Why intent-based aggregators will dominate retail prediction market volume

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Prediction market interfaces continue to rely on legacy order types inherited from traditional financial exchanges. On platforms like Polymarket and Kalshi, users can submit two kinds of trades:

1. Market order: takes liquidity off of the book and execute at the best available price
2. Limit order: places liquidity on the book which will be matched if there is a requisite matching order

When a user sends a market or limit order, they are instructing the frontend to build a transaction with their exact parameters. The frontend then sends the constructed order to the orderbook matching engine. The user must decide exactly how their trade should execute, even if another venue, route, or execution path could deliver a better price.

In this post, I outline why intent-based aggregators will be the preferred retail prediction market interface and provide specific examples of novel orders enabled by intents.

An intent is a set of constraints that allow a user to outsource transaction creation to a third party without relinquishing full control to the transacting party. They allow the user to tell a third party "what" they want without caring about "how" it is achieved.

These intents are typically sent to an aggregator. The aggregator is responsible for figuring out how to give the user the best price. Aggregators achieve this by asking a set of fillers for a quote and routing to the filler with the highest quote.

UniswapX was the first major aggregator to implement intents. Today, all the major DEX protocols operate as an intents-based aggregator, albeit with slightly different implementation nuances.

Intents are a meta-order type that allows for more flexibility. This enables novel user interfaces that serve as wrappers around different intent orders. One such novel user interface is an LLM-based interface that accepts user orders in plain English. These LLM-based interfaces can then easily translate and encode them into an intent order.

Equities and tokens are fungible assets. 1 share of META = 1 share of META (within a given share class). 1 BTC = 1 BTC.

Prediction market assets are non-fungible, as each individual market may have different resolution criteria. In the simplest case, the same event might point to different resolution sources. For example, crypto markets on Polymarket resolve based on the Binance spot price, while Kalshi markets resolve based on CF Real-Time Indices.

Problems and potential design solutions for market interfaces

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The interfaces of Polymarket and Kalshi have remained functionally identical since 2022, copying familiar exchange designs rather than building novel interfaces from first principles.

This is what Polymarket and Kalshi looked like in 2022:

Three years later, their interfaces are almost identical:

While this made sense initially for early user acquisition, the lack of any meaningful changes directly affects market efficiency, liquidity provision, and price discovery. Opinionated frontends and aggregators should differentiate themselves along different axes to develop specific tools that people want.

Third-party frontends have similarly failed to innovate thus far, despite the clear opportunity. Prediction markets are still awaiting their native trading interfaces.

In this post, I outline six problems with prediction market interfaces today. For each problem, I offer commentary and a solution.

The Wikipedia model

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Play money prediction markets were once the only legitimate venue for participating in prediction markets due to regulatory concerns. They trained the first generation of prediction market traders and served as the first large-scale instantiations for how these markets actually work.

Many play money platforms, such as Manifold, have strong communities that persist to this day. However, the rise of Polymarket and Kalshi provides concrete incentives for sharp traders to trade on real money prediction markets instead of play money platforms. Many early Manifold traders became early active users of Polymarket and Kalshi.

In this post, I examine the performance of play money prediction markets compared to their real money counterparts. Following this analysis, I outline several advantages in their market structure and describe a future where they function as important actors in the prediction market industry.

The Brier score is a scoring rule that measures the accuracy of probabilistic predictions. It ranges from 0 to 1, where 0 represents perfect accuracy and 1 represents the worst possible predictions.

The score is calculated as the mean squared difference between predicted probabilities and actual outcomes. For a single binary prediction, if you predict probability p and the outcome is o (1 if an event occurs, 0 if not), the Brier score = (p - o)². Lower scores indicate better calibration and resolution of predictions.

Brier.fyi uses Brier scores to evaluate prediction market accuracy across four prediction market platforms: Polymarket, Kalshi, Manifold, and Metaculus. Polymarket and Kalshi are the two largest real money prediction market platforms. Manifold and Metaculus are the two largest play money platforms. Manifold uses "mana", which is play money currency. Metaculus operates as a forecasting website with slightly different prediction mechanics, but can be interpolated into many of the prediction market aspects.

Polymarket, Kalshi, Manifold, and Metaculus have Brier score as follows:

Unsurprisingly, real money prediction markets outperform their play money counterparts. This makes intuitive sense: if price discovery occurred on play money prediction markets, someone could easily arbitrage between play money markets and real money markets.

Nevertheless, they perform better than one might expect and actually outperform real money prediction markets on certain axes such as science markets (Metaculus vs Kalshi).

1. They do not fall victim to numeraire effects.

Context is that which is scarce

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One of the most alluring things pro-prediction market people say to skeptics is, "If the price is incorrect, you can fix it and make money doing so!"

Polymarket's own website states this equivalent as fact:

Prices = Probabilities.

Prices (odds) on Polymarket represent the current probability of an event occurring. For example, in a market predicting whether the Miami Heat will win the 2025 NBA Finals, if YES shares are trading at 18 cents, it indicates a 18% chance of Miami winning.

These odds are determined by what price other Polymarket users are currently willing to buy & sell those shares at. Just how stock exchanges don't "set" the prices of stocks, Polymarket does not set prices / odds, they're a function of supply & demand.

The logic mirrors the question posed to market bears who performatively claim asset prices are overvalued: "Are you short?" Here is a liquid venue to express your view and profit if you're right. When asked, most aren't actually short.

Last year, Dan Robinson posted a theoretical question about prediction markets in an X poll, asking what the price of a prediction market would trade at, given a known probability of an event occurring:

According to Dan, only 403 out of 2,293 people answered this correctly.

Stop here to think about it before scrolling down for Dan's solution.

If the title didn't already give it away, it is not the most popular answer: $0.75 (A).

Dan claims that the answer is D: Anything between $0 to $1. His rationale is as follows:

1. Prediction market payouts are denominated in a base currency such as USD. Users use USD to buy contracts and are paid out in USD if the market resolves in their favor.
2. Some prediction markets result in changing the value of the underlying base currency on a per unit basis.
3. Traders engage with prediction markets with full context on the value of the base currency with respect to its ability to purchase goods and services, not in a dollar for dollar vacuum.
4. This means that the different outcomes prices do not necessarily correspond to respective probabilities but rather the average of every trader's utility function in those two cases.

7 explanations

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The Iowa Electronic Markets pioneered the first modern instantiation of a prediction market in 1988, allowing academic researchers to trade contracts on political outcomes. DARPA experimented with prediction markets for intelligence gathering through its Policy Analysis Market in the early 2000s, but was quickly shut down due to some markets being associated with assassination markets of political leaders.

