In this issue:

• blindsided
• “end behavior”

Friends,

Blindsided

My cousin Nicole just had her first child in the past year and is now compiling resources for new parents in light of where her attention has obviously been.

In our family chat, she asked:

When you became a new parent, what blindsided you the most?

I’ll share my answer, which I qualify with both the awareness that having a child is a gamble on many levels and that conception itself is a miracle and should never be taken for granted. What was I blindsided by?

[9:16 AM, 9/16/2026] Kris Abdelmessih: that they were gonna be so awesome

[9:17 AM, 9/16/2026] Kris Abdelmessih: that last one is important when we live in times where people have less kids and talk about it as though it’s a chore (it is) but not the amazing upside

The most transcendent single moment of my life thus far was to hear my son’s voice the day he came into the world. I don’t know if that’s every parent’s experience, but immediately I felt the joy and clarity of purpose. Before that cry, I don’t think I would have said I had no purpose, but the moment revealed that I didn’t believe I did. The speed and intensity of this rush of belief was a novel feeling. Thus, irreparably blindsided.

Share your own answers in the comments and I’ll share them with Nicole. Thank you!

I offered a couple of less serious answers to her question as well.

On that last one, I wrote about that 2 years ago in our minds love to betray us:

When I was at the Sphere with my family over Spring Break, I wouldn’t ride the long, exposed escalators. I took the elevator where I found the rest of the scaredy-cats.

If there’s anything good about having a phobia, it’s empathy for the range of what can go on in people’s minds and bodies.

I’m watching this poor guy thinking, don’t do it man, it’s not worth and all he wants is a glimpse:

Man crawls through his phobia of heights to get a view of the Atlantic Ocean from the edge of a cliff. 😅

1:44 PM · Sep 12, 2026 · 36.9M Views

2.99K Replies · 5.3K Reposts · 131K Likes

The comment section understands, and based on the number of “likes”, many others do too.

Anyway, I blame my kids for my embarrassment when I go to the Sphere to see Metallica with a group of guys next month and have to explain that I’ll meet them at the seats.

Money Angle

A couple of Option Trench episodes to share:

📺Terminal vs Path-Dependent Value Explained Using Collars | 39 min

📺The (Not So) Efficient Market Hypothesis? | 59 min

The first one will be useful for anyone wanting to learn more about option collars, which I’ve been writing a lot about. A video might be a gentler format so check that out.

The second one applies to investing broadly. I also use the Paradox of Provable Alpha at the end to answer a good question Erik asks.

Money Angle For Masochists

Alex is an options trader you should follow in case he ever tweets a lot. Because he doesn’t, when he posted the question below a year ago, it got few responses. I took the liberty of posting it myself this week.

This was fun because it led to a lot of discussion on the timeline and DMs. I was told it sparked a bunch of quant debate on one trader’s desk.

The most popular answer, which was still less than 1/3 of the responses, was the correct answer.

Why?

The maximum value of a put is the strike. The maximum value of a call is the stock price.

Straddle is C + P so $100+$100 = $200

Notice how this means all call spreads go to zero since the calls are worth the same — the stock price. All put spreads go to their max value— the distance between strikes because the puts themselves are worth the strikes.

Logic for delta:

Delta is the change in option price per change in stock.

But the put’s strike is fixed, so the value of the put doesn’t depend on the stock price. The put has zero delta. It’s always worth $100. Which means it has no gamma either 🙂

The call is $100 because the max value of the call is the stock price. The call value moves 1-to-1 with the stock, so it has a delta of 1 or 100%

The max value of a straddle is therefore the stock price plus the strike price.

If you sell the straddle or either option at max value and hedge on its delta one time (this is known as a static hedge in contrast to dynamic hedging where you would rebalance as your hedge ratio changes), you cannot lose. It is that simple fact of arbitrage that makes it the upper bound.

To address the second most popular response in the poll, those who said the straddle is $100 (wrong) and has a 1.00 delta (correct), we will demonstrate why this is incorrect.

What’s your p/l if you sell 1 straddle at $100 and buy 100 shares against, if the stock goes to $300?

The straddle will be worth $400, so you lose $300 but make $200 on your long share.

Hmm, maybe I’m just underhedged. Fine, what if I hedge on a 200 delta?

In that case, you actually make money; you win $400 on your 2 shares more than offsetting the $300 straddle loss. But what if the stock went to zero?

Your straddle p/l is unchanged, but you lost $200 on the long stock position. Arbitrage max value means you cannot lose if you sell at that price. Since we found a losing scenario, the price is not the maximum arbitrage bound. If you sell the straddle at $200 and buy a single share of stock, there’s no scenario where you lose. It is the lowest straddle value for which this no-lose scenario is true, thus it’s the arbitrage bound.

Of course, this is but a toy problem where the call and put go to their maximum values because it’s a degenerate case of infinite time or vol. But learning how a function (an option price is just a function) behaves by observing its boundaries is good for intuition. You did this in 9th grade. Khan Academy can jog your memory:

In the real world, you can fleetingly find options that trade beyond their arbitrage values:

Earlier in the week, @DeepDishEnjoyer aka p4 wrote a thread about a dividend mispricing.

It led to some back and forth with passersbys who use options but appear to have large gaps in the fundamentals.

Between the maximum value poll and p4’s dividend lesson, it’s worth saying it:

In a proper option education, you spend a lot of time on arbitrage relationships, cost of carry, and synthetics before you ever hear the word “volatility”.

I didn’t study formal math but I imagine there’s a lot in common with the process of proofs. Arbitrages rest heavily on assumptions. So to understand the relationships, you are forced into an intimate familiarity with the assumptions. And in the extremes of everything, it’s the failure to examine assumptions that leads to being blindsided. But also, when things get extreme, to go on the attack means asking yourself, “Who’s on autopilot? Is this price resting on a stale assumption?” The arbitrage relationships give you the highest conceptual ROI that derivatives offer, you never learn the most useful thing derivatives can teach…passage over the “bridge of asses”.

If you want to see more examples of why option basics are so key to understanding assumptions and opportunities when things get weird:

• Financial Hacking: ETF vs Negative Oil Futures
• the art of paranoia

From My Actual Life

I leave you with another pic from our family chat where my wife posted a photo of where she was walking.

I don’t know how many Egyptian Arabic speakers we got in the crowd but “shib-shib” is like a slipper. Mom’s weapon of choice. Apparently this is a broader thing:

Mothers and shoes

Stay groovy

☮️

Moontower Weekly Recap

• why i’m not more bearish equities compared to 6 months ago
• trade prospecting ahead of the Fed meeting

In this issue:

• slow burn
• mental bet sizing for everyone
• sizing for real-life decisions

Friends,

Riddle me this:

Over the years, I’ve administered 2 reader polls to take the pulse of why you bother reading this newsletter. Both times I’ve asked the above question but with an open-ended fill-in-the-blank template. The answers come back in all shapes and sizes, but most of them can be boiled down to “smart” or “stupid”.

My reactions to the “stupid” responses:

My writing has a style that can be, at times, counterproductive. It’s often unfiltered and parenthetical. My wife constantly tells me that if I write more clearly, I’d have a bigger audience, and she’s, of course, right. The writing is not optimized for clarity. Scott H Young is my canonical example of such writing. I’m a big fan, but my lack of self-control forbids this common-sense approach.

My writing is aspirationally in the spirit of a Pixar movie where it serves 2 masters: the child and the adult who brought them to the theater. I’m trying to teach relatively basic or intermediate concepts while leaving Easter eggs for readers who arrive with more finance/trading context. This will leave the more basic readers feeling like they are missing a joke.

Fellow trader and writer RobotJames, once told me that Moontower is a “slow burn”. There’s no single banger that you could send to someone and say “this is why you should read this”, but reading it week in and week out leads to something more than the sum of its parts. My lament over never having a truly massive hit notwithstanding, I will not complain about the “slow burn” status. Many of my favorite things often took time to warm up to. Acquired tastes are rewards for crossing rugged terrain. (Except when they’re status cosplays. Our wants are even opaque to ourselves. There’s a thin line between appreciation and snobbery and its placement is up to the onlookers, not the admirer.)

To feel stupid when reading moontower would be natural. There’s technical or domain-specific minutiae mixed in with the meat and potatoes. This is further exacerbated by the cohort of readers who only show up for the personal sections and whose interests lie especially far from any of the material I cover. But if I read the blog of a doctor friend because it’s a way to follow along their life, the object-level material is gonna make me feel stupid.

And of course sometimes you feel stupid because I fail. The sequence of words fails to unlock the concept. Today I’m actively trying not to f this up. I want to teach you a neat topic. My goal is to really cement it for you. To make it accessible in real life for you in the same way multiplication tables are at your disposal.

If you don’t regularly read Money Angle, maybe see if today works for you. It’s long, but that’s because we are going to drill the concept. If you succeed in walking away with a new mental tool to practice, then the ROI on the reading time will look like a steal. If you’re not interested, then I’ll channel one of my college roommates whenever I tried to bail on hitting the gym, ”That’s cool man, I’ll just take you off my list of successful people for today” and leave you one of the OG memes that deserves annual visitation.

Commencing demystifications now…

Money Angle

One of the most important concepts in risk-taking is bet sizing. Which is unfortunate because people are quite bad at it, while the effort to be way above average is quite low.

A jarring and famous demonstration of this is the Haghani-Dewey Coin Flipping study, which showed how even college grads with business, economic, and technical backgrounds incinerated their capital or massively underperformed the expected profits presented to them by a game they knew was rigged in their favor.

You can read my synopsis in Bet Sizing Is Not Intuitive.

