AI Workers With Newfound Wealth: A Guide to Giving It Away

Roy Bahat's two-part essay on charitable giving, now in its second installment, targets people in AI who have acquired sudden wealth and want to direct it toward making the world better. The piece begins with a foundational principle: start by defining the outcome you want to achieve, not by deciding upfront how much money should go to nonprofits versus political candidates versus other vehicles.

Bahat argues that most donors work backward—they choose categories first, which prematurely constrains their options and, in some cases, backfires. He cites the cautionary example that "more than one big foundation has wished their donor hadn't bonded the money to strictly nonprofit giving." He notes that the universe of giving is far larger than nonprofits alone: donors can give to political candidates, angel invest, start their own initiatives, host events, pay off neighbors' student loans, or become activist shareholders in public companies. What vehicle to choose depends on your "theory of change"—your belief about what needs to happen for the world to turn out differently.

Bahat emphasizes that AI workers are watched more closely than most donors. He illustrates this with a concrete example: "An OpenAI engineer who used his sale proceeds to open a community makerspace in the East Bay made local news." This visibility, he suggests, adds weight to the choices AI workers make.

The essay then poses a series of questions for potential donors to wrestle with. First: who decides how to use the money? Bahat introduces the concept of "participatory grantmaking," in which donors share decision-making power with those most affected by the issues they care about. Second: should you back existing organizations or insist on new things? He expresses enthusiasm for both. Third: how much control will you exercise over how funds are deployed? Here Bahat draws a sharp contrast: startup investors rarely dictate how founders spend capital, yet nonprofit donors routinely impose "restricted giving" conditions. He objects to this double standard, noting that donors often understand nonprofits less well than startups, yet are far more controlling. Fourth: how will you measure what works? Bahat acknowledges that measurement is hard and honesty is harder to come by; he suggests triangulating from multiple sources and recognizing that some results take time to materialize.

Bahat also touches on the role of philosophy in giving. He mentions Effective Altruism, which he notes is "omnipresent" in AI circles, and points readers to Stanford philosopher Rob Reich's critique of philanthropy as "essentially taxpayer-subsidized power." Reich's model, Bahat reports, frames philanthropy as higher-risk R&D that government might later adopt—a way of thinking that repositions the donor's role.

Critically, Bahat warns against one pitfall: isolation from reality. "One of the great dangers of having a lot of money is that it disconnects you from others' reality," he writes, noting that people rarely maintain friendships across more than a one-order-of-magnitude gap in wealth. Giving, if done well, can reconnect a donor to reality—especially if they show up and learn from those closest to the work.

Finally, Bahat stresses that giving money away is only part of the solution. He invokes Lilla Watson's words: "If you have come here to help me you are wasting your time, but if you have come because your liberation is bound up with mine, then let us work together." The deeper questions, he suggests, are personal: What privileges will you sacrifice? What power will you give up? How will you examine the hardest choices within your family and company? How will you steel yourself when the road is hard? For him, the work is harder and more rewarding than it looks, and the questions never end.

Bahat's essay frames charitable giving not as a logistics problem (how to distribute money across buckets) but as a strategic one rooted in what you believe needs to happen in the world. This reframing matters especially for AI workers because they inhabit a moment of unprecedented wealth creation and, simultaneously, unusual public attention. A small makerspace gift from an OpenAI engineer made local news—a sign that donors in the field cannot give quietly. That visibility creates both opportunity and obligation to think carefully.

The essay also draws a subtle contrast between two models of philanthropy. Startup investing operates on trust and founder autonomy; nonprofit philanthropy, historically, operates on donor control ("restricted giving"). Bahat suggests this asymmetry is misguided—that the nonprofit sector, where donors often know less, is where they most insist on control. His implicit argument is that ceding power to those who know the work best—and to those most affected by the problems—produces better outcomes than top-down micromanagement.

Central to his framing is the idea that giving money is only one piece of the work. He quotes Lilla Watson: "If you have come here to help me you are wasting your time, but if you have come because your liberation is bound up with mine, then let us work together." This suggests that the deeper question is not how much to give, but what privileges and power each donor is willing to examine and sacrifice. For AI workers with accelerated wealth, that examination may be the harder and more consequential work.