I Have Two Harvard Degrees. They're the Wrong Qualification for What's Coming.

Last Wednesday a documentary film put a name to something I'd chased for a decade in Silicon Valley. The ghost I could never quite catch.

I'm on the board of the San Francisco Foundation. About ten of us gathered in the board room to screen a new film called Ghost in the Machine. Most of the room was junior staff and middle managers, the people who usually sit along the walls during board meetings, not at the table. We pulled the shades on the Bay and watched.

The film traces artificial intelligence back to an uncomfortable root. It draws a line from the Victorian men who first tried to measure and rank human worth, through the founders of Silicon Valley, to the AI boom we're living in now. The argument is that the whole project rests on one old idea: that some people are simply more deserving of wealth and power than others, and that you can measure who.

When it ended, no one spoke. The whole thing felt insurmountable. The room was set up panel style, three chairs up front facing rows of seats. It felt wrong. We didn't need a sage on a stage. We needed to be in conversation with each other. So we pushed the chairs into a circle, and the program officer moderating asked us for our reactions.

I was one of two panelists, with Lili Gangas. She went first. I stared at the ceiling, trying to turn a deep feeling into a sentence, and what came out was Stamped from the Beginning. In Ibram Kendi's deeply researched, 511-page sweeping history of racist ideas in America, he argues that racist ideas didn't come first. The self-interest came first, and the ideas were built afterward to justify it. The hierarchy was the alibi.

The hierarchy in Silicon Valley presents itself as intellectual. But it grew out of the same old project: the attempt to prove that human worth can be ranked and measured. And it still does the same work. It keeps a certain kind of person at the top and lets them feel they have earned it.

I know that hierarchy from the inside. I have an engineering degree and two master's degrees from Harvard. I worked at Bain, at Bridgespan, and at Google for a decade. My credentials were unquestioned, and they helped me climb. Before I was promoted to Director at Google, my boss told me a C-Suite executive had asked her where I'd gone to school. I earned that promotion on the work. But a decade out of grad school, the question still got asked. And two Harvard master's degrees were the answer that satisfied it.

I say all of this because the same assumption now sits under almost every conversation about the coming wave of AI philanthropy. It's under Nan Ransohoff's smart, widely shared piece on the third wave of philanthropy. It's under every room where this new money is being discussed.

The assumption is this: the people best equipped to solve our hardest problems are the people who can think about them most rigorously.

I don't believe it.

It comes with a method. Rank the problems by expected value. Trust the calculation over the testimony of the people living closest to them. Treat sentiment and local loyalty as bias to be stripped out, and call the result objective. But choosing whose testimony counts as evidence, and whose counts as bias, is the oldest bias there is. I understand the appeal. I've spent my whole life inside it, and I know how seductive it is to believe a hard enough mind can reason its way to the right answer.

This isn't an argument against intelligence. It's an argument against worshipping it, and against the quiet belief that the people who have the most of it have earned the right to decide everything else.

Here is what that belief does, whether it means to or not. It takes moral authority away from the people who have actually suffered, and hands it to the people who can argue most fluently about suffering.

Those are not the same people.

Moral authority doesn't come from a diploma. It comes from proximity. The person who has lived closest to a problem knows its contours in a way no model captures. They know what exclusion feels like. They know what it costs to be born on the wrong side of a line. That knowledge is not soft, and it is not a lesser form of expertise. It is the most reliable expertise we have about how oppression actually works.

I learned this in the work, not in a seminar.

Years ago at Google.org I made a $2 million grant to a Black-led movement against gun violence. They organized the people closest to the shootings around an approach the evidence backed. The grant wasn't large enough to need executive approval, but the company asked me to get a senior vice president to sign off anyway. I still notice that detail. The work made the system want a higher signature.

The leaders of that work had a moral authority no credential confers. One of them put it simply: the people closest to the pain should be the ones leading the response. But when I sat with other funders working on criminal justice, some of whom came out of effective altruism, those same community leaders were heard but not centered. They were treated as people to be assessed, not people to be followed. The prime seat went to the funders and the analysts. The ones who actually knew were expected to defer.

It confused the leaders. For a while it confused me too. Then I saw it was the hierarchy doing what it always does.

The history is worth being precise about.