For a binary wager (ie win or lose), if you know the payoffs and the probability of winning, both of which were known to the participants, the solution is to use the Kelly Criterion.

The tragedy is that it is incredibly simple to compute and applies to many conventional gambles and decisions (the examples in the quiz will span various life situations!).

If something is both easy and widely relevant, it should be common knowledge. So let’s fix that today. I’ll show you how easy it is to use, and you’ll forever be able to do it in your head.

First, a succinct definition:

Kelly is the bet size, as a fraction of bankroll, that maximizes the long-run compounded growth rate of your wealth. It’s a mathematical solution to bet size that doesn’t seek to maximize expected profit per trial, but the size that optimally balances compounding rate and survival.

We will use this formulation of Kelly because it’s general:

The easiest way to learn it is just jump right in with a few worked examples:

Fair coin wager (even odds style bet)

p =50%

q= 50%

b =1 (ie even money, for a $1 bet you either lose a $1 or make a $1 profit)

f* = 50% – 50% / 1 = 0 → bet nothing, zero edge

Coin biased in your favor (even odds style bet)

p =60%

q= 40%

b =1 (ie even money, for a $1 bet you either lose a $1 or make a $1 profit)

f* = 60% – 40% / 1 = .20 → bet 20% of your bankroll

Roll a 6 on a die (underdog bet where you get odds)

p =1/6

q= 5/6

b =8 (for a $1 bet, you either lose a $1 or make an $8 profit)

f* = 1/6 – (5/6) / 8

f* =8/48 – 5/48 = 3/48 → bet 6.25% of your bankroll

If f* is 0 or negative, you have no edge, so not betting is prescribed

Sports moneyline (betting as a favorite where you lay odds)

A −200 favorite. You risk $2 to win $1, and the line implies 2/3, but you think it’s closer to 3 in 4.

p = 75%
q = 25%
b = 0.5 (getting 50% return on the amount you risk)

f* = 75% − 25% / 0.5 = 75% − 50% = 25% → bet 25% of your bankroll

Wait a minute, these are large bets?!!

If these bet sizes seem surprisingly large for the given advantages, then your senses are well-tuned. For most people, “full” Kelly is too big!

Kelly maximizes long-run growth on the assumption your probability is correct. Well, it probably isn’t because the world is messy. We can inject some humility by using a fraction of Kelly:

• “Half Kelly” gives up about a quarter of the growth rate and roughly halves the drawdowns. If you invert that, you see that the Kelly scaling law means as you bet bigger, you get diminishing returns per unit of risk. Extrapolating that logic, betting more than “full Kelly” is incinerating compounded wealth even if the individual bet has positive EV.
• “Quarter Kelly” or less is far more common in practice.

Please don’t let the equation scare you, it’s intuitive and easy to remember

Look at the equation again:

f* = p − q/b

It’s just “how often you win” minus “how often you lose.” It’s just that the second term incorporates the payoff. The loss term gets divided by b, which represents the return you collect when you’re right.

• When b = 1, you’re getting even money. A 100% return. Dividing by 1 leaves q alone, and the equation collapses to pure hit rate: p − q. That’s the coin case where you bet $1 to make $1.
• When b > 1, you’re getting long odds. The division shrinks the loss term. This is why the die works. You lose 5 out of 6 rolls. Straight subtraction says you’re down 66 cents on the dollar and should never play, but you’re paid 8-to-1, so that 5/6 becomes 5/48, and suddenly the 1/6 win percentage is the bigger number. Long odds forgive a bad hit rate.
• When b < 1, you’re laying odds. Now division stretches the loss term. The moneyline: you only lose a quarter of the time, but at −200 each loss costs you two units to earn back one, so that 25% loss percentage behaves like 50%. Being right three times out of four barely clears the bar. Lay enough odds and even a very good record is a losing proposition.

The graphic shows how you can think of the odds (the denominator) as shrinking or inflating q, as you collapse your thinking to a comparison of p vs q.

A word on b

b trips people up because “odds” is loaded gambler jargon. A wider interpretation of b is that it’s a percent return.

It’s what you make divided by what you risk. Even money is b = 1: risk a dollar, make a dollar. That’s a 100% return on the amount at stake. 3-to-1 is b = 3, a 300% return. Laying −200 is b = 0.5 because if you risk two to make one, it’s a 50% return.

[Return is a profit, while multiples don’t subtract your initial risk. It’s the difference between “I 2x’d my money” vs “I made 100%” or “I 10x’d my money” vs “I made 900%”. The percent return is the multiple minus one because we subtract our initial risk.]

The reason it’s a return and not just “the odds” is that Kelly assumes a loss wipes out the whole stake. The denominator is always the same number: everything you put up. b is comparable across a coin, a die, and a moneyline because it’s the return on risk, always measured against a total loss.

b = 1 is a natural reference point. At 100% return, a win exactly cancels a loss, so you need to win more than half the time. The breakeven hit rate changes with b.

Set f* = 0 and you get p = 1/(1+b).

Read the table as a menu of the hit rates you’re allowed to have. At b = 24 you can be wrong 24 times out of 25 and still be flat. At b = 0.25 you can be right four out of five and still be flat

Applying to real life: when is Kelly the right tool?

Kelly needs a few inputs: a bankroll, a payoff you know, and a probability estimate.

Which of these is a Kelly problem?

1. A prediction market contract trading at 30¢. You think it’s worth 45¢.
2. You’re all-in-or-fold on the river with a read that you’re good 40% of the time, getting 3-to-1 from the pot.
3. How much of your 401(k) to put in equities.
4. Writing checks as an angel investor across 30 startups.
5. Buying a weekly call on a biotech ahead of an FDA decision date.
6. Whether to take the new job.
7. Your buddy offers you 5-to-1 that it rains in Oakland tomorrow. The forecast says 30%.
8. Buying homeowners insurance. The premium is clearly more than the expected loss — that’s how the insurer stays in business.
9. Your neighbor doesn’t carry homeowners coverage. She banks the premium instead.
10. Your auto policy offers a $500 deductible or a $2,500 deductible, for a $340/yr discount on the premium.
11. You’ve got vested startup options. Exercising costs $40k out of pocket in strike, and you think there’s maybe a 15% chance the company gets somewhere that makes them worth $1M.
12. A merger arb spread. Target’s at $46, deal price is $50, and it trades back to $38 if the deal breaks. You think it closes 90% of the time.
13. Your agency spends 20 hours of unbilled time on a speculative pitch. You win about a quarter of them, and a win is worth 80 billable hours.

Money Angle For Masochists

Solutions to Kelly Problems

1. Yes. Cleanest case there is. Binary, known payoff, and the price provides b directly. Risk 30¢ to make 70¢, so b = 2.33. f* = 45% − 55%/2.33 = 21%.
2. Yes. This is the canonical one. p = 40%, b = 3, f* = 40% − 60%/3 = 20% of your stack. The wrinkle is that in poker your stack isn’t really your bankroll. There’s a whole literature on pros using Kelly for bankroll management across sessions rather than for a single river decision.
3. No. Not this version of it. Stock returns aren’t generally binary so there’s no p, q, or b. There’s a continuous analog called Merton’s Share, which is similarly rooted in reward vs variance. What Gamblers Can Teach the Buy-and-Hold Crowd can get you started.
4. Sort of. The structure is right: repeated, roughly binary, long odds. The problem is that p is a guess and b is a bigger guess, and Kelly is violently sensitive to overestimating your edge. Garbage in, garbage out.
5. Approximately. If you treat it as approve/reject it’s binary enough to size with. If the expiry aligns with the date such that you are betting strictly on the terminal intrinsic value, the option piece will inherit the binary modeling you imposed on the stock.
6. No. The variables are too opaque.
7. Yes. p = 30%, b = 5, f* = 30% − 70%/5 = 16%. Note, you’ll lose this bet more than twice as often as you win it, so your most likely scenario is losing 16%. You can shrink the Kelly fraction if this makes you uncomfortable.
8. Wrong side of the equation. Run f* on this and you get a negative number, because you’re buying a negative-EV bet.
9. Yes. It’s the same policy, so notice that the bet only exists on the insurer’s side of it! Every year your neighbor doesn’t buy, she collects a premium and writes a tail. Rebuild cost $500k, premium $3,000, call it a 1-in-500 chance of a total loss.

   p = 99.8%
   q = 0.2%
   b = 3,000 / 500,000 = 0.006 (risk $500k to win $3,000)

   f* = 99.8% − 0.2%/0.006 = 99.8% − 33.3% = 66.5%

   Positive, as expected since insurers price premiums well above fair value. In this case, her bet size is the $500k house. If the house is most of her net worth, she’s at 100% on a bet capped at 66%. Rather than overbet, she should buy the policy. If she’s worth $5M, she’s betting 10% when she’s allowed 66%, which puts her near quarter Kelly (~14%), and she could skip the insurance. If she’s worth $1mm, it’s a 50% bet, which is more than half Kelly. I’d say take the insurance but I’m a wimp. There are other considerations (would she have the liquidity to rebuild the home or maybe taking the insurance with a high deductible is a better fit), but just doing this exercise gives you a sense of how risky or conservative your choices are relative to the bet share that maximizes long-term wealth.

10. Yes. Raising the deductible is like you writing a $2,000 policy and collecting $340 a year for it. You’re the insurer again, so work out what you need to believe. Take the high deductible and save $340. Have a claim, and you’re out $2,000 more, but you already banked the $340, so the loss is $1,660.

    b = 340 / 1,660 = 0.205

    f* = p − q/0.205 = p − 4.88q

    Set that to zero, and you get p = 4.88q, which, with p + q = 1, means q = 17%. Your breakeven is a claim every 5.9 years. Anything less frequent and you’re the one with the edge.

