Throughout 2025, the headlines in Science and Nature read like a slow-drip obituary. Dreadful developments accumulated, tracked by full-time beat reporters and dedicated “bad times for science” sections on their websites. Each day brought a new layer to the unfolding drama.

I’m concerned, but not surprised.

Years ago, I wrote that a financial shake-up was coming to academia. My prediction was milder than actual events, but pointed in the right direction. The heart of the issue was a long-overdue contractual renegotiation. I used Hollywood’s transformation in the 1950s as an analogy for how science might evolve. But the events of the year revealed two mistakes: I started the story in the wrong place—The Endless Frontier, like nearly everyone else— and I fixated on the wrong outcome.

What follows is an attempt to mend those original mistakes and articulate my current theory of change. A longer view helped surface an obvious point: science makes meaningful advances not just by federal policy and funding, but by widening the circle of participation. That circle is defined by the cooperative interface—a concept I’ll explore below.

The year also reinforced the aspects of my perspective that haven’t changed. I still believe that when institutions struggle to adapt, outsiders often can. The parallel social infrastructure they create can become a lifeline to the future.

This essay is mostly a reminder—to myself, and maybe to you—that the situation requires more than concern. The moment demands a sort of imaginative resilience: the ability to acknowledge real damage while still insisting that better futures can be built.

Post-Pandemic Science

The COVID-19 pandemic affected science in ways we’re only beginning to understand.

Early in the pandemic, I sensed something important was happening in science. It wasn’t any specific discovery or tool I was watching, but rather a larger, subtler shift. Many of the scientists I found most interesting were mapping their careers onto a new archetype—a fuzzy outline at best, but certainly a different way of being a scientist in the world.

I started documenting conversations I was having with scientists and science-adjacent entrepreneurs to understand this new terrain. I called the effort “Science Better”. The content struck a chord with a small but committed audience of (mostly) scientists. The project was loosely tied to the philanthropic science funding experiments we were running on Experiment, but the conversation broadened. I found like-minded people who were charting a similar path through the uncharted territory between science, tech startups, and philanthropy.

I wasn’t alone, of course. Others were writing and documenting, too—a scene was emerging. Sam Arbesman created the Overedge Catalog as emerging and alternative research organizations began to crop up, which gave visibility and a loose sense of connectedness to the efforts. Adam Marblestone and Sam Rodriques published a white paper articulating their vision for more Focused Research Organizations. Long-time open science advocates, realizing that another generation of reformers was revving up to improve the institutions of research and discovery, added their informed perspective. The whole discussion got tagged under the emerging genre of metascience.

Soon, books appeared, think tanks took up the issue, and international conferences convened around the topic. Notably, Michael Nielsen and Kanjun Qiu wrote the pièce de résistance of the emerging scene: A Vision of Metascience. They made a powerful case that science could evolve—should evolve—through a process of imaginative design and evaluation. Improving science, they argued, would require the exact form of experimentation that scientists espouse, and would likely involve more than any one specific fix:

Rather, the point is that a flourishing ecosystem would rapidly generate and seriously trial an enormous profusion of ideas… The best of those ideas would be rigorously tested, iterated on, debugged, and scaled out to improve the entire discovery ecosystem.

Beyond the thinking and writing, things were actually happening. COVID threw science and medical research into the center of the civic arena. The use of preprints for quickly sharing research outcomes skyrocketed in both use and legitimacy. And initiatives like Fast Grants and Operation Warp Speed showed that science could be done at a different pace and with novel organizational arrangements. The rigid boundaries of science’s ivory tower seemed malleable and movable. Science, the grand civilizational pursuit, was evolving.

Thanks to the booming metascience corpus, I felt like I had gotten the picture—a good-enough mental model of what would happen next. I’d seen the shape of what science was becoming, or so I thought.

I wrote up that vision in an essay called “The Hollywood Analogy”. It seemed to me that science in 2020 was in a similar predicament to the filmmaking industry in the 1950s. The old order—the impossibly powerful studio system, which controlled all the financing, production, and distribution of films at the time—was suddenly shaken and weakened, owing to the rise of television and a damaging antitrust lawsuit. It opened the door for the next generation to forge ahead. The newly established talent agents renegotiated the deals for their biggest stars, and the balance of power tipped dramatically from studio executives to talent. The industry evolved quickly and irrevocably.

The Hollywood transition was uncomfortable for anyone with a financial interest in the old way of doing business, but a liberating opportunity for the writers, actors, and agents willing to bet on themselves. A New Hollywood emerged, and the movies got much better as a result.

From my vantage point, the same general trend was happening in science. The best scientists had options. They were no longer constrained by the academic career path of old. They were starting companies, securing grants, and building labs on their own terms, rather than those dictated to them by their universities.

I stand by that general prediction. But as the actual events play out, I’ve realized some important aspects I got wrong. My first error was when I started the story.

Like everyone who writes about the American research enterprise, I began the story with Vannevar Bush’s Science: The Endless Frontier as the Big Bang of Big Science—the birthdate of the modern research system. There’s a reason everyone starts here. It is the beginning of the current arrangement, as we’ve come to know it. The major components were cemented into place through Bush’s recommendation to President Roosevelt about a post-war blueprint for American research. Bush laid out how the wartime machine could be pointed at noble causes like curing disease and inventing futuristic technologies, and it happened just so.

But I should have gone back further. There was as much to learn in the decades before WWII—through the failed attempts to launch a national research program—as from those critical years after.

The Endless Frontier was more than a beginning. It was also an ending.

Interwar Science

Just like COVID-19, World War I was a shock to the scientific system in the United States.

Prior to the war, scientists enjoyed a precarious social position. The prestige of the profession was steadily increasing with awards like the Nobel Prize, established in 1901, and new relationships with captains of industry. However, the general popularity of science was dwindling after the turn of the century. The great age of science popularization, which had followed the boisterous debates around the theory of evolution, was fading away and people were losing interest. It wasn’t a purposeful retreat but rather a result of advancing frontiers. As science professionalized and specialized, it became harder to maintain an ongoing conversation with other scientists in distant fields, let alone a general, more public dialogue. Science was firmly decentralized and heading further in that direction.

The Great War changed all that. The historian, Ronald Tobey, documented the period in his book, The American Ideology of National Science, 1919-1930:

“During the First World War, nongovernmental scientists in universities and research institutions were recruited to work on defense problems. Their scientific activity in the war was distinctly different from their earlier work. Before the war, they had done research on problems whose solutions were of interest mainly to men of their own specialties. These researches had been individual enterprises in which they had worked without supervision. Their professional activities had been conducted on the local or regional levels except for the annual or semi-annual national conference in their fields. In contrast, during the war many men left their homes for research centers like the New London Experimental Station or Washington D.C. Their research was a team effort, supervised and coordinated with that of other teams by a central agency. And these scientists had the deep satisfaction of knowing that they contributed directly to America’s survival.”

Scientists had tasted Big Science—larger budgets, bolder projects, and deeper coordination—and they wanted more. When the war concluded, discussions immediately continued about how to maintain the momentum of cooperation as well as the generous financial appropriations. According to Tobey, the situation “impelled the scientists to find substitutes if they wished the accelerated scientific progress of the war to be continued in the peace.”

The National Research Council (NRC), which was created as the National Academy of Sciences’ coordinating apparatus for wartime research, was poised to lead the effort. But debates about exactly how a national strategy should proceed kept scientists tied up for years. The scope of disagreement: how to engage industry, how centralized the planning and coordination should be, and how much should be spent on federal laboratories as opposed to building capacity at the universities. The NRC argued and lobbied for dramatic forms of centralization. Everyone agreed on a bigger pie, but couldn’t agree on how to slice it.

Beyond the mechanics, there were philosophical debates about what science should be aimed at—about who science was for. Was it a tool of democracy? An extension of American individualism? Was science an inevitable form of cosmic progress?

It wasn’t until the early 1930s, when the National Academy of Science attempted to fundraise for a National Research Fund, that the failure to unify behind a common vision came to a head. Tobey again:

“The failure of the fund was ideological. The original goal of their campaign was to cultivate public awareness of the values of the pure scientists. This goal was part of the general effort of the national scientists to convince the public of their ideology and thereby to promote cultural unity. The scientists thought that public recognition would bring financial support from the corporations. But the scientists never were able to establish a connection between the public acceptance of their ideology and the corporate donations. Consequently, as the campaign for funds progressed from 1926 to 1929, the scientists’ attention shifted away from constructing a primary relationship between themselves and the public to constructing such a relationship with the corporations. By 1929, their central concern was no longer to relate the method of pure science to the progressive values of individualism and democracy, but to demonstrate to industrialists that pure science, rather than engineering or applied science, was the basis for industrial profits.”

This is the essential bargain of American science. Scientists, in order to justify their budgets and fix their social position, realized they must align with both the military and corporate goals of the nation. Big Science—despite the lofty ideals of the individual participants—could only be achieved through national science. This is the concrete foundation laid beneath the ivory tower.

The failure of the National Research Fund was both lesson and prelude. As soon as “World War II began, the new generation of scientists who had been doing graduate study in the 1920s, would undergo an experience similar to that of the previous generation.”

The Second World War was another chance for scientists to lock in their aspirational social arrangement.

Vannevar Bush got the call to lead the National Defense Research Committee (NDRC), which then became part of the larger Office of Scientific Research and Development. The committees and discussions that stalled out the National Research Fund gave way to a streamlined organizational structure and the decision-making centered around Bush. He employed a famously flat reporting structure, with division leads across different fields, like the Radio Research Lab at Harvard and the Radiation Laboratory at MIT. They funded quickly and the results of their investments enabled important developments like radar and the atomic bomb.

Bush’s lasting impact on American science was not just what he funded, but also how. The NDRC set an important precedent when, as Bush described in his memoir Pieces of the Action, they “decided that we would make contracts for research directly with universities, not individuals therein.”

The decision was important for the war effort and beyond. They were underwriting the cost of research by paying overhead rates to the universities that maintained the infrastructure. Bush credited the post-war outcome as “literally a lifesaver for the universities.”

This simple fact—a contracting decision made in the fog of war, mostly without consultation—has accounted for trillions in research funding. It’s no wonder the current debate and gnashing over science budgets has centered on this contractual detail—the indirect research costs—and the administration has focused its pain-inflicting there.

But that’s not where the solution lies. Mending the contractual details isn’t enough. The situation requires a deeper fix. It’s time to examine and fix the foundation. We need to update the essential bargain between science and society, which is a decidedly larger conversation.

We’re in the same situation as the interwar period, or should be, at least; new ideas, big dreams, and fierce discussion. Ours is a time for Popper-esque philosophizing, not just Bush-like dealmaking (push that off, if possible, until we learn a few things).

Echoing Nielsen and Qiu’s call to action: now is the moment for bold imagination and experimentation.

Improving the Scientific Project

The second big problem with my Hollywood analogy: I focused on the wrong outcome. I was following the money, which is a common refrain in the metascience scene.

I wrote about the stars of science and how they were pioneering a new type of career by diversifying their identity outside of academia, and moving into industry and private research organizations. Founder culture, made famous in startup land, was bleeding into science. The repercussions, I thought, would produce similar results to Hollywood: scientists would get wise to their true value, universities would evolve quickly or get left behind, and the new arrangement would settle into an updated, talent-centered equilibrium.

Although this prediction has proven accurate, it was limited, plucking only the most obvious of conclusions. Of course, the star scientists are going to be fine and full of good options for continuing their work. Duh.

But it wasn’t the stars that needed attention; it was everyone else.

The product of a large, national research budget was always more than published papers, headline-grabbing breakthroughs, and Nobel Prizes. What’s hidden in those indirect costs being paid to universities are all the tools, laboratories, and people—the scientific proletariat—that make discovery possible. Slashed budgets mean gutted infrastructure, and especially the people.

The headlines tell the story: the NSF and NIH spigot has seized up. The agencies froze new awards and shrunk paylines. The flagship Graduate Research Fellowship pool was reduced to the lowest intake in 15 years. Early-career scientists are the collateral damage. The traditional “apprentice–tenure” pathway is collapsing, and might already be damaged beyond repair.

At this point, the biggest hit to science is invisible. The lost grants are bad, but the missing generation is likely going to be worse—an acute pain that will be noticed in the years and decades to come. In a testimony to the House Committee on Science, Space, and Technology, Dr. Margaret Leinen, the director of the Scripps School of Oceanography, said that the leading oceanographic institutions had to make pre-emptive cuts in admissions, accepting half of the usual amount of graduate students in preparation for reduced budgets.

Some scientists will jump to industry, while others might find homes at the new science organizations or research institutions outside the United States, but an even larger number of young people won’t enter the system. They’ll miss those critical years watching and learning in the laboratory, in the field, or at the bench.