Companies like Google and Microsoft have experimented with internal prediction markets to forecast project timelines and product success. InTrade emerged as one of the first major public platforms, gaining attention for its accurate predictions of elections and other events before shutting down in 2013. Augur launched in 2018 on Ethereum but struggled to gain traction.

In 2020, Polymarket did $11.3M of volume during the week of the 2020 US presidential election and had effectively zero volume until 2024.

Various implementations of prediction markets have been attempted over the past decades. Yet they only achieved cultural product-market fit within the past year.

Today, prediction markets process over $3 billion weekly. New partnerships emerge daily to integrate prediction markets into sports, news, and search.

In this post, I provide seven explanations, ranked from most to least likely, for why prediction markets took so long to find product-market fit.

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1. US regulatory agencies outlawed the creation of prediction markets.

The CFTC effectively banned prediction markets for decades through aggressive enforcement. InTrade shut down in 2013 after regulatory pressure. Augur launched in 2018 but struggled to gain traction, partly because operating in regulatory gray areas limited legitimate user acquisition. The few attempts to obtain licenses faced years of legal battles with uncertain outcomes.

Sophisticated participants (traders, market makers, institutions) avoided unlicensed platforms due to legal risk, instead opting to trade correlated assets or one-off event contracts OTC. Without sophisticated participants, markets remained thin and poorly priced. Without good prices, platforms couldn't demonstrate value to regulators.

Priority batch auctions as one better market structure to create more liquid prediction markets

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The four major prediction market platforms, Polymarket, Kalshi, Opinion, and Limitless, all facilitate price discovery through an orderbook. Each market has an orderbook for each YES/NO outcome, which lists buy offers waiting for a compatible sell offer and vice versa.

The matching engine for the top four orderbooks occur offchain, as onchain orderbooks still remain unviable. All markets are matched first-come-first-served (FCFS), with the exception of live sports on Polymarket (more on this later). FCFS means that trades are processed exactly in the order that they are received. These orders are then processed by the orderbook matching engine based on price-time priority.

FCFS requires the public to trust that the companies running the infrastructure are operating honestly and not re-ordering transactions. In traditional markets, there is robust regulation to ensure that orders are processed on a FCFS basis. In prediction markets, these guarantees are much weaker given that the state of regulation for prediction markets is still in its infancy.

Nevertheless, orders are still very likely processed FCFS as existing prediction market platforms are not incentivized to risk their reputation by modifying their ordering mechanisms without notice. This is generally confirmed by rudimentary testing.

FCFS creates a latency war to update prices as close to real time as possible. This creates a large incentive to co-locate with the matching engine of the orderbook to have the lowest latency and the ability to react to news events the fastest.

Recall that market makers make money by buying at a lower price and selling at a higher price. They make money when the fair price is between their bid and ask spread.

When the fair price moves before they can pull their stale quotes, they are picked off by takers.

When market makers have less certainty that they will be able to update their quotes in time to react to a new fair price (as is the case with FCFS), they are forced to widen their quotes. Wider quotes means worse prices for traders.

15 million free riders and counting

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Thirty-seven years after the first modern prediction market was launched at the University of Iowa, prediction markets have finally found product-market fit.

Weekly prediction market volume crossed $3B for the week starting October 27, 2025, and it is on a clear trajectory to play a large role in finance, news, and everyday life.

Prediction market volume is growing at 20-30% week-over-week:

For the week starting October 27, 2025, there were 9 million transactions across all prediction markets, and it is increasing by roughly 20% week-over-week:

It is too early to predict where the growth rate will begin to plateau. Extrapolating using a 5% week-over-week growth rate, weekly prediction market volume will be $10B in May 2026 and $36B in November 2026.

For reference, weekly volumes for global FX, US equities, and US equity options sits around $40 trillion, $2 trillion, and $750 billion, respectively. Spot volume across crypto onchain exchanges was $56 billion for the same week of October 27, 2025.

Some see the rise of prediction markets as evidence that society is at last catching up to the theories they've championed for decades. Others argue that this is a temporary equilibrium, heavily distorted by the gambler archetype willing to consistently take negative expected value bets, and that prediction markets are not an incentive-compatible market structure.

Productized scapegoating

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Instagram and Twitter were the defining cultural companies of the 2010s. These platforms created explicit status games where users compete for social capital via metrics such as followers and likes, creating a world where online perception is upstream of real-world outcomes. The social dynamics of these platforms has been extensively written about.

Companies are now finding themselves dragged into the same status games. They are increasingly going direct, with edgy X accounts and marketing stunts. In the PR-driven information age, companies have higher budgets allocated towards managing their social perception. Companies are particularly interested in paying for services that reduce their likelihood of being associated with negative press. These social dynamics create demand for professional blame absorption.

Just as Software as a Service lets companies rent specialized technology services instead of building it, Blame as a Service (BaaS) lets companies rent scapegoats instead of becoming them. These third-party BaaS firms absorb the backlash from unpopular but profitable decisions, allowing their clients to pursue what actually drives their bottom line without sacrificing their carefully cultivated brand image.

The characteristics of a typical BaaS company includes:

1. Offers a bundle of services that conceal their true value proposition of blame absorption
2. Shields elite decision-makers from decisions with negative externalities
3. Benefits from network effects as their blame-absorption capacity scales

We are beginning to see more BaaS companies in the Average is Over era. The elite class across industries is smaller but growing in power and increasingly willing to pay for institutional lackeys that protect their interests while maintaining plausible deniability. BaaS companies engage in third derivative work. They don't do the work directly or build the tools, instead deciding what should be done and absorbing the blame for the consequences.

In this post, I examine the market structure of three BaaS companies operating today and one future BaaS company archetype.

McKinsey is the canonical example of a BaaS company. The decision to hire McKinsey is made by the executives of a company, nominally to improve the company's bottom line.

A company may be genuinely interested in hiring McKinsey to get an outsider's view on their reasoning before undergoing an action, as decisions could affect billions in enterprise value. But companies usually know what needs to be done before McKinsey walks through the door. At minimum, they hire McKinsey to execute and check financial projections. They have the deepest contextual knowledge of their field, whether the plan is expanding product lines or cutting thousands of jobs.

But now it comes stamped with the authority of an "unbiased third party." When layoffs hit or unpopular restructuring begins, executives can point to McKinsey's recommendations. This enables company executives to take unpopular decisions by outsourcing blame to McKinsey.

38 observations

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My previous post makes it clear that I do not think you can raise someone’s agency levels via a 1000 word blog post.

This post is my contribution to the agency industrial complex.