    Let’s say real-world collision frequency is more like 6%. So p = 94%:

    f* = 94% − 6%/0.205 = 94% − 29.3% = 64.7%

    Which says risk at most ~65% of your bankroll. The risk here is $1,660. That clears as long as you have about $2,600 in liquid savings, which is to say the sizing check is trivially satisfied for almost everyone so you should generally opt for the higher deductible. For quarter Kelly, we’d need savings of $1,660/(.647 * .25) = $10,262.

11. Approximately. It’s not truly binary, but if you frame it in a way where you are comfortable with the no consolation prize of a medium outcome, you can see it as paying $40k for a 15% shot at $1M. b = 24, so f* = 15% − 85%/24 = 11.5% of your liquid net worth. This is quite sensitive to your estimate of p of course.
12. Yes, a classic example of binary-type risk in markets. You risk $8 to make $4, so b = 0.5 — you’re laying odds, same as the moneyline. f* = 90% − 10%/0.5 = 70%. That number is only as good as the 90%. Revise p to 75% and f* is 25%.
13. Yes, in a subtle way! Your bankroll is capacity, not cash. b = 80/20 = 4, so f* = 25% − 75%/4 = 6.25%. Twenty hours has to be 6% of what you’re working with, which means you can’t run this pitch out of a 100-hour month. If you’re the manager, you can put it in dollar terms by converting to wages.

    ---

Finally, I strongly recommend William Poundstone’s book Fortune’s Formula: The Untold Story of the Scientific Betting System That Beat the Casinos and Wall Street

Description:

In 1956, two Bell Labs scientists discovered the scientific formula for getting rich. One was mathematician Claude Shannon, neurotic father of our digital age, whose genius is ranked with Einstein’s. The other was John L. Kelly Jr., a Texas-born, gun-toting physicist. Together they applied the science of information theory—the basis of computers and the Internet—to the problem of making as much money as possible, as fast as possible.

Shannon believed it was possible for a smart investor to beat the market—and William Poundstone’s Fortune’s Formula will convince you that he was right.

And this is from my notes, Insights From Fortune’s Formula:

a gripping narrative full of 20th century trivia that ties together the birth of information theory, some of the greatest scientific minds of the 1900s, the rise of quantitative finance, and the role of organized crime. These topics come alive in a fresh, memorable way when discovered through the lens of its colorful characters.

It chronicles the history of the efficient market hypothesis (MIT, U Chicago, Paul Samuelson). You can organize its conclusion around this excerpt:

There is much truth in the efficient market hypothesis. The controversy has always been over just how far the claim can be pressed. Asking whether markets are efficient is like asking whether the world is round. The best way to answer depends on the expectations and sophistication of the questioner. If someone is asking whether the world is round or flat, as fifteenth-century Europeans might have asked, then “round” is a better answer. If someone knows that and is asking whether the earth is a geometrically perfect sphere, the answer is no.

Stay groovy

☮️

Moontower Weekly Recap

• “Achievement” is a fickle mistress
• betting on direction with short-dated spreads

In this issue:

• energy as the ultimate currency
• “middle manager” fiction

Friends,

A year ago I wrote a piece called Capitalism Is a Temporary Condition. Buried in it was a thought:

An investor in an age of acceleration watches as commodity prices, real things, hover in familiar territory while “future cash flows discounted” reward attention — in some cases because of optimistic stories, in some cases because the company exchanges cash for BTC (an act which adds no economic value), and in some cases for nostalgic lolz.

Today, it’s embarrassing to think of actual value. The most basic form is stored energy**. An obvious example is oil. Slightly more abstract —a building is a collection of atoms that took energy to arrange and provides utility. It’s proof of work. Same with a good reputation.

The ** footnote:

I increasingly think that a durable concept of a risk-free or least-risky rate is more likely to come from Vaclav Smil than U Chicago.

I knew about Smil from a profile piece that celebrated his suffer-no-fools rigor in making sense of the world.

I’m finally getting around to How the World Really Works, and I dig how quickly it’s zeroing in on what I anticipated to be the biggest muscle movement of all. I’ll let it unfold as he does the book’s intro.

Smil asks us to imagine an alien probe watching Earth, programmed to wake up whenever something important changes in the way energy is captured or used.

For a very long time, it sleeps.

Then life figures out photosynthesis: sunlight can be captured and stored as chemical energy. Much later, humans learn to control fire. Then agriculture. Draft animals. Wind and water.

The gaps are enormous. Billions of years. Millions. Thousands.

Then they start collapsing.

Coal gives humans access to immense stores of ancient solar energy. You can see a parallel to wealth in that phrase alone. Steam will turn that energy into mechanical work, replacing what he calls prime mover energy (human and animal labor). Oil makes enormous amounts of energy portable while electricity makes it transmissible and almost infinitely adaptable.

Smil puts numbers on this.

In 1800, the world had access to roughly 0.05 gigajoules of useful energy per person each year. By 1900 it was 2.7. By 1950, about 10. By 2000, 28. By 2020, roughly 34.

In a little over two centuries, useful energy per person increased nearly 700-fold.

The average person today commands roughly the energy equivalent of 60 adults working continuously, day and night. In affluent countries, the equivalent is closer to 200–240 permanent laborers.

And the averages hide enormous differences.

Measured in primary energy, someone in one of the world’s poorest countries may use less than 10 GJ a year. India is around 20–30. The global average is roughly 75–80. China is above 100. Western Europe and Japan generally sit around 120–160. The United States is closer to 250–280.

It sounds a lot like how GDP per capita is distributed. We measure GDP in dollars, but money can be printed, thus distorting its exchange rate versus the stored value of work. In that sense, energy may be civilization’s only uncorruptible currency.

If we continue pulling on the thread, wealth might be thought of as an account from which we can spend to hold entropy at bay.

• A 72-degree house in Scottsdale.
• Mango salad in during a Minnesota January.
• Clean water pumped up the Hollywood Hills.
• A sterile operating room.
• Skyscrapers defying gravity.
• Data centers (I couldn’t resist).

Much of the AI commotion revolves around what it means for humans to directly turn electricity into intelligence. That the product is intelligence which can recursively accelerate knowledge instinctively unsettles us because it violates our sensibilities around balance. Like energy is somehow not being conserved, leading to the type of divergence we associate with chain reactions.

I’m actually struggling to put my finger on the right analogy, which validates another point Smil makes. He argues that at precisely the moment when we have become capable of commanding extraordinary quantities of energy, most of us have become almost completely detached from how any of it happens.

Smil calls this our “comprehension deficit.”

Modernity is a black box. Light comes from a switch, and meat comes from a carton lined with a maxi pad. It’s all so effortless. Students of economics read I, Pencil to appreciate how capitalism’s profit-motive and competition actually lead to complex chains of cooperation. The side effect of the story is a (demoralizing?) inference that the world is so invisibly complicated now that you should feel good in your choice of major because it’s strategic to be a symbol-pusher since details are hopelessly infinite. (Sorry, did I go to far here? I’m an econ major, so it’s kind of like Chappelle using the n-word. Kinda? Nevermind.)

Smil’s punchy take on specialization:

You could meet real Renaissance men on Florence’s Piazza Signoria in 1500, but not for too long after that…By the middle of the 18th century, Diderot and d’Alembert could still assemble a group capable of summing up much of their era’s knowledge in the Encyclopédie…In 1872, a century after the appearance of the last volume of the Encyclopédie, any collection of knowledge had to resort to the superficial treatment of a rapidly expanding range of topics…today, it is impossible to sum up our understanding even within narrowly circumscribed specialties…experts in particle physics would find it very hard to understand even the first page of a new research paper in viral immunology …Highly specialized branches of modern science have become so arcane” that many practitioners must train into their thirties “in order to join the new priesthood.

All I’ve done is read the introduction to the book, but the “comprehension deficit”, which he’s fixin’ to remedy with respect to how the world works (at least from a physics/chemistry macro point of view), is just a fascinating idea of its own. Before the printing process and hyper-connectedness, it was hard to learn much of what was a relatively small body of knowledge, but since then, even though you could learn more in a lifetime, it was a smaller percent of what could be known.

But that I can sit here in an air-conditioned house on a 90-degree day typing contemplations about abstractions like how wealth is really the temporary abatement of nature’s randomness and how the accounting of such wealth should be denominated in units of work, which is traditionally measured by money and its continuously leaking exchange rate to said work is itself a wild collective achievement fueled by eons of carbon remnants from lives I never knew.

I guess I feel like I owe it to the universe to be curious about its ways even if it’s a Sisyphean fantasy to close the comprehension deficit. Looking forward to continuing:

A thought worth sharing more widely from hemispheres this week’s post about discretionary trading:

In this AI era, brains and consciousness are major topics du jour. I increasingly see us as technology centaurs with both a meat and silicon brain mediating structured and unstructured data, which in turn can be private or public.

On the private side, I have Claude connected to:

• my personal knowledge management or PKM (Notion)
• MCP to moontower.ai data
• git repos across work and personal
• email (which means it can access all my writing)
• project management software (Linear)
• Google drive
• Google calendar

And of course, the entire world of public info lives in the model’s training data and ability to go online.

You can be taking a walk with voice mode on spec’ing a prototype that will connect to data with nearly any context you want to provide from your private knowledge, the internet, or what’s in your head that moment.

The future is here for anyone who feels haunted by dozens of ideas a day that they previously couldn’t or wouldn’t act on.