The new science startups are trying to fill the gaps by hosting fellows or creating resident PhD programs, but these one-off solutions won’t fill a gaping generational hole. We need New Deal-sized ideas about how to engage more people in the scientific project.

Another problem with focusing on the money is that the perspective ignores the core truth of the scientific project: an international community of striving researchers who freely share their best ideas and theories in the hopes of contributing to the storehouse of human knowledge, all for the glory and credit amongst their peers, current and future. The funding of science is downstream from this delicate miracle of global cooperation.

If your story starts with The Endless Frontier, you can make the mistake of thinking the federal appropriations created this community. That’s wrong, of course. The Bush decision helped underwrite the momentum, but it didn’t create the core infrastructure, let alone the core ideas. Just like Google Adwords didn’t create Google Search—it was simply the business model wrapped around an already great product.

The science historian Lorraine Daston tells the against-all-odds history of scientific cooperation in her book Rivals: How Scientists Learned to Cooperate. Starting with the early Republic of Letters and continuing through the 1960s, when the term “scientific community” came to resemble the international collaboration we recognize today, Daston’s history lesson makes clear that creating this ever-growing circle of scientists required continual leaps of imagination. Early global projects like the International Cloud Atlas and the World Meteorological Organization created the observation networks and communities, which showed the path others could follow.

Science gets bigger and better when the circle of participation gets wider. And the big jumps in cooperation are not rational or logical. Daston writes:

“But as historians have shown and political theorists have acknowledged, no viable collective is ever just the result of cool cost-benefit calculations. It is a shared vision of what it would mean to be part of a collective that surmounts hesitation and commands allegiance. What that collective vision should be for science already has a 350-odd-year history, and the work of the imagination is still ongoing.”

The problem with many of these metascience schemes and manifestos (which pop up every few months, and I’m guilty of, as well) is that they obsess about some new economic angle. We need to fix the incentives, find more industry funders, create a marketplace, etc.

But true scientific evolution happens when the tools for cooperation change—when the social circle widens to include more minds. The scientific journal, for example, was a new cooperative interface that expanded the concept of who could participate in the scientific project by moving ideas in smaller chunks across time and space, opening them to both critique and contribution beyond the published books and meetings of the learned societies.1 The open science crusaders (bless them) have been closest so far, but the cooperative interface (the scientific paper) was the same. Preprints—while very useful—are just a paper, sooner.

AI has a real chance to become a new cooperative interface—a machine-mediated guide to the entire written corpus of science. This is no secret. Almost every scientist is busy working AI into their process and workflow, and seemingly every major AI company has zeroed in on science as their next focused effort. AI for Science startups are busy connecting LLMs to lab equipment in the hopes of creating “self-driving labs”. Even the administration, with its newly announced Genesis Mission, is getting in on the action.

Whether AI becomes a truly autonomous discovery agent—running its own experiments, discovering new laws of physics, etc.—remains to be seen, but I think it’s worth taking the “AI as new cooperative interface” idea seriously as its own unique outcome. What will it mean to contribute to science in this form? (Large, unique datasets seem far more important, to point out one obvious thing.) And what will it mean to consume science in this form? If anyone can ask an AI science engine a question—with full command of the literature, evolving data, and experimental infrastructure behind it—what use are professionals for explanation and interpretation? The line demarcating the bounds of the scientific community is about to get a lot blurrier. What happens to the standards, ethics, and status mechanisms that the community has established over the past century? Will they translate to this expanded circle of participants or will new ones emerge?

The interface matters. For decades, the walls of the ivory tower of academia have been shrinking around the scientific community, with anti-science sentiment swelling along with a general revolt against experts and institutions. The phrase “War on Science” is bandied about, referring to everything from the politicization of science to the influence of moneyed interests. The data behind the distrust isn’t as dramatic as the stories would make it seem. Regardless, scientists certainly seem to feel threatened by their drifting societal role, and miffed about how to fix it.2

Martin Rees, former President of the Royal Society and Emeritus Professor of Cosmology and Astrophysics at the University of Cambridge, spent a large portion of his career in the upper echelons of science, thinking and writing about the existential risks to humanity. His most recent book, If Science is to Save Us, describes our cultural conundrum: science is the common denominator to all of the major global risks. Safely navigating the century ahead, he argues—through the challenges and opportunities of climate change, artificial intelligence, and biotechnology, among many others—will require an expanded role for the scientific project. Science is the most useful tool in the civilizational toolbox and, going forward, it will need more participants, advocates, and friends.

The book was published in 2022, in exactly the moment of the COVID-19 pandemic response when science seemed most triumphant. The mRNA vaccines had brought the world back to some semblance of normalcy, and Rees was championing the role of science to address more of our global issues. From his perch, he couldn’t see the swelling anti-vax sentiment or imagine that, just two years later, federal funding for mRNA research would be halted. Rees correctly identified the science-culture fault line, but underestimated the magnitude and nature of the divide.

Rees’ perspective is representative of science as a whole, which seems unable to act on this cultural blind spot.

The shape of science has always had a mirror image: the society within which it operates. And you can’t change one without the other.

Parallel Social Infrastructure

Remaking the scientific enterprise will require new cooperative interfaces, which are hard to prototype within strained institutions.

Rather than rebuilding in place, the more reliable approach is to build next to them: salvage what’s useful from the collapse and put it to good reuse. There’s a relevant historical lesson here from the Soviet Union.

Mathematics in the postwar Soviet Union—roughly the 1950s through the 1980s—is a paradox of progress. In many ways, it was a productive period, full of achievement, producing numerous Fields Medalists and breakthroughs in the fields of topology and group theory. The period is often referred to as the “Golden Age” of Soviet Mathematics.

It’s a most unlikely outcome, given the challenges facing Soviet scientists at the time. The editor Ross Andersen used exactly this period in the history of Soviet science as the throughline story and analogy for his Atlantic story on the current situation in American science, “Every Scientific Empire Comes to an End”, which detailed how corruption and cronyism ruined a vibrant ecosystem of ideas and scientific rigor.

The Soviets sabotaged their own success in biomedicine. In the 1920s, the U.S.S.R. had one of the most advanced genetics programs in the world, but that was before Stalin empowered Trofim Lysenko, a political appointee who didn’t believe in Mendelian inheritance. Lysenko would eventually purge thousands of apostate biologists from their jobs, and ban the study of genetics outright. Some of the scientists were tossed into the Gulag; others starved or faced firing squads. As a consequence of all this, the Soviets played no role in the discovery of DNA’s double-helix structure.

It was a textbook fumble. And Soviet mathematics should have been just as hampered—under the same political pressures, after all—but it wasn’t.

The Soviet leadership had placed tight constraints on all the formal and official research environments, like academic institutions and government positions. First, the authorities forced isolation. Soviet scientists couldn’t travel, which meant they were cut off from the international mathematics community. Ideas were also confined; books and journals became scarce, and translations were limited. Faculty was filtered for political and religious purity, and the remaining university administrations were left with strict instructions about curriculum, which failed to adapt and evolve as new fields emerged. To top it off, the research institutions had guards who checked the students’ IDs and limited access—literal fences in addition to the ideological barriers to knowledge. Slava Gerovitch, the MIT historian of Russian sciences, described the situation in a paper on the period, Parallel Worlds: Formal Structures and Informal Mechanisms of Postwar Soviet Mathematics:

All these factors worked against the creation of a fully functional mathematical community. In other words, the conditions in which Soviet mathematics developed in the 1950s-80s looked like a recipe for disaster, not for a “golden age.”

However, despite the restrictions, curiosity found its way through the cracks. Enterprising scientists took it upon themselves to create alternative forms of academic scholarship and havens for intellectual ferocity, which Gerovitch referred to as a “parallel social infrastructure, which existed apart from and in some sense in opposition to the official institutions.”

The scientists went around the obstacles, rather than through them. This parallel social infrastructure wasn’t just one solution, and hardly, if ever, official—it was all makeshift.

The most visible parts of this parallel world were the open seminars, with the “most famous and influential” of those being led by Israel Gelfand, which he ran from his post at Moscow University for more than four decades. Gelfand had done work for the Soviet military, so he enjoyed a modest amount of immunity from administrative backlash, which he flaunted by creating a seminar series to explore emerging and frontier ideas in mathematics.

“These seminars covered a wide range of topics beyond the rigid Mekhmat curriculum. The system of open seminars, which gave instruction in the most recent, booming fields of mathematics, became a key component of the parallel social infrastructure. Since these seminars were offered outside the regular curriculum, attending them did not bring students any credit. In fact, many participants were not university students at all but came from outside the university, figuratively or literally climbing the fence.”

The seminars were only part of the picture. At almost every level—young and old, amateur and professional—a generation of mathematicians took up the cause. They acted for each other, in defense of intellectual freedom and resistance to administrative influence. A network of math circles, or kruzhoks, emerged where students would teach each other concepts and curriculum. They created their own schools, too. Prominent mathematicians parlayed the influence they developed by participating in the nuclear program into lobbying for the creation of specialized schools with rigid adherence to meritocracy and technical ability.

I originally heard about these seminars from a second-generation mathematician, now at Princeton, whose father had grown up in the Soviet Union and attended these seminars. Even a generation removed, with the stories secondhand, I could hear the excitement and purpose that were present in those evening sessions. Mathematics had ceased to be a profession and had instead become a cultural movement, fueled by enthusiasts.

This idea—parallel social infrastructure—is relevant now. It proves that federal budgets aren’t destiny, and ivory tower decrees are not the only voice of response.

The shape of science to come is open for interpretation—and for invention. It could be rebuilt on a currency of enthusiasm, radical participation, and merit. It could break down cultural barriers and rise to meet the issues of our time. What it cannot do is retreat to the arrangements of the last century. There’s no back button here.

If we can act with enough imaginative resilience, the scientific project will continue, remade soundly for the century ahead.

Key ideas:

Keep the discussion focused on people and ideas. As the meetups multiplied, the Quantified Self quickly encountered a problem: the startup pitches started to dominate, so they instituted a protocol for talk-givers. They had to answer three questions:

1. What did I do?
2. How did I do it?
3. What did I learn?

This first-person requirement kept the conversation lively and helped QS “articulate an ethic.”

Mind the experience gap. In any technical scene, experienced veterans race ahead while newcomers repeat the same beginner questions—an adaptation of the eternal September problem. If you don’t bridge that gap, the community stalls. Wolf suggests this might be the natural life cycle of a field-building effort.

Key ideas:

Good theory must be “rewilded” back into the information ecosystem. Any idea raised in captivity (a group chat, a research lab, etc) eventually has to make contact with the world, and that’s where you’ll learn its true value.

Maintain a portfolio of inspiration. Venkat learned the craft of research management by raiding history—Bloomsbury Group, the International Geophysical Year, Solvay Conferences, and more. Don’t copy any one model, he advises. Instead, know the lineage, then cherry-pick lessons that fit the moment.

Design for artifact-field fit. The Summer of Protocols has experimented with a variety of artifacts—tension workshops, kits, a digital magazine—to spread the concepts. Growing a field often involves inventing a social object to help move the idea along.

Ryan Phelan was there at the beginning, part of the team that gave rise to the idea and the organization, Revive & Restore. Now more than a decade into the pioneering effort, Phelan has become the field-building archetype that others emulate and study. She’s showing us all how it’s done.

Key ideas:

Find the people who will egg you on (in a good way). It takes a special combination of personalities to bring a new field into being. The ideal dynamic is a group of people who can “push and pull” and bring out the best in each other’s ideas. Stewart Brand, George Church, and others were good complements to Phelan in the early days of de-extinction discussion.

Phelan outlines her perfect workshop formula. No one organizes a more productive workshop than Phelan. Years of scientific discussion and progress happen over a few focused days. I’ve seen her events firsthand—they work. Every field builder should be copying Phelan’s playbook.

Key ideas:

Small grants matter. Academics only think about funding for writing papers. Industry folks scoff at the amount. But small grants can be useful for funding new tools, which are often overlooked, even if they help people justify spending a little extra time. Small grants are more than money; they’re a responsibility and an excuse to start.

“ Start small and just build stuff” - Will Zeng

Field-building organizations are “a special kind of institution.” The non-profit, for-public-good nature of field building relieves the pressure for monetization or “citationization” of their work. They’re able to build ecosystem-critical projects with a longer time horizon.

Key ideas:

RIFS — Homeworld’s field-building philosophy: Roadmap, Ignite, Fund, Synthesize. They have refined their field-building strategy to a repeatable cycle. It’s more than just grant funding—they coax the frontier questions out from the collective intelligence of their community, then take bold action to try and solve them.

Problem Statements are a better way to develop questions. Homeworld did something unique with the antiquated format of the grant proposal: they separated the problem statement from the solution idea. This allowed the problem portion to become a radically collaborative document, while also allowing scientists and technologists to keep any intellectual property rights on any private solution ideas.

