The Vietnam era sent some of America’s best young scientists to Canada. It’s happening again.

Kyoto, Japan, 2016. There was a break between sessions, and I was out on the patio of the conference center venue talking with the President of York University in Ontario. We were both at the STS forum, one of the premier global science policy events, held each October in Japan.

“You know, most of my most senior neuroscience faculty immigrated from the United States,” he said.

I was pretty skeptical. With brain science powerhouses like Toronto, McGill, and the University of British Columbia, that didn’t make sense.

“Surely, that’s not the case,” I responded. “Canada is historically a leader in brain research. Look at McGill, alone.”

“I’m not talking about all my faculty,” he responded. “Just the most senior ones. They came over during Vietnam to avoid the draft.”

That conversation was ten years ago. Now it’s resurfaced in my memory for obvious reasons. Every time I visit an embassy here in D.C., the question comes up about my views on who should be on a list of up-and-coming young American neuroscientists open to emigrating.

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I was a teenager in Pasadena when the unpopularity of the Vietnam War and the draft sent a remarkable cohort of American scientists to Canada. Among them were people I knew well: Harvard classmates of my older sister, trainees in my Dad’s Caltech lab, teachers at my school. Many of them weren’t marginal figures. They were disproportionately elites in the STEM labor pipeline, self-selected for their willingness to leave the familiar behind for a principle. Canada didn’t just absorb them; it built around them. McGill, Toronto, UBC, and, yes, York, became genuine centers of excellence in part because of this infusion of talent. And most importantly, many did not come back. After President Carter provided amnesty, analysis from the 1986 Canadian census suggests that of the 30-50 thousand individuals who migrated to Canada to avoid the draft, roughly one-half stayed on. Careers, families, and institutions had taken root. The lesson from this era is important today: a brain drain isn’t a temporary loan. It’s a transfer of capital—human, intellectual, and institutional.

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How does what’s happening now connect to what happened in the 1960s and 70s? Then, a single acute shock — the draft — produced a discrete, visible cohort exodus. The border helped: a U.S. driver’s license and a few cursory questions got you across. Physical migration was nearly frictionless, which is why the movement happened quickly and in plain sight.

Today, the mechanism is almost entirely different. There’s no single precipitating event — instead, a slow-motion but accelerating pressure system. NIH and NSF funding cuts and organizational disruption to grant infrastructure. Indirect cost cap threats destabilizing university research budgets. Uncertainty around F-1 and J-1 visas is choking the graduate student pipeline. Political targeting of specific institutions and research areas. None of these is like the Vietnam-era draft. But, together, they are changing the calculus.

Also, there is considerably more friction than in the Vietnam era. A lot of it is in the decision itself. Scientists considering a move are embedded in a complex institutional infrastructure: active grants, dependent trainees, and long-standing collaborations. Leaving requires dismantling all of that. But that same complexity means that those who do make the move are deeply committed to staying on after they move.

The cumulative effect is that the expected value of an American academic science career has declined sharply, especially for early- and mid-career researchers. This isn’t a single push — it’s a dozen smaller ones, which makes it harder to see as a pattern, but no less real.

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I should be precise about what I mean when I describe my embassy visits. These aren’t casual social calls. Over the past year, I have been contacted by representatives of several foreign governments — European countries foremost among them, but not alone — with a request that has become almost formulaic: could I identify promising young American neuroscientists who might be open to conversations about positions abroad?

The request is always framed diplomatically. No one uses the word recruit. But that is precisely what it is — organized, deliberate, and sophisticated. These aren’t opportunistic conversations at conferences. They are structured efforts, backed by government resources, to identify and cultivate specific individuals at a specific moment of American institutional vulnerability.

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Canada isn’t simply waiting for Americans to show up. It has been actively building the infrastructure to receive them. The Canada 150 Research Chairs program, launched in 2017, was explicitly designed to recruit exceptional researchers from around the world and drew heavily on talent from the United States. The Canada First Research Excellence Fund has made major institutional infrastructure investments that give Canadian universities genuine competitive standing. These created an ecosystem in which the positions are not consolation prizes for Americans who couldn’t land a U.S. position but are first-choice destinations.

There is also a factor that rarely surfaces in policy discussions but matters enormously at the individual level: healthcare. American researchers who leave academia lose employer-tied health insurance. Canadian researchers face no such risk when moving between institutions. In a country where a family health crisis can be financially catastrophic, this is not a minor consideration.

The result is that Canada is playing a long game while the United States is, at the moment, folding its poker hand.

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The temptation is to imagine today’s American scientific emigrants as the disaffected — unhappy postdocs, marginal researchers, people who were already looking for the exit. That is not who I am being asked about, and it is not who is going.

The concerning cohort is mid-career: people with established labs, active grants, graduate students, and reputations built over a decade or more of productive work. When they move, they don’t move alone. They take their entire ecosystem — trainees, collaborators, years of accumulated institutional relationships. One full professor emigrating may represent fifteen or twenty years of American scientific investment walking out the door. And the trainees they bring with them, or recruit once they arrive, will become foreign scientists. The pipeline reorients.

The Vietnam parallel holds here with uncomfortable precision. It wasn’t the marginal figures who left and stayed. It was the most able.

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This brings me back to the York president’s implicit lesson — the one he wasn’t even trying to teach. Many of the Vietnam cohort didn’t come back.

After Carter’s amnesty, some did return. But roughly half had built lives, careers, and families in Canada, and they stayed. The institutions that had grown around them stayed too.

Why would the current cohort be different? If you have rebuilt your lab in Toronto, enrolled your children in Canadian schools, secured stable funding through one of the Canadian funding agencies, and watched from a distance as American science policy lurches from disruption to disruption — what exactly are you returning to?

Political conditions in the United States may eventually stabilize. But institutional damage takes a generation to repair. The graduate students being trained in Canadian labs today will become Canadian faculty. The networks being built now will be Canadian networks. The scientific culture that is quietly relocating will not quietly relocate back.

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The United States has long treated its research enterprise as a fixed asset — something that could be drawn down, disrupted, and defunded without permanent consequence. Brain drain is the market’s verdict on that assumption. Scientists, like capital, move toward expected returns. When the expected return on an American academic science career declines — through funding uncertainty, political interference, and institutional instability — talent moves elsewhere. This is not a political statement. It is an observation about how labor markets work.