And a discretionary trader is nobody if not someone who starts 50 sentences a day with “I wonder what happens if…”

Related:

Money Angle

I ask for your indulgence, for what follows is an informal word-wall connecting conversations I’ve had with some W2 friends.

Pick your favorite gospel on disruption. Schumpeter’s creative destruction, Christensen’s innovator’s dilemma. Most companies, sometimes even industries, will eventually recognize themselves as a melting ice cube.

In a great talk back in 2009, media investor Peter Chernin warned:

One of the things I always used to say to the people who work for me is that you can’t protect your business, and your job is not to protect your business. Your job isn’t to protect. Your job is to maximize your business at any given time, but your real job is to grow new businesses faster than the old ones decay.

He uses record labels as the cautionary example. They set out to protect their business, but “to the degree you’re going to try and do it you’re going to get killed because technology is going to liberate audiences in such a way that they’re going to get what they want regardless.”

It feels like there are only bad choices for companies that are now run-off businesses. Re-investment feels too speculative, but panic will preclude any chance of a soft or at least dignified landing. Navigating these moments well is a sign of grace, but if grace were easy we’d call it something else.

Instead we get to witness corporate pathology.

The ice cube is melting. But it started as a very big ice cube. Big enough to bridge a middle-aged middle-manager’s soft landing into an early retirement if they can claim a larger share of a shrinking cube. Welcome to corporate hunger games on steroids.

The middle manager is a risk-averse incrementalist. That’s WHY they are middle managers. They will do no such thing as grow new businesses faster than the old ones decay. They need an accomplice to secure their spot on the life raft. This accomplice comes from an unexpected place. The partners.

You see, entrepreneurs have deranged brain chemistry. My friend Jason Buck and I were drinking coffee in my backyard on Wednesday when he described an entrepreneur as someone who works 100 hours a week for themselves to not have to work an hour for someone else.

The entrepreneur, the founder, the partner is a delusional optimist. A melting ice cube can be refrozen if you just find the right segment to sell to, the one that somehow eluded you when things were going well. The middle manager latches on to this hope.

He crafts a business plan to do things “radically” differently. Of course, radical in this context is a mere gesture in comparison to the extinction-level shift in the business environment. The partners, never ones to back down from a fight, support this can-do attitude from the manager who stepped up.

The manager, energized and enabled, moves to consolidate influence. By promoting their plan, any plan, doomed as it is, they portray unsupportive colleagues as quitters by virtue of their dissent.

“If you’re not on board with my [dumb] plan, you clearly hate the company and are not a team player.”

There’s no serious appraisal of the dissenting argument’s merit. And that’s probably because the dissenting argument is “Umm, we’re f’d, so we should focus on retention, not growth, because [insert analysis that actually makes sense]”. Any analysis that maximizes EV in a losing game will be hard to sell against a bad strategy employed with hope. We gamble to get back to even and we’re risk-averse when we’re ahead.

Our middle-manager doesn’t just knife out the dissenters. They fully larp optimism with expansion. They hire loyalists, veterans of this obsolete-but-not-yet-obviously-so strategy. The loyalists are relieved. They, themselves, were cooked seeing the same writing-on-the-wall, but they just got thrown a lifeline by the last firm running the old plays. The only plays they’re familiar with. The reunion with their middle-manager buddy is as predictable as you expect, complete with brown liquor and nostalgia for those nights during training when they were chasing tail in Murray Hill and scarfing khati roll together if they struck out that night. Ah, Indian food before bed. To be young again.

The loyalist cluster is pragmatic. Every year of health insurance and private school tuition extends the polyester harmony that is their home life. But for the middle manager, the loyalist cluster is strategic. He’s like an old piece of tile covering himself in linoleum. It’s all wrong but a bigger nuisance to remove. Become hard to kill by entangling yourself deeper in a hierarchy that you created and from which you are a convenient buffer between partners and minions whose names they never want to know.

The misalignment, politics, and the waste of human life force in the name of self-preservation of an artificial environment (that’s a big one to unpack, but y’all might have to come have coffee or maybe something a bit stiffer for that convo) are regrettable.

But you know what I find worse?

That our middle-manager protagonist was not as cunning as he seems. That his primary offense was stupidity. That he sincerely mistook the situation for something he could fix instead of a noble impulse towards calculated self-preservation.

Stupidity is uncivilized. It’s unpredictable. “Say what you want about the tenets of national socialism, at least it’s an ethos. These guys are nihilists.” That’s how I feel about our manager if he is stupid. That’s a barbarian. It might be unpleasant, but you can reason with the merely deceptive.

I’m not sure if our middle-manager character (whose likeness to any real-life individual is purely coincidental) is sincere and stupid or just trying to survive, but those accomplices on high, blinded by desperate optimism, channeled the old guy at the club instead of having the courage to reinvent or the grace to just go home.

Then again, being who they are is what got them to giant ice cube status in the first place.

Money Angle For Masochists

A recommendation

Besides writing, part of my role here is to find good stuff to share with you.

One of my favorite online discoveries this year has been the X account of @SowingAlphaSeed. It’s an anonymous account that goes by “Farmer”.

The bio says:

Retail investor w/ a stretch goal of 2 Sharpe + 30% CAGR. Please DM me if you have fund recommendations. Live portfolio and track record on my website.

The main draw is that this account has steadily been learning and doing “in public”. It’s a constant source of interesting and smart ideas. It’s also focused on what it says in the bio, so the percentage of useful posts to total posts is extremely high.

Tip of the hat to Farmer (who I never met and don’t know).

An observation:

1-year collars in MU are close to the cheapest they’ve in the past year. If you’re bullish you can budget a trade that can offer multiples of return per dollar bet. If MU already made you rich, the cost to hedge is actuarially small.

Then again, you didn’t get rich by hedging amirite

This week:

Zooming in:

Moontower Weekly Recap

• a common marginal thinking blindspot
• hemispheres

Friends,

I’m visiting my family in NJ so Moontower will return August 12th.

That said, today is a longer essay.

the sound of inevitability

The market is 12-15.

12 bid. 15 offer.

The broker sizes up the offer.

“How many you got there? How about you? And you?”

A couple of the market makers get flakey once they see him counting.

“You know what, I’m 17 now.”

“Fine, but can you fill the size there?”

“This second, no more shopping.”

“Mine.”

A few minutes pass.

The broker comes back around. “How now?”

“Go fuck yourself. 20-40. Small.”

And that’s it.

It’s a liquidity-clearing trade. The market’s version of punctuated equilibrium.

Small lot sizes from here on out.

The price drifts higher over time. The same amount of volume moves the price by larger increments. The least-capitalized shorts who are also the smallest in the trade begrudgingly cover. Better to live another day. The larger ones lay off some of the headline greeks in related assets, but the basis leaks against them the whole way. At least it’s not a fresh flesh wound every day. Paper cuts aren’t mortal risks, but the job will be demoralizing for a while. We’re gonna do this again? Why don’t I learn? I’ve done well enough. Right? I don’t need this shit I say as I fire up loopnet on yet another chrome tab hoping to find a cap rate in the shape of an eternal palm tree.

Weeks pass. Maybe longer. Who’s counting?

You see the price. 63. Numb. Doesn’t mean anything anymore. You can’t taunt a ghost.

More time.

Wait, 56?

“Anybody doing anything in this?”

“Nah, maybe just recent sympathy with hawkish Fed chatter.”

“You think this thing being up 300% has anything to do with basis points. C’mon.”

“Yea, I don’t know. I’m trying to book Odyssey tickets on IMAX for 3am, can we do this later.”

“Neverm—”

[Ringing. The hoot flashes.]

[Groans and picks up.]

“Sal, I thought I told you to cut the line. The fuck you want?”

“How is it today?”

“I don’t know, Saaaal, why don’t you tell me how it is?”

“At 45”.

“We’re 5 minutes from the close, I can’t show.”

“At 33. They’re gonna trade”.

“I’ll round you out just to be social.”

Next day.

How?

“15-25. Your move, Sal.”

Leo

Matt Levine:

[On the modern AI thesis] Aschenbrenner was early and smart and articulate about it. This allowed him to raise money, and the fact that it has been basically correct allowed him to return 270% through May.

Leopold Aschenbrenner’s fund, Situational Awareness LP (SALP) started in late 2024 when he was 23. He raised $225mm and, through the use of leverage and being right as hell, rode a legendary heater with assets at peak over $25B about a month ago.

His portfolio concentrated heavily on hardware and chip shares of CoreWeave, Nebius, Bloom Energy, Iris Energy, Micron and the Korean company SK Hynix, as well as a sizable private stake in Anthropic. Meanwhile, he was shorting traditional software companies. You only need to pull up the charts of his longs and shorts to explain how his returns had been so stellar.

Many of his longs peaked in late June. On July 24th, he sent a memo to investors stating that the fund “has not been immune” to recent market volatility. He invited existing investors to commit fresh capital effective Aug 1, citing the most attractive opportunity set since early 2025.

Within the week, SALP would proceed to lose 2/3 of its assets and liquidate its public portfolio to Citadel.

Back to the pit

Trading is about pricing liquidity. Handicapping the price to move a chunk of risk in a particular period of time.

Leopold was very right on his security selection. So right that even net of the liquidation, the fund is still up 80% on the year! That’s got to be unprecedented. But being liquidated in the first place was inevitable.

Let’s go back to the stylized story from the opening.

When an asset rips higher as quickly as these longs did, it exhausts the supply of offers that maintain a sensible relationship to a concept of fair value. For the sake of legibility, we’ll call those sellers the natural investors. The ones whose bids and offers are tied to some semblance of a fundamental model.