Key Ideas:

Name an idea with enthusiasm, but define it with caution. Giving a field a name can unite disperate efforts and attract interested others. Trying to add a definition to the name can cause division. Better to embrace a loose definition and a porous boundary, which keeps a scene dynamic and interesting.

The job changes. Field building is never one job, and it changes over time. Sometimes it means being a cheerleader and other times a critic. The field builder helps a better narrative unfold by being a voice of reason—a manager of expectations.

The previous two laid the groundwork:

“Small, Fast Grants at the National Science Foundation” — On the blindspots and weaknesses of the federal funding ecosystem, especially regarding small, fast grants.

“Where are the field builders?” — Lessons from our funding experiments at Experiment and an appreciation of the unsung, emerging heroes of technoscience: the field builders.

The third piece—how to measure and improve the pipeline of field builders—remains unwritten, but for good reason.

After publishing the second essay, I heard from many field builders, both seasoned and aspiring. They shared their stories, frustrations, and hopeful nuance. I’ve delayed the third essay to account for the added interest in the idea, which needed more time to steep.

In the meantime, I’m taking the research public. Here are two updates:

1. On Field Building

I’m recording an interview series with established and successful field builders. I’ve just published the first with Tito Jankowski of Airminers. More will be published soon.

You can listen to these interviews on all the regular podcasting platforms, but it’s not a podcast. They are in-depth, unpolished discussions meant to tease out lessons and wisdom. It’s open-source research for future essays and curriculum development.

2. An Experiment experiment

As Matt Clancy mentioned on his Substack today, we’re looking for a researcher to study the science funding experiments we’ve been running at the Experiment Foundation, including the field building idea mentioned in the previous essay. The scope of possible engagement is wide. Read the matchmaking memo for more details.

We are interested in research questions such as:

• Who is effective at funding science through the Science Angel program?

• What characteristics define successful Science Angels?

• How do microgrants influence the trajectory of new scientific fields and researchers?

• How do field builders operate, and what strategies make them successful?

• What lessons can be drawn to improve future models of decentralized science funding and field-building?

A mix of methodologies could be employed, including:

• Quasi-experimental designs (e.g., difference-in-differences or matched comparisons)

• Surveys and interviews with past and current Science Angels, field builders, and grantees

• Analysis of grant outcomes, including follow-on funding and scientific progress metrics

• RCT feasibility assessment, considering how controlled experiments could be structured for future iterations of the program

Are you the right metascientist for the job? Email us.

It feels strange to offer your whole organization up for an RCT. I suspect few CEOs, politicians, or directors would willingly subject their entire corpus of work to such rigid constraints or public accountability. It’s an existential gamble for the organization.

But it matters, now more than ever.

Startups make bet-the-company decisions all the time—it’s where all the upside is. They’re supposed to take those odds. The metascience experiments in science—the funding, research, and institutions—should aspire to the same type of risk-taking.

Good on for instigating.

Key Lessons:

Narrative Composure - Field builders help generate the narrative to keep the story moving. They add momentum in the good moments and help reframe setbacks as opportunities.

Pick a problem so big that you don’t care about getting credit. But…

Find a niche only 10 people are working on. Start with an issue so specific that you can list the 10 people in the world who are working on it.

Do small grants matter? Yes, definitely.

Would the “science angel” model work to find overlooked people and ideas? Yes, and more could be done.

Could grant funding spur more crowdfunding? Not in a meaningful way, but on occasion, and maybe we could do it differently.

Does seed funding lead to follow-on funding from larger science funders? Yes—it’s still early but it’s happening.

I could write a long essay about each of those answers. But the more interesting observations came from questions we never thought to ask. At this point, those ideas are closer to strong hunches rather than testable hypotheses, but they are the truest lessons we have from our experience. Here are two of them.

The first idea comes from working with more than two dozen science angels—scientists-turned-funders who have the discretion to allocate small grants to projects they deem worthy. I was constantly surprised by the varying approaches of these angels. Some struggled to get grants out, while others easily accepted the challenge. I have an emerging intuition about the characteristics of a good science funder (which I’ll explore below), but I’m stuck on this thought: we still can’t predict who will be good at this.

We aren’t the only ones who are unsure; nobody knows. Being a good science funder seems to be a completely separate skill from being a good scientist. And outside of ARPA program managers, there is very little effort to identify or develop the talent.

The second idea isn’t from our work specifically, but our type of work more broadly. As we’ve been operating in the uncharted area between academia, tech startups, and government agencies, we’ve found fellow travelers—other individuals and groups operating in the same in-between space. Many of these people identify as scientific “field builders” and they specialize in generating momentum among funders, technologists, and researchers toward some new field of technology or science. They drive funding, make connections, and facilitate progress toward real-world outcomes—not just published papers. Companies, tools, and industries are birthed in their wake. Field builders are opening the doors to new possibilities.

Seeing these operators up close—mostly through the Experiment Foundation’s lens as a peer organization—has profoundly affected my perspective. I’m convinced that technoscientific progress is being hindered by a lack of field builders.

This essay explores these two ideas—their origins and possible explanations. It also sets up the big question we’re considering at the Experiment Foundation, emerging at the intersection: can we use the science angel model to embolden more field builders?

“OK, you do it.”

Scientists love to gripe about money. In both private circles and public forums, scientists jump at the opportunity to explain their vision for how federal budgets should be managed, how quickly grants could be administered, or how new funding mechanisms be employed. Their opinions seem to grow stronger with age and experience.

"Money problems" topped the list of a survey of academic researchers in 2016. I suspect you'd get a similar result if you ran the questionnaire again today. I spent a year interviewing scientists and mostly heard the same. The final question of my interview was always, "What is your best idea to make science better?"

And the answer was almost always a variation on more diverse funding models.

Our science angel funding program has challenged scientists to become the science funder of their dreams by giving them discretion (within the bounds of scientific legibility and non-profit legality) over a small budget, the ability to make quick decisions and a simple charge: OK, you do it.

Here is the original pitch:

Foundations and companies seed fund the program. A group of scientists are selected and each given a budget of $50-100k to contribute to projects on Experiment. They are free to use their discretion in how they recruit, select, and allocate that amount. At the end of a one year period, they will have a portfolio of experiments to show their work. All of this is done in public on Experiment, with minimal overhead and the potential to leverage additional support from the crowd.

Three years later, I'm proud to say we ran the experiment. Bold philanthropic partners helped us enable more than twenty individuals (and occasionally small teams) to participate as science angels and we've funded nearly 250 projects as a result of the program. The initiative has even won awards: our partnership with Robert Downey Jr's FootPrint Coalition won the 2022 Falling Walls "Breakthrough of the Year" in the Scientific Management category.

After three years of running these programs, my perspective on the problem has changed. I've lost interest in trying new and exotic funding mechanisms, like lotteries. Some of these will work, I'm sure, but my forays into these ideas have been underwhelming. Instead, I'm convinced the big opportunity for fixing science is by improving funding dynamics, especially at the earliest stages.

The best part of the science angel program was giving scientists the freedom to experiment with those dynamics—the subtle human aspects that happen before, during, and after the actual grant funding. Their efforts were revealing.

The characteristics of a truly great science angel are still up for grabs, yet to be discovered. The metascience of the question is worth further study. But we’ve found a few basic truths—the ABCs—to screen for the right types of people.

A - Availability. This seems obvious, but it's an easy mistake. To be a good science funder, you need to dedicate time to the endeavor. A lot of smart people take on too many commitments, and overestimate what they can accomplish. In the face of overwhelm, a part-time science funding gig is one of the first candidates for procrastination. Famous and established academics sometimes turn out to be ineffective science angels, mostly because of their availability, or lack thereof. They're too busy.

We could improve here. We could create an interface and cadence that works better for the busy schedules of established scientists, but I’d rather focus effort on those who can dedicate time and energy to the job. Besides, I’ve learned there are more important characteristics to optimize for, like…

B - Belief. Done well, science angel-ing is more than money. Like their startup-investing counterparts, they also inject a shot of enthusiasm and momentum into a promising (although sometimes rough) idea. The best science angels can imbue a sense of possibility alongside the grant. These are the grants that change careers. Here's Nobel Prize-winner Katalin Kariko writing about her first true believer, David Langer:

“What was unusual, though, was how deeply David believed in the work. Not only had he managed to secure a small grant—about $25,000, if I recall correctly—for our work, he’s also delivered a paper at a conference in Arizona: “Bypassing the Nucleus: mRNA as Gene Therapy.” I guess it’s fair to say that David wasn’t merely a believer in mRNA—he’d become an mRNA evangelist.”

Money can be a vessel for belief, which is often more sustaining. Conveying belief—authentically filling another with self-confidence in their nascent idea—seems to be an innate talent, almost a personality quirk. Startup investors with this skill tend to rise quickly. Unfortunately, the behavior is discouraged in the scientific ranks. Scientists are trained from an early age to be critical and skeptical of their peers. In science, belief is never rewarded and sometimes punished. But it's essential to being a good angel investor, whether financial or scientific.

Rigor is good, welcome. But early-stage ideas, especially when they're novel, need a heavy dose of belief when they begin their journey through the gauntlet of criticism and peer review.

My best idea to identify this trait is to go earlier—start more folks as science angels early in their careers and see if the job suits them. If they have the knack, we should provide an opportunity for career progression, with the best rising into the talent funnel at one of the emerging ARPAs.

C - Curiosity. Like availability, curiosity is an obvious characteristic, but you'd be surprised. Unfortunately, many scientists become so thoroughly absorbed in their milieu of papers, grant paperwork, and lab management that they lose the playful curiosity of the beginner's mind. They become hyper-fixated on the cutting-edge novelty of their specific academic focus—the new papers, competitive research, applications of AI to their field, etc—and lose the ability to see possibilities outside their current perspective. An effective science angel must have real curiosity towards new ideas or research directions, even and especially if they're not fully fleshed out. They should point out and nurture the surprising bits of a proposed question, rather than getting hung up on potential pitfalls. This characteristic creates a feeling of co-conspiring with grantees, as opposed to pure critique.

I don't know how to test for curiosity yet, but it's worth creating some sort of filter. So far, the best wisdom here comes from a short story from Holly Witteman: avoid B Lane swimmers.

And a new addition: D - Different. I recently asked Bridget Baumgartner, an experienced trainer and coach of ARPA program managers, what she looks for in a potential funder. She replied with a simple heuristic: they must have a vision for their field or the world that is fundamentally different from what exists today.

“How will the world be different if you’re right?” is a simple and potent question to ask a potential science angel. We incorporated Bridget’s insight into our screening process immediately.

These lessons—the ABCs—are just a baseline filter. They help us identify who won’t succeed in the role. But there’s still an upper bound to explore: who can be great. To answer that question, we need an ideal—a benchmark or target that we’re aiming to emulate or exceed—otherwise we’re just wandering around in the dark. Enter the field builder.

The Technoscientific Field Builder

We aren’t alone. Our science funding experiment is one tiny star in a constellation of new organizational and institutional experiments playing out in research.

The broad strokes of this movement and trend are clear. A frustration with publication-obsessed academia and a growing opportunity in startup venture creation has pushed talented scientists into the driver's seat. No longer wanting to be stuck in the lab, endlessly publishing papers, their scientific dreams have turned toward building companies, low-bureaucracy research autonomy, and impact. The best historical analogy is 1950s Hollywood—the moment when the studio system broke down and a new, talent-centric model emerged.

The best scientists have options. They're moving between non-profits, universities, and startup circles—often all at once—to build their purpose-fit empires. And these entrepreneurial scientists are paving the career path just as fast as they are carving it—with help. Buoying many of these efforts, working alongside the entrepreneurial scientists, is an emerging persona: the field builder.

The field builder operates between the insular worlds of academia, startups, and non-profit philanthropy. Sometimes they are giving grants. Other times they are educating investors. But they're always relentless cheerleaders for a new and emerging field of technology or science—their enthusiasm acting as a siren call to other researchers, builders, and doers to get involved. To invoke the Hollywood analogy again, the field builders are serving the same enabling role that talent agents played in show business. Agents helped advocate for talented actors, directors, and writers—they facilitated a grand renegotiation of power and process.

Crucially, in the aftermath of Hollywood's revolution, from the Golden Age to New Hollywood, there was a renaissance in filmmaking. We should hope for the same in science. And field builders are making it happen.

I'm thinking of Isha Datar, who coined and kickstarted the field of cellular agriculture. Or Ryan Phelan, whose entrepreneurial effort brought biotechnology into the world of wildlife conservation. Or Antonius Gagern who helped bring ocean alkalinity enhancement into the forefront of carbon removal discussions.