What would reverse it? Restored and predictable federal research funding. Visa stability for international students and researchers. A federal posture toward universities that treats them as assets rather than adversaries. None of that is imminent.

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I think about the York president on that Kyoto patio, reporting what he had observed without alarm, almost as a casual aside. He wasn’t issuing a warning. He was describing the outcome of a natural experiment that had played out over decades, visible only in retrospect.

I am watching a second such experiment begin, in real time, from an unusual vantage point. And unlike the York president, I’m not describing it in retrospect.

The current cadre of science policy folks in this country worries about research security—stolen intellectual property, occult foreign funding, and the like. They’re not seeing the tsunami: when the human talent isn’t there, research security is airtight. But there aren’t any discoveries, because there aren’t any discoverers.

How a global roadshow built the political cover for America’s brain initiative

It had been a long haul from Newark — eighteen hours plus, but something is clarifying about the final approach into Changi. The city materializes out of the South China Sea, improbably ordered, all reclaimed land and green corridors, a small nation that has collectively decided to simply will itself into a planned, successful future. I took a photograph out the window with my 2009-era iPhone, a gesture that now looks hilariously lo-fi in my photo archive. It was my first return to Singapore since a childhood trip with my parents, and I was arriving this time not as a tourist, but as a science entrepreneur.

The goal: pitch the Decade of the Mind as a global neuroscience initiative. The thesis — which I hadn’t yet fully articulated to myself — was that the road to funding success in Washington ran through foreign capitals first.

I demonstrated the limits of that thesis approximately twelve hours later when I showed up for my appointment at the Prime Minister’s office in a business suit.

“Lose the jacket and tie,” said the science liaison officer, not unkindly, when I arrived. Equatorial Singapore is always the same: very hot, very humid, approximately the temperature of a sauna with better architecture and excellent mass transit. The boss was in short sleeves when we walked in. The suit, I realized, was doing exactly what American assumptions often do in foreign capitals — arriving with confidence about what the situation requires and getting it wrong.

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Why go abroad first?

The state of play in Washington at the end of the 21^st century’s first decade was this: our Big Neuroscience concept had genuine champions — neuroscientists such as Mort Mishkin and Jay McClelland, and even celebrities like Oprah Winfrey — but it lacked political traction. The idea that we should mount a coordinated national effort to understand how human brains produced the thing we call “mind” was scientifically compelling and strategically murky. Who would lead it? Which agencies would own it? What would it cost? These were questions that tended to produce long meetings rather than commitments.

The insight — and I’m not sure whose it was originally, though I’m happy to take credit if no one objects — was that American science initiatives gain real leverage when they can claim global momentum. This is not cynical; it’s accurate. Policymakers respond to the sense that history is moving. A proposal that arrives as “we should do this” is a harder sell than one that arrives as “this is happening, and here’s where America stands.”

We’d seen versions of this playbook before. The Human Genome Project was in many ways a response to international competitive pressure — the fear that if the United States didn’t lead, someone else would. The framing of a race, even a friendly one, is extraordinarily useful when you’re trying to move bureaucracies. We see this most recently in the context of AI and quantum computing.

The decision, then, was to build genuine international excitement before making the full domestic ask. Not to manufacture enthusiasm — you can’t fake that for long, and scientists especially see through it — but to find the real partners, have the real conversations, and come back to Washington with something tangible.

Singapore was the first stop, and not by accident.

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The roadshow

Singapore made sense for several reasons simultaneously. The scientific capacity was real — DSO and A*Star — and the government’s receptivity to big science concepts was well-established. Singapore had been making deliberate bets on research infrastructure for years; they understood the genre. There was also a strategic symbolism to starting in Asia. This wasn’t going to be a European initiative with Asian endorsement bolted on afterward.

The pitch itself was a particular kind of performance art. You’re asking for commitment before you have anything to commit to. You’re offering a partnership in something that doesn’t yet fully exist. The skill is making that feel exciting rather than embarrassing.

What I was asking for varied by stop. In some capitals, it was matching funding, or at least a signal of funding intent. In others, it was scientific leadership — names attached to something, which is its own form of currency. In others, frankly, it was the meeting itself: a photograph, a letter, a line in a future proposal that said officials in Singapore, Sydney, and Seoul have expressed strong interest.

You learn quickly on a trip like this to tell the difference between genuine excitement and polite diplomatic interest. Both can look identical in the room — the nodding, the questions, the expressions of enthusiasm. Genuine excitement has a different texture: people start proposing things. They say, ” What if we involved Professor X?” or “There’s a funding mechanism that might work for this”. Polite diplomatic interest stays at the level of the concept. It’s warm, and it goes nowhere.

I got both. The ratio varied by city.

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The cultural translation problem

The suit was a metaphor, but, like most metaphors, it pointed to something real.

Americans arrive at international science meetings with American assumptions baked in. We assume that “global initiative” means something that America conceived and now invites others to join. This is understandable — we have historically been the largest funder of basic research, and the model has usually worked — but it lands poorly in rooms where the people across the table are sophisticated enough to recognize the structure.

What we had to learn, stop by stop, was that genuine partnership required genuine co-creation. Not of the science — the science could be international on its merits — but of the meaning of the enterprise. Why did this matter to Singapore specifically? To South Korea? The answer couldn’t just be that it mattered to the United States.

Later that same year, in Berlin, I heard about a nascent competitive European idea: the Human Brain Project, which was being put forward entirely independently of our own efforts. Folks were happy to hear about our ideas, but they had their own. The European effort would be, in some ways, more ambitious than what we were proposing: simulating the human brain in silico using computational neuroscience models.

In Japan, we heard about a different idea that would take advantage of sophisticated animal models in neurobiology, going beyond the then-dominant American view that a mouse is just like a small human being. The Japanese also had an advantage in using advanced spectroscopy techniques, such as nuclear magnetic resonance.

I left some of those meetings having said things I would not have said, at the time, in Washington. More accurately: I left certain meetings having heard things that changed what I said in subsequent meetings in D.C. This is how science diplomacy works — not as the export of American ideas, but as a genuinely iterative process that changes the ideas themselves.