1. They have exceptionally clear writing.

2. They say things that allow you to connect dots in your own life.

3. They are consistently dissatisfied with the state of their work.

4. They are dissatisfied with the state of the world.

5. They will always ask the question, no matter how dumb it may make them seem.

6. They are internally disagreeable.

7. They believe that agency is an environmentally-induced trait.

8. They often keep read receipts on.

Against the Agency-Maxxing Industrial Complex

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In No One is Even Trying, Applied Divinity Studies provides three compelling examples and relevant statistics showing that high performers often succeed through basic effort rather than exceptional talent. This leads to the reader asking themselves, “Am I even trying?”

This blog post archetype is often the most popular posts from a given blogger. Some other examples include:

• Maybe you’re not Actually Trying - Cate Hall (18,159 likes, her #1 most popular blog post)
• How to be more agentic - Cate Hall (3,330 likes, her #2 most popular blog post)
• How I practice at what I do - Tyler Cowen (142 comments, compared to 41 and 61 comments for the other two posts that day)
• The unreasonable effectiveness of just showing up everyday - Kishore Nallan (his #1 most popular blog post)

Every single decently written instantiation of this blog post garners high engagement. Or maybe I’m falling into this trap:

At the risk of sounding arrogant, I wrote a decent instantiation of this type of blog post as my third post, No One is Really Working. That post went viral and is still my most popular piece by far. Without sounding even more arrogant, I knew that it would take off, even though it existed in obscurity for over 135 days. Just look at how many variations of “you can just do things” went viral on X in the past year.

The predictability of virality demonstrates that there is an underlying structure to these agency posts that can be analyzed and replicated. No One is Really Working set out to test my theory in production. It achieved virality, so either my theory is true or I just got lucky.

In this post, I first describe the structure and steps to replicate popular agency posts, then explain why these posts impose a strict negative externality on the world.

Agency posts send the reader a clear message: you are capable of doing much, much more.

• No One is Even Trying shows the reader that famous directors, YouTubers, and writers are the only ones even putting in a modicum of effort. If you did a fraction of this, you could realize outsized gains.
• Maybe you’re not Actually Trying tells a story where her husband does the obvious things of contacting relevant authorities to resolve her stalker. If you applied similar obvious problem solving processes to your own life, imagine all the problems you could solve.
• How I practice at what I do outlines Tyler’s day of reading, writing, talking, and eating. If you did the things you already do with more intentionality and consistency, you could be as productive as Tyler.

21 assorted thoughts on derivative levels

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The following are an assortment of notes and observations from Why do people who get paid the most do the least?.

1. A single worker can output a product that maps to different derivative levels.

A YouTube philosopher outputs work with components on every derivative level:

• First derivative: uses existing tools to upload videos on the YouTube platform
• Second derivative: builds systems and pipelines to increase content creation velocity
• Third derivative: identifies which philosophical ideas to popularize
• Fourth derivative: changing the thoughts of the third derivative people

The shape of their output depends on their objective function.

2. Nobody likes being told what to do, especially as you move up the derivative levels.

Third derivative workers really hate being told what to do.

3. Second and third derivative workers are often very stubborn in their personal lives.

The anecdotal evidence is overwhelming. I would be very curious to see data with respect to their personal life metrics (divorce rate, life satisfaction, etc.).

4. Professional and romantic relationships across large chasms in derivative levels are difficult to navigate, often due to very different interests and values.

Third derivative work

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CEOs and professors are both highly compensated, albeit with different combinations of financial and social capital.

Consider the average day of a CEO:

1. Wake up
2. Go to the gym
3. Go to the office
4. Get briefed by your assistant
5. Respond to some emails
6. Go to some meetings
7. Lunch
8. Sit through a strategic initiatives meeting
9. Send some emails
10. Go home

And now consider the average day of a professor:

1. Wake up
2. Drink coffee
3. Give the same lecture you’ve done 1000 times with nobody listening
4. Go to a research meeting
5. Lunch with other faculty you don’t really like
6. Talk with graduate students about research
7. Write a grant you probably won’t get
8. Go home

Everybody who isn’t a CEO or professor looks at these schedules and thinks to themselves, “These people aren’t doing anything”, followed by “I can do that.” On most days, this is probably correct. The trajectory of Chipotle would not change if I was CEO for a day. College students around the world would still get their protein slop bowls that day, and life would go on.

Because of this, some people believe and rationalize this as oppression. Why are all these high status and well-compensated people not really doing work, while I get paid much less to do much more physically and mentally demanding work on a day-to-day basis? While this is certainly true in some cases of nepotism or otherwise, labor markets in the US are pretty competitive and there really isn't much evidence to believe that wages are far exceeding productivity in the long-run.

In No One is Really Working, I offer seven explanations as to why professionals get paid high salaries to do seemingly nothing. One rationale goes as follows:

2. A single breakthrough covers everything.

A worker comes up with the idea of a widget that increases internal productivity 1000-fold or creates a new product that everyone wants. The firm asymmetrically benefits from capturing the economic value of this breakthrough and does not compensate the employee proportionally to the value they've created.

You don't know who will do this ex-ante (and neither does the employee) so you have to pay everyone an inflated salary to attract the innovator.

Compensation impact: High in select industries, low otherwise

This explanation elicited the strongest reactions amongst emails and comments, as it depicts the asymmetric reality of value creation. This is succinctly captured by the comment:

12 observations and opinions on financial markets

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This post draws insights from a previous post: Financial Markets.

Many of these insights are well-understood by finance practitioners but are still worth re-iterating.

1. The success of an exchange is a function of the number of interesting assets available to trade and how much people want to trade on them.

Exchange success = (assets) * (volume) + (selling data)

2. The success of a brokerage is a function of how much money people deposit onto the platform and how much people want to trade on them.

Brokerage success = (assets under management) * (volume)

3. Liquidity network effects are stronger than social network effects.

Liquidity network effects enable easier price discovery when everything is in the same centralized venue. Additionally, exchanges accrue technical debt and impose increasingly higher switching costs for integrators.

User preferences are constantly changing, putting significant pressure on the social network effect of brokerages. Furthermore, regulation is designed to incite competition among brokerages.

4. Consumers are generally very well-protected and oversight is quite robust.

Exchanges and brokerages from first principles

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The global financial system exists to price, transfer, and distribute risk. Every asset (stocks, bonds, derivatives, etc.) encodes a unique risk profile and payoff function, allowing market participants to express viewpoints.

A market is a place where people can go to trade various assets. In finance terms, markets are where buyers and sellers meet to trade assets at prices they both accept. Functioning markets match people who want something with people who have it and determine what price clears the market. Today, almost all financial assets are traded electronically.