If a stock you sold when it 3x’d continues on its way to a 10x in a short window of time, like on the order of months, there is a paradigm shift in its liquidity. In a squeeze, there is a shortage of supply, but those episodes are faster and less mysterious. In the opening example, it’s not a supply squeeze but reluctance.

With the “naturals” long taken out of the stock, the marginal liquidity provider on the offer is an atheist. In other words, a trader. They have no religion about the short. It’s an HFT, a market maker trading some low-capacity intraday basis they discovered in a linear regression, or some passive mechanism with a rebalance toggle.

What do these sellers have in common?

They’re hyper-tuned to the risk. They have a volatility number somewhere in their trade lifecycle.

Fine, who’s buying when the stock is now twice the price that itself was twice the price of anything sensible?

For starters, the original fanatic, especially if they are receiving inflows based on the marks they are reinforcing. Who else is buying? The momos and fomos. Weak hands. The momos are executing a simple bandwagon python script. This is a weak hand by design. The fomo weak hands come in 2 forms. The ones who haven’t had an original thought in their life and the ones chasing a benchmark because they devoted their life to mimicry with just enough leeway to preserve the illusion that their creativity matters. There’s a price for everything, so no shade, but calling a spade a spade, this is the weakest hand.

The stock is in a liminal zone. Levitating on the echo flows from the original disturbance. The stock market’s microstructure always presents a wash of back-and-forth trading. The withdrawal of real liquidity is less visible than the example in the opening sequence, which caricatures a market in a derivative or obscure contract that trades on Clearport by appointment but lacks a DOM. But make no mistake, the liquidity of the super stock is broken just like the fake derivative example.

The liminal zone, to quote Kindergarten Cop, “lacks discipline”. The sellers are atheists, the buyers are momos, fomos, and the original mover gathering flows, literally “high on his own supply”, with a mandate to trade on a thesis he publicly telegraphed in an adversarial game. The marketing effect of this strategy was powerful but not free. Why not? Because it rings the dinner bell which reverberates with the sound of inevitability.

Mordecai

When I graduated college in 2000, my mother took my sis and me to visit our family in Sydney and travel Australia for 3 weeks. In Darwin, we took one of those boat tours on the Adelaide River where they hang massive slabs of meat over the sides so us tourists can watch the crocs coil below and then leap high for their meals. Once you get on the river, the swarm of eyes comes out of the weeds as the sound of the motor signifies meal time. I’ll never forget the sheer size and thus the name of the croc they told us was the river’s alpha — Mordecai.

Leo’s wild success summoned Mordecai.

Crocs don’t need to chase. They don’t waste energy. The strike happens in a muddy thrash, and shortly after, the ripples of water slow as the trees and surrounding fauna relax in the wake of violent awe.

And even if the alpha crocs turns over, replaced by a new alpha, this species lives forever.

If I can prove how apt this analogy is, you will believe, like I do, that this liquidation was inevitable.

If you’ve been following this saga, you will notice I haven’t yet introduced the true culprit — Leverage + Concentration. The crocs aren’t the villains. In the words of Jack White, “if you’re headed to the grave you don’t blame the hearse”.

The moment Leo chose 4x leverage on a concentrated book, he splashed loudly into the river. From there, crocs just do what they do.

Byrne Hobart, in the Diff:

When there’s an economic actor whose day job is to identify forces that will lead to short-term flows in and out of particular stocks, and whose long-term model is to periodically pounce on distressed companies, these models will tend to converge into a model where they trade in advance of the blowup, and then exit and reverse that trade in the rescue.

The economic actor Byrne is referring to specifically is Ken Griffin’s Citadel, but generally, it is the dealer. The primary function of a dealer in any market, whether it’s securities, art, cars, or even being a link in a supply chain, is to price liquidity and manage inventory. SALP’s performance was a confession of Leverage + Concentration. Crank the virtuous loop of momentum and flows into thin liquidity, and those dead eyes surface for a look. If understanding liquidity was easy, then market-making would be less profitable. It simply would not be as valuable a service. So we can forgive Leopold for not realizing his gross market value was in shallower waters than he thought.

Market-making is a psychological grind. Again, go to the story from the open. You get paid $10 to flip million-dollar coins, and every now and then you find out you’re on the wrong side of a rigged coin. But even rarer than getting picked off is the chance to feast on fat prey. Now, to be fat, they must have been doing something right, but the weight makes it harder to maneuver than it used to be, and in Leo’s case, “used to be” was quite recent. It only takes a moment of indiscretion to show your belly. Markets are unforgiving because you’re only as sturdy as your worst mistake.

The crocs are always there. Griffin was also there to buy Amaranth out of their positions. Citadel has been in nat gas since the Centaurus era, and with John Arnold retired, Citadel has been an alpha croc in gas trading for well over a decade. If you search my writing, you’ll see a recurring theme of “what equity traders can learn from commodity futures markets”. Futures are zero-sum, so not all the lessons apply, but the ruthlessness will let you borrow a healthy amount of paranoia.

This is @LepoulpePoulpo:

So this was the view I always had wrt equities before
Vs commodities where a lot of shady stuff happens all the time but everyone knows about these games

On the other hand, I’m really less sure now. “It wasn’t certain how close SALP were to a margin call. Wasn’t certain they would have to liquidate in a block” -> I think if you had an idea of their leverage, and saw the price action on all his names, significantly worse than other semis, you could anticipate he would be close to force unwind/liquidate and try to squeeze him.

I admit I don’t know any equities trading team where people would do this kind of thing, but it happens often in commodities.

The word liquidity is a reminder that this is a biological system. Leo priced his trade as if the distribution were exogenous. He would never admit that, but his actions suggest his understanding was purely academic.

Byrne Hobart again:

AI people obsess about existential risk in theory and Leopold has publicly spoken about being aware of it, from a financial standpoint, in practice. But if you want someone who really feels existential risk in their bones, you’re better off talking to a hedge fund manager in Miami.

A mental model for the commodity market I’ve at times lamented and at times celebrated is that for the most part it’s a boring business of blocking and tackling. But now and then a well-capitalized outsider hops Chesterton’s Fence to see if he can force the market to cry uncle. If they’re especially crafty, it can work for a while. You can always beat a dealer on the way in. But you need liquidity to get out and now you don’t have the element of surprise on your side. Eventually, the old illuminati of the business lock arms to go on a hunting expedition. We used to call this “running them in”. If you are forced to cover, there’s little risk to me to bid ahead of you. (That’s not to say the “clean up” isn’t competitive. This is a good thread.)

When the Hunt Brothers cornered silver, the exchange eventually disallowed opening buy orders and raised margin requirements, depleting all the fuel. The exchange used to be owned by traders. They were literally called “members”. You can imagine their position at the top.

The sound of inevitability

In Jurassic Park, Michael Crichton folded an introduction to the field of complexity into the story. The park’s creators’ overconfidence in linear scientific thinking led to disaster. Complexity focuses on chaotic systems like weather (the proverbial butterfly flaps its wings and causes a hurricane across the globe) where models resist equations. There’s a greater emphasis on simulation and higher-order effects. A popular analogy from complexity science is the sandpile. Eventually the sandpile collapses but nobody would say the nth grain of sand causes the avalanche. It simply reveals that the pile angle had become unstable.

When observers consider the timeline (SALP’s peak was likely in late June) they are trying to label the nth grain. Fully embracing the butterfly, here’s my list:

• the SpaceX IPO
• the World Cup
• the uptick in long-term yields
• box spread rates reflecting funding costs rising as demand for leverage increased, with those costs passed straight through to levered ETFs
• a slowing trend increasing chop, which increases drag in levered ETFs, which wears down the momos’ patience faster
• the Knicks winning
• Kris visits Rome for the first time

Inevitability means none of these matter.

So why was a liquidation inevitable? Why was at least one croc guaranteed a meal?

I already said it. It’s for the same reason LTCM, Hwang, Alameda, and Brian Hunter remain cautionary tales:

Leverage + Concentration.

But what’s so lethal about this combination? Why must it lead to liquidation?

Stated as plainly as possible:

As soon as you assert leverage, you are saying not only am I right, I’m right on timing AND path.

It’s a continuous time parlay. Even if he is right on the destination within the time frame of his choosing he can’t tolerate a large drawdown in the interim.

There’s just no give in the math.

Here’s quant Richard Craib:

But the outcome was never about being right or wrong on AI. At ~150% vol, variance drag alone is ~113%/yr, and risk of ruin is roughly a coin flip over the fund’s life. A child can do the math on a napkin (Claude did it for me: “ruin wasn’t unlikely, it was roughly even money”).

Volatility that high pierces every other fact about a portfolio: the thesis, the timing, the talent. The initial success and the margin call are draws from the same distribution.

And this isn’t really conditioning on the reality that a market’s price discovery function means they will push to a clearing price on a faster schedule than your lender would like. If you didn’t have a lender, this is not a concern!

Martin Shkreli had great coverage on the SALP story on TBPN. But thrown in at the end of the interview is this terrific section:

Kelly famously came up with what is now called the Kelly Criterion. It started as a gambling concept before becoming a finance concept, and it mathematically proves the optimal bet size. The formula is your edge minus the reciprocal of the odds. So, if you have a 55% edge, your optimal bet size is about 10%.

Even that is quite volatile for most people, which is why many investors use half-Kelly or quarter-Kelly sizing. The reality is that most traders don’t actually have an edge, yet they trade as if they have a four- or five-times Kelly edge.