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The leverage moment

Returning to Washington with something tangible is a different experience from returning with nothing but optimism. I had letters. I had commitments, or near-commitments. I had names attached to the concept — serious names, from serious places.

And this, I found, changed the domestic conversation in ways I hadn’t fully anticipated.

When I got back, it was at a dinner with NSF’s leadership that I found real resonance: the host invited us to submit a two-page white paper that eventually made its way throughout the US government. Importantly, the paper had no author. It practically invited any decision maker to adopt it as their own. And, the framing was the global moment.

Part of what international buy-in provides is cover — a way for cautious domestic actors to justify enthusiasm that they might otherwise suppress. The political logic runs something like: if Asia and Europe are interested, we can’t afford not to be. This is not a beautiful logic, but it is an operational one.

The other thing it provides is a proof of concept for the pitch itself. If you can walk into a room in Singapore and make a compelling case, you can probably walk into a room on the Hill and do the same. The roadshow is, among other things, practice.

The Decade of the Mind didn’t become the BRAIN Initiative instantly or cleanly. The Obama administration recast the idea, initially as something called the Brain Activity Map Project. But the international groundwork was part of what made the domestic momentum possible. When the BRAIN initiative finally had its launch moment three years later. it arrived with the credibility that only genuine global interest can provide.

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What this tells us about how big science gets done

The official history of a science initiative tends to emphasize the science: breakthrough papers, visionary proposals, legislative milestones. The unofficial history involves an enormous amount of what I’d call pre-political work — the meetings that don’t appear in any record, the relationships built on conference sidelines, the carefully calibrated sequence of who gets approached in what order.

The international roadshow is one piece of this. It is, at its core, an exercise in creating the conditions for a political decision that hasn’t been made yet. You can’t force that decision. You can make it progressively easier to say yes.

Science diplomacy as a practice is genuinely undervalued and undertheorized. We train scientists to do science and administrators to administer, but the hybrid skill — understanding both the science and the diplomatic register well enough to move between them — tends to be developed ad hoc, by people who find themselves in rooms they weren’t fully prepared for. That ad hoc aspect is certainly how I found my sea legs.

Looking back on the Singapore trip — my suit, their short sleeves, the PM’s office, the iPhone photograph — what strikes me is how little I understood about what I was doing while I was doing it. I had an intuition that the path ran through international buy-in. I didn’t have a theory of why. The theory came later, assembled from the specific failures and specific successes of the trip itself.

Which is, I suppose, how most things that work work. You do the thing imperfectly, and then you understand the thing.

They are buying the label on the bottle

“This is not my wine,” announced the billionaire as he sipped from the elongated crystal goblet that our university president had just poured. It was his wine. I had pressed my local distributor, a 30-something Virginian with a name straight out of Gone With The Wind, hard to find a case of his stuff. The plan had been to celebrate his promised big check in support of the Institute with a dinner at the President’s house, and his red wine would be the guest of honor.

We were all horrified. Our university president presented him the bottle, with his label, X Vinyards, prominently displayed.

“Of course it is,” the President said.

“No, this is fraud. This is not my wine.”

I focused intensely on my shoelaces. In my mind, I wasn’t at the President’s house. I was 500 miles to the north at my Woods Hole lab bench, working out the role of protein kinase C in sea urchin development. I wondered how fundraising for science had come to this.

“Mr. X,” let’s try some white, no? That’ll go with the first course, I think.” Distracting him with Chardonnay was my plan.

Disaster, right? Actually no. The check cleared three weeks later. A big one. And it was unrestricted.

What had just happened in that dining room wasn’t a near-disaster. It was, in retrospect, a perfectly normal evening — just with the subtext briefly visible.

Here is what I think major donors are really buying. Not the science. Not the graduate students who will spend five years chasing a result that may never arrive. Not the state-of-the-art equipment, the postdocs, the administrative overhead that your development office will explain in careful detail. They are buying the label on the bottle. They are buying the wing with their name on it, the lecture series, the endowed chair, the wall plaque that they think will outlast everyone in that dining room. The Institute exists, in their imagination, as an extension of themselves — a form of biological immortality dressed in the language of public good.

Your job, as Institute Director, is to make that narcissism feel like generosity. To them, and honestly, to yourself.

This is not cynicism. The science is real. The graduate students are real. The results — when they come — are real. In neuroscience, my field, some of those results can save lives. But the transaction at the center of scientific philanthropy is a negotiation between two kinds of legacy hunger: the donor’s need to be remembered, and the researchers’ need to do work that matters. An Institute Directorship is, among other things, the art of holding both of those needs in the room simultaneously without letting either reveal the reality of the other too clearly.

Nobody teaches you this. There is no course in graduate school called Asking Rich People for Money in the Service of Science. Your thesis advisor taught you to think carefully, to doubt your results, to follow the data wherever it went. That person, your mentor, had no idea what an Institute Director does on a Tuesday afternoon.

What you learn, slowly and mostly by humiliation, is that federal funding and philanthropic funding are different games with different rules and different currencies. There were many times when the audacity of asking for a lot of money in a personal, one-on-one context led me to disaster. On one occasion, the donor started laughing at the ask. It was two orders of magnitude too small. On another occasion, the donor glared at me and remarked that they had thought we were just having a friendly conversation about science, not money.

At NSF and the NIH, the currency of the realm has been peer review — you are accountable to your scientific community, and the process, whatever its flaws, is fundamentally about the work. In the world of major donors, the currency is relationship. The science is the framing context; the relationship is the product. You are not making a case to a peer review panel. You are making a person feel that their money will become part of something larger than themselves — and that you, specifically, are the right steward of that transformation.

This requires a particular kind of split attention. The version of me that spent years at a lab bench, working out how molecular signal transduction works in a living cell, had to be held in reserve — not abandoned, but kept somewhere safe. In contrast, the other version of me learned to read a room, manage an ego, and pivot to some white wine when necessary. The bench scientist and the Institute Director are not antagonists. But they are not quite the same person either.

The BRAIN Initiative taught me this at scale. Billions of dollars, multiple agencies, the White House, and, somewhere underneath it all, the actual neuroscience. Keeping those things in productive tension — that’s the job. The wine dinner, Mr. X, was just a smaller version of the same problem.