If done well, field building can be wildly generative. Once they start the flywheel, the usual suspects show up to go bigger: philanthropists and researchers, entrepreneurs and investors, governments and policy-makers. Datar, Phelan, and Gagern have all helped catalyze communities of practice and directed millions of dollars into their respective fields. Many of those initial investments have been eclipsed by entrepreneurs, funders, and government agencies that have taken those nascent ideas to larger scales. Field builders are fire starters.

The term “field building” is gaining traction in philanthropic circles. The Bridgespan Group, an advisory firm that monitors and advises many large foundations, has been promoting the concept for decades. Their most recent report was published in 2020, Field Building for Population-Level Change, which describes and promotes the concept as a vital philanthropic strategy. They were using it in the context of social change, applying it to causes like democracy, bail reform, or homelessness alleviation. Their 40-page report, which analyzed the development of more than 36 different fields, reads as a good introduction to the idea. There are some useful contributions, like segmenting field building into distinct phases, but the tactical recommendations seem vague and tentative. It’s still early days.

The term has a somewhat separate meaning and significance in technoscience, and it’s newer. More than a philanthropic strategy, it’s a job to be done—a persona to inhabit. The stark boundaries between academia and the marketplace, non-profit and for-profit, have opened up a grey area where field builders can make a large difference.

The momentum of the field building concept in science owes a debt to Tom Kalil’s definition and promotion. Kalil has spent his career in the upper echelons of the ivory tower as an administrator at the University of California, Berkeley, director at the White House Office of Science and Technology Policy, and most recently as an executive at prominent tech-focused philanthropies. I first heard the term from him—he calls them field strategists. Throughout his career, he noticed the rare person who can take a longer, wider view of their discipline, whether by identifying common bottlenecks or opportunities or simply seeing a new direction a field could move in. Recognizing their outsized impact, he bet big on new organizational structures like Focused Research Organizations to support these outliers. His encouragement has also helped to create new resources on the topic, like Ed Boyden and Adam Marblestone’s paper, “Architecting Discovery”, or Eric Gilliam’s essay, “When do ideas get easier to find?”

Gilliam’s message, in particular, speaks to the value of field builders. He documents the generative nature of the work, but also highlights the paradox of the current system: the incentives in science go against creating new branches of knowledge in favor of digging ever deeper silos of understanding.

There’s more to learn, too. A good place to start is by asking and studying the people who are doing the work. I’ve started a crowd-sourced list of technoscientific field builders to help with that process. There’s an emerging suite of tactics being employed. The field builders are in contact with each other now, recognizing their shared philosophy and overlapping interests. For example, here’s Homeworld Collective writing about their field building methodology, and the tools they’ve utilized to be effective.

I’ve talked to many of them and the commonalities go far beyond strategy. They’re all onto something. Listening to them describe their mission, one gets the sense they are sprinting to catch up with the opportunity. And they’re eager to share their playbook so others can get involved.

Building Field Builders

The backgrounds of these field builders might surprise you. More than half of the people listed didn’t come from prominent labs or research institutions—some have no formal scientific training at all. And many who did have a scientific education also had important life experiences outside of research. Datar credits her party planning experience. Phelan was a biotech executive. Gagern worked as a consultant after studying economics. Unsuspecting people are thriving in the role.

This matches my experience working with science angels. It’s hard to correlate their effectiveness with any sort of traditional measure of scientific achievement or advancement. The trend suggests we aren’t casting a wide enough net. And given the efficacy of technoscientific field building, more serious questions arise: how do we get more people to do it? And from where do we draw?

Before getting to specifics, it’s worth reiterating that field building is a positive sum game. In nearly all the examples above, the field builders made something from nothing—they brought in new funders, inspired new entrepreneurs, and gave opportunities to more researchers and technologists. Scientists are so accustomed to fighting for their slice of resources, like grant funding or talented grad students, that they often react skeptically to activity that grows the entire pie.

In that spirit, there may be many ideas for creating more field builders. I’d like to hear them. I also want to pitch our idea: turning our small science angel experiment into a larger, full-on field building accelerator.

Almost all the great field builders get into grant funding. If they don’t start as funders, they tend to evolve the trait quickly. Distributing money and resources to a growing scene seems to be a core competency. Running a micro-grant program, a la science angel-ing, is a good way to isolate and evaluate the ability for larger amounts—it’s a perfectly sufficient starter pack.

Our model is particularly well-suited to the job. Here’s what we could do:

Philanthropic partners underwrite a cohort of 50 aspiring field builders. Each gets a budget of $75,000 to start micro-granting out to new ideas and researchers in their respective fields. We run a bi-weekly interview series with experienced field builders to tell stories and answer questions. At the end of one year, we’ll assess how their efforts impacted the field.

We have the administrative and legal infrastructure to run this experiment tomorrow. Added twists and details could make it even more fun, interesting, and impactful.

In the final essay of this series (coming next), I’ll explore this pitch in more detail—how we’d structure it, how we’d measure it, and what aspects still need consideration.

If you missed the first installment, here’s part 1:
Small, Fast Grants at the National Science Foundation

Also, it’s fundraising season in non-profit land. Before I ran a 501c3, I wondered why everyone made the same canned push at year-end trying to drum up donations. They can’t possibly work, right? How many people have a tax situation that requires a sudden charitable resolution? But now, sitting on the other side of the table, I realize how much those spur-of-the-moment donations can add up. These fundraising emails are little flags planted in the ground, offering a reminder of the organization’s existence as well as a chance for people to rally around a cause they deem important.

So, these next few essays are also an invitation to join the Experiment Foundation as a supporter and fellow traveler on the mission to embolden scientific curiosity—to make the world safe for new ideas and questions that need a little bit of time and money to take hold. You can donate here. Or email me—david@experiment.com—and we can set up a time to talk more about the organization.

Now for Part 1…

A Decade of Small, Fast Grants at the NSF

I should start with my admiration: the National Science Foundation (NSF) is the best federal science funding agency for both small grants and fast funding. Their EAGER and RAPID mechanisms have been catalytic for starting new research directions and responding quickly to urgent issues, like the COVID-19 pandemic. They’re the gold standard among federal research agencies. But they could be better.

I recently wrote a proposal for the Day One Project, suggesting a tweak to the current arrangement: creating Micro-ARPA program managers whose only job is allocating small, fast grants. Below is an addendum with the data behind the argument. More importantly, I added suggestions for what this data means for science funders everywhere—not just the federal agencies.

In my original “Science Angels” writing, I leaned heavily on a 2013 paper by Caroline Wagner and Jeffrey Alexander called “Evaluating transformative research programmes: A case study of the NSF Small Grants for Exploratory Research programme”. The paper analyzed the Small Grants for Exploratory Research (SGER) program at the NSF—the small, fast grants program that predated and inspired EAGER and RAPID—and found the mechanism to be wildly effective and efficient: one in ten of the small grants turned into transformative results, which they defined rigorously. One in ten! As far as research funding goes, those are good stats.

The other major point of the paper: for some unknown reason, the NSF program officers were not using the SGER mechanism as much as they could have been. It was effective and underutilized.

This was supposed to change with EAGER and RAPID. In 2009, when Arden Bement, NSF Director at the time, launched the EAGER and RAPID program to replace SGER and “urged NSF program officers to spend up to 5% of their budgets on them.”

Viewed from the agency level, that’s never happened. Not even close. Even in the RAPID-heavy year of COVID response in 2020, the combined EAGER and RAPID budget didn’t crack 3%. The Wagner and Alexander paper, with their glowing report card, didn’t change the trajectory much, either.

Here’s the total amount of EAGER and RAPID funding in the decade following the report:

There was a hopeful bump, then the COVID spike, but now tailing off. Here’s how that relates to the rest of the NSF budget:

Still far from Bement’s hope of 5%, and trending in the wrong direction.

Another interesting way to view the data is by the volume of grants given—not just the total dollar amounts. After all, these are small grants, given with the expectation that a small amount could go a long way to proving a new idea. The numbers tell a similar story there:

Again, the data shows blips of enthusiasm, but overall it’s a steady decline.

For fun and perspective, I added the grant volume of our small non-profit, the Experiment Foundation. Our numbers will increase by ~50% again this year and—if trends hold on both sides—we could eclipse the volume of NSF EAGER grants in 2026 or 2027, which is wild to imagine. We’ve had one half-time staff member (me) and a few dozen science angels doing this work. To be fair, our grants are much smaller—mostly less than $10k. But it’s worth making the point: more is definitely possible. I’ve lived it.

A commitment to early, exploratory grantmaking matters. An experienced ARPA funder told me that she couldn’t find a correlation between the success of a project and the size of the grant. Any financial investor would tell a similar story. Given the uncertainty of outcomes, small grants should be favored simply because we can do more with less.

Every EAGER grant that goes out represents a new and hopeful research direction. The more funding we allocate to EAGER and RAPID, the higher the volume of grants the agency can deploy, and the more scientists are on the playing field.

So what? What to do about this data? I have three suggestions.

One is for the federal funding agencies: try something different. Don’t just admonish program officers to use these mechanisms. That approach has flatlined. It’s time to test a new arrangement. My Day One Project proposal to create Micro-ARPA program managers to disperse the funding is one such tweak, but there may be others.

Also, the NSF has been the leader so far, but there’s no reason other agencies couldn’t implement the mechanism and improve the process. NOAA, BOEM, the Forest Service, etc—I’m looking at you! Your research budgets aren’t nearly the size of the NSF or NIH, but you could turn that into your advantage. There is a niche to fill here.

The second suggestion is for the philanthropists, foundations, and donors funding scientific research: don’t mimic the NSF. I’ve noticed a tendency for philanthropists and foundations—when faced with the prospect of setting up a new research funding program—to replicate the example set by the NSF and NIH: put famous scientists in charge, run an extensive peer-review process, and try to correlate their dollar amounts to the grant sizes of the federal agencies. That’s all fine and good, but there’s a missed opportunity. The federal agencies have limitations, and small, fast grants are currently one of them. Philanthropic funders could fare better, and their funding could go further.

Lastly, to the scientists: become the science funder you want to see in the world. I’ve now worked with more than a dozen science angels who’ve ran their micro-grant program with the Experiment Foundation—finding and filling niches in the scientific funding landscape, as well as helping to kickstart hundreds of new projects and careers. One lesson is clear: the skill of being an early-stage science funder is unique and distinct from being a good scientist. We need more people to try their hand at this role—taking on the persona of a science angel—to maximize the opportunity for small, fast grants to turn into more transformative results. Please consider the job. It might be your great talent, with the whole scientific ecosystem improved by your effort and attention.

I learned that, along with a host of other interesting facts, by attending a presentation by a team of engineers and scientists who are trying to develop a new type of non-invasive brain-computer interface. The skull was a critical tool for the team to assess the viability of their proposed technique.

It would be impossible for me to tell the story better than Marley and the team already did in their entertaining thread on X: 

To sum it up: a group of friends and collaborators rented an Airbnb for a focused period of hacking and work. Their goal was to test the possibilities of two new brain imaging methods using an acoustoelectric strategy and another using functional ultrasound. 

I had been paying attention to neuromodulatory ultrasound after reading Sarah Constantin's compelling series on the topic. The ability to non-invasively interact with precise regions in the brain appears full of both research and therapeutic potential, so I was primed to notice Marley's post and story about the Airbnb experiments. Anytime I see a technology go from the research lab to the garage (or in this case: the Airbnb living room), I pay attention.   

Meeting the team, and hearing their enthusiasm for the new questions they've discovered, gave me confidence that they're on an interesting path. I'm cheering for their ongoing experimentation. But this post is not about functional ultrasound, although I suggest you follow both the team and Sarah for further updates on the technology. Instead, the experience caused me to reflect on the nature of ambition and, more specifically, how far the technology community has drifted from what I call "right-sized ambition". 

I made a few simple diagrams to explain. 

The first is a rough sketch laying out the scale of ambition. The stark jump comes when you're making something. Before that, you're dreaming. In the earlier stages, there are prototypes and experiments, which progress steadily towards products or feats of discovery. 

The first milestone should be the early prototype—the "wouldn't it be cool if..." experiment—that helps chart the course towards the next big question or goal. 

I love this stage. This is exactly what Marley and the team achieved in the Airbnb: a goal just beyond the reach of their skills, resources, and know-how. The reason they were able to pursue the adventure was owing to a small grant from Protocol Labs who helped underwrite their equipment purchases. As you can see from the celebratory video in the X thread, this moment is FUN.

On the far end of the spectrum is a project of maximum ambition. From my perspective, and probably many others, the Starship booster tower catch is the most ambitious project in decades. Again, the goal was at the limit of the skills and resources of the SpaceX team to achieve. It was recently unimaginable, barely possible, and now, amazingly, a historic moment. As you can see from the celebratory room full of SpaceX engineers, this moment is also VERY FUN.