That might sound like they’re simply taking a lot of risk, but if you run the simulation, you’ll go to zero almost every time. The simulator is a really powerful tool because it shows that even if you had a 60/40 edge on every trade—which nobody has in the stock market—you’ll still go bust if you overbet.

That’s a real eye-opener. Position sizing matters just as much as having an edge. It’s something I had to learn the hard way over many years: I was almost always overbetting. I think most hedge funds do it to some extent, and certainly most retail investors do. Very few people actually simulate their portfolios to understand what the appropriate position sizing should be.

After I left the Tiger Cub fund where I worked, I spent a short time in the office of a former SAC Capital (now Point72) portfolio manager. He was one of the best managers I’d ever seen—a quiet guy that almost nobody has heard of, now retired. I had the chance to watch him for a few months before launching my own hedge fund, where I proceeded to do the exact opposite and massively overbet everything.

I group LTCM in with other victims of the Leverage + Concentration poison. As a quant fund, their business was actually to lever diversified edges. But once the correlations of their positions converged, their cocktail was spiked with mathematical Concentration. On the surface, you might say what does a position in corn have to do with Treasury basis, but when a single commingled fund cross-collateralizes its leverage, then their size in the market imports a temporary synchronization of price returns.

Horse@TheFlowHorse

Leo knew… H/t @QuiteMidlife

7:45 AM · Jul 30, 2026 · 149K Views

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20 Replies · 42 Reposts · 1.08K Likes

Elm Wealth’s Victor Haghani has done a public good by commuting his pain as LTCM partner to teaching the necessity of sound bet sizing. His famous coin-flipping studies show how econ and finance professionals manage to continuously blow up 60/40 advantages by betting far more than what Kelly prescribes.

It gets better. In Fortune’s Formula, I learned that someone betting the prescribed Kelly fraction of their bankroll (which btw implies they have an edge in the first place) has a 50% chance of experiencing a 50% drawdown and a 1/3 chance of experiencing a 50% drawdown before doubling up. In other words, the Kelly fraction is not even conservative. Many traders and gamblers I know will max their betting at half-Kelly.

[The book points out that halving your Kelly fraction will cut your drawdown risk in half but your return only by a quarter so even though your long-term wealth compounds more slowly, the risk-reward is better. That fact alone tells you that the scaling law is extremely punitive if you overbet at all nevermind overbet at the rate Leo was.]

Why Leo, why?

[His defenders are quick to point out that he’s still up 80% for the year, but all this does is highlight the thin line between zero and hero. We overfit narratives with a comfort that is comically out of tune with what is warranted by circumstance. If Leo loses an extra 25%, an utter blip given his vol, at 4x leverage his investors are zeroed. He made a great call to liquidate, but the presence of liquidity to do so is never a given. In the final hours of a deeply fragile situation, every routine event, hell a Trump tweet, has butterfly potential. Putting yourself in a situation where there is no margin for error is itself a mistake. Leo’s future will revise and buff down the pointy edges of the story’s path dependence, but honesty demands acknowledging that no matter what he becomes, today he is neither lion nor lamb but liquidation was inevitable. He is a man alternating as we do between grace and folly, with neither ever being our full legacy.]

Let’s proceed.

Structure Mistakes

Matt Levine:

If you are all-in on this thesis, you might be more than all-in on this thesis. You won’t put 100% of your money (and your investors’ money) into the AI boom. You’ll put, like, 300% of your money into the AI boom. You’ll borrow money to lever up your bets on the AI boom. As your AI stocks go up, you’ll borrow more to buy more. Getting a 200% return on your money by buying SK Hynix stock is great, but getting a 1,000% return on your money requires borrowing more money to buy more stock. This is a naturally long-term trade. Aschenbrenner’s famous June 2024 essay series is titled “Situational Awareness: The Decade Ahead.” The point is not, like, “SK Hynix will beat earnings expectations next quarter”; the point is stuff like “by the end of the decade, we are headed to $1T+ individual training clusters, requiring power equivalent to >20% of US electricity production.”

You have a vision of the future and want to make a fortune; you need to match your funding to the duration it will take to see the thesis play out. The use of recourse leverage, subject to daily revaluation, is wholly inconsistent with the horizon.

He must know this.

That’s why companies issue equity. They don’t want to worry about the next loan payment. The duration of the financing and vision are aligned. A business that cannot fund ops from cash flows is depleting capital and will need to issue more equity. In that sense, its leverage is not reevaluated every day its beholden to investor appetites at discrete points when it refinances. Meanwhile, a self-sustaining profit machine is more like permanent capital. If it doesn’t like investor bids, it can create its own liquidity by buying itself back.

There was a failure in appreciating structure. The vehicle you use to express a vision is no less important than the vision itself. Founders Fund is Peter Thiel’s GOAT-level investing vehicle, which uses locked-up capital to invest in private companies. Meanwhile, Clarium, his hedge fund that invested in public markets, lost 90% and closed in the wake of the GFC. That a hyper-opinionated genius could succeed and fail so loudly in seemingly similar tasks should alert you to the nature of edge, its prerequisites, and limitations with respect to how you express it.

Hubris?

The most famous Leopold I knew of before Aschenbrenner was another genius.

Nathan Leopold.

In bullet form:

• First words at four months and three weeks
• Studied fifteen languages, claimed five fluently.
• Graduated in his teens Phi Beta Kappa at Chicago, headed for Harvard Law.
• A nationally recognized ornithologist at nineteen

Enamored with Nietzsche’s Übermensch (“supermen”) as transcendent individuals with superior intellect, Leopold wrote to his a precocious friend Loeb, that such a man is “exempted from the ordinary laws which govern men.”

They conspired to get away with the perfect murder as proof and tribute to their superiority. They spent 7 months planning the abduction, disposal, and even a ransom demand purely as misdirection.

All this only to be caught by eyeglasses dropped near the body. While the glasses had an ordinary prescription and an ordinary frame, they featured an unusual hinge sold to three customers in Chicago. One was Leopold.

Kelly math would have been trivial to Aschenbrenner by the time he was 10. He probably would have used the word “trivial”.

Ed Thorp, another genius, was able to connect the dots from John Kelly’s equation to its use in investing, effectively inventing the world’s first quant fund (which incidentally seeded Ken Griffin when Thorp shut down and gave Ken all his documents since he saw Ken knew what to do with it all). But I’m increasingly of the mind that Thorp’s genius also included suppressing his own ego enough to take Kelly seriously. It’s an intersection of classical genius and wisdom which itself needn’t be so rare. It’s neither here nor there, but I think the public recognizes that the type of genius we are getting out of Silicon Valley is far narrower than the Thorpian variety.

[Related: The connection and friendship between Thorp and Buffett, whose approach to investing was vastly different, was one of the audience’s favorite parts of the talk I gave at Arbor].

Back to Shkreli referring to a trader he once worked with:

What amazed me was that he managed roughly $300–400 million of his own capital but almost never used it. Eighty to ninety percent of the portfolio was simply cash. He would make these tiny trades—little nibbles—and over more than 20 years, I don’t think he ever had a down quarter. He generated 20–30% annual returns while barely putting capital at risk.

It was an incredible lesson. Then, of course, the moment I got the opportunity to manage capital myself, I was running eight times leverage. Looking back, it was one of the dumbest things I could have done. You live and you learn.

Apparently, you can’t learn risk management. You can only live it. Allocators take note.

The allocator’s mistake

I mean, what makes us human, right?

We love stories. We need stories. We want to see athletes fly. We want the impossible dream. We love that truth is stranger than fiction, after all, fiction is restrained by its need to make sense.

The optimism required to back the impossible is the same optimism required to attempt the impossible. Leopold’s investors were not teachers’ pensions and bean counters. It was his singularity-pilled entrepreneur brethren.

Even if he zeroed the damage would be contained to those who can afford it, so my view is no harm, no foul.

Nobody is bigger than the market

A lesson we learn again and again, is that nobody is bigger than the market. Not even the crocs. They survive because they respect it. They’ve seen so much in the course of both providing liquidity and also occasionally taking it to manage risk, that they can have no other relationship to it other than respect.

That means keeping concentration away from leverage. There’s no price worth giving up control of your fate.

There seems to be something deep about risk management that eludes genius alone.

• Leopold couldn’t have been ignorant of betting math.
• Leopold should have been able to understand structure.
• Leopold knew growth would require getting thesis, path, and timing right.

I’m left to conclude that the ancient root of most major errors is at hand. Hubris.

I’m even more confident in this because history tells us that being smart doesn’t inoculate you from hubris and, as the murdering Leopold story suggests, can actively fuel it. But since I have never spent a day being a genius, any more than I jump like Jordan or sing like Sinatra, my sense of limitation leaves me unable to empathize with Leopold’s blind spot.

This is a point of encouragement to everyone.

When you are limited, you seek approaches in light of your limitations, which explains the reality we seek all around us. That the quality of our decisions which determines flourishing has no relationship with excessive intelligence.

Since investing and managing money is a decision overlay that sits on top of research and analysis functions, the manager’s efficacy is rate-limited not by brains but by wisdom.

We should be a bit more obsessed with where wisdom comes from than continue to fall for dazzling minds.

If there’s a bit of poetry in this episode, it’s that Leopold’s only out was a singularity bigger than the market’s boring old constructs like liquidity and collateral. Maybe he could have gotten there. And he still has a chance. His mistake was turning it into a race with the crocs.

If you don’t mix Leverage + Concentration, you never have to get in the water.

Money Angle For Masochists

📺Leopold Aschenbrenner’s Probability of Ruin Explained | The Options Trench

Leo might be Neo. The One. Because the fund is still standing. Given the leverage, they only needed to lose a fraction of its vol to be zeroed. If he emerges as a great fund manager, it’ll be a story for the investing ages. This episode goes a bit more into the brutal inevitability of the math.