So, what do you do with all of this?

The honest answer is that you get good at it, and getting good at it changes you in ways that are hard to fully inventory. You learn to pick up social cues in the first seconds of a conversation. You learn that silence from a major donor is not the same as a no. You learn that the ask, when it finally comes, should feel inevitable — the natural conclusion of a relationship that both parties have been carefully tending, even if only one of them knew that’s what was happening.

And you learn, eventually, to make peace with the transaction. The donor gets their label, their legacy, their wall plaque. The Institute gets the unrestricted funds that quietly multiply into something a directed gift never could. The graduate students get their five years at the bench. Some of them produce results that matter. A few of those results, in a field like neuroscience, find their way into treatments and into lives that are measurably better because the science has been done.

That is not nothing. That is, in fact, quite a lot.

But I still think about that dining room sometimes. The crystal goblet. The bottle with his label on it, the wine he grew on his own land, and his absolute certainty — in the face of all evidence — that it was not his. What he was really saying, I think, is that the label was never enough. That what he wanted was something no check could buy: the certainty that the thing bearing his name was genuinely, irreducibly his.

Scientists understand this feeling better than donors know. Every result we ever cared about felt that way — earned, irreplaceable, ours in a way that had nothing to do with money.

We just learned, some of us, to work with people who express it differently.

Blogpost On the Way

I will be publishing a blogpost later this week about the mass firing.

But rearranging the deck chairs can work

The call came from Jo Handelsman.

She was the White House microbiome czar — formally, Associate Director for Science at the Office of Science and Technology Policy — and one of the most respected microbiologists in the country. That mattered to me. This wasn’t a communications staffer working a headline. It was a scientist, operating inside the executive branch machinery, doing what that machinery required of her.

I was the Assistant Director for Biological Sciences at NSF — the head of a directorate called BIO, which meant I was the person she needed to talk to. About a minute in, she let me know the President was looking for about $140 million from our directorate. I was already mentally recoding program money across four related BIO program areas to hit that target. They were seeking a similar amount from colleagues at the National Institutes of Health. I figured they were aiming for around $1 billion.

I was annoyed. Not at Jo — she was doing her job with characteristic seriousness. I was annoyed by the situation itself — by the structural position we both occupied and by what the exercise revealed about how presidential science policy functions.

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The constitutional problem hiding in plain sight

Here’s what never appears in White House press releases about presidential science initiatives: only Congress has the power to appropriate taxpayer money. This isn’t a technicality — it’s the foundational architecture of federal spending. When NSF receives its annual budget, those funds have been appropriated by Congress for specific purposes under statutory authority. Program officers and directorate heads are stewards of a congressional trust, ultimately answerable to the legislative branch, not the executive.

And yet the White House’s actual influence over that budget is, in practice, more limited than it appears. There’s an old saying in Washington: the President proposes, the Congress disposes. The President’s budget request is largely a messaging document — Congress typically starts over, setting topline numbers and leaving agencies to determine specific research directions from there. The Obama White House, for all its scientific vision, understood this. The bully pulpit was the real instrument, not the budget pen.

Which makes Jo’s phone call more interesting, not less. She wasn’t calling to redirect taxpayer funds — Congress would have the final say on that. She was calling to build a message. What she wanted from me was not new money but a credible number: existing programs that could be rebranded under the new initiative’s banner and counted toward a headline figure. The $140 million from NSF BIO wasn’t $140 million in new microbiome research — it was $140 million in ongoing biological research that could be plausibly connected to the microbiome, renamed, reframed, and counted.

This is a meaningful distinction. The White House wasn’t telling me where to spend money. It was asking me to redescribe how I was already spending it. That’s a subtler thing to object to — and I want to be precise about my objection, because I’ve had the chance to think it through more carefully in the years since, including in conversation with Jo herself.

I could have said no. Francis Collins, then the NIH Director, did exactly that, and the request was accepted. These were genuinely requests, not commands. The annoyance I felt on that call was partly at the exercise — the gap between the headline and the reality — and partly at myself, for how quickly I complied when I could have pushed back.

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The science, though

Here is where I must complicate my own annoyance. I believed in the Microbiome Initiative. I still do — and the decade since its announcement has only deepened that conviction.

The microbiome is not a niche subject. The human body contains roughly as many microbial cells as human cells, and the collective genome of those microorganisms dwarfs our own in complexity and metabolic diversity. We are, in a very real sense, superorganisms: our health, our immune function, our neurological development, even our susceptibility to depression and anxiety, are all shaped in ways we are only beginning to understand by the trillions of organisms living inside us. The gut-brain axis — the bidirectional signaling pathway between the enteric and central nervous systems, mediated in part by the microbiome — may ultimately transform how we think about psychiatric disease. The relationship between the microbiome and cancer immunotherapy is already reshaping oncology: the composition of a patient’s gut microbiome can predict whether they will respond to checkpoint inhibitors, one of the most significant advances in cancer treatment in a generation.

Agriculture stands to be transformed just as profoundly. The soil microbiome — the invisible ecosystem beneath every field and forest — drives nutrient cycling, carbon sequestration, and plant immunity. Manipulating soil microbial communities offers a plausible path to reducing synthetic fertilizer use, improving crop drought resilience, and sequestering atmospheric carbon at scale. These are not speculative futures. They are active research programs producing results.

In 2016, this frontier was real, large, and seriously underexplored. The research questions weren’t manufactured for a press release. This wasn’t a vanity project. The science was — and is — among the most consequential in biology.

And the presidential megaphone, despite its distortions, is a real instrument in ways that go beyond simple optics. Industry investment follows demonstrated government commitment — companies want to see federal support before putting their own dollars in. The Initiative generated buzz that translated into real new resources from non-federal sources. It also had a genuine scientific ambition at its core: to get researchers working across systems — comparing, for instance, the recovery of the Gulf microbiome after the Deepwater Horizon disaster with the recovery of the human gut microbiome after a course of antibiotics — to develop broad ecological principles that no single-system study could reveal. The goal was comparative microbiome science at scale. That was, and remains, a serious idea.

So, the question I kept returning to during that call and afterward wasn’t whether the Microbiome Initiative was good science policy. It was whether the way we announced and funded it was honest — and whether I had been honest with myself about my own role in it.