This is where the idea of right-sized ambition comes in. In some startup circles or non-profit prize efforts or best-selling thought leadership, there's an emphasis on "big bets" and highly ambitious projects, which is not a bad idea on its own. I'm all for the moonshots. But it should be noted—and I haven't seen anyone write this anywhere—that starting a moonshot from scratch and struggling to muster the resources together to do something of maximum ambition is NOT FUN.

Too often, the work turns into constant fundraising; chasing big donors or patrons and being tempted into making bigger and bolder claims. I've seen many good people get stuck on the speaking circuit, not for want of the spotlight (although sometimes that happens), but because they view publicity as the only way to achieve their goal.  

Trying to close this gap can be painful:

Another way is to focus on scaling your ambition over time. Start with an ambitious-to-you prototype that demands further iteration and exploration. Don't worry about organizational structures or fancy pitch decks. Resist the temptation to formalize the effort—keep it a project for as long as possible. Be like Marley and the team. 

Again, this is not a knock against the moonshots. By all means, if you can afford it, take a big swing. But know that there is another path—well-worn and productive—of right-sized ambition.

Many of your favorite ambitious projects started this way, even though those early stories don’t get the headlines. Starting small builds the confidence to go bigger. For every Waymo, there is a Stanley. For every SpaceX, there is a BFR. For every new non-invasive brain-computer interface, there might be a skull from skullsunlimited.com.

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Note: I’ll be publishing several pieces in the coming weeks about small grants—lessons learned from three years of running a micro-grant program. I’ll also be announcing the next batch of grant programs from the Experiment Foundation. Stay tuned for that!

It comes with the territory. Our non-profit, the Experiment Foundation, uses a peer-discovery model to award small, fast grants for scientific research. We’re using “science scouts” to give out lots of grants. The efficacy question often comes from experienced program officers—folks who’ve been in philanthropy or science funding at the federal level—who are used to bigger budgets.

There’s another common response, too, which often comes from a scientist, inventor, or entrepreneur—they tell me a story.

When I explain our funding model to this audience, I’ve opened the door for them to tell me about the person who—at some critical junction in their life—made a bet on them with an early grant, investment, or job. They understand what we’re doing because they lived it. I lived it.

It turns out, the best way to respond to the skeptic is with stories—many of them, and ideally in one place. So I thought I’d start a list.

Below are a few of my favorite small grant stories. If you have one, please send it or leave it in the comments. Small is, of course, relative.

The goal is to end the skeptical response once and for all. Then we can move on to the important—and unresolved—question: how to effectively scale small grant programs?

I.
Jet Propulsion Laboratory (JPL)

The informal predecessor of the NASA Jet Propulsion Laboratory (JPL) was the “suicide club”, consisting of California Institute of Technology (CalTech) graduate student Frank Molina as well as amateurs Jack Parsons and Ed Forman. They garnered the nickname for their explosive rocketry tests on and around the CalTech campus. 

Rocketry was fringe science at the time, in the 1930s. Robert Goddard was conducting early tests, and the German rocketeers were just getting going, but the field was not taken seriously. As Theodore Von Karman explained, “the literature of rocketry was more or less regarded as part of science fiction.”

Von Karman, a CalTech professor at the time, gave the suicide club a boost anyway. He admired the persistence and enthusiasm of the young group, but he was mostly intrigued by their unique combination of skills. Parsons and Foreman didn’t attend college, but each had developed a self-taught expertise and manual literacy that was critical for building rockets—Parsons the chemist and Forman the mechanic. Molina added an academic rigor to the whole operation. 

Von Karman gave them tacit approval by allowing the use of his laboratory in off hours but required they raise their own funds. And money became a constant problem for the nascent project. The group was forced to scavenge parts from junkyards and save up for anything critical. In The Wind and Beyond, Von Karman tells a story about their low-budget beginnings: 

Actually, the only funds the group received came one day in 1937 from an unexpected source. A student named Weld Arnold was so impressed with the possibilities of rocketeering that he offered to donate a thousand dollars if he would be allowed to act as photographer for the group. When Malina agreed he brought the first five hundred dollars in bills wrapped in a newspaper.

The laboratory privileges ended up being short-lived after explosions frustrated neighboring scientists. The group moved out to the desert—the Arroyo Seco, just outside Pasadena—to continue their testing far away from meddling administrators. By 1938, they had amassed a handful of results that seemed promising, like characterizing the thermodynamics of rocket motors. One of Malina’s papers was even accepted by the Institute of Aeronautical Sciences. However, industry and government still showed little interest in the group or the potential of rockets, except for one person.

The head of the Army Air Corp, Hap Arnold, was intrigued by the potential of using rockets to assist the takeoffs of large military aircraft on short runways, the type one might find in the South Pacific. He offered Von Karman and the team a $1,000 contract to start work investigating the problem. The second contract was for $10,000 and set up a consequential field trial for rocket-powered aircraft. 

In August 1941, Lieutenant Homer Boushey was strapped into one of Parson’s explosive airplanes. Everyone was nervous. Von Karman tells the story: 

Boushey revved up the motor and let off the brakes. As the plane rolled down the field and gathered momentum, he kicked the ignition switch. Smoke billowed out as the rockets ignited. The plane shot off the ground as if released from a slingshot. None of us had ever seen a plane climb at such a steep angle. Boushey leveled out, circled the field, and returned in a few minutes. When he stepped out of the plane, he was grinning broadly. And so were we.

That contract, as well as the ensuing tests, turned out to be foundational for both JPL and Aerojet, the commercial spinout. 

Malina, Parsons, and Von Karman would all take their place in the history books of technology, making their patrons—the aspiring photographer and the prescient Army chief—the unlikely benefactors of American rocketry.

II.
The Submarine

The inventor of the submarine, John P. Holland, had a difficult time getting anyone to underwrite his underwater dreams. 

He tried to get funding from the United States Navy in 1875, but they didn’t think the design would work. Instead, he found an unlikely benefactor in an Irish revolutionary group, the Fenians. The expat group had formed in the United States with a mission to fight back against British rule in Ireland. Holland presented his plans and submarine designs—a far-out science fiction idea at the time—to the leadership. They gave him $4,000 to pursue it, paid out of the “skirmishing fund”  which was a collection of donations the Fenians had gathered from other Irish immigrants to help fight London. 

III.
mRNA Vaccines

Katalin Karikó, the Nobel-winning hero of science who discovered the potential for mRNA vaccines, had a well-publicized hard time in academic science: denied tenure, demoted, and overlooked for grants. Her story has become a symbol of curiosity and perseverance.

In her autobiography, Breaking Through, she names names. She calls out the bureaucrats and colleagues who withheld opportunity and put up barriers. It doesn’t come across as bitter—just honest—because Karikó also highlights the people who did help. The obvious protagonist is her colleague Drew Weissman, who she describes as her “lock-and-key” scientific partner. Another was a colleague at Penn, David Langer. When shifting leadership winds threatened her position, Langer went to the head of the neurosurgery department and demanded Karikó stay employed with the team. Karikó explains:

“Hiring me, he insisted to Eugene Flamm, was exactly what the place needed to be great. David planned to partner with me on research. Shoulder to shoulder. Indefinitely. And we could do that only if I became a member of the neurosurgery department.”

The story is a good reminder that full-throttled belief can be as valuable as money. Delivered together, even the smallest grant can become a powerful force for forward momentum. Karikó again:

“What was unusual, though, was how deeply David believed in the work. Not only had he managed to secure a small grant—about $25,000, if I recall correctly—for our work, he’s also delivered a paper at a conference in Arizona: “Bypassing the Nucleus: mRNA as Gene Therapy.” I guess it’s fair to say that David wasn’t merely a believer in mRNA—he’d become an mRNA evangelist.”

IV.
Artificial Intelligence

The term “artificial intelligence” was coined on a grant proposal. 

The ideas and concepts around thinking machines were prevalent in the early 1950s, but a small group of practitioners and academics—including John McCarthy, Marvin Minsky, Claude Shannon—forced the issue by proposing a summer of research study focused on the topic. They approached the Rockefeller Foundation with “A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence” which outlined their research agendas and requested funds for stipends and travel. Their all-in budget was $13,500.

The summer program became legendary—the conversations and discussions were lively and the visiting guests were frequent. 

The field of artificial intelligence was born over those eight weeks.

V.
Y Combinator*

The original Y Combinator investments were $20,000. It’s a relatively small number now, but it was a small number back then, too.  Paul Graham and his co-founders, Robert Morris and Trevor Blackwell, had received a small bit of funding themselves. The number was a function of their experience of what early money plus good advice was worth. Graham explains: 

We modelled YC on the seed funding we ourselves had taken when we started Viaweb. We started Viaweb with $10k we got from our friend Julian Weber, the husband of Idelle Weber, whose painting class I took as a grad student at Harvard. Julian knew about business, but you would not describe him as a suit. Among other things he'd been president of the National Lampoon. He was also a lawyer, and got all our paperwork set up properly. In return for $10k, getting us set up as a company, teaching us what business was about, and remaining calm in times of crisis, Julian got 10% of Viaweb. I remember thinking once what a good deal Julian got. And then a second later I realized that without Julian, Viaweb would never have made it. So even though it was a good deal for him, it was a good deal for us too. That's why I knew there was room for something like Y Combinator.

*Not a grant obviously, but worth including for the lessons.

Hopefully, this collection of stories sparks a memory or anecdote of your own. Again, please send it my way. I’d love to hear it.

I’ll add a Part II in a future post.

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I sat down to read Ashlee Vance's new book, When the Heavens Went on Sale, with anticipation. 

Finally, I hoped, we were getting the type of technology story that the world could use: aspirational and pragmatic. The book sought to capture the recent evolution of small satellite designs and the industry changes that resulted. Vance told the story through the challenges and triumphs of three startup companies. I was paying attention because the trajectory of one of those companies, Planet Labs, had been a personal north star over the years, with their idealistic business approach overlayed onto an ambitious engineering challenge.

If any Silicon Valley company deserved to have a book written about them, it was Planet. And that’s not just my opinion. The technology writer Kevin Roose, in a profile of them for NYMag, titled the piece “A Tech Start-Up Just Restored My Faith in Humanity”.

They set an admirably high bar. 

Before the Vance book, I had seen the story up close. I've known Will Marshall and Robbie Schingler for more than a decade. My teammate and co-founder Eric Stackpole spent time living in the Rainbow Mansion depicted in the book. Stackpole built the first underwater robot prototypes (of what would become the OpenROV project and company we co-founded) in the same garage where they built the first satellites. Stackpole told me that he and Marshall would often joke, referring to parallel missions of space and ocean exploration: "Ok, you go up, and we'll go down." 

Beyond the garage, Stackpole and I visited them in their office in the days following the early images coming back from the first Dove satellite mission. They were thrilling moments. We met with them dozens of times over the years. The experience of watching a humble garage project grow into a public company—running a space program out of a San Francisco office building, no less—was pure inspiration. 

So I say this with a heavy bias towards the story's main characters: Vance left out a critical thread. 

Don't get me wrong. He told a great story. It was factful and thorough. And it’s good writing. The accounts of Pete Worden and his role in assembling a generation of doers at NASA Ames Research Center were especially riveting. Along with Lori Garver's book on the uphill battle she faced at NASA, we're finally getting a clearer picture of one of the great institutional transformations of the past century. Just like he did with his Musk biography, Vance captured the entrepreneurs in their element, with the risk-taking aspects of their personalities on full display. The arc of entrepreneurial zeal set against bureaucratic stagnation is irresistible storytelling. Do read it!

My only note: the story needs more CubeSat. 

It's there, but only briefly. He mentions it when discussing the PhoneSat project, a pre-Planet endeavor from the team to test whether smartphone components would work in space. 

With the launches behind them, the PhoneSat team shifted from putting a smartphone through its paces to producing an actual satellite. The engineers opted to mimic something called a CubeSat, which was a four-inch-by-four-inch-by-four-inch cube of metal scaffolding that could be packed full of electronics. The CubeSat concept had been pioneered by universities* looking for a way to simplify and standardize the construction of small satellites in the hope that more students would have a chance to work on and launch real spacecraft. By having a common satellite design to work from, students could trade information on which solar panels, electronics, sensors, and other components worked well in the device and not have to repeat the efforts from scratch at all their respective schools. 

The problem with the flyover is that it distorts the role of the CubeSat standard's democratizing effect on satellite development and deployment. The economics of launch were permanently altered by the simple design. It enabled hundreds of prototypes from new teams, the first large constellations, and countless innovations in remote sensing for scientific research. The design brought satellites out of the realm of nation-states and into the likes of Kickstarter projects.1 

The origin story of the CubeSat was just as unlikely—and just as heroic—as the efforts made by the entrepreneurs. It was two professors—Bob Twiggs of Stanford and Jordi Puig Suari of Cal Poly—on a mission to embolden their students. Amidst a graveyard of failed attempts to lower the cost of launch, they achieved real progress for their students and beyond. And their story belongs in the history books. 

