In this episode of Professional Punters, we sit down with Tim Wu, who traded at some of the most sophisticated firms in the world before walking away from trading to build AI models for the natural world.
Tim studied computer science and mathematics at Stanford. He’s had experience at Weiss Asset Management, Jane Street, and Five Rings, moving from a hedge fund hunting statistical edge to two of the sharpest market-making shops in the business.
A little over a year ago he left trading entirely to become a founding machine-learning engineer at Coolant, a climate startup that raised their seed round from General Catalyst and Floodgate to build spatial intelligence and 3D vision; flying drones over forests and reconstructing every tree at centimeter resolution.
This conversation is about what separates a hedge fund from a market maker, where edge really lives in relatively efficient financial markets, and how that trader way of thinking informs one on how to run their own life.
Hedge Funds vs Trading Firms
Weiss, Jane Street, and Five Rings aren’t three versions of the same job. What separates them isn’t really “hedge fund versus market maker” but whose money they run, and how much risk each one is built to carry.
Weiss is a single-manager hedge fund, which means it runs outside capital. A fund like that lives inside a mandate( the strategy it raised money to run) and if it’s branded as, say, a high-Sharpe stat-arb shop (Sharpe being the ratio of return to the volatility you take on to get it; stat-arb being systematic, statistically-driven trading), it’s broadly held to that.
You can innovate within the mandate, and good desks do it constantly, but you can’t wander off it: LP money comes with real regulatory and investor constraints, so it’s innovation on a leash, not the wild west. When Tim interned there it had a single quant desk and it did everything statistically, because that’s what the mandate was.
Jane Street and Five Rings are the other kind of firm: proprietary trading shops, running their own pools of capital. That allows for freedom because there’s no outside LP to answer to, no mandate to justify, no particular return profile you need have; isntead, you make money for the firm, however the firm has decided is smart.
Both are nominally market makers (they quote a two-sided price and collect the spread) and both operate under market-maker privileges from exchanges (reduced fees, extended locate requirements for shorts, etc…).
In practice, Jane Street is far more willing than Five Rings to lean into a directional view and warehouse the variance that comes with it; Five Rings sits closer to the traditional market-maker archetype with less appetite for swings.
The Dumbest Idea in Quant
Tim previously said that he’s heard some dumb ideas at his previous employers, so I asked him for the dumbest idea he’d heard at these firms, half-expecting a story about a blown-up trade. His answer was better, because it’s a mistake a lot of shops are still making: treating AI as though it has no place in markets.
The objection used to sound sophisticated. Signal-to-noise in markets is too low; the distribution shifts too fast to isolate anything; there are too many players; it’s too game-theoretic; etc... Tim (and I) heard every version of this early in his quant career, and every version has aged badly.
Look at the scoreboard: HRT’s profit-per-head now rivals or exceeds Jane Street’s, and Jane Street is out building data centers, which tells you exactly what kind of method they’ve decided to bet on, because you do not need a data center to run classical inferential statistics. The asymmetry is stark. The cost of exploring deep learning is low: hire a handful of cracked PhDs, pay them a few dozen millions a year, hand them compute, let them dig. The cost of not exploring it is that you eventually get quietly competed away by the shops that did.
In high competitive industries, the risk of underinvestment is often greater than the risks of over-investment; an observation that also applies to frontier generative AI.
The Use of Modeling Volume & Volatility
For those outside of trading, modeling volume does not appear immediately useful since it can’t be directly monetized. However, for a market maker, volume informs how much slippage a trade will cause, how much inventory risk you should be willing to warehouse, how deep to quote and where. A retail trader can safely ignore all of it; a market maker lives on it. The lesson generalizes past volume, very often the edge is in modeling the regularities the market treats as boring and secondary.
