 0 at the margin.

Crypto people lost sight of basic economics. They envisioned Yield on the Y-Axis above instead of CPI. But yield doesn’t acquire users, it retains users. This is where I get excited, if we can utilize the power of traditional UA and combine it with more robust retention — driving customer lifetime up — we can actually create a greater LTV than if we didn’t have P2E mechanics. Indeed, you could even think of ways to drive ARPU up with P2E.11

Conclusion

The story’s not over for web3. But building from here on out will be slow. The world exploded when it saw the initial promise of web3. This created an expectation that was impossible to meet. Just because impossible expectations weren’t met doesn’t mean the technology or the products will die away, it simply means that we get to operate in an environment unencumbered by impossible expectations. Unfortunately, that also means we get very limited investment exposure.

Utilizing wisdom of the gaming industry along with a little engineering know-how will lead the web3 games industry out of the dark. So, go acquire some users the old-fashioned way, and then get them to stick around the new-fashioned way.

Castronova and I wrote this article a number of years ago. We haven’t quite found a place to publish it, partially because we lack the energy and partially because, at least when we wrote it, it wasn’t relevant enough for mainstream econ literature. But attention has become increasingly important in recent years, hitting several mainstream venues. Had we fought the battle with our paper with more vigor, who knows whether we could have had a major voice in the discussion. That said, I’ve been thinking more and more lately about social media and its effect on society.

I’m a free marketer. I believe that incentives are the single most important economic concept needed to understand our world. I strongly believe that if we could instill the axiom of incentive into every person on earth, it would be a much more well-functioning place.

While chatting about these issues with a good friend of mine the other night, it occurred to me that each of the major social media sites has it’s own set of incentives that promote certain content. Each website incentivizes specific content and so that content dominates that site. I want to share my theory with you today and, lazily, suggest academic researchers to explore the topic empirically.

Incentives: Pecuniary and otherwise

First, I want to define, probably for the hundredth time, incentives and why incentive mechanism design is so important. Incentives are the carrot at the end of the stick. They are the thing that economists spend most of first year grad school maximizing. We maximize incentives subject to constraints. The most common incentives are pecuniary but, as we have found in recent years, there are powerful incentives that aren’t money; attention, for example.

I’ve actually spent most of my working life studying non-pecuniary economics. For example, when I was 21, I ran my first experiment in college. I was studying altruism in the dictator game and found that people really don’t like to be mean. Or they don’t like to be perceived as being mean. They dislike it so much that they are willing to pay a price for positive social status. Social status is not monetary, yet people care deeply about it. It’s a powerful incentive.

Sometimes, incentives can take the form of social status or, more primitively, dopamine. Different platforms reward clicks, likes, comments, and virality in a different way. The goal of this peace is to attempt to formalize these mechanisms. In fact, I’ve come up with an acronym: Social Media Incentive Mechanism — SMIM. SMIM determines a platform’s content, culture, and experience. Certain platforms incentivize educational content, others incentivize funny videos, and some incentivize incendiary content. If we’re to understand how these platforms impact society and our individual lives, it’s important to formally address SMIM.

The Levers of SMIM

Social media sites like Facebook, Instagram, X, and Reddit all rely on trickling dopamine and other brain juices to their users in order to maximize engagement and retention. There is nothing inherently wrong with this. That said, it does establish an incredibly strong, yet potentially perverse, set of incentives. Unlike alcohol, drugs, sex, LEGO, board games, or any other physical thing, social media is ubiquitous, “free” and inexhaustible.1 This distinguishes it from any other form of consumption in history. As a natural consequence, platforms each differentiate themselves slightly through providing different experiences to their users.

Sometimes, differentiation is quantitative or objective: a photo versus a video, long-text versus short-text. Other times, it is qualitatively different: a funny video or a sad video. This qualitative differentiation is largely driven by the incentives behind providing content. Platforms that reward harmful content, will perpetuate harmful content. Platforms that reward funny content, will provide funny content. Platforms that reward educational content, will naturally perpetuate educational content.

But the rules of incentives do not simply appear out of nowhere, they are built slowly over time and determined by three primary building blocks — or levers for you business people.

Users - The user base will ultimately heavily influence what type of content gets rewarded and, as a result, perpetuated on the platform. Young people like to laugh, old people like to grumble, and academics like to bicker. Users heavily influence what type of content gets rewarded on a platform.