Modern financial markets facilitate the trading of assets through order books. Order books are digital ledgers that track all pending buy and sell orders based on price-time priority. Price-time priority dictates that trades are matched based on the best available price, and if multiple orders have the same price, the earliest order by time takes priority.

Order books have bids and asks. Buy orders (bids) stack up on one side, ordered from highest to lowest price. Sell orders (asks) line up on the other, from lowest to highest. When a bid meets or exceeds an ask, the matching engine executes a trade automatically. This process repeats millions of times per day, constantly discovering the price where supply meets demand.

In the US, the market infrastructure is split into two key players: exchanges and brokerages.

Exchanges are venues that define market structure rules to facilitate the buying and selling of an asset. Trades typically occur on an order book on a server. Notable exchanges include NYSE, Nasdaq, and ICE.

Brokerages are entities that connect users to exchanges. Users submit trades to brokerages who execute trades on your behalf through exchanges.

The separation between exchanges (execution venues) and brokerages (order-originators) was the direct effect of the Securities Exchange Act of 1934. Some countries implement a similar bifurcation (Canada, United Kingdom, Australia), while others operate exchanges and brokerages as a unified entity (China, Hong Kong, Singapore).

The vast majority of crypto exchanges operate both the exchange and the brokerage, including prediction market venues such as Polymarket and Kalshi.

This post explores the primary revenue sources of exchanges and brokerages.

Path-dependency protects us from ourselves

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The following are examples of laws and economic structures that are not incentive-compatible, yet very few people seem to care.

Florida has a state law called the homestead exemption that enables people to declare bankruptcy while keeping their primary residence. Notable people who have leveraged this exemption include OJ Simpson and Bowie Kuhn (former MLB commissioner).

There is a long legal precedence for utilization and makes it attractive for wealthy residents to leverage preceding bankruptcy.

The rules are as follows:

1. It must be your primary residence
2. Covers all property contiguously connected
3. No dollar limit on the homestead exemption value
4. Must have owned the property for at least 1,215 days before filing. If owned for less, the exemption is capped at ~$200,000

This provides the incentive for people to buy expensive homes while taking on massive personal risk. If you know you’re about to go bankrupt in the next 5-10 years, move to Florida. If there’s even a chance that you’re going to go bankrupt, the rational strategy is to:

1. Liquidate all your assets
2. Move to Florida and buy the biggest house you can afford that you can pay off in full
3. Live there for 1,215 days
4. Towards the end, take out as much credit as you can: credit cards, personal guarantees, etc. You can probably rack up $250,000 to $500,000 in debt
5. Declare bankruptcy, keep your house. Liquidate your house after bankruptcy proceedings.

Turn $10 million in exposed assets into $10 million in protected assets, while also extracting six-figures of discharged debt.

And yet neither Florida or Texas are on the top of the list for state bankruptcies.

Churning is the process of applying for new credit cards to farm their sign up bonus. People who participate in churning open and close credit cards multiple times a year, accruing points, miles and cash back in the process. The mark-to-market aggregate value of each sign up bonus ranges from $100 to well over $1,000.

Save us, Sumner

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Every so often in conversation, I’ll hear someone say:

• “People aren’t buying gas right now because the price is high.”
• “People aren’t buying houses now because interest rates are high.”
• “Everybody’s wages are rising, which causes inflation.”

Every time a statement like this is uttered, a piece of Scott Sumner dies inside.

Sumner has a famous meta-insight: “Never reason from a price change”. The principle references instances where people treat prices (outputs) as causes rather than effects. When we observe a price change, it's always the result of underlying shifts in supply, demand, or both. The price itself tells us nothing about which force is at work. Responses should target the cause, not the symptom.

If you haven’t studied economics or really thought about these issues, it’s understandable how one could fall into these traps. Ceteris paribus, you typically buy less of something when the price is high and more of something when the price is low. But prices here are the effect, not the cause.

But it’s not just the random layperson making these mistakes. Sumner cites famous, well-seasoned economists making these errors:

• Robert Shiller viewing low real interest rates as an incentive for investment
• Larry Summers equating low interest rates with easy monetary policy

The correct interpretation of each statement is:

Markets operate via supply and demand. Consider the most basic Econ 101 graph:

The best way to predict the future is to build it

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A handful of engineers can now build attention algorithms that shape political and cultural discourse more effectively than any government. A team of two can create economic systems where millions transact daily and generate millions of dollars of profit per day less than one year after launch. A single engineer can rug model weights and cause people to commit suicide.

A small number of people don’t just participate in the world, they construct the rules the rest of us have to live inside. This is world building.

In a recent talk, Bruno Macaes succinctly enumerates the world building meta-game:

“There’s no greater power in the world than to build the landscape or the system within which others have to operate…A superpower encodes the game, a normal power plays the game.”

The distinction is not left/right or establishment/anti-establishment. It’s about world builders and world players. World builders and players used to be part of government-controlled institutions and organizations, but this is no longer true.

Macaes accurately notices that the stratification between players and builders is increasingly dominated by teams developing powerful technologies without explicit government control. He is able to parlay his knowledge to read and predict cultural signals. In a 2020 piece, he draws a sharp East–West contrast rooted in linguistic and epistemic norms:

“‘Do you know why the Chinese are so naturally good at deep learning? Because the black box has been part of Chinese society and Chinese culture since the very beginning. Zen meditation, yes, but not only. Chinese medicine. There is an input, some herb or infusion. You have no idea how it works, but it does. All you can do to get a different result is enter a different input.’”

Macaes’s framework, while provocative, reads like an oversimplified Grand Unified Theory that assumes a starting point and ending point, dismissing the middle as irrelevant path-dependency. This isn’t a unique indictment of Macaes; rather, it’s a more general academic and journalistic critique of caring about the messiness of reality. Traditional geopolitics operated through legible channels via sovereign states, formal treaties, and clear hierarchies. The new paradigm operates through institutional bypass and digital leverage, which remain opaque without deep immersion in frontier digital culture.

The shift from world player to builder isn’t new. History is filled with individuals who demonstrated competence, transcended their initial roles, and reshaped systems. What is unprecedented is the velocity and scale of this transformation.

• Shitposting your way to controlling the world’s social graph
• Participating in YC to running YC to running one of the most important companies
• Trading to architecting new economic market structures for exchange business models

How novel titles reshape talent allocation

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Over the past couple years, some tech companies have begun refactoring traditional job titles:

Refactoring titles is a form of title arbitrage. Titles are an imperfect signal of how one contributes to an organization. They confer varying levels of status across different groups. Title arbitrage shifts the relative status of certain positions and changes what people want to work on. Some title refactors are simply name changes while others are signals that a company is taking an opinionated view on the world. Many companies also pair this with title deflation, abandoning external leveling schemes in favor of flatter hierarchies with these new designations.