Another regularity is the pattern of volatility realized. The observation there is that realized volatility doesn’t arrive linearly through the day. It clusters at the open and again into the close, and it’s asset- and clock-specific: a stock with heavy Chinese ownership bleeds more of its volatility during Chinese trading hours than during the US session, so you decay its vol differently depending on the clock (and set your price differently at the US opening auction). The same logic extends to stocks which may have other regional or commodity exposures.
Why Jane Street
Of the three firms, Tim’s clear favorite on culture is Jane Street. Tim’s observation is that these firms are extraordinarily good at attracting talent and, increasingly, less good at retaining it, because the competitor for their best people is no longer another prop shop, but the frontier AI labs.
What sets Jane apart is that the people at the top genuinely think about the employee experience, and that this is harder than it sounds, because the higher you sit the worse your information gets. Ask your own people how they’re doing and they’ll tell you it’s incredible; the honest signal never reaches you.
Most firms under-allocate to fixing that precisely because they can’t see the problem. Jane, Tim thinks, corrects for it better than most.
Look Past The Comp
On the topic of trading and Jane Street, it is impossible to avoid noting that pay numbers are enormous ($850K Total Comp for New Grads); however, it is noted that you should weight this number very little.
In the grand scheme of life they aren’t actually that big, and optimizing the starting figure is optimizing the one thing everyone can see: the price, not the value. Above all, price what different opportunities teaches you and which doors it opens, because that’s the thing compounding underneath the salary.
By his accounting there are exactly two good reasons to go into quant: you want to make money for five years and coast (high value placed on not working), or you genuinely love trading (high value placed on trading). Absent those, Tim notes that “if I could go back, I wouldn’t have,” because of how much is happening everywhere else right now, especially in AI.
Variance Adds in Square Roots
Tim only half-jokes when he calls this one of the most important rules of life: Expected value adds linearly but variance adds in square roots. Over a long enough horizon, twenty, thirty years, the square-root term shrinks relative to the linear one, which is the formal version of the folk wisdom that people usually end up roughly where they should over the long run.
Two consequences fall out of that. First, you should take many independent bets and stack them (i.e. try now things, take risks when yu are young), because independence is what makes the variance term behave. Second, and less obvious, is that your trading portfolio should be wired to the risk in your life.
If you’re already the kind of person taking startup risk with your career, and you’re simultaneously holding all your retirement in the S&P (with no leverage), there’s a contradiction sitting in plain sight: levering up, especially since those bets aren’t actually that correlated for most people. The covariance is real but small next to the enormous idiosyncratic variance of any individual life.
The Case for Leverage
His view on optimal leverage is simple: take the thing with high enough expected value and a long enough horizon (the S&P, a global stock basket) and lever it.
And the tooling to do this responsibly is now trivial. You can one-shot a Monte Carlo simulation in Claude Code that shows you the full distribution of outcomes at each leverage level, so you can stare at every percentile and decide how you actually feel about the bad ones before you take the position.
The compounding math is startling. QQQ is up roughly 16x since 2010 (from ~44 to ~720). TQQQ, the 3x-levered version, is up not 3x that but around 350x over the same window, from about 0.22 to 77, because leverage compounds.
The most under appreciated form of leverage, though, is on your future income. When you’re young, your current net worth is a rounding error against your expected lifetime net worth, so 3x leverage on 1% of your wealth is still only 3% of what you’ll eventually be worth, which is not a crazy decision at all.
The right posture, Tim argues, is to live as though you hold an infinite call option on yourself: a standing bet that you can always get back to a high-earning seat if you need to. And once you internalize that, it changes not just how you invest but how you think about money, because you stop being afraid of the downside that was never actually going to end you.
Trading to Trees
A little over a year ago Tim joined Coolant, then a seed-stage startup, because his heart had always been in climate and in the geospatial corner of computer vision.
Coolant scan trees. A drone carrying ordinary RGB imagery flies a forest, and the pipeline reconstructs it in 3D (i.e. a digital twin of the natural world) from which vision models read off the metrics clients pay for.