Algorithm - Some algorithms push certain types of content. Some platforms have more influential algorithms that perpetuate very specific content. LinkedIn, for example, specifically filters content that doesn’t follow a very specific structure. Another, more grim, example is that of Grok: Mecha-Hitler.

Medium - The actual medium used such as photos, videos, writing, or books can heavily dictate what type of content gets rewarded. Content that is quickly consumed will be judged by a larger audience due to a lack of filtration. Content that is long and exhaustive will be voted on by a more devoted audience. Medium and Users are closely linked.

At the very center of everything, we must not forget, is the platform’s desire to maximize engagement. While they differentiate by adjusting the levers above, one thing reigns supreme above all:

Golden Incentive: Attention

Social media platforms aim to maximize attention and they do this through differentiating the three content levers above.

The Rewards of SMIM

Below I’ve posted a table that lists some of the most popular social media apps along with their associated building blocks and subsequent Rewarded Content.2 Rewarded content refers to the type of content that gets immediate traction such as votes, reposts, shares, and comments. As each of the 3 levers changes so too do the incentives.

Pulling it together

Algorithm is likely the most important driver in determining what gets rewarded. For example, on a platform like LinkedIn, certain post structures are borderline omitted from the feed. LinkedIn has been explicit about the role the algorithm has in promoting specific types of content. Indeed, this is likely the reason that most of the content on LinkedIn looks the same and the reason AI has such success on LinkedIn. The pattern is followed to a T.

But algorithms can only go so far. Sure, they determine how much a piece of content gets pushed on the site.3 But they don’t control users’ preferences. This is where the user base and medium come in.

On the userbase front, it’s obvious that a post on Truth Social about what a moron Trump is will get downvoted to oblivion. On the other hand, a post on r/politics on Reddit that Elon Musk is a good guy will be eviscerated in the comments. Both platforms have a very strong sense of community and, as a result, going against these norms can have detrimental effects. The algorithm plays a very important role in molding preferences over time, but the user base ultimately has the final say. It would take on the time scale of decades for Reddit to become right-leaning and even longer for Truth social to lean left.4

Medium is a subtle but powerful lever. You’ll notice that long-form content typically results in more high quality being pushed. This isn’t always the case. Friday by Rebecca Black remains one of the most watched videos of all time on YouTube. But platforms like YouTube (and Substack for that matter) will always tend toward high quality content because the medium lends itself to long engagement sessions. Because judgement is slow, so too is the content. Content that takes a long time to develop is, on average, higher quality.5

The Perfect Counterfactual

I think an example is in order. Consider an author who has developed a simple thesis: Eugenics is good. Furthermore, there are two potential platforms on which they can post their thesis: X or Substack. In our hypothetical, the author experiences writing this thesis on both platforms simultaneously — the perfect counterfactual.

When they write the thesis out on X, given the character limit and incentive structure — shorter is better — they opt for a simple “Eugenics is good.”6 They know that most of the people that will get exposed to their post, due to strong algorithmic feedback, will agree with it. X rewards quick, explosive comments and doesn’t lean heavily on exploration. Thus, the short and sweet version is more likely to stick than a longer, drawn out version. The author thinks for about 30 seconds and then makes the post. Only users who agree with him see the post and thus the reception is very positive. Incentivizing further similar posts.7

On Substack, the author posts the same 4 words and his post is met with disdain and dozens of facts, stats, and philosophical rebuttals that render the post a failure. Two identical posts, two completely different outcomes all due to the treatment variable: X or Substack.

Our author can’t simply post 4 words on Substack nor can he post an essay on X. This is due to the levers at play on the platform. Primarily, the user base of Substack wouldn’t tolerate a simple 4 word post. The article would get no traction because the users demand more. Combine this with a relatively weak algorithm (much traffic is directed via sub recommendations anyway). Given these rules, our author starts with an introduction, moves into the body of their argument, and provides a conclusion. During this writing process he does two critically important things: thinking & research.

