Quarterly Letter · Q2 2026

Winners, losers, and the law of large numbers

In one NFL season, we bet $20,000 to win $125.

“The transaction we are announcing today represents a strategic proactive move to optimize our financial flexibility and maximize long-term shareholder value creation.” — Anat Ashkenazi, Alphabet CFO

Right after college, a couple of my best friends and I decided we’d get rich building a model that could beat the NFL betting markets.

We spent six months of the offseason building and refining a neural network* that could take inputs ranging from player injuries to how far away teams were flying and predict the outcomes of the games.

Now, in order to make money betting on NFL spreads and totals, after you take into account the sportsbook’s take, you need to get it right roughly 52% of the time. Our model, after months of work, suggested we’d be right…54% of the time.

We each chipped in a couple hundred dollars week one, and each weekend we followed the model (probably ruined the fun of gambling for me), bet on nearly every game, and recycled our winnings into the next week. Some weeks we made tons of money (relatively), some weeks we lost even more; the swings were large for kids eating Domino’s.

In the end, we placed over $20 thousand across hundreds of bets and won just over 53% of them. Good for $125 and about $0.75 per hour of my time.

More importantly, it was a lesson in the volatility of single bets or even single weeks, and the impossibility of evaluating whether one is right or not with too small a sample. It seems like the right time to revisit this lesson.

Looking at the market today, there appear to be a lot of winners and losers being crowned well before it’s possible to know the truth, and probabilities have gone out the window.

To us this seems a mistake.

* In this letter it may sound as if I’m incredibly bearish on AI, while in fact the opposite is true and has been since this story, well before ChatGPT.

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