Modern statistics was built, in large part, by eugenicists. Francis Galton coined the word, and developed correlation and regression. Karl Pearson, who gave us the correlation coefficient, held a literal academic chair in eugenics. So did Ronald Fisher, whose work underpins most of modern statistics, and who was still defending innate racial hierarchy as late as 1950. This isn't ancient history, and it isn't fringe. In 2020, University College London stripped Galton's and Pearson's names from its buildings, and Cambridge took down a window honoring Fisher.

I'm not saying the math is tainted by the men who made it. Regression works. An idea is not false because its author was a bigot. My claim is different.

Eugenics was never only a set of tools. It was a project: ranking human beings on a single scale of measurable intelligence, then deciding who deserved to flourish. That project didn't end. It changed its name.

It became the IQ test and the SAT. The SAT was designed by Carl Brigham, who sat on the advisory council of the American Eugenics Society and had just published a book on the superiority of the "Nordic race." He recanted years later. The test was already built. It still shapes who gets through the doors of the academy. When we're told that the people best equipped to run philanthropy are the ones who scored highest and reason most rigorously, we are being handed the most recent costume of a very old idea.

That is the pattern Kendi described. People build these hierarchies to justify their own place inside them, then call the result merit. The worship of measurable intellect is its newest form. It seats its own architects at the summit and tells them they belong there.

And it didn't only shape who got in. It shaped what got built. William Shockley shared the Nobel Prize for the transistor and then founded the lab that brought silicon to the orchards south of San Francisco. His engineers left to start Fairchild, which became Intel and much of what followed. Gordon Moore, who cofounded Intel, said Shockley put the silicon in Silicon Valley. Shockley also spent his last decades insisting that Black Americans were genetically inferior, and proposing that people with low IQs be paid to be sterilized.

The reverence for artificial intelligence is that same instinct pushed to its conclusion: the dream of an intelligence even greater than the men who already sit, by default, at the top.

When you hold that much wealth and power, you need a story that makes it feel deserved. Intellect, dressed up as something neutral and measurable, is that story.

Nan Ransohoff sees part of this, and her piece does real work, forcing the sector to reckon with the sheer scale of the money coming. She is right that the measurement tools of the last wave, the ones effective altruism gave us, are a poor fit for the questions this wave has to face. But the talent frame she builds on top of them smuggles the old hierarchy right back in.

Look at who appears in the world she sketches, and who does not. Her builders are tech founders. Her capital allocators are philanthropic VCs, modeled on Sequoia. "Tech-caliber talent" is the standard, and she predicts these funders will be wary, by default, of the people who come out of traditional philanthropy.

Nowhere in that ecosystem, funders, allocators, builders, do the people closest to the problems appear, except as the problems themselves. They show up the way they always have, as case studies and quotes in a deck, assessed by the room instead of seated in it. A third wave built on that frame will hand the pen to the same people who have always held it.

Nan also hopes we don't lose sight of human flourishing: aesthetics, civic life, moral imagination, the cultural conditions for a good life. I share that hope. So ask yourself honestly where human flourishing actually lives.

It is not in Silicon Valley. It is not in high intellectualism.

It's in cultures that never stopped valuing community, rest, nourishment, and connection to the land and to the sacred. It's around a campfire. It's in the things our most intellectual spaces have been quietly starving for. The academy was never built to credential the people who kept that knowledge alive.

So I'm not asking us to abandon intellect. I'm asking us to stop centering it. Not in who accumulates this capital, and not in who gets to distribute it.

The people we should be looking for are bridgers. People who carry the lived experience of communities that have suffered, and who can also navigate the academies and boardrooms well enough to move real money. People who can sit in both rooms and refuse to let either one erase the other.

That is harder to find than a brilliant analyst. It's also the only thing that will keep this third wave from intellectualizing, one more time, on behalf of people it never bothered to include.

That night at the foundation, the people in the circle were not the ones who usually hold the pen. The circle was the right shape for the conversation. It may be the right shape for the money too.

The money is coming. The real question was never whether we're smart enough to deploy it.

It's whether we'll trust the people who actually know.

Justin Steele is co-founder and CEO of Kindora, a Public Benefit Corporation using AI to help social-sector organizations find and win funding. He previously directed nearly $700 million in philanthropy at Google.org over a decade and serves as a trustee at The San Francisco Foundation. This piece was originally published on LinkedIn.