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.

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.

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.

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.

His letter in SCIENCE, here. Very useful perspective and even somewhat hopeful. Hat tip to my old friend and colleague, PR.

Dan led the US National Science Board as its chair, served as provost at Utah, and did important work at Microsoft. His thoughts on HPC (and what’s happened) here. As my colleague TC always says, self-recommending.

Matt Lakeman’s most recent long piece on his recent trip to Afghanistan is also his best, I think. I do count travelers as discoverers, and with this piece, we have a series of real discoveries–I leave it to you to glean them.