Turning Risk Appetite Into Impact - by Eric Gilliam

FreakTakes

SubscribeSign in

Engineering Innovation<br>Turning Risk Appetite Into Impact<br>An Ode To Warren Weaver

Eric Gilliam<br>May 22, 2026

11

Share

When it comes to the philanthropic ecosystem, we live in exciting times. A duo of recent posts — one from one of SF’s great thinkers, the other from one of its great general managers — are dedicated to this fact.<br>Last week, Dwarkesh Patel closed submissions for a blog prize searching for the best answers to the question: How do we deploy (possibly) hundreds of billions of dollars to “make AI go well?” He specifically wanted to know how readers might approach the problem of converting money into impact from the POV of the OpenAI Foundation.1<br>In a second blog post, The third wave of American philanthropy, Stripe Climate GM Nan Ransohoff put the scale of the opportunity in context. She points out that if $50 billion from AI donors were to enter the philanthropic ecosystem each year, that would be enough to fund the annual budgets of the following organizations:<br>6 Gates Foundations (~$9B/yr), or

67 Coefficient Givings, formerly OpenPhil (~$1B/yr), or

100 GiveWells (~$500M/yr), or

333 Arc Institutes (~$150M/yr), or

5000 Institutes for Progress (~$10M/yr)

Nan then breaks down the challenge into a set of seven subproblems that, from an ecosystem design perspective, we must overcome to use these dollars well. Today’s piece zeroes in on a solution to one of these problems: figuring out how these new funders can leverage their unusually large risk appetites to world-changing effect.<br>Incidentally, this is exactly what my submission to Dwarkesh’s blog competition was about. In it, I profiled the best scientific philanthropist of all time, Warren Weaver, and his (exceptionally risk-tolerant) playbook for willing new fields into existence. Like great VCs, he only took big swings. Unlike VCs, he did next to no diversification. To Weaver, field creation was an endeavor that required focus; responsibility was not about hedging your bets, but giving carefully selected fields everything they needed to flourish.<br>Over the course of twenty years, Weaver bootstrapped the field of molecular biology into existence. He then funded a course of crop research that would grow into the Green Revolution. Weaver had the courage to live with concentrated bets for years on end, and the world is immeasurably better for it.

A 1949 photo taken from an experimental wheat field in Mexico. Weaver is the shortest man in the photo.

Subscribe

Share

Given the blog competition word count, I’m briefer than usual in many spots. If you’re curious to learn more about any topic mentioned, check out my long piece on Weaver, my many Weaver tweets, or ping me. And if you have the urge to act on today’s piece, contact me — egillia3@alumni.stanford.edu, or on my Twitter. If urgent, I can fly to SF.<br>Two more of Nan’s subproblems — (1) the need to create new philanthropic capital allocators and (2) to make the problems that need solving more legible — will be the subject of a coming piece.

A Technology of Historical Consequence

To become a technology of historical consequence, we must proactively make good things happen using AI. In the not-so-distant past, we find kinks in mortality graphs representing hundreds of millions of lives saved — kinks sparked by the action of a few smart people. Human brains did that. In partnership with a superhuman AI, we should have no less ambition.<br>Our ambition should extend beyond longer, healthier lives. I take a somewhat Robert Gordonian view of human flourishing: people working less without getting poorer, finding ways to make housing and food more affordable, and so on.<br>In pursuit of these goals, the philanthropic efforts of the AI labs should focus on problems that take advantage of two Silicon Valley comparative advantages: R&D and risk-tolerant capital allocation.2<br>Key individuals at the OpenAI Foundation have the opportunity to cement themselves as historically great, as more consequential than even J.C.R. Licklider or Bell Labs’ Mervin Kelly. But to do so, they must internalize the lessons of history’s best research funders. Why? Turning money into impact is closer to a problem of organized complexity than one of simplicity. Working from empirical data trumps “first principles” in areas of this sort. Funders need to decide on some heroes, and learn from them.<br>If I ran the OpenAI Foundation, a photo of Warren Weaver would hang in the lobby. From his perch at the Rockefeller Foundation, which he took over in 1932, Weaver made two contributions that cement him as the greatest scientific grant funder of all-time. He:<br>Funded molecular biology into existence.

Was key in funding the Green Revolution into existence.

(If the reader wonders whether Weaver simply got lucky twice, he also spotted the computing wave. Purchase the book-form of Claude Shannon’s The Mathematical Theory of Communication, and you’ll find...