Launched this week at Moontower.ai

A quick reminder of how option enjoyyyyers are sorted into 2 camps.

Camp 1: Investors who start with an opinion on a stock. They go to the option chain to express it and accept the posted price as fair. This is the vast majority of option users. Using options for delta or possibly “income” which still maintains a delta.

Camp 2: Vol traders invert the process. They’ll concede the stock is priced about right, since that’s not the skill they bank on. Instead, their discernment goes into the derivatives where they search for relative value of contracts on the volatility surface.

Moontower originates from camp #2, but our new Workflows feature is to serve camp #1.

There’s 3 suites, each one is organized around an intent that drives your screening process married to the Moontower lens for finding value.

Income is for selling covered calls and cash-secured puts.
➡️ Introducing Income Workflows

Defensive is for buying protection, or replacing a long position with calls or spreads.
➡️Introducing Defensive Workflows

Collars is for investors willing to finance protection by selling some of their upside.
➡️Introducing Collar Workflows

The posts explain each suite in more detail, but we built them after talking to clients about their specific processes. The interface will be intuitive based on how investors usually screen for these ideas, except our opinionated analytics are quickly narrowing and sorting to accelerate your workflow. Hence the name.

This is the call-to-action part: Sign up

Seriously, it’s bad-ass.

Stay groovy

☮️

Moontower Weekly Recap

• “best faculties of a mature human being exist in the child”
• a talk I gave at a quant bootcamp

In this issue:

• free lunches and non-tradeoffs
• VIX and buy signals
• delta-hedged risk reversals

Friends,

As one of my favorite HS teachers used to say, every silver lining has a cloud. (That this is one of my favorite teachers, you could probably predict my teenage affection level for rainbows and pop music). It seems I was destined to take to the idea of no free lunch easily.

Costs and Benefits

Trader and author Brent Donnelly, like most of us, struggles with the drawbacks of the otherwise transformative tech packed into our smartphones.

I Want It, But I Don’t Like It | 8 min read

The shocking and amazing thing about the unprecedented economic success of surveillance capitalism is how easily many of us (including me) surrendered to the extraction layer without much of a thought or a fight.

It’s a great example of what DFW called our default groove or “water”. That he’d spend the commencement speech warning us about lapsing into zombie mode BEFORE the smartphone was even invented indicates just how hard it really would be to overcome infinite scroll.

The short book I most commonly recommend to people is Neil Postman’s lengthy essay Amusing Ourselves To Death (my notes). It was published in 1985. My edition as a prophetic foreword:

Brent offers his Easy, Medium, Hard interventions to combat his phone. I share the struggles and have had with various levels of success tried many of these myself.

While the article is ultimately practical, I appreciated Brent’s abstract observation that the phone has both an extraction layer designed to monetize your attention as well as an agnostic technological utility layer (phone, camera, processing). His strategy is to minimize the former while maintaining the benefits of the latter. In other words, this is not the realm of a tradeoff.

In The Sydney Opera House Exam Question Dan Davies writes:

You are almost certainly not at the efficient frontier of managing your phone’s costs and benefits.

So there must be a free lunch after all. Check out Brent’s interventions.

Accelerated upskilling

Wednesday’s oh well included some links about learning and upskilling. Here’s another one I’ve come across since:

How to ‘git gud’ at Games (Faster Than Everyone Else) | 4 min read

This is from SIG’s gaming blog.

“One of the least efficient ways to improve at a game is simply playing it.”

In our latest gaming blog, Adam, a competitive gamer who has reached Master rank with all races in StarCraft II, cracked the top 50 in North America in Hearthstone Battlegrounds, and is currently ranked #1 in the world in Patchwork on BGA, looks at how you can “git gud” at games (faster than everyone else, of course).

It offers 5 tips to accelerate learning. Actually, “tips” is a flaccid description of Adam’s suggestions. They are the difference between the preparation of amateurs and pros in any skill-based activity. It’s more like an advantage loop. Combining it with talent (which is why matching your activities to your abilities is so important) and persistence is a very simple recipe to achieving rare outcomes.

I didn’t say easy. Just simple.

Maxen-Art

This past weekend I stood up a website for 10-year old to host his art. In the age of AI this is easy even without a website builder.

I bought the domain name on Namecheap, Max found gallery sites he liked that were minimalist, and I told Claude to mimic the format. The HTML is produced is hosted on Github along with a folder where we upload his images. Vercel is the host serving the webpage. There is an automatic webhook from Git to Vercel so that anytime Git updates, Vercel updates the page.

🔗maxen-art.com

Money Angle

Here’s Victor Haghani:

A high VIX1 is widely considered to be one of the cleaner buy signals out there. A recent piece in The Financial Times made the case directly: when the VIX climbs above 30, forward returns have been well above average, positive most of the time, with double-digit six-month gains.

The Financial Times case is “buy the f’n dip” logic with a VIX gate. It’s exactly the type of thing that a layreader numbly nods at when the SPX is sitting near an all-time high. The Financial Times’ case is lazy from the perspective of both investors and active traders. For the investor, it’s just survivorship bias. Knowing what we know now every pullback has just presented a bargain. The market literally “going on sale” like it’s Prime day. VIX spikes over 30 just coincide with the sales.

The question you care about is one that an active trader hearing that statement would think to hypothesis test. Given that buying any time before an all-time-high has been worked out well, how do I distinguish between relatively better or worse buys?

Back to Victor:

What that leaves out is risk. Buying the spike means taking on a lot more of it, and the strategies that did the opposite, trimming exposure when fear ran high, held up better. So the popular signal may have it backwards.

Raw returns aren’t the right thing to optimize. You care about compounded returns since investing is a repeated game. Compounded returns are risk-adjusted returns because a geometric growth process penalizes volatility.

Elm Wealth tests FT’s claim not on raw return but Sharpe Ratio, or how much return you’re getting per unit of risk taken, as the variable to maximize if we care about risk-adjusted returns.

When Fear Spikes, Should You Buy? Elm Wealth | 5 min read

What they found when they ran the numbers on S&P 500 and VIX data from 1990–2026, they found:

• A plain static stock/T-bill portfolio: Sharpe ratio of 0.50
• A strategy that buys more when VIX > 30% (the popular advice): 0.47 which is slightly worse than the null case
• A strategy that reduces exposure when VIX is high (inverse sizing): 0.54
• A simple momentum strategy (cut exposure when the market is falling, which is typically when VIX is elevated): 0.59 — the best performer

It’s always bears repeating how risk scales:

When volatility doubles, the risk of holding stocks is actually four times as large (because variance, not standard deviation, is what matters to risk).

To merely hold your position when VIX doubles, expected returns would need to quadruple. To justify doubling down, they’d need to increase eightfold, which the authors deem practically implausible.

This post led to some smart quants chiming in on X.

Here’s @ptuomov:

This is a very low-resolution statement because each word represents many variable choices when you get into research:

Define “high”, define “trending”, “six months” was probably just a placeholder term

The degrees of freedom on the choice notwithstanding, the idea makes sense:

You are using the signals from the derivatives market, a place where leverage attracts early movers and smart money, to give a leading indicator on “the market environment is changing from the status quo” and collective anchoring biases make the wider market underreact. The way to profit from the seeds of this new information is to follow the trend.

There’s that line what the wise man does in the beginning, the fool does in the end.

The quant view is trying to find the signal of moving from the end of one cycle to the beginning of another. Trend following in a sense has a long option flavor. The premium is all the false starts and the payoff is when you finally catch a trend.

Meanwhile, buying the dip is a short option strategy in that it is betting on mean reversion as opposed to further divergence. Buying stock when VIX spikes is a mean reversion trade. But when you examine that as a strategy from the vantage point of all-time highs, it takes for granted that the mean is a good thing.

When you read a claim about a course of action, it’s good mental hygiene to first triage it as: is this directionally long or short vol?

Money Angle For Masochists

We recently added multi-leg support to our Attribution Visualizer, our tool for allowing you to track an option contract’s p/l assuming you hedged the delta daily. The tool breaks out the p/l according to gamma + theta (which sum to realized p/l) and to implied vol (vega p/l).

With multi-leg support, you can now entertain yourself with countless questions. Like “how would a masochistic skew trade work out if I trade a risk reversal and hedge daily?”

I ran a few risk reversals through the attribution tool.

USO: Buy call/sell put after the Iran war started

Date: March 13

Expiry: June 18, 2026 (~ 3 months)

Spot: $119.92

Risk reversal: 140c / 100p (equidistant strikes ~ each 17% OTM)

Initial hedge: Short 73 shares per risk reversal (the RR had .73 delta)

The war had already flipped the skew hard toward upside strikes. The $140 call traded 94% vol against the $100 put’s 83% IV. It cost $5.83 in option premium.

At expiration, the stock expired at $114.87

So how did it work out to buy the premium IV?

Not good. The cumulative delta-hedged p/l was a loss of over $4.50 as you lost to both realized vol and vega. At the initiation of the trade, paying the premium vol meant you were flattish gamma but paying theta.

You were also long vega because, despite the options being equidistant, at a generally elevated vol level the lognormality of the underlying distribution and its associated positive skew pumps up the delta of calls. In fact, the 140 call was ~.47 while the 100 put, which is closer in dollar space, was only .27d. The higher call delta says the 140 strike is much “closer in vol space”. That’s why the equidistant risk reversal cost so much premium to buy the call. You are buying at OTM that has a delta that we usually associate with near ATM options!