Title arbitrage and deflation represent an attempt to rewrite tech’s status hierarchy and reshape its culture from the top down. In recent years, this has been led by companies attempting to usher in a new era of tech giants, most notably AI research labs and adjacent entities.

Companies implement title arbitrage and deflation for the following reasons:

1. New titles increase the status of certain jobs that are core to company success.

Certain roles are less sexy than others. Talented people naturally gravitate toward high-status positions and titles regardless of actual fit. New titles increase the status of certain roles and attract talent to roles they would have dismissed under their original labels.

Palantir pioneered the Forward Deployed Engineer (FDE) title. FDEs are critical to the success of Palantir as they develop custom solutions and relay frontier context that gets fed back into Foundry or Gotham. While FDEs are there to nominally serve as a solutions or integration specialist, sending sharp people (1) creates tighter feedback loops for customer satisfaction and (2) signals company strength, as most companies deploy average talent in customer-facing roles. When clients interface with Palantir’s top-tier FDEs, they are left impressed and ask themselves: if the FDE they sent to us is this impressive, how impressive is the rest of the company?

The status of the company and role enabled them to recruit sharp software engineers into technical consultant roles. Palantir was able to recruit out of the more technically-savvy (and likely higher g-factor) FAANG talent pool instead of the management consultant pool. You have to give someone a lot of perceived status to convince competent people to travel to random cities around the world to integrate data pipelines at 2 am.

To be sure, socially engineering smart people to work in customer-facing roles was necessary but not sufficient for the success of FDEs at Palantir. Rather, it is in conjunction with their business strategy and company architecture that enables the creation of software that stays years ahead of what everyone else thinks governments and enterprises need.

2. Fewer labels reduces siloing, allowing talented employees to contribute across functions and naturally shift into new areas of work.

12 learnings and takeaways

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When I was in university, I played a lot of poker. Looking back, I was sniped by poker because it promised a meritocratic environment where I could prove my skills while indulging my libertarian and anti-establishment instincts.

Every weekday, I was four-tabling Bovada and GGPoker games on my primary monitor while watching lectures on my second monitor at 3x speed. When I wasn’t playing, I was studying poker charts to improve my abilities.

I played seriously for about 6-8 months. I still play occasionally in-person now, on the order of 5-10 times a year.

The following are my takeaways from playing poker:

1. Game selection is more important than theory.

Figuring out how to get into better games will yield better marginal results than studying charts and theory. There are times of day that are much, much more profitable to be playing than others. This forces you to think through incentives and run experiments on your own.

2. Zero-sum games like poker are capped and you can quickly find yourself trapped in a local equilibrium.

The earlier you realize this, the better.

3. The best players don’t play poker.

Ben Affleck, Nate Silver, and Haseeb Qureshi all played professionally or semi-professionally. They all could have made a career in poker but instead chose to utilize their skills in careers with uncapped financial and social upside.

The strongest counterarguments against increasingly popular beliefs

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Three ideas have gained remarkable momentum, even becoming consensus in certain circles:

1. Consultants don’t do real work.
2. Affirmative action puts diversity over merit and blocks the most talented candidates.
3. Alcohol is bad for you and you should never drink.

True intellectual rigor demands we seek out the strongest possible counterarguments, even when they challenge beliefs that feel obviously correct. Laying out the strongest arguments for the opposing side ensures that we understand the other side from first-principles. If you can't articulate the strongest possible case against your own beliefs, you don't understand the topic well enough to have strong beliefs about it, and are subject to the public intellectuals and other cult-like individuals who present these ideas.

In this piece, I steelman the case for consultants, affirmative action, and alcohol consumption. Note that this is not what I necessarily believe, but what I believe are the strongest arguments in favor of each.

Zeitgeist belief: Consultants don’t do real work. Consultants may work nominally long hours but do not add any significant value to firms, billing high rates for meaningless PowerPoints.

Steelmanned counterarguments:

1. Consultants enable executives to take unpopular decisions by outsourcing blame to a third party.

Consultants are presented as unbiased third parties who are stewards for the company to increase its bottom line. Improving the bottom line often means cutting costs by laying people off. Executives utilize consultants as a scapegoat in laying people off to deflect the blame and ultimate responsibility for unpopular and difficult actions.

2. Consultants can be more incentive-aligned than internal stakeholders.

Consultants typically bill per hour with milestone goals. Company employees are typically salaried employees who are not conditionally employed for a set of scoped projects. Employees are more incentivized to do the minimum to not get fired while consultants are incentivized to work much harder to extend their contracts. Additionally, it is common for consultants on a project to eventually work for the company down the line, another incentive for the consultant to work hard for potential equity.

Thank you for increasing my blogging ambitions

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I have a lot of very sharp and capable friends. As I grow older, the more I witness an ever-widening chasm between what people can achieve and what people actually end up achieving.

The simplest explanation would be that some people are innately more capable than others and that their skills compound at a faster rate. While this is certainly true, it does not explain what you choose to work on and how hard you work.

Rather, anecdotal evidence continually suggests differences in outcomes are downstream of ambition. While some amount of ambition is probably innate, I have come to believe that much of ambition is environmental.

There aren’t very many reasons to be ambitious and work hard if you don’t have billionaire-level aspirations. Most somewhat educated people in America (and especially in Europe) act rationally by not working very hard at their job, yet lead a pretty comfortable life: baseline healthcare, money to afford most things, and ample free time.

If you’re one of these people that sees their job as a means to an end, odds are that you will assort with others who think similarly. With all your friends in a similar position in life, the group naturally homogenizes around comparable ambitions levels and naturally decays as you grow older. Once you find yourself in this local equilibrium with your identity inextricably linked to your job, friends, and lifestyle, it’s very hard to change.

One of the only ways to change this at scale is through an external entity telling you that you are capable of greater things. Omniscient entities are credible people and institutions who increase ambition at scale by providing proactive positive reinforcement before any significant evidence of realized potential.

Modern secular omniscient entities include:

Examples of omniscient entities in action:

• X/Substack/Internet Media: A collective of people provide early signals that you are producing valuable work in some dimension. You are capable of producing such good content that the audience signals that they are willing to allocate time in the future to consume the content you haven’t even created yet.
• Job/Titles: Jobs and job titles not only signal competence to future employers but also tell you that you are the type of person capable of everything associated with the title. The intern turned junior developer is likely still a net negative on the organization, but after two years on the job under a good manager, they are able to autonomously contribute to an organization. This is the time where they earn the senior engineer title.or engineer.
• Early-Stage Funding: A VC or an angel gives you money before you have any evidence of product-market fit. They believe that you will be able to figure everything out (attract talent to work for you, build a compelling product), maybe even more than you do yourself.
• Grants/Fellowships/Awards: A committee or person decides that you are talented and grants you money and/or prestige along with a peer group of similarly skilled individuals. Proactive grants may be the catalyst that leads you to break out of your local equilibrium, change peer groups, and go on to do greater things.
• Universities: Two similar students, one who went to Harvard and another who went to their local state school, start and end university with a similar skill set. The student who attended Harvard receives a Harvard diploma, but more importantly a sense that they are capable of doing greater things.