The author understands that if he writes this article, intellectuals will read the post and respond to it in a critical way. They will find the flaws. This is very much driven by the fact that most people on Substack are academically inclined and critical. Thus, a very important differentiating lever between X and Substack is the user. At the end of the day, our author writes the same thesis, except now it has more facts, more research, and more thought. Whether or not our author is “right” is irrelevant. Now his piece has facts, statistics, and statements that can be rebutted and debated. His argument is made stronger for it.

The point here is not to say whether eugenics is good or bad. It’s to prove a point. The type of content that gets perpetuated on one platform compared to another can vary dramatically depending on the levers that platform decides to pull. Now, my personal disposition is that Substack is a far more powerful and useful mechanism to communicate complex ideas on. I also believe that “hot takes” lead to incorrect conclusions. Indeed, I think they are harmful to industry, crypto in particular.

Concluding Thoughts

I don’t want to come off as saying one platform is better than another, though I am certainly partial to Substack, my baby. I want to bring attention to the fact that each of these platforms is positioned for different purposes. Society needs to figure out which platforms specialize in what type of content. Go to Facebook and TikTok for memes and cultural trends. Go to X, BlueSky, and Threads for breaking news. Go to Substack and Medium for science, politics, and philosophy. Go to Reddit for your hobbies.

No platform is an island. Each serves its own purpose. Understanding how content is perpetuated is critical for us to adapt as a society and not devolve into a post-truth, post-fact civilization. Just like humanity had to adapt and use nuclear power appropriately, so too do we need to use social media appropriately. If we don’t, the consequences could be catastrophic.

You walk into an empty concert hall. At first, you are alone. You look around at the architecture and admire your solitude. A friend follows you in a few minutes later to appreciate the hall too. You begin speaking together. You take turns speaking and responding to one another. The only other noise is the reverberation of your echo bouncing off the hall’s walls. The conversation is delightful. You like your friend and what you both speak about.

After several more minutes, more of your friends enter the hall and join the conversation. Words are given more quickly and the reverberation is bouncing more rapidly. The conversation doesn’t diverge until a dozen or so people join the group. Now there are two conversations happening and the group begins to separate in order to make room for the two voices. You still hear your friend but notice the other voices in the background.

After some time, another group hears the activity and enters the hall. Enjoying the energy of pleasant conversation in an open room, they begin discussing the hall, it’s architectural characteristics, it’s great size, and potential performances they might attend in the future.

Several more groups join in the room and now what was once two conversations prattling against the concert hall walls, is a series of a dozen different discussions, all with varying topics and volumes. You overhear something you are interested in, bid your friends farewell, and go join the other conversation. They welcome you.

For now, you can easily move from one conversation to the next.

But more and more people keep pouring into the room. They each add a new voice and a new conversation to the grand hall. It becomes more difficult to squeeze between the people and get to conversations your interested in. In fact, you can’t really overhear other conversations at all so you can’t find other conversations even if you’d like to. You’re relatively content in your corner of the hall and can still hear things just fine.

More people squeeze into the already full hall.

But you no longer move. It’s too hard to find new conversations and other people are so loud. They get in the way of you being able to listen to the conversation at hand. You grow frustrated. Why do the other conversations have to interfere with your enjoyment of your small corner of the room?

Eventually, people start bringing small radios into the room so that they might have music for their conversations. The hall becomes loud with music and voices. But each group has their own radio. Some people bring multiple radios to make it louder in their corner of the hall. Two radios are louder than one. And three are louder still.

The hall now numbers thousands of people and each person has many radios. Thousands of people and tens of thousands of radios. The hall is pure chaos and no one can hear anything.

You discover a microphone on the floor and pick it up. You speak through it and realize that it’s connected to the loudspeaker. There is no ignoring your voice, it booms over everything and everyone. They get quiet. The radios turn off. You’ve found your voice.

There are other microphones. Four in total. Eventually, the hall is dominated by four microphones. Conversation and radios re-ignite. The conversations revolve around these four microphones. Each group is annoyed by the existence of the others because they make it more difficult to hear their locality. But the room reaches an equilibrium. Four primary conversations with other conversations emitting from that one.

Still, no one can move from one part of the room to another because it is too tightly packed. You accept that you cannot move and stay put. You don’t know whether you would prefer the conversation coming from one of the other 3 microphones. But it doesn’t matter. You have no choice. The room is a cacophony. You can’t even get out of the room if you wanted to. It’s too crowded to move.

You’re stuck.