Let’s adjust the strikes so that our call and put are both ~.25d

To equalize deltas against the $100 put you have to buy…drum roll please…

The $190 call! 58% OTM for 101% IV. Now you collect a $2.17 credit to own the call and short the 100 put. Your initial Greeks mostly vanish.

The trade still loses, but it fares much better as the loss is only $1.29.

It’s tempting to conclude paying a premium vol doesn’t work. But if you bought the much cheaper call and shorted the put on a hedged riskie in SPY before the war started, then you got smoked if you chose April 30th expiry (SPY bottomed the last day of Q1), recovered once the market started rallying, only to lose again as the market…continued rallying! SPY riskie:

I’ve said it repeatedly over the years in different ways, but riskies are the whips and leather of the option world. If you bought the call on the SPY Feb 720/650 risk reversal on the first trading day of the year and hedged daily until expiration, you actually would have lost $.25 despite the following:

• the trade collected about $2.75 in premium at the outset
• the stock’s closing prices stayed inside the range of $675-$700
• the call you bought was 10.2% IV and the put you sold was 16.8% IV

In Financial Hacking, Philip Maymin invents an optimistic junior trading assistant who sits down his bosses at the bank to explain that he has found an infinite money machine. Selling the high IVs in SPY puts and buying the cheap IV in SPY calls. Maymin asks the reader to figure out why this logic doesn’t work.

Our tool provides the day-by-day audit which feeds the charts. Armed with that, Claude does an admirable job of not only answering Maymin’s prompt to the reader but also pinpointing exactly which days carry the biggest weight in the answer.

I’m excited about the tool even though using it feels like performing surgery on myself. Which weirdly reminds me, I have an option trivia question for readers who made it this far:

Stay groovy

☮️

Moontower Weekly Recap

• oh well
• war only happens at night

In this issue:

• summer reading
• a bunch of option videos
• AI Traders?

Friends,

As I mentioned on Wednesday, traveling mercifully forces me into quiet periods to read. In my normal routine, reading for pleasure can feel like an indulgence, but the combination of travel and my juvenile attachment to “summer vacation” is enough to put the guilt in remission. Of course, if you are of sound mind, you need no such permission, but just in case, here’s more than permission.

A Library of Distractions

If you are looking for recommendations for a book or show to get into this summer, this list by Chris Arnade might be just what the doctor ordered, and it opens with what I can only describe as a prescription:

I walk to learn, which is why I read, since each is a different way to do that. The Metis versus Techne split described by James C Scott, although there are plenty of other terms to describe experiential versus formal learning. I use his because I prefer the framing, which emphasizes that the two differ not only by methodology (talking versus reading) but by where that knowledge resides. Metis is the epistemology of the masses, and it is decentralized, local, and bottom-up versus Techne, which is that of the elite, and so is codified, formal, and top-down. Common sense versus book smarts, in Metis terms, and folk wisdom versus fact, in Techne terms.

Neither encompasses truth, so I believe you have to engage with both. If you focus solely on one, you will end up like the guys at the gym who never work out their legs. That analogy is especially appropriate for today’s elites, who seem to only do Techne days, never Metis, and so come out top-heavy, with spindly legs, too fragile to walk among the masses. I get it, going out into the world, dealing with people on their terms, can be intimidating to intellectuals, which has consequences, because while I value both, most people in the world are Metis, and consequently understanding it is essential, especially in a democracy.

That is one of my concerns about AI, which is that it will codify, then metastasize Techne, since that is what it draws from. Think of it as a grand aggregator of Techne, consuming it, then regurgitating its own watered-down, smoothed-out version as undeniable fact.

The History Beneath My Feet: Two Years in Valle de Bravo (21 min read)

Tiago Forte moved his family to a mountain town in Mexico. Find a quiet place or a cramped seat in coach, grab a coffee, and enjoy a captivating history lesson and a meditation on matters that actually matter.

The closing is more of a prompt than a spoiler, so I share it as enticement:

Who will we choose to become when work is not the central priority around which all others revolve? How will we decide to spend our time when most of it is not already spoken for by a job defined as “9 to 5”? How will we define ourselves when our work ceases to be an identity, and becomes more like an implementation detail?

I don’t know, but Valle de Bravo is beginning to suggest answers out of the deep well of its 500 years of history and culture.

As for me, my leisure summer reading:

Dominion fans, that book is not to be confused with:

And for podcasts, I’ve queued about 25 pods from Rest Is History. I just finished:

This is probably the only episode you should not listen to with the kids in the car.

Money Angle

This week’s Option Trench will be very educational to anyone whose traded an equity option since they are American-style (meaning you can exercise them early). Erik was assigned on IBIT puts 22 days before expiration and thought it was a bit strange. I agree. I think it was a sub-optimal early exercise, but in this chat you can see what factors influence the assessment of “optimal” and the surface of reasonable disagreement.

This is a link to the calculator in the video:

https://moontower.ai/tools-and-games/american-options-early-exercise

Also, Erik and I pre-recorded our Options Trench podcast episodes before I went on vacation. If you want to catch up…

📺Volatility in 5 Levels of Difficulty: An introduction to various meanings of volatility.

📺All Implied Volatility is WRONG: This one goes well right after the “vol in 5 levels of difficulty”. It’s a topic that is mathematically simple, but conceptually, I notice it just seems to warp people’s brain. I explain who does and who doesn’t need to care about it. If you are in this section, you very well might need to care.

📺An Inside Look At How SIG Trains Traders: See if you can answer some old interview questions and learn about bootcamp.

Money Angle For Masochists

Any moontower.ai subscriber can prompt our trained agent. Even if you aren’t a sub you can give it a try for free. Our team plans have included an API but we just launched an MCP allowing users to connect their own AI’s to our API endpoints.

This gives users maximum flexibility. We are tuning our agent on a regular basis, but if you prefer your own tool stack and AI you have that choice now.

We use evals for automatically RLHF’ing Moontower Agent and I also have a manual process where I give the agent and the MCP (using Claude Code) the same prompt, and then judge them myself. Very old-fashioned. I’ll share more about what we’re learning from this in the future, but in the meantime, here’s a relevant article from the market-making firm Optiver:

Where AI Trading Models Work and Where They Still Fall Short (4 min read)

Optiver’s Applied AI team did a different kind of eval. They gave several leading large language models the same assessments they give human interns and junior traders.

The results indicate where LLMs excel…

• grasping trading theory
• calculating fair value
• recognizing risk

…and where they still stumble:

• multi-step reasoning
• updating beliefs on the fly
• maximizing expected value under pressure

Even before AI was dominating the conversation, traders have always been obsessed with learning from data. A common example is in transaction analysis. Looking at the trades you did filtered by counterparty, venue, method (ie voice/electronic) as you suss out where you are most likely to be adversely selected. This is a hard problem even with structured data. For example, it might be straightforward to filter by how you do against live option orders (as opposed to delta neutral packages), but there are so many possible permutations. Should I consider how the quote was framed before the order came in? Do I treat a resting order differently than if I’m hit or lifted? Does time of day matter?

But now consider the scope of the unstructured data problem. The counterfactual. The order a broker showed me, I passed on and proceeded to trade without my participation. You’d need to record every phone call (actually this is already done for compliance reasons. In fact, when I interned at a bank in 1995 one of my tasks was to change the giant reel of tape!). But you’d need to link the audio of what the order was to the print when it hit the tape. Or track the fact that it never even traded. It’s like tracking the p/l of a non-trade that could have been. With transcription so cheap, this is feasible now, but it wasn’t when I was thinking about it. You could have traders note when they passed on a trade, but this would be so tedious that it was always a non-starter on a high-volume market-making desk.

My guess is that some trading shops might be doing things like this now (if not, you’re welcome for the idea). But this Optiver article made me wonder when trading rooms will be mic’d up. Jarvis listening to all the conversations, meetings, and debates to cheaply turn unstructured data to structured data.

Your voice, its quiver, your cadence, your pauses, your keystrokes, your glances, your heart rate. Insofar as humans will still be trading, it’s hard to imagine the data obsession that’s already penetrated the MLB not make its way to desk talent.

You’ll know singularity is close when the employee handbook stipulates bathroom breaks as the only acceptable cause to remove your electrodes. Buy stock in Gillette. Every man on a W2 will need to shave their chest for a clean connection.

Related

Elm Wealth let AI compete with humans in their popular Crystal Ball Challenge. You can give it a try yourself:

https://crystal-ball.elmwealth.com/

Elm’s founder Victor Haghani:

A couple of weeks ago we let you loose on our Crystal Ball Challenge: tomorrow’s headlines, $1 million to trade in stocks and bonds, and four AI models to beat. Humans showed up in force, logging thousands of plays and adding over 1,500 entries on the leaderboard.

Here is how the AI models are doing against human players so far:

– Claude: winning 65% of the time
– ChatGPT: 50%, a coin flip
– ️ Grok: 43%
– Gemini: 40%

Both the Wall Street Journal and The Economist covered the experiment this month, and both keyed on the same finding: the AIs are great at reading market-moving news, but they struggle to size their bets appropriately. Knowing what to trade turns out to be the easy part. Knowing how much is what trips them up.

If you have not played yet, three of the four AIs are losing more than half their matchups. Pick your fight. If you have played but not lately, your spot on the leaderboard might no longer safe.

And finally, just before I scheduled this to send out I came across Dwarkesh’s:

Subtitle: “Labs are throwing away the most valuable data”.

🗒️transcript

Stay groovy

☮️

Moontower Weekly Recap

• DFW
• if you want to hide in cash for a bit…