One of the best CubeSat stories was written by Eric Hand in an issue of Science: 

Over lunch at a sandwich shop in San Luis Obispo, Twiggs and Puig-Suari sketched out options on a napkin. They thought hard about the potential capabilities of a 10-centimeter cube with a mass limit of 1-kilogram—the size and weight of a liter of water. Clad in solar cells, the cube would also eke out perhaps a watt of power, enough to power a small computer and a radio: “a Sputnik,” Puig-Suari says. Back at Stanford, Twiggs found the perfect life-size demonstration model: a plastic box used for storing the insanely popular stuffed animals known as Beanie Babies. A standard was born.”

The Beanie Babies box has become CubeSat lore, but it was another development that moved the standard from idea to reality: the P-Pod launcher. It’s one thing to design satellites to common spec, but far more important that they adhere to a common interface with the launch vehicle. P-Pod launchers were designed to hold three CubeSats and attach to any number of launch vehicles that were willing to let them piggyback a ride into space. 

Ridesharing wasn’t an easy sell. American companies like Lockheed Martin wouldn’t bother integrating, so the first CubeSat launches were with Russian providers. It wasn’t just the companies ignoring them, either. NASA and the relevant federal agencies were slow to recognize the potential, too.

The early CubeSat adopters were mostly student groups, thrilled that they could finally afford to send their creations into space. Once they showed it was possible, the cheaper launch economics drew early adopters, like the Planet Labs team and the National Science Foundation (NSF). 

The CubeSat effect is undeniable. Check the numbers. Check the science. Ask the entrepreneurs.

While there have been other innovations in the microsatellite class (CubeSats are considered nanosatellites), and new satellite designs like the ones used for the Starlink constellation are becoming more common, the CubeSat cracked a problem that had bottlenecked aerospace engineers for decades. It turned launch ridesharing from an afterthought to one of the main events. 

Hand wrote a multi-page profile of Planet Labs in the same issue of Science, running just ahead of the CubeSat creation story—the heroic entrepreneurs and the enabling protocol.2 That was published in 2015. A decade later, while the company legend continues to grow, the story of the standard is losing fidelity. 

I had a recurring thought while reading Vance’s book: This is where we lose them. At the threshold of history, the role of the enabling standards seems to get overshadowed, even forgotten. 

It’s bigger than Vance. I recently read another well-written and researched piece by one of my favorite technology writers, Anna-Sofia Lesiv, called The Satellite Renaissance, which made the same omission. Something has changed over the past few years. We're in a unique moment in space technology where the modern era—reusable rockets, small satellites, and the new government contracting mechanisms—is becoming modern folklore. 

This is not unique to satellites, either. It's a common occurrence in technology writing. The standards and protocols become, at best, footnotes in the entrepreneurial biographies, even though the protocols often create those new frontiers to begin with. 

The easy explanation for this discrepancy is an incentive problem. Entrepreneurs and companies have marketing budgets, PR teams, and obvious reasons to make themselves known. Protocols don't have the same resources or goals. In fact, to spur adoption, protocols aim for the opposite: fully-distributed heroism. Successful technical standards aim to cultivate a quiet respect amongst engineers, one that makes new contributors and adopters feel a sense of ownership and pride in collaboration. Good protocols avoid the pomp to maintain a shared sense of purpose. 

The longer explanation is that it’s hard to tell these stories. They don’t lend themselves to character arcs and storytelling tropes. They’re complicated and nuanced and involve too many people. Without the creative tension, it’s unlikely they would ever crack the best-seller lists. And so our protocol histories go (mostly) unwritten.

Narrative Oral Histories

Against this sad reality, I have found one hopeful antidote: oral history. 

Just ask the people who were there. Sit them down and make them explain the story from their biased perspective. Record it so the next generation of historians can make an honest go of it.

While researching Standards Make the World, oral histories became my preferred source of ground truth. Finding the specifications of any given standard was often straightforward with a quick Google search. Sussing out the standards-making process—the procedures and committees that went about creating (and eventually maintaining) the standard—was doable from there. The next layer down—the people, the politics, the honest challenges and disagreements—was often difficult to uncover. And that’s where all the action is.

Where I found oral histories, I found gold: MIDI, ROS, ARPANET. If you want to learn how the standards actually got implemented, you have to ask the people who did it.

In that process, I learned there’s another step that turns oral histories from archival dust collectors to culturally relevant artifacts: blend ‘em up. Interweave all those stories together in a way that gives an outline and an honest shape to the project. We could call this genre the narrative oral history.

Here are the origins of USB told in this style. It’s a unique and effective form of storytelling.

[Note: I hacked together a CubeSat narrative oral history by pulling quotes from various interviews to give a taste of that perspective. It lives further down at the end of this essay.]

The master of this art form is James Andrew Miller, the investigative journalist who has published books on ESPN, Saturday Night Live (SNL), and others using exactly this technique. They’re all compelling reads. And it translates seamlessly to podcast format, which Miller has done with Origins, so it easily fits into today’s media landscape.

It’s worth noting the tools for creating these types of oral history projects have never been better. You don’t need to travel to get the interviews—use Zoom. You don’t need an elaborate plan to host or organize the interviews—just put them on Youtube. The new AI tools make transcription cheap and easy (Descript is my preferred tool) and are enabling all sorts of interesting new possibilities, like creating a GPT fine-tuned with the wisdom of engineering and technology legends. 

All this to say: I wish we had more of these protocol histories. If you’re working on this flavor of project, please consider going the extra mile with documentation. Interview everyone while their memory is fresh.

We need these stories. The omission of protocol histories has consequences.

If we lose these stories, we don’t learn the right lessons. For example, in How Big Things Get Done, researcher Bent Flyvbjerg and his co-author Dan Gardner make a strong case for improving our capacity to do big infrastructure projects like high-speed rail or large bridges or freeway tunnels. (It’s a great book—I recommend it!) Building off their database of megaprojects and their corresponding costs and timelines, the authors lay out critical lessons for success. One of the major design considerations they suggest: start small and modularize. Planet’s network of Dove satellites is one of their cited examples:

Space has long been dominated by big, complex one-off projects, and priced accordingly, with NASA’s James Webb Space Telescope—$8.8 billion, 450 percent over budget—just the latest example. But there are promising signs that the lessons of modularity are taking hold. To make satellites, a company called Planet (formerly Planet Labs, Inc.) uses commercial, off-the-shelf electronics, like those mass produced for cell phones and drones, made into 10 × 10 × 10 cm (4 × 4 × 4 inch) modules as cheaply and easily as possible. These are their Lego. They’re assembled into larger so-called CubeSat modules. Assemble three CubeSat modules and you have the electronics for one Planet Dove satellite. In sharp contrast to the large, complex, expensive satellites that have long been the norm, each Dove satellite takes only a few months to build, weighs eleven pounds, and costs less than $1 million—peanuts by the standards of satellites and cheap enough that failure will result in learning, not bankruptcy. Planet has put hundreds of these satellites into orbit, where they form “flocks” that monitor the climate, farm conditions, disaster response, and urban planning. Despite privacy concerns that need addressing by policy makers, Dove satellites are a powerful illustration of the adaptability and scalability of modular systems, especially when contrasted with NASA’s bespoke approach. 

Again, there is a brief mention of CubeSats, but not in a way that accurately conveys what the standard is or does. Nor is there any real attention to the standards-making process. Enabling standards and protocols beget modularity. If you want to apply the lessons of Planet to your own megaproject or plan, you’d do best studying how the CubeSat was made, not just the Dove. 

But it’s not just associative lessons, like building modular systems, that are lost. These missing stories make it hard for us to build better standards. Or get people excited about standards-making in the first place. As I wrote in Standards Make the World, when we started the Bristlemouth project we were at a loss for information. Our inspirational project, the CubeSat, was a miraculous anomaly. The standards model we sought to emulate seemed almost a secret.

Narrative oral histories help fix this problem. 

CubeSat Revisited

Let’s try the CubeSat story again, but only using pulled quotes from the creators:

Twiggs: It started in 1999 when I was at Stanford University. In 1995, I had went and started a small satellite program in the aeronautics and astronautics department. The satellites we had been building were microsatellite-class satellites and one of the things that was my objective was I wanted to try and get students through the process of actually building and launching a satellite when they were doing their master's degree in a period of about two years.

It turned out the students, if you have a larger satellite, they kept coming up with ideas of more things they want to add to it. I had a terrible time trying to get them to finish the satellite. I'd tell them I wanted it finished and they said, "Well, you get us a launch and we'll finish it." I said, "When you finish it, I'll get you a launch."

It ended up taking until after 2000 until we got our first launch. It wasn't something we could afford -- the launch -- so we had to depend on other people. We got this launch through some collaboration with some people that got DARPA funding. It was a launch on a satellite called OPAL and it actually launched some picosatellites, a mother satellite with some daughter satellites in it.

After we made that launch, I thought why don't we come up with something roughly the size of this picosatellite, and it was about the size of a Klondike ice cream bar. It was flat and long, and the thought of building a spacecraft was that if you're not going to have an attitude stabilization on it, you have to have solar cells on all sides. So what I did is I started looking for something to pattern after.

I went to a plastics shop and I found a 4-inch plastic tube that was used for storing Beanie Babies. There was a Beanie Baby craze at that time, so I bought this box and looked it over and thought about how do I hold the box to launch it, how many solar cells could I put on it, and I come to the conclusion that I could put enough solar cells on it to probably get about an average of a watt of power out of it.

So I designed this thing such that I wanted to put them in some sort of launcher tube and so I designed a tube with little rails on it that would hold the box along the corners. That's how the design of the launcher came along and we decided we could put about three of them in there. (Source: Spaceflight Now)

Puig-Suari: We knew that launch vehicle flexibility was important. It was hard to get onto a launch vehicle. We had all these custom boxes everybody was doing their own little size and that meant you need to find a nook and cranny on a launch vehicle for your specific spacecraft. And that was difficult. So we decided to try to use standards, and at the time there standards in spacecraft would come up and go away and come up and go away, and they really never went anywhere. The mission always took priority. But we decided to give it a try. (Source: Keck Institute for Space Studies Lecture)

Twiggs: It all started as a university education program satellite. It was kind of funny. I didn't think that people would criticize it as much as they did, but we got a lot of feedback, you know, "That's the dumbest idea I've ever heard. Nobody's going to use this toy." We said, "Who the heck cares. We'll go ahead and use it. We're using it for education." (Source: Spaceflight Now)

Puig-Suari: It was primarily a university project issue and we thought industry may be interested in paying for it so we put that thing about “it could be an industry testbed” but that was really not what we were trying to do. We were just trying to find some funding so that that's where that came from, so we came up with a standard. And it's a very simple standard and there's reasons for that. One, it's small. It's a Pico sat — one kilogram, basically just a dimension standard: if you fit in this box, you can go fly. We didn't really tell people what to do and what to do inside because that was too much work and we wanted a simple system that universities could manage. (Source: Keck Institute for Space Studies Lecture)

Twiggs: Another thing that was kind of funny is we had no interest from NASA or any of the military organizations. It just wasn't anything they were interested in, so it was all funded without any funding from those aerospace organizations. I'm kind of glad that NASA didn't help us, or we'd probably never got it done. It was developed for the education of students. If you make it small, they can't put much in it, so they get it done quicker, and hopefully you can get it launched for a lot less money. I don't think Jordi Puig-Suari at Cal Poly or myself had any idea that we'd see days like this. (Source: Spaceflight Now)

Puig-Suari: [The P-POD design is] extremely simple. It’s the world's most expensive jack-in-the-box. You have a pusher plate with a spring you put the satellites inside, and when the door opens they come up they come out. And it fits three cubesats and a lot of people have asked “why three? why not four or five?” … that was about the room we had on a Delta II secondary space, and we actually have never flown in that particular location. (Source: Keck Institute for Space Studies Lecture) 

Twiggs: We say that you have to build it with materials that meet space standards. You don't want to build it out of something that outgasses a lot after you get it into space, and that kind of dictates you build something out of metal. So aluminum is the easiest thing to do it with, and, of course, it has to fit that form factor to fit in what they call the P-POD launcher. We just said, "Rather than you build what you want and we'll try to fly it for you, we've got this launcher, it does some good things and you should use it."