The market structure and incentives of US sports leagues

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Every major US sports league today was founded in the 20th century. None of the ten largest leagues by revenue were created after 2000:

There have been over 65 well-funded attempts to create new sports leagues since the year 2000, with many billionaires and high-profile individuals including Thiel, Chernin, and Cuban backing various leagues that have all failed.

To put this in perspective, YC’s eight company first batch included Reddit (currently valued at $50B), Justin Kan and Emmett Shear (who went on to co-found Twitch), and Sam Altman.

The three leagues with even an argument for being not total failures are the NWSL, League of Legends Esports (LoL Esports), and LIV Golf. All have unique advantages over their counterparts, and yet none of them are profitable today or even on a very convincing path toward profitability.

• NWSL: subsidized by the US Soccer Federation, Canadian Soccer Association, and Mexican Football Federation during its early years, currently still reliant on investor money
• LoL Esports: loses hundreds of millions of dollars annually, exists solely as a marketing mechanism to get people to play the actual game
• LIV Golf: backed by the Saudi Arabia sovereign wealth fund, poaching top players from the PGA by offering exclusive contracts to only play in their league

In this piece, I examine how market structure and incentives explain the absence of new sports leagues in the United States over the past 25 years.

Following these explanations, I outline some first-order insights and strategies I would employ if starting a sports league.

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1. Legal structure and precedent greatly benefit incumbents.

The MLB has a legal monopoly over baseball in the US. They are able to block the formation of competing leagues, control franchise locations, and maintain the minor league system without antitrust scrutiny.

Investing in Anthropic, Cursor, Figma, Circle, Solana, Sui by breaking the rules

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Venture investing doesn’t scale. For an industry obsessed with investing in tech companies that scale through technology, venture is a tight network of individuals deploying relatively small amounts of capital (total VC AUM is ~$1T while private equity sits around $6T; global equities are around $78T). When venture firms add more partners or capital, they almost always perform worse in aggregate.

There’s a massive incentive for people to figure out how to scale venture firms. If a firm cracks this code can deploy capital across a wider array of companies and secure larger allocation into the winners. Numerous funds and platforms have tried different strategies to scale under their respective theses with varying degrees of success:

• AngelList: anyone can sign up and spin up a venture fund at cost
• Pioneer: the internet provides a unique substrate to identify talent globally via gamification and program metrics
• SoftBank: founders optimize for the best financial terms and offering better prices can generate outsized winners
• Y Combinator: young technical talent is underserved, business acumen is learnable on the job

Unlike many of their financial industry counterparts (investment bankers, consultants, etc.), venture capitalists are not known for their work ethic. Over the past decade, few firms have attempted meaningful innovations to scale venture funds. This can be partly attributed to the low personnel turnover rate and many less-than-ambitious people. Some are playing the management game, others spend twenty years coasting towards retirement, and some just aren’t that sharp.

FTX entering the venture game was a rare occurrence of an ambitious, resourced team deploying capital. FTX Ventures deployed over $5.2B in capital across more than 400 companies, with significant equity stakes in Anthropic, Cursor, Solana, and Sui. Today, that portfolio would be worth well over $20B.

FTX’s actions were illegal and inexcusable. But unlike most other venture firms, they employed a strategic, differentiated approach that leveraged their brand to deploy capital at scale. While they adopted some tactics from other firms, their implementation was a distinct point on the capital-strategy frontier and continues to influence venture firms today.

1. Get onto the field and operate, double down on your best theses and comparative advantages. The best returns to investment are your social capital.

FTX started with Alameda when they were trading on Binance market making and running a hedge fund. Instead of using money and social capital from Alameda to start a venture firm, they started FTX to compete with Binance as they saw the benefits of creating a centralized exchange. (Perhaps too many benefits.)

Matt Huang, general partner at Paradigm and investor in FTX, is now running Tempo. Huang is uniquely positioned to lead Tempo given his experience as the founder of one of the largest crypto venture firms along with the backing of Stripe (where he also sits on the board).

To be sure, FTX did not start this trend. Keith Rabois started OpenStore while working as a partner at Founders Fund. Fellow PayPal mafia member Max Levchin started Affirm while running his venture firm SciFi. FTX pushed this approach further, running multiple operations in parallel to create compounding value and showing that running multiple operations is a strategic advantage, not a distraction.

16 observations on hospital culture and incentives

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I recently shadowed a hospitalist during a 10-hour shift. A hospitalist is a generalist doctor responsible for patients while they are in the hospital. They go through similar training to the doctors you see for your annual check-ups.

Unlike ER doctors, who handle immediate crises hour-by-hour, hospitalists manage the day-to-day care of patients with serious but not immediately life-threatening conditions.

At any given time, a hospitalist usually manages 8–10 patients, most of whom are new to them. Their job is to quickly gather information from medical records, patient conversations, and test results in order to make diagnoses and eventually discharge patients.

Hospitalists function as medical coordinators. They are trained in high-level diagnosis but quick to refer to specialists for specific interventions. In many ways, they act like PMs of the hospital: aligning stakeholders, tracking progress, and ensuring patients are on course. They can order tests such as MRIs but typically leave interpretation and detailed recommendations to specialists (e.g. surgery).

The following are a select assortment of notes from my visit:

1. Everyone jokes about death.

Everyone including the doctors, nurses, and support staff all joke about death in a way that is jarring to an outsider. This is best interpreted as emotional compartmentalization as a survival mechanism, not callousness. This makes sense, as it enables them to create a barrier between themselves and death. I imagine it’s one of the most effective ways to create a positive work environment while enabling everyone to do their best work without being depressed all day seeing people who are about to die.

2. Verbal communication dominates for up-to-date knowledge transfer, while written notes are often an afterthought.

When I first arrived, four surgeons were holding a stand-up meeting at a nearby table. The overnight surgeon recounted the nuances of a surgery she had just performed, while the others tried to listen and skim the patient’s chart at the same time. She had to repeat herself multiple times as they asked questions she had already answered.