I don’t think I had been entirely. Jo has since reminded me that I had a choice. Collins made a different one. The pressure to participate is real, but it isn’t irresistible. The annoyance I felt on that call was legitimate — the gap between the headline figure and the new money was real, and the public deserved to understand that distinction. But the annoyance I should have felt, and didn’t quite feel until later, was at my own compliance.

And I was not alone in the complexity of that position. Jo was navigating her own version of it — a scientist of very high distinction, channeling her expertise into a process that was as much political theater as scientific strategy, trying to leverage the bully pulpit for science she genuinely believed in. We were both serving the machine. That’s what made the call feel the way it did.

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A later chapter

I left government in 2018 and returned to George Mason, where I had been a professor for many years before my time at NSF. I came back to teaching and research with a different perspective — more impatient, perhaps, but also more aware of which scientific questions mattered to people outside the academy.

One of those questions turned out to involve the microbiome.

In 2020, I published a paper in PLOS ONE with Kyle Brumfield, Anwar Huq, Menu Leddy, and Rita Colwell—a comparative analysis of whole-genome shotgun and 16S amplicon sequencing methods using publicly available data from the National Ecological Observatory Network. Rita Colwell is one of the foundational figures in environmental microbiology and a former Director of NSF. The fact that I ended up doing microbiome science alongside her is the kind of irony the federal science system occasionally produces and almost never acknowledges.

The NEON connection deserves a moment. Readers of this newsletter may recall an earlier piece — “Nothing But Tundra” — about how I flew to Alaska during my NSF tenure to inspect a NEON construction site that the paperwork said was progressing on schedule and found nothing but primeval tundra stretching to the horizon. What followed was one of the hardest decisions of my time in government: firing the managing organization and bringing in Battelle to rescue the project. NEON eventually came online in 2019, fully operational, producing open ecological data across nearly two hundred data products.

The data in our 2020 paper came from NEON.

I don’t say that to take credit for the paper’s existence — the science belongs far more to my collaborators, and NEON’s data is freely available to anyone. I say that because it illustrates how federal science infrastructure works on timescales longer than any administration or initiative. The NEON I helped rescue produced the data that underpinned microbiome research I did after leaving government — research that sat within a scientific ecosystem the National Microbiome Initiative had helped legitimize and accelerate. These threads connect in ways that no press release captures and no budget line reflects.

I thought about that call with Dr. Handelsman more than once while we were working. She had done her job. I had done mine. The arithmetic had added up. And somewhere downstream of that transaction — through the relabeled dollars, through the rescued observatory, through the open data and the collaboration with Colwell — there was a team of scientists doing the work the headline had promised.

That team included me.

The money wasn’t new. But the science was.

What the geometry of a room taught me about the architecture of American science

Nearly two decades ago, from the mezzanine of the Krasnow Institute for Advanced Study, I had an accidental view into the federal government’s central nervous system and its central pathology.

Below me, perhaps sixty deputy-level civil servants had gathered for a reception following a day of lightning talks. These are the people who run the federal science enterprise between administrations: the Senior Executive Service officials and GS-15s who preserve institutional memory, make quiet go/no-go decisions that shape enormous investments, and keep the machine running while political appointees cycle in and out every four years. I had helped organize the day. I should have been down there working the room. Instead, I found myself watching.

The geometry was diagnostic.

At opposite diagonals of the square great room, the military establishment and the health establishment had each formed their own gravitational field. Uniforms clustered near the windows on one side: NIH and FDA people near the bar on the other. In the center, NSF, the National Labs, and NASA circulated politely among themselves, moving between the two poles without quite bridging them. In an hour of watching, I did not see a single sustained conversation cross the diagonal. People talked past one another in the most literal sense — bodies angled slightly away, addressing their own.

We had brought them together to discuss the possibility of a very large, coordinated federal investment in neuroscience. What would eventually become the BRAIN Initiative had been conceived by a group of about eight academics, and we had started planning before the 2008 election: something worth pausing on. Designing a major scientific program before you know who the president will be requires a particular kind of institutional literacy. You’re not lobbying a specific administration. You’re trying to shape the landscape that any incoming administration will find. The goal was to socialize the concept across agencies before new leadership arrived: to get the deputies thinking in the same direction, because we understood something easy to forget: political will at the top is necessary but nowhere near sufficient. The deputies would have to cooperate. And cooperation, as the mezzanine made clear, was not their natural state.

What the room taught us was that the coordination problem was worse than we had imagined. These were talented, dedicated people who understood their own domains with real depth. They were not cynical or lazy. They were not obstructionist in any simple sense. But their domains didn’t speak to each other, and neither did they when placed in an unstructured social setting.

What we eventually learned and what the BRAIN Initiative’s structure was specifically designed to reflect is that interagency coordination on a scientific bet of this scale doesn’t emerge from shared interest, goodwill, or a well-intentioned convening. It requires a power center above the agencies, willing to spend real political capital making it happen. In our case, that meant the White House. Not a memo from the White House. Not a task force. Not a working group with a White House name attached. Active, sustained leadership from the Office of Science and Technology Policy, with the explicit backing of the President, is publicly committed.

That architecture is harder to build than it sounds, and easier to dismantle than anyone would like.

To understand why the view from the mezzanine looked the way it did, it helps to understand what happens inside federal agencies over decades and why the standard explanations for bureaucratic dysfunction miss the point.

The political science literature on interagency conflict tends to reach for two explanations: turf protection and bureaucratic inertia. Both are real. Neither is primary. The deeper explanation is cultural, and it runs much further down.

Large federal science agencies are not bureaucracies in the pejorative sense. They are genuine intellectual cultures, with their own histories, epistemologies, and ways of assigning value to scientific work. NIH thinks in terms of disease mechanisms, and the pathway to clinical translation — its entire grant review apparatus, its study section culture, and its publication norms are organized around proximity to the patient. NSF thinks in terms of fundamental discovery and the long-term health of scientific disciplines; it is institutionally suspicious of applied framing and has spent decades defending basic research against short-term thinking in Congress. The Department of Energy thinks in terms of national security infrastructure and large-scale systems; its scientific culture comes out of the weapons labs and reflects a tolerance for massive, centrally managed projects that neither NIH nor NSF can quite replicate. DARPA is a different animal still — structurally flat, program-manager-driven, deliberately averse to peer review — existing in permanent tension with the academic science norms that dominate NIH and NSF.