First, it holds three CubeSats. The expendable launch vehicle guys like it because we don't have some student thing that can come off and run into the primary satellite, so that solved the problem with them. They were very cautious about putting something out in the open that parts could come off of, so we told them we've got this thing inside this metal container, and if a radio comes on, you're not going to hear it because it's inside of a Faraday cage. If parts come off it, the primary payload is going to get launched before we do, so when we launch, if it comes out in a whole bunch of pieces, it still won't affect you. It was really designed to help protect the primary satellite on the launch vehicle, and that loosens up the restrictions.

But, in fact, even with that, we went quite a few years where the only launches we could get were from the Russians. It wasn't until the Minotaur (rocket) came along that we were able to get any of our satellites launched in the United States. (Source: Spaceflight Now)

Puig-Suari: And it worked! It actually was successful… I've stopped counting [the number of launches] because it's a continuous barrage now. We launch from everywhere… We have launches in the US, in India, and Russia, Vega launched in South America but it's a European launch vehicle. And truly it's a regular launch scene. Every month or two or six months there's a launch somewhere, so it's very hard to keep up with it, but it's very nice to see that happen…

We have a very large developer community. We have universities and governments and industry and it’s worldwide. Everybody's doing Cubesats. We have dedicated workshops. And one thing that I'm very proud of is that we brought in a bunch of new players. And that's actually true from the beginning: new countries and new universities. People that have never thought about launching spacecraft were some of the early adopters of the standard.  (Source: Keck Institute for Space Studies Lecture) 

Twiggs: We figured out a way to do it, and somehow that became the standard. We built the launcher to launch it, and that required that the CubeSat be a certain size. We never told anybody what to put in them. All we said is you have to be a 10-centimeter cube, and we did that just so people would know the physical size. But there was never any rule, and there still isn't any rule, about what to put inside of them. (Source: Spaceflight Now)

Puig-Suari: To me, a few things are important. Some are obvious and some not so much. It is important for universities and people to be able to put a satellite up without the kind of risk NASA and commercial companies faced. They could try some crazy thing. Before cubesats, we were so conservative nobody was willing to try anything out of the ordinary. When we did, we discovered some of the things everybody said would not work, did work. The fundamental change was that there was a mechanism to go try to those things. Some will work and some will not, but it allows us to try them and that was very infrequent before cubesats arrived. That was really important. That was the big change. Commercial electronics were exploding at the same time. It was serendipitous, and we demonstrated that they did work fairly well in space, at least in low Earth orbit. That was a huge change in the capability of the small spacecraft. When Bob and I started, we really wanted a Sputnik. We didn’t feel like there was much more these things could do until students started pillaging cell phone technology and all kinds of other stuff. Next thing you know the National Science Foundation is interested in smallsats. (Source: SPACENEWS)

Twiggs: [Responding to the question: “So CubeSats are a more modern example of American innovation?”] Yes, exactly, which makes it ironic that the early launch providers were Russian. We did go to some of the American launch providers, Lockheed Martin comes to mind, and they said, "If you give us a half-million dollars, we'll study it, and then if it makes economic sense for us to launch it, we'll do it." We kept asking them to take some of the lead (ballast) off and fly some of these things as secondaries, but they just didn't go along with it. I was really disappointed that the aerospace industry couldn't see the benefit other than profits from it. They couldn't see the educational benefit from it, and the potential of the educational benefit turning into commercial applications. Now, you see the commercial applications coming along with Skybox Imaging, with Planet Labs. Oh my goodness, they look like they have a tremendous economic potential. (Source: Spaceflight Now)

If you liked this sampling of the story, I have good news: Aaron Zucherman is collecting more stories as part of a CubeSat History Project, with the goal of turning it into a book. I’m optimistic his efforts will fill this narrative gap.

More importantly, Zucherman is setting a good example for the rest of us. Don’t just hope for more protocol histories; go forth and write them.

I spent the summer researching technical standards. More than any particular standard or spec, I was interested in the idea of standardization: the history, the methods, the heroes.

Most people, when I told them about my research topic, gave me a puzzled look: Why are you doing that?

The reason was self-serving. Based on my involvement in the creation of a new marine connectivity standard, the Bristlemouth project, I was convinced that standards-making was an underappreciated and powerful way to shape the technological landscape. I wanted to explore and tell that bigger story.

The culmination of that research is out now: Standards Make the World

It’s a long essay, but I do hope you’ll read it and consider the ideas. My initial hunch was confirmed: the world needs more standards entrepreneurship. I hope this piece contributes to a better conversation about these engineering rules that shape and maintain our modern lives.

I have more to write about the topic—footnotes and stories that turned into standalone pieces. I’ll publish them here in the months ahead.

Here’s a short excerpt from the essay to get you started.

Standards are everywhere. These nearly invisible rules establish trust between engineers and give rise to commerce, industry, and possibilities. Even now, just by reading these words, you are relying on dozens, if not hundreds, of guiding technical standards. Some of them might be familiar, like the World Wide Web (WWW) or the Internet Protocol (IP) that delivers packets of information to your device. What about the standards that went into manufacturing the device you might be reading on, like the allowable Radio Frequency Interference (RFI) and Electromagnetic Interference (EMI) limits for that device? What about the shipping and transportation standards that brought it across oceans? Would you know where to find the specification? What about the group that created them? Or who maintains them?

The rabbit hole of questioning extends to almost every object in our lives. Technical standards form the foundation of our built environment. They're often regarded as burdensome constraints on creativity, which can happen when they're poorly constructed, but if they're well designed and effectively implemented by engineers on the front lines, standards can become enabling technologies: the Internet, shipping containers, time.

Startups and companies get all the headlines, but the tools we use to cooperate—standards and protocols—drive an equal measure of civilizational progress.  

Despite their importance, standards often go unnoticed. Most people, if they're aware of them at all, think they’re boring and overly bureaucratic. This is partially due to the word itself—standard. It sounds basic, and it’s broad enough to cause constant confusion. A standard could refer to anything from the Unicode system that approves new emojis to the bacteria levels allowed in pasteurized milk. Standards end up as outcomes—agreed-upon measurements, terms, and rules—but they always involve a process as well. For the purposes of this essay, standard refers to the specification and standards-making to the process. 

The term protocol is also diluted from overuse. The word can be used to describe everything from the steps of scientific experimentation to royal etiquette. In this essay, I’m mostly referring to protocols in the way that computer scientists use the term: a specific set of rules and instructions for handling and exchanging information on digital networks–standardized protocols. In that sense, protocols are a specific subgenre of standards. 

The names are just a small reason that standards get overlooked. A bigger issue is first impressions.

The popular portrayal of standards is through coverage of “standards wars” where similar implementations compete for supremacy in the marketplace: VHS vs. Betamax, Blu-Ray vs. HD DVD, AC vs. DC power. These famous examples get attention because of the public nature of the competition and the investment on either side, but standards wars are relatively uncommon. Standards-making is always a negotiation, with competing ideas and trade-offs on multiple sides, but the majority of those disagreements and differences are settled in committees and small groups through defined processes well before they ever become an open conflict in the market. Still, the visibility of standards wars remains, and it makes the whole endeavor seem corporate and dangerous.

The other common interaction with standards is as a boundary or constraint. People encounter them on the way to some other goal. For example, a product designer runs into several safety and interoperability standards through the course of making a new product. An architect is bound by building codes in designing a new home. Even managers are guided by standards—such as ISO 9000—when they try to add quality assurance measures to company processes. Then, when anyone starts asking why the standard is the way it is, they find a committee or a consortium or some other process that seems impenetrable.

Understandably, this is where most people stop thinking about standards. They adhere to their basic legal and technical obligations and they move on.

This is unfortunate. A deeper understanding of standards-making—and how that process has evolved over time—creates a healthy respect for the scale of influence. Standards are some of the most powerful tools we have to affect our world. And here’s the kicker: you can make them. 

Standards are not divine laws. They are made and remade by (usually small) groups of people and projected into the world through various means and with varying levels of effectiveness. And that process is dynamic. Standards-making is something that anyone can engage in, even though almost no one thinks to do it. But they should. You should. Too often, better societal outcomes —overcoming technological bottlenecks or ensuring tools are safely deployed—are held back by poorly designed or missing standards. 

What is needed is more widespread literacy in the language of standards. And as with any other language, developing this literacy starts with a new vocabulary.

MORE — Download the full essay from the Summer or Protocols page.

Someday, when the robots eventually take over and decide to write their own history books, they will devote an entire chapter to Willow Garage.

The organization was central to robotics progress over the past twenty years, even though its role is relatively unknown outside of technical circles. And even there, the story of how they succeeded in creating a common Robot Operating System (ROS) is not well understood.1 The lessons — especially their strategy to embolden the following generation — are applicable to other aspiring scenes.

Willow Garage was technically a company, but it operated as (and amounted to) a well-heeled amateur scene. It was started by Scott Hassan in 2006. Hassan was a computer scientist and entrepreneur who had already made it big. He had started a company and sold it to Yahoo. He was also a functional co-founder of Google, having helped Larry Page and Sergey Brin write the original code for the search engine as well as invested in the nascent company. Financially speaking, he was set. 

His fortune was an important factor in being able to start Willow Garage, but so was his motivation and philosophy. Hassan recognized the important role that open-source software had played in his business success. By building on the stack of open tools, he was able to quickly get his software company, eGroups, off the ground and serving customers without having to build everything from scratch. He theorized that robotics needed a similar boost — contributions at the core operating system layer that would make building applications easier for everyone. 

He started with a building and a budget. The building was a nondescript single-story office complex in suburban Palo Alto, not far from the Stanford campus and the SRI research facility. The budget was enough to support up to 60 people and researchers, which he recruited carefully and doggedly. 

Two of his first recruits were Eric Berger and Keenan Wyrobek, graduate students at Stanford who were finishing their PhDs in robotics and leading the Personal Robotics Program there. When Hassan approached them, they expressed skepticism about the plan, mostly because “it didn’t fit any natural mental model” as Berger would say. The novel organizational structure and circumstances of the funding arrangement were unlike anything they had come across in academia. Ultimately, they rightly recognized and seized the opportunity.  The money was good, but the mission was better. As students, the pair had experienced firsthand the barriers holding back the field. The nature of robotics required diverse skillsets: hardware and mechanical expertise, electronics, as well as a comprehensive set of software skills like path planning and computer vision. They saw the same opportunity that Hassan had identified: an open-source scaffolding for robotics would be critical for progress. 

Hassan continued to pluck the best and brightest from Stanford, SRI, and beyond. Initially, the team was set to work on two projects: an autonomous car and an autonomous boat. When those efforts stalled, Hassan put the focus back on Berger and Wyrobek’s Personal Robotics project. While at Stanford, they developed PR1 as a hardware platform for the open software component. Willow Garage, with all its resources and talent, was now going to build the PR2.2 They set technical milestones, like navigating the building and plugging itself into a wall outlet, as well as manufacturing goals, which wound up being twenty machines; ten to be donated to research universities and ten to keep around the office. ROS, the Robot Operating System, was decided on as the software to run the PR2.

At the time, ROS was still mostly a Stanford project, having been started by Berger and Wyrobek and still leaning heavily on the open-source robotics work done by Morgan Quigley, another Ph.D. student at Stanford who was working in the Stanford Artificial Intelligence Laboratory. Eventually, an effort was made to formally merge various efforts to focus on ROS. Quigley would later comment that the fact that the project was between organizations — separated by both institutional boundaries and physical distance — helped because “it forced you to have a little more formality baked into the software to allow you to work on different components in parallel.”

As PR2 developed and Willow Garage grew, ROS development centered there. Quigley and the team published the ROS white paper and pushed version 1.0 on SourceForge. In addition to ROS, the PR2 became the other focus for the team, and the PR2 beta program — developing and delivering the common hardware to academic labs around the country — was the main strategy for gaining adoption of ROS amongst developers. However, it was another initiative at Willow Garage that would prove more effective: the internship program. 

The internship program at Willow Garage, led by Melonee Wise, was legendary. The story, in her own words: 

When we started really working on the Personal Robots Program and on ROS, I went to Steve and said, “The way that you make companies grow, and the way that you get people excited about what you’re doing, is to have a really big internship program. If you want to make sure that people start using ROS, the best way to do it is to bring people here and make them use ROS, and then send them all back to their labs.” I knew this because I’d done a lot of internships, and it was obvious to me how they were successful. Internships were about selling the Kool-Aid.

The internship program made a huge impact on robotics. It wasn’t just about learning stuff; I made a real effort to make their entire time at Willow awesome. We had parties, we went rafting, wine tasting, laser tag … And that’s important, if you want to create this connection. We had jackets. I know it’s stupid, but we had to make sure that when they went back, everyone saw it. People called it the Willow Mafia.