When doing the morning rounds, the hospitalist never took notes during the meeting. The two medical students doing their rotation were taking notes in their notebook. In the hallway after meeting with the patient, the doctor and the medical students would discuss diagnoses and recommendations for next steps. There was very little disagreement as the next steps were usually clear. In one instance, the medical student reminded the hospitalist of one of the symptoms that the hospitalist had forgotten about during the 15 minute conversation. The hospitalist usually would keep everything to memory but sometimes wrote a quick 1-5 word note on their phone for future charting.

Great work always finds the people who matter

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On his podcast with Scott Alexander and Daniel Kokotajlo, Dwarkesh makes the claim that everything that is good gets read by all the right people:

“I feel like this slow, compounding growth of a fan base is fake. If I notice some of the most successful things in our sphere that have happened; Leopold releases Situational Awareness. He hasn’t been building up a fan base over years. It’s just really good…I mean, Situational Awareness is in a different tier almost. But things like that and even things that are an order of magnitude smaller than that will literally just get read by everybody who matters. And I mean literally everybody.”

Scott responds with:

“Slightly pushing back against that. I have statistics for the first several years of Slate Star Codex, and it really did grow extremely gradually. The usual pattern is something like every viral hit, 1% of the people who read your viral hits stick around. And so after dozens of viral hits, then you have a fan base. But smoothed out, It does look like a- I wish I had seen this recently, but I think it’s like over the course of three years, it was a pretty constant rise up to some plateau where I imagine it was a dynamic equilibrium and as many new people were coming in as old people were leaving.”

Watch the full clip here: Dwarkesh Podcast

The underlying assertion that Dwarkesh is making is that the content market for ideas is very efficient. Scott agrees conceptually but to a much lesser degree, citing his own experience in the early days of Slate Star Codex, and indicates that he considers the market to be less efficient than Dwarkesh does.

As a recovering efficient markets believer, I am very skeptical of anyone claiming that any market is efficient. However, Dwarkesh is correct here. Stated precisely:

The content market for novel and interesting ideas is efficient, enabled by incentive-aligned market microstructure.

To avoid ambiguity, let me define exactly what I mean by that claim:

• Content markets refer to markets that operate on internet rails. They have zero or effectively zero marginal cost and are non-rivalrous.
• Efficient means optimally connecting suppliers (content creators) in such a way that maximizes consumer satisfaction. People are bounded by their time and care about consuming the best ideas and content.
• Market microstructure refers to the mechanisms, tools, and systems that govern how content is discovered and distributed. [1]

Work harder, bet bigger

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I’ve taken a lot of career and financial risk, more than almost all of my peers. While I view my decisions as largely rational and effective, I tend to keep most of my decision rationales and work habits to myself.

Much of my decision to take early career risk can be attributed to select blogs, books, and talking with a lot of people, especially older mentors. Since most of my friends already think I'm pretty weird, I figured I'd open-source some of my work habits and decision rationales that have shaped my approach:

• Working multiple jobs for a period of time and using the earnings to have a period of unemployment (what I’m doing currently). This has enabled me to prevent myself from (1) being anesthetized by my job and becoming complacent, and (2) giving myself full latitude to work on projects in-depth, not as perpetual half-assed “side projects”.
• Two super intense deep work days per week where I go zero to one with no distractions. This is usually Sunday and Tuesday, where I work with the aim of having the highest output day possible. The other days are spent largely editing the work product from these days or otherwise lighter work modes.
• Doing way more and trying way harder; no one is even trying. Successful people just have way more output than their peers who are often equally as smart and capable. “Work smarter not harder” is some of the worst advice I’ve received. You need to put in the hours and understand the context to determine what’s important and what’s not. July was a particularly high-output month for myself, writing 50k+ lines of code and 10,000 words, averaging 4 hours of deep work for 6 days a week.
• Reading long-form books regularly, with 1-3 intensive subject deep dives per year on specific topics or classics. I schedule reading during lighter work periods since books become a major distraction during my high-output deep work sessions.
• Not working on the hardest thing I can. You can make large contributions to many fields and working on the hardest thing is often irrational. My driving thought process is asking myself where I think I can make a large marginal contribution with my skill set.
• Writing on the internet to sharpen my thinking and increase the surface area of serendipity. I write across a variety of topics and personas which allows me to have edges and peer groups at the frontiers of different subject areas. Take the initiative to meet people in person and learn about their production functions.
• Understanding the long-term games worth playing. Becoming the best badminton or chess player is impressive but very capped. Think about playing in positive-sum environments where you can own a unique spot on a growing frontier.

I certainly do not advocate for most people to follow my work habits or career choices. It is best interpreted as real work habits from your friendly internet anon who isn’t successful (yet) and who has no incentive to lie. (If I really was successful, would I really be toiling away writing this blog?)

The Kelly Criterion is utilized in binary outcome environments and describes how much to wager for a bet given certain constraints. It is used to optimize long-term expected value of your bankroll. The inputs are:

• The size of your current bankroll
• If your bankroll is replenishable
• The probability of an event occurring
• Your edge in the market

In kind environments where feedback is honest and frequent, Kelly is a useful model. You can place 1 million sports bets and determine if you are a winning or losing player on average and use Kelly or fractional Kelly to give pointers on how much you should bet.

Kelly formalizes the natural intuition that you should bet larger when you have a bigger edge and less when your edge is smaller. The less intuitive part is how the math works out:

For even money (1:1) odds, Kelly suggests:

1. 80% market probability, 10% edge → 50% of bankroll
2. 50% market probability, 10% edge → 20% of bankroll
3. 10% market probability, 10% edge → 11% of bankroll

For many parents, hyper-optimization is the preferred method for brute-forcing their children out of mediocrity.

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The NBA is one of the most competitive domains in the world. There are 450 active players in the NBA at any given point in time (15 players per team, 30 teams total). The distribution of annual income for the top 1000 basketball players in the world looks something like this:

In the 2024-2025 NBA season, the average salary was $9,191,285, and the median salary was $3,657,120.

With such a steep drop in income and prestige among the top 1000 basketball players in the world, it is no surprise that parents are doing all they can to surpass the 450 threshold and climb along the NBA salary curve to get as close as possible to the NBA supermax.

Young children who have a shot at making the NBA typically show a strong signal from a young age. The baseline attributes include height and athleticism. On-court IQ is becoming increasingly important and better measured with advanced statistics and cameras. These traits are correlated with parents with above-average genetics and the resources to provide their child with the best environment to develop. Parents who are 2+ standard deviations athletic and domain-intelligent are much more likely to have children capable of playing in the NBA.

This is intuitively true and becoming more and more empirically true every passing year in NBA data:

Naturally, these parents want their kids to be successful. When they see that their kid has the genetic makeup to be in the NBA one day, they pour in resources to have their kid achieve that goal. Parents center their lives around enabling their kids to develop their abilities and make the league.