These are not trivial differences in vocabulary. They reflect decades of accumulated mission, shaped by the constituencies each agency serves, the appropriations subcommittees that fund them, and the scientific communities that define their identity and police their boundaries. A neuroscientist trained in the NIH system and one trained in the NSF system have, in many cases, genuinely different intuitions about what good science looks like. They aren’t wrong to have those intuitions. The problem is that cultures, once formed, defend themselves — not cynically, but almost automatically, the way an immune system responds to foreign tissue.

Consider a seemingly simple question that arose during the early planning for what would become the BRAIN Initiative: where should federally funded neuroscience publications and data live? What cyberinfrastructure would anchor a shared repository?

NIH’s answer was immediate: PubMed. They had built it, maintained it, and trusted it. It was already the de facto home for biomedical literature globally. That it was showing its age as a platform and that a genuinely new large-scale data infrastructure might warrant a genuinely new architecture were beside the point from NIH’s perspective — it worked, it was theirs, and extending it was the obvious path.

NSF’s answer came just as quickly, pointing in an entirely different direction: use the Department of Energy’s platform. Whether this reflected a genuine assessment of DOE’s technical capabilities in large-scale data infrastructure — and those capabilities were real — or a reflexive resistance to being absorbed into NIH’s scientific ecosystem was never entirely clear. Probably both. The two things are hard to disentangle from the inside.

What was clear was that no one in either room was discussing compromise, nor was anyone discussing what the scientific community needed from a shared repository. The conversation was about which agency’s infrastructure would anchor the enterprise, which meant which agency’s culture would shape it, which metadata standards would prevail, which access controls would govern it, and whose bureaucratic stamp would appear on a decade of scientific output. The White House, watching from above, grew visibly frustrated. Here were two agencies that shared a nominal commitment to advancing American science, deadlocked over a question any genuinely neutral party might have resolved in an afternoon. But there was no neutral party. There was only the accumulated weight of two institutional identities, each pulling toward its own gravity.

This is what silos look like from the inside. Not obstruction. Not incompetence. Just two organizations being, with perfect fidelity, exactly what they had each spent decades becoming. The tragedy of it is that the people involved often know it’s happening and can’t stop it anyway.

The PubMed standoff faded, as many such disputes do, into institutional stalemate — each agency continuing to do what it had always done, the shared infrastructure question deferred rather than resolved. No one had been explicitly asked to back down. No one had been forced to. The question simply became too costly to revisit.

The Biden Cancer Moonshot episode was different in kind, and I was there.

The scientific logic was almost painfully straightforward. The Department of Defense maintains medical records, including biological samples collected at peak physical condition, for millions of young, healthy service members, who are among the most comprehensively documented populations in the country. The Veterans Administration maintains longitudinal health records on many of those same people decades later, including those who developed cancer in the intervening years. Linking those two datasets would give cancer researchers something extraordinarily rare: a biological baseline matched to long-term health outcomes, at scale, across a population of millions. The potential to identify early biomarkers, track environmental and occupational exposures, and understand the gap between apparent health and latent disease was enormous. This was not a speculative idea. The scientific community had been pointing at this dataset for years.

Biden wanted it done. He had staked his political identity on the Cancer Moonshot. Senior officials from both DOD and the VA were in the room. Their answer, expressed through lawyers and deputy secretaries rather than principals, was no.

The refusal came wrapped in legal and technical language: privacy regulations, incompatible record architectures, classification concerns, HIPAA obligations applied in ways that seemed to expand whenever the conversation moved toward a specific plan. These were not entirely fabricated objections. The legal and technical barriers were real enough to be inconvenient. But they were not the real structure underneath. The real structure was territorial: two agencies, each with its own medical infrastructure, its own relationships with its patient population, its own institutional identity built around serving a defined constituency, being asked to subordinate that identity to a shared project they hadn’t designed, didn’t control, and couldn’t shape. The language of legal compliance had become the language of institutional resistance, dressed in a costume that made resistance look like responsibility.

Biden pushed back. With visible anger. This is worth pausing on: a Vice President of the United States, sitting in his own office, pressing senior officials with the kind of controlled fury that comes from watching something obviously right fail in real time, because two agencies couldn’t agree to share what they already had. The frustration wasn’t abstract. He understood exactly what was in those records and exactly why they weren’t being linked, and he was watching it happen in front of him anyway.

Eventually, under that sustained pressure, the agencies moved toward compliance. Plans were made. Commitments were extracted. Progress, of a kind, was achieved.

But note what it took. Not a policy directive. Not a new framework. Not a cross-agency working group. A Vice President of the United States, in a room, pressing senior officials personally and with considerable force. And note also what happened the moment that pressure lifted: the agencies returned to being exactly what they had always been, because the underlying structure had not changed at all. The legal and technical barriers had dissolved under sufficient political heat. Which meant they had never quite been the barriers they appeared to be, but the institutional logic that had generated them was entirely intact.

This is the second face of the coordination problem. The first is passive: agencies that simply don’t interact, like the clusters in the Krasnow great room, or NIH and NSF talking past each other about platforms, because talk’s their natural mode. The second is active: agencies that resist coordination when it threatens their domain and reach for procedural language to make that resistance look like something other than what it is.

The passive version is frustrating. The active version is dangerous because it is nearly invisible. It doesn’t look like resistance. It looks like due diligence.

What eventually worked was a structure we had begun designing before most of the relevant agencies knew the BRAIN Initiative existed as a concept. The eight or so of us who conceived it understood from the beginning that the science was the tractable part. Mapping the functional connectome of the human brain is hard. The coordination architecture was harder.

The core insight was simple, even if executing it was not: no agency would willingly subordinate itself to another. This is not a character flaw. It is a structural feature, almost a law of institutional physics. Any architecture that handed one agency primacy over the others, even for good programmatic reasons, would fail before it started, for exactly the reasons the PubMed dispute illustrated. The losing agency would not simply accept the outcome. It would use every procedural and legal tool available to relitigate, delay, and hollow out the shared project until it resembled the losing agency’s preferred alternative.