The timber use case makes the value obvious. If you run a timber operation you manage enormous tracts of land and know shockingly little about them, because the incumbent method is to send a person out with a tape measure. It’s slow, it’s expensive, and it samples a few acres of a vast holding.
This is a small-area estimation problem, and the square-root law again: your sampling error is brutal when the thing you’re sampling has huge local variation. Disease is localized. Species competition is localized. You specifically want to catch the localized thing, and a sparse tape-measure sample is blind to it.
It’s also just inaccurate; you can’t easily measure the height of a tree with a tape, a laser pointer, and some trigonometry. Coolant’s reconstructions are centimeter-resolution: thickness and height to within an inch, every tree, from a single drone covering hundreds of acres in the time it takes a person to measure a handful.
The use cases fan out well past timber: carbon-market verification (proving the trees you planted for credits actually exist and are growing), wildfire management, coastline monitoring. And underneath all of it: a large and growing proprietary dataset of the physical world, which in this era is the moat.
Lifetime Net-Worth Swaps
The most fun idea in the conversation is one Tim actually trades with friends, mostly his more risk-seeking ones. It’s a lifetime PNL swap. You pick an expiry (usually something like each other’s 40th birthday) and agree to exchange five basis points of the difference in your net worths at that date.
The logic is pure variance reduction. In the founder-heavy corner of SF Tim lives in, everyone has a high expected net worth and a much lower median one (a lottery-ticket distribution, empirically) and a swap like this is a clearly positive-utility trade whenever it moves money from the friend who hit to the friend who didn’t.
It’s a bet on your friends making it, priced better than their cap tables; blindly buying YC companies has historically returned something like a 20% IRR, but do you actually want to buy your friend’s startup at a $40M valuation? The swap sidesteps the entry price entirely and aligns incentives better (a founder can get acqui-hired into a $100M package while the company nominally dies and the equity holders get nothing).
It works best early in life, when there’s the least information about who will win and pricing is therefore most uniform; by forty the risks have largely realized and there’s little left to hedge, which is why you’d never write the contract then. And it’s especially good for lower-risk people, because the floor is zero; the worst case is that your friend goes broke and you give up five basis points of your net worth.
Tim’s half-serious coda: once on-chain identity (a World Coin-style proof of personhood) makes these enforceable, a swap like this could become significantly more popular, as well as replace a good chunk of what insurance companies do.
Professional Punters is presented by Freeport Markets: Freeport lets users trade 24/7, weekends included, with no KYC and up to 200x leverage across stocks, indices, commodities, crypto, rates, and more, with access to pre-IPO exposure in companies like OpenAI & Anthropic. For traders who already follow sharp sources across Twitter, Substack, hedge fund filings, corporate insider activity, and political trading disclosures, Freeport uses AI to read the sources they trust, connect the dots across markets, and surface trade ideas as narratives develop.
About the Host: Lihong Wang is the founder and CEO of Freeport Markets. Before starting Freeport, he traded discretionary semiconductor names at quantitative market-making firms including Jane Street and IMC Trading, building both systematic and discretionary strategies. Lihong graduated from Duke with a degree in mathematics and statistics, and has raised over $2.5M to build Freeport from investors including Y Combinator, Alliance DAO, and Informed Ventures. For more of his long-form research, visit freeportlogbook.substack.com.
Disclaimer: The information provided on TheLogbook (the “Substack”) is strictly for informational and educational purposes only and should not be considered as investment or financial advice. The author is not a licensed financial advisor or tax professional and is not offering any professional services through this Substack. Investing in financial markets involves substantial risk, including possible loss of principal. Past performance is not indicative of future results. The author makes no representations or warranties about the completeness, accuracy, reliability, suitability, or availability of the information provided._
This Substack may contain links to external websites not affiliated with the author, and the accuracy of information on these sites is not guaranteed. Nothing contained in this Substack constitutes a solicitation, recommendation, endorsement, or offer to buy or sell any securities or other financial instruments. Always seek the advice of a qualified financial advisor before making any investment decisions.