The average NBA player is far more athletic, skilled, and smarter than NBA players just a decade ago. The average NBA player from the 80s would have no shot at being in the league today with how the game is played today. The game itself has been optimized to a local equilibrium of high-volume threes.

Because the skill floor is so much higher today, NBA players are expected to enter the league with a much higher baseline skill set. This is advantageous for kids who have been playing basketball for their entire lives and attending camps with retired NBA players. It is now normal to see kids play AAU, travel ball, and varsity basketball throughout the entire year.

Even the NBA referee pipeline is becoming increasingly optimized.

Justifying the High Salaries of Early-Career Professionals

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The following are anecdotes of a typical work schedule for young professionals in established, tracked professions.

Following these profiles, I provide explanations for why young professionals command such high compensation, relative to what their work product would indicate.

Adam has been a SWE for four years. He first started coding when he was a young boy and quickly found that he had a knack for solving puzzles. He always loved video games and was excited to learn that he could have a lucrative career that included programming the very games he enjoyed growing up.

His day consists of pushing updates to backend servers in Go and writing relevant Typescript client code. Most problems don’t require much brainpower, meaning that he can push a change and spend the next 30 minutes on TikTok. Typically, when he refocuses himself, he finds that he one-shots the problem and moves on to the next problem. A good day would be merging a couple of PRs and some friendly Slack banter.

Adam has compounded his skills over the years, thanks to a strong culture, well-defined tasks, and a competent manager. He has a good work-life balance as he is able to finish projects quickly, though most do not even have hard deadlines. He makes sure to not work too fast or set expectations too high. This is an implicit learned behavior from his boss, who is also competent and not incentivized to ask for or create more work.

On average, Adam puts in 0-10 hours of deep work a week. The rest of his work hours are spent mindlessly coding, listening in on various meetings with his camera off, and on TikTok.

Adam went to a large engineering school. He was much sharper than his fellow students and didn’t have to work too hard to get good grades. He leveraged his connections and grades to eventually work at the gaming company he’d always dreamed of.

His typical week includes merging a couple of PRs and periodically managing a few interns. He generally prefers to work alone. He is conflicted about whether to go the IC route or the manager route. He’ll probably go the manager route because his manager told him it is the path of least resistance.

Adam thinks AI and AI-adjacent tools are crutches. He does not use Twitter.

Brenda is a writer at a marketing agency, helping top brands with positioning and providing materials for advertising campaigns.

Clinton Foreign Policy, $TRUMP and $MELANIA, and the Foundations of the Internet

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This post is about how relationships, emotions, and incentives drive real-world outcomes over reason, ethics, lawful behavior, and market forces.

In 1995, Bill Clinton had a very public affair with Monica Lewinsky. In response, Hillary Clinton did not speak with Bill for many months due to the affair and the second-order effects of his infidelity.

The re-commencing of their relationship and the eventual forgiveness by Hillary is claimed to be traced back to her strong-arming her husband to bomb Serbian villages: [1]

Bombing Serbia was a family affair in the Clinton White House. Hillary Clinton revealed to an interviewer in the summer of 1999, “I urged him to bomb. You cannot let this go on at the end of a century that has seen the major holocaust of our time. What do we have NATO for if not to defend our way of life?” A biography of Hillary Clinton, written by Gail Sheehy and published in late 1999, stated that Mrs. Clinton had refused to talk to the president for eight months after the Monica Lewinsky scandal broke. She resumed talking to her husband only when she phoned him and urged him in the strongest terms to begin bombing Serbia; the president began bombing within 24 hours. - Source

The primary source here is shaky at best. But is something like this really beyond the realm of possibility for one of the most ambitious and socially intelligent Western female leaders with a long history of hawkish foreign policy tendencies? Hillary’s love language could very well be acts of war.

Early-stage startups run on secrets and secrets often spread like wildfire. NDAs are only as good as the ability to enforce them. Your favorite application (Facebook, pump.fun) has a much more complex founding story than is publicly available, likely with some questionable espionage-esque behavior as a core propellant to reaching their heights.

Most participants understand this social contract and end up contributing and extending in-group dynamics. This includes connected individuals carefully crafting narratives for public consumption.

The founding stories of successful companies including Paypal, Flexport, and Notion are well-documented and leveraged as lore to attract customers and prospective employees. How much of these stories reflect reality versus a curated Girardian story to persuade others of their divine purpose?

Verkada is a security camera company founded in 2016 and is currently valued at $4.5 billion. They offer a full-service physical security solution to institutions, installing and managing the logistics of their security camera operations.

Clients include the Los Angeles state government, prisons, Tesla factories, Equinox, healthcare systems, and leading biotech firms.

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There’s a graph from Tim Urban that I think about often:

It never ceases to amaze me that you can literally see all the weeks of your entire life on a single sheet of paper. You have a very finite number of weeks in your life. [1] Every decision matters.

Time is the one scarce resource that affects everyone equally. Regardless of your background or socioeconomic status, everyone plays by these same constraints. [2]

However, reality is even more pernicious than what this graph shows. As you cross off each week of your life, you falsely believe that you are operating in a linear regime where each week is an equal proportion of your perceived life. In reality, your perception of time in life is better modeled on a logarithmic scale rather than a linear one.

At age 5, one additional year is another 20% of your life. At age 50, one additional year is only 2% of your life.

While our perception of time is not perfectly logarithmic, talking to people older than me leads me to believe that this heuristic is at least directionally true.

What should we do about this? How can we maximize our ability to get what we want in our lives?

As early as possible, you should develop a thesis for what you want to do given your risk profile, work/life balance, and skillset. Your thesis can and should evolve as you grow older.

Intelligence is getting what you want out of life, and most people complain they aren’t getting what they want without any evidence of even a modicum of effort. You can get ahead of basically everyone in any field by putting in an ounce of effort. No one is even trying.

Don’t spread yourself too thin, but realize that there are unexpected returns to being a polymath and deriving cross-pollination insights. Choose 2-3 areas to do ten times as much in life. Deliberate practice over 2-3 areas for even a few weeks will put you at a massive advantage over the vast majority of the population. Compounding is still greatly underrated in practice, even though everybody knows about it.

Assuming you have some basic education, you can learn most things rewarded by the free market in a very short period of time. Most people dramatically underinvest in “temporal leverage points” – where small investments of time can radically alter the trajectory of your life. Examples include: spending time really understanding how the talent system works in your given industry, learning the basics of probability theory, having lunch with someone 30 years older than you in your field, or starting a podcast. All can be done within a week with unbounded upside.