The Biden model, relying on a senior official’s personal fury, was not a system. It was a workaround that depended entirely on the political will, personal energy, and continued engagement of a single powerful individual who had approximately ten thousand other things demanding attention. That’s not architecture. That’s heroics, and heroics don’t scale and don’t persist.

The only authority above the agencies that was not one of them was the White House itself. So, we designed around that.

The BRAIN Initiative was structured from the outset with active OSTP leadership. Not OSTP as a convener or facilitator, roles that are easy to ignore, but OSTP as a genuine power center with the explicit backing of the President and a mandate that the agencies understood was real. Interagency working groups were stood up with White House participation, which changed the political valence of every meeting. When NIH, NSF, DARPA, the National Labs, and DOE sat down together, they were no longer negotiating as peers protecting their own turf in a vacuum. They were operating under a shared mandate from above: one that came with an audience, because the President had announced the initiative publicly and tied his name to it.

This last piece was not incidental. It was load-bearing. Obama’s public commitment changed the incentive structure in a specific way: failure to coordinate was no longer just an internal bureaucratic inconvenience, invisible to outsiders and costless to the agencies involved. It was a visible failure in a project the President had claimed ownership of, in a domain (neuroscience and brain disease) that commanded broad public sympathy. An agency that stonewalled the BRAIN Initiative was not just slowing down a program; it was undermining it. It was creating a political liability for the White House. That kind of exposure concentrates minds in ways that no amount of memo-writing or task-force-convening can replicate.

The territorial instincts didn’t disappear. They were overridden. There’s a crucial difference. An instinct that is merely overridden will reassert itself the moment the override lifts. But consistently overriding it across multiple decision points and over multiple years can create precedents, working relationships, and shared infrastructure that outlast any individual’s political engagement. The goal was never to eliminate the silos. It was to build enough scaffolding above them that the work could proceed despite them, and that, over time, the scaffold itself would become part of the institutional landscape.

None of this was accidental, and none of it was obvious at the time. It was designed by a small group of scientists who had spent enough time studying the geometry of these rooms to understand what it required.

I tell these stories now for reasons that go beyond neuroscience history.

The conditions that made the BRAIN Initiative’s coordination architecture work are precisely the ones most difficult to sustain and easiest to dismantle. They require a White House that understands interagency coordination as a design problem, not a personnel problem — not something you fix by installing the right people, but by building the right structure and maintaining it actively. They require OSTP to function as a genuine scientific leadership office rather than a ceremonial one. They require a President willing to publicly and visibly stake political capital on a specific scientific program, so that agencies feel the cost of non-compliance.

Large scientific bets, the kind that require a decade of sustained investment, genuine coordination across agencies with different cultures and different constituencies, and a tolerance for uncertainty that normal appropriations logic resists, are exactly the kind of programs most vulnerable to the coordination problem. Neuroscience was one. Pandemic preparedness is another. Climate modeling. Nuclear fusion. Quantum computing. The list is long, and what unites them is that no single agency can do them alone, and the gap between what a single agency can do alone and what a well-coordinated federal enterprise could do is, in many cases, the difference between success and failure.

The mezzanine view is still available to anyone who looks. The question is whether the people who need to understand what it shows are in any position to act on what they see.

How our bias towards recency in scientific discovery hurts our understanding

The white lab rat moved towards the food dispenser. It evidently heard the tone and correctly interpreted its meaning. The ten electrodes implanted deep within its brain were each recording the fingerprints of multiple neurons at millisecond time resolution—key to deciphering the neural code. A wireless transmitter relayed the massive data train to an analog-to-digital converter, where it was fed into a computer, first sorted by neuronal fingerprint and then collated and curated to make sense for the human experimenters. The year was 1975. The computer was a DEC PDP-8/E minicomputer—about as big as a dorm fridge and five orders of magnitude less powerful than your smartphone.

We are surprisingly bad at knowing when things began.

I’ve been thinking about this for a while, partly because I lived through several of the transitions we now misremember. In 1987, I used the Internet for early text-based email, file transfers, and reaching colleagues at other universities. In August of 1991, in the face of an impending direct hit of Hurricane Bob, I moved all of my image data from Woods Hole to NIH in Bethesda in a matter of minutes. This was entirely unremarkable at the time. And yet when I mention it today, people often look mildly startled, as if I’ve claimed to have owned a smartphone in 1987. In their minds, the Internet began sometime around 1994 or 1995, when the Web arrived and made it visible to everyone. Before that, apparently, there was nothing.

But of course, there was something. There was a rich, functional, and genuinely useful network that predated the Web by decades. And invented at the same time as the Web was Gopher, an ancient app for navigating and retrieving documents that worked elegantly and simply before the Web achieved wide public adoption. There were mailing lists, FTP archives, Usenet — an entire ecology of networked communication that the Web didn’t replace so much as it superseded, in the way that online streaming superseded television programming. TV is still there if you know where to look. Most people don’t look.

This isn’t just a historical curiosity. When we misplace the origin of a technology, we lose something important: our understanding of why it evolved the way it did. The Web wasn’t designed in a vacuum. It was a solution to specific problems regarding the use of hyperlinks to navigate the nascent Internet. The decisions Tim Berners-Lee made in 1989 were shaped by what already existed. If you don’t know what already existed, you can’t understand those decisions. You inherit the outcomes without understanding the tradeoffs. And some of what was traded away was worth keeping. The now-forgotten contemporary of web browsers, Gopher, was simple and decentralized. And this looks appealing again now that we’ve seen where social media and commercialization have taken us.

The same logic applies in science. The experiment I described at the top of this piece was real and took place in a Caltech lab. Multi-electrode neural recording, wireless transmission, real-time spike sorting — these capabilities existed fifty years ago. The folks doing that work understood aspects of the neural code in the context of learning and memory that are often invisible to current neuroscience trainees, partly because the papers were published a long time ago and because all of science is biased towards the most recent, shiny things. The finding doesn’t disappear. It just becomes functionally unavailable.

The field of artificial intelligence may be the most dramatic case study in collective chronological confusion we have. Most people who interact with today’s language models and image generators believe they are witnessing something genuinely unprecedented — a technology that sprang into being sometime around 2017. What happened is more complicated and more interesting.

The mathematical foundations for neural networks were laid in 1943, when Warren McCulloch and Walter Pitts published a paper describing how neurons could, in principle, compute logical functions. Frank Rosenblatt simulated a working perceptron at the Cornell Aeronautical Laboratory in 1958 — a system that could learn from examples. The 1986 backpropagation paper by Rumelhart, Hinton, and Williams, which most practitioners treat as a founding document, was itself a rediscovery and refinement of ideas that had been circulating since the early 1970s. Yann LeCun was training convolutional neural networks to read handwritten digits for the U.S. Postal Service in 1989. The architecture underlying those systems is recognizably the ancestor of what powers modern computer vision.

None of this was secret. It was published, presented, and in some cases deployed in real systems. What happened instead was a kind of institutional forgetting, accelerated by two “AI winters” — periods when funding dried up, interest collapsed, and computer science turned its attention elsewhere. Researchers who had spent careers on neural approaches moved on or retired. Graduate students who might have built on their work were instead trained in other paradigms. When the hardware finally caught up with the ambitions of the 1980s, around 2012, the rediscovery felt like a revolution. In some ways, it was. But the conceptual foundations were not new, and the people who had laid them got less credit than they deserved, partly because so many of the field’s new practitioners didn’t know they existed.

The practical cost here is the same as elsewhere: repeated investment in problems that had already been partially solved, frameworks that were novel mainly to their authors, and a set of origin myths that flatter the present at the expense of the past. The deeper cost is that we don’t understand what was tried and discarded and why — which algorithms were abandoned for reasons of computational expense rather than theoretical inadequacy, and which might be worth revisiting now that the expense has fallen.

Climate science offers a different version of the same problem — one with considerably higher stakes. The standard cultural narrative places the discovery of anthropogenic climate disruption sometime in the 1980s, anchored perhaps by James Hansen’s 1988 Senate testimony, or by the formation of the IPCC. If you read serious journalism about the climate, you might push it back to the 1970s. If you are diligent, you might encounter the Keeling Curve, which has been tracking atmospheric CO₂ from Mauna Loa since 1958.

The scientific recognition of the greenhouse effect and its potential consequences for global temperature dates to 1896. That year, Svante Arrhenius published a paper in which he calculated, with considerable accuracy, how much warming a doubling of atmospheric CO₂ would produce. He arrived at a figure somewhere between 5 and 6 degrees Celsius — higher than modern estimates, but in the right direction and for the right reasons. He then speculated, in print, that industrial combustion might one day alter the atmosphere’s composition enough to matter.

This was not forgotten in the way a 1975 neuroscience paper was. Arrhenius was a Nobel laureate; his work was well-known. What happened instead was that the question was considered, examined, and provisionally set aside — partly because mid-century scientists underestimated how rapidly fossil fuel consumption would grow, and partly because they assumed the ocean would absorb most of the excess carbon. These were empirical mistakes, not failures of reasoning. The framework was sound. The inputs were wrong.

What we lose when we date climate science to Hansen or to the IPCC is the understanding that this is not a young field with tentative conclusions. The core physics has been understood for over a century. The measurement of its consequences has been underway for nearly seventy years. When people argue that science is “still developing” or “too uncertain to act on,” they are often unconsciously drawing on a mental model in which the field is young and its conclusions preliminary. Knowing the actual timeline does not resolve all the uncertainties — science is always uncertain at its leading edge. But it changes how you should reason about the weight of evidence.

Economics has its own version of this confusion, though the consequences are harder to tabulate. The efficient market hypothesis is widely understood to have originated in the 1960s with Eugene Fama. The idea of index fund investing — holding the market rather than trying to beat it — is associated with John Bogle and the first retail index fund, launched in 1976. The behavioral critique of rational actor models, which demonstrated systematically that real human beings make predictable and consistent errors in judgment, is credited to Kahneman and Tversky’s work from the early 1970s.

All of this is broadly correct as a matter of attribution. What gets lost is the prior landscape of ideas these researchers were responding to. The observation that markets were difficult to beat systematically appeared in Louis Bachelier’s 1900 doctoral thesis on the mathematics of speculation — work so ahead of its time that it was largely ignored until Paul Samuelson encountered it in the 1950s and recognized what it contained. The psychological research on judgment and decision-making that Kahneman and Tversky formalized was in some respects a rigorous extension of observations that Herbert Simon had been making since the 1950s under the heading of “bounded rationality” — the recognition that human cognition operates under constraints that classical economics had simply assumed away.

Simon won the Nobel Prize in Economics in 1978. Kahneman won his in 2002. The ideas are connected clearly. And yet the field repeatedly had to be reminded that people are not rational actors, as if this were a new finding rather than a conclusion established, contested, partially absorbed, and then re-established over the course of half a century. Each rediscovery brought energy and refinement. But it also brought the inefficiency of not quite knowing what had been tried before.

This is the practical cost of chronological confusion: we reinvent. We pour resources into solving problems that are already solved, we fund theoretical frameworks that are novel mainly to us, and we write introduction sections that inadvertently misrepresent the state of the field by simply not knowing what came before the Internet made everything searchable.

But there’s a subtler cost, too. When we don’t understand how a technology or a scientific field evolved, we become poor navigators. We don’t know which roads were tried and abandoned and why. We don’t know which detours led to unexpected places. We can’t reason well about where to push next, because we don’t have an accurate map of where we’ve already been.

There is also a political cost, which the climate case makes vivid. When the historical depth of a finding is obscured, it becomes easier to argue that the finding itself is uncertain or contested. The chronological error licenses a kind of epistemic innocence: we can treat as open questions things that have, in fact, been largely closed for a long time. This is not a problem unique to climate science. Wherever institutional memory is thin, motivated actors can exploit the gap between what has been established and what is widely understood to have been established.

Technological and scientific genealogy isn’t nostalgia. It’s a form of rigor. The rat in the 1975 experiment knew something. So did the Caltech scientist, looking at the brain recordings. Arrhenius knew something in 1896. Bachelier knew something in 1900. Rosenblatt’s perceptron knew something in 1958. We could stand to know it, too.

In Undark, here. Michael Schulson interviewed me, among other of my former NIH colleagues for the piece. As they say on Tyler Cowen’s blog: self-recommending.