It worked. Not only was Willow Garage attracting all sorts of new talent to the team, but they were sending back those talented students to their respective academic labs, and with ROS freshly installed into their working memory. When they got back to their Universities, they were biased towards using ROS on whatever robotics project their team was trying to concoct. Unexpectedly, ROS was spreading out into the world before the common hardware platform had been shipped. Ken Conley credits the internship program: 

Originally, we thought that the vehicle for ROS was going to be the PR2, but what happened, and this is really a credit to Melonee and what she did with the Willow Garage internship program, was that because we had so many interns come through from labs that had robots, it was the case that ROS was already running on lots of different lab robots at many different universities before we ever officially released the PR2. 

A youth movement can buoy an amateur scene. Almost no one thinks about design on a generational scale, but it’s a powerful way to shape the future. Get the kids comfortable with the new tools and ideas, then wait for them to take over the world. 

Companies and organizations often think about internships as a way to get cheap labor, or as a way to find and evaluate talent. This kind of program usually gets bucketed into the “education” or “recruiting” category and is often outsourced to schools or human resource departments. Wise and Willow Garage took an expansive view of the idea and got meaningful results.

Youth movements on their own aren’t usually sufficient to kickstart a scene — they must be connected to other ingredients like catalogers, kits, or open standards.

We saw a version in the underwater robotics world. As a high-school student, my teammate Eric Stackpole participated in the international MATE competition, a program where teams of students would design, build, and deploy a remotely operated vehicle (ROV) to achieve some required tasks. The experience was transformative for him. It exposed him to the world of underwater robotics and helped build his confidence in his emerging design skills. The event series has been popular among students around the world, as well as appreciated by marine technology companies who are eager to capitalize on talent development. At OpenROV, we hired several MATE graduates and always found them resourceful and knowledgeable. It’s a great program, but not an amateur technology scene (as I’ve defined them).

Every student group that came to the event started at square one. While this was probably good from an educational standpoint — everyone has to learn everything from scratch — it held the group back from changing the technological status quo.3

Contrast that with iGEM, the international competition where students design and build genetically engineered microbes. In the early 2000s, when Tom Knight, Randy Rettberg, and Drew Endy were puzzling about how to turn biology from a basic science to an applied engineering field, they turned their attention to the meta techniques of field-building. Biology was hard to engineer, but it could be made easier, they figured, if more minds started working on the problem and everyone was sharing their work — learning by everyone doing. The iGEM jamboree was a “coopetition” model where, every year, student teams compete to create the best new biological part or microbe. Each qualifying project is then added to the Registry of Standard Biological Parts, a growing database for the community to use and build on. iGem is a youth movement and an expanding biology kit — the combination has made it a major part of the growing synthetic biology scene.

Scientific publishing saw a similar generational change in the late 1800s with the emergence and transition to scientific journals. The uptake of the periodical format was not sudden or orderly. Nature started on the scientific periphery. The X Club was an elite club of scientists, known as “Darwin’s Bulldogs” for their aggressive espousal of Darwinian evolution. They helped lend credibility to the fledgling publication started by Norman Lockyer, but they viewed it largely as a mechanism to influence public opinion. It wasn’t considered high scholarly work, which was still the realm of the book and the deliberations of the learned societies. It wasn’t until a younger generation of scientists, those growing up in the shadow of the X Club, began to adopt the method of communication as a preferential publishing mechanism that things began to change. Nature became the venue and the winning benefit was speed. In this case, a youth movement combined with an accessible new communication medium permanently changed scientific publishing.

It can take a full generation (or several) for a new medium or tool to find its footing. Early adoption is a hint, but the true impact and format-defining work will come from the following generation — from the group that knows no different. In doing so, they re-route the path to the top. 

Willow Garage took the long view and succeeded. They did it, in part, by building an effective youth movement. And their tools have spread well beyond academia. In 2019, a report showed that ROS was set to be installed on more than half of commercial robots being sold around the world. Achieving this level of ubiquity took foresight and patience on the part of the initial team. Ken Conley remembers the criticism at the time: 

When we first started doing ROS, one of the things that was consistently said was that, “ROS is only for research robots, no one is going to use this in commercial robots.” We always knew at the time that it was more [than that], that we’d have to be patient for that. We knew that no one who had a current robotics company was going to throw away all of their software and switch to ROS. That would be an unrealistic expectation. But, what we were really trying to do was set a foundation for future growth in the robotics industry. I think we did a pretty good job of predicting that, and being ready for it.

If you want to change the world, equip the kids.

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“Now there is a popular fallacy about this business of setting standards. It is the belief that it is inherently a dull business. One of the reasons that I am glad to see the present history appear is that I believe it will help to dissipate this misunderstanding. Properly conceived the setting of standards can be, not only a challenging task, but an exciting one.”

- Vannevar Bush, 1966
MEASURES FOR PROGRESS: A HISTORY OF THE NATIONAL BUREAU OF STANDARDS

~~~

A few years ago, I became deeply interested in the world of technical standards: how they work, how they’re made, and what they are.

The term “standard” is ubiquitous enough to have become nearly invisible. It can refer to the size of screw threads, the required pasteurization levels of supermarket milk, or a million other things. Once you notice them, you realize everything in our modern world is touched and shaped by these simple rules.

I was interested in standards for pragmatic, specific reasons related to marine technology. I expected the topic to be boring: long committee processes, grandstanding engineers, endless bikeshedding. I couldn’t have been more wrong.

Done well, standards-making could be radically creative and impactful. After spending a decade building a company and being immersed in the startup world, standards-making felt like discovering a new continent.

More than anything else, I ached for a bigger conversation about my newfound passion. I wanted to find other standardizers to share lessons and stories about this important toolset. Others were looking, too, but they were using different language, like public goods and protocols.

To that end, I’m excited to say that I’ll be joining the Summer of Protocols program as a core researcher this summer. I’m going to be writing about the Bristlemouth protocol and sharing what we’ve learned about developing a new marine technology standard from scratch. But most exciting is the opportunity to work with all the other researchers, from a variety of different backgrounds and experiences, to explore this underrated idea of standards and protocols.

Expect to hear more this summer.

~~~

In researching amateur scenes and technical standards, I’m finding oral histories to be the most valuable resources. For example, here’s Dave Smith, the creator of MIDI, telling the story of the technical standard that would shape electronic music:

~~~

Robin Sloan, writer and media inventor, spent time reflecting on the question: “What do you want from the internet, anyway?”

His answers sent him down a path of uncovering the early discussions of the IETF and culminated with him deciding to build out his own idea for a protocol, which he’s calling Spring ’83. It’s a stripped-down version of the behemoth we have today. It’s an idea, Sloan hopes, that other creators and interested people could follow and learn from. I found it notable to hear him reflect on the process: 

Before I go further, I want to say: I recommend this kind of project, this flavor of puzzle, to anyone who feels tangled up by the present state of the internet. Protocol design is a form of investigation and critique. Even if what I describe below goes absolutely nowhere, I’m very glad to have done this thinking and writing. I found it challenging and energizing.

Sloan tapped into the thrill of standardizing. There are no promises of riches or even success, but there is a sort of personal power that comes from shaping the tool that shapes the tools. It’s rarified air — a Highlander-like competition taking place above the realm of startups and FAANGs that quietly determines the direction of our techno-scientific future. 

Every year, the World Science Fiction Society gives out the Hugo Awards for the best science fiction and fantasy of the year. It’s the genre’s highest honor. 

The award’s namesake is Hugo Gernsback, the late publisher of Amazing Stories magazine. He is often referred to as one of the “fathers of science fiction” for his role in popularizing the category. But Gernsback was interested in much more than science fiction. He was also an avid tinkerer and inventor. Prior to publishing Amazing Stories, he documented the rise and evolution of amateur radio, all the while participating himself. 

While he is remembered for his role in science fiction history, Gernsback also pioneered a separate and important style of writing that has gone less appreciated. He was the quintessential cataloger—a chronicler, supplier, and cheerleader for a burgeoning amateur technology scene.

Cataloging is a specific type of writing that co-evolves with technical communities and plays an active role in shaping the destiny of a given technology or set of tools. Catalogs themselves are ephemeral and cyclical, rising and falling with the scenes they cover. But the skill and importance of cataloging springs eternal.

Gernsback showed the path.

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Judging by his report cards, Gernsback was an average student in everything outside of physics. His childhood consisted of avid reading, with H.G. Welles and Jules Verne being particular favorites, and dreams of electricity projects. In 1904, he emigrated to New York City at the age of 19 and immediately got to work building off his passions and publishing his design ideas, first in Scientific American. He also opened his own shop in the Lower East Side, Electro Importing Company, which imported and sold electrical equipment from Europe. The business created one of the first mail-to-order catalogs for radio technology in the country. The clientele was mostly fellow amateurs. 

The catalog evolved into Modern Electrics and spurred offshoot publishing projects like how-to manuals and pamphlets. With their broad reach, Gernsback also helped form the Wireless Association of America to try to organize the network of radio builders. Gernsback realized it wasn’t enough to simply supply equipment, he needed to become a purveyor of information and imagination. From the introduction of The Perversity of Things: Hugo Gernsback on Media, Tinkering, and Scientifiction, an analysis and collection of his work, edited by Grant Wythoff: 

In his monthly editorials, feature articles, and short fiction, Gernsback pioneered a kind of writing that combined hard technical description with an openness to the fantastic. Using interwoven descriptive and narrative frameworks to describe a particular device, experience, or vision of the future, Gernsback followed the smallest of technological developments through to their furthest conclusions: the increased availability of a light-sensitive alloy implied that the coming of visual telephones was near, and the number of amateurs sending in their own designs for primitive television receivers only served to confirm the imminence of this new mode of communication.

The world-building and storytelling that would come to define science fiction was intertwined with the actual tools available to the amateur. The combination provided heavy doses of agency which the reader could bring into their own experimentation. Sometimes Gernsback would intervene directly by listing off the inventions he’d most like to see. In an issue of The Electrical Experimenter (one of his many successor publications to Modern Electrics), he made a short list of all the next great inventions: wire insulation, storage battery casings, heavy current microphones, tele-music. He made sure the supply of “what’s next” was endless.

The Type

The cataloger archetype is well-worn and consistent. They're writers at heart, usually without formal training—a sort of makeshift journalism. They often run a small operation peddling kits and equipment to the scene, hence the catalog format, but the businesses rarely do better than middling financial outcomes. In addition to Gernsback, other famous examples are Stewart Brand and the Whole Earth Catalog, Tim O'Reilly and the technical books of O'Reilly Media, Dale Dougherty and Make: Magazine.

There are notable similarities between the efforts. What the catalogers provide is different than traditional press coverage or financial investment. They are dealers of agency. Through a combination of tools, information, and imagination they help draw a map around new communities. 

If you ask them to describe what they do, you’ll get a meandering answer. As Brand says: “I find things and found things.”

You’ll get an even more varied response if you ask how they do it. In an interview with Dale Dougherty and Tim O’Reilly, I asked this directly. O’Reilly said he keeps a sort of mental map of what technology should exist and then elevates people and projects when they start to fill a role—a “know it when you see it” type of skill. Dougherty says he follows enthusiasm. He recounted a story of meeting Tim Berners-Lee at an academic Hypertext conference in 1991. Berners-Lee was relegated to a poster session from the main program because the organizers didn’t think the Web was interesting enough. Dougherty was taken with the project. Everything else at the conference was moving slowly and feeling stale, but Berners-Lee was offering something—building a web page—that he could do himself. Dougherty became a champion for the nascent Web.

From an interview with Dale Dougherty and Tim O’Reilly:

Whatever the skill, it seems to be widely transferable. It’s common for a cataloger to play this role in multiple scenes during the course of their career. Brand is notorious for following his interests into several: the ‘60s counterculture, the environmental movement, and the personal computer industry, among others. O’Reilly and Dougherty can claim pivotal roles in the Web, open-source software, and the Maker Movement, among many others. Windy career paths make perfect sense through the lens of cataloging. Their impeccable timing—showing up at critical moments throughout technology history—is self-reinforcing. They get involved when things get interesting, and things get more interesting when they get involved. 

There is a chain of influence that's worth mentioning, too. When we started OpenROV—our own attempt at kickstarting an amateur marine technology scene—we were emulating Chris Anderson who, while editor of WIRED, cataloged the amateur drone scene through his website, DIYDrones.com. Dougherty spent much of his career at O'Reilly Media. A young Tim O'Reilly's initial writing goal was to get published in Brand's CoEvolution Quarterly. Even though they can’t define it, catalogers seem drawn to the work, and most can place themselves in some informal lineage.

The catalogers also have a long association with science fiction, beyond just the obvious Gernsback connection. The Whole Earth Catalog recommended Dune in the inaugural issue, and Tim O’Reilly wrote a biography of its author, Frank Herbert. The opening paragraphs in the first issue of Byte, which cataloged the early personal computer industry, quoted Robert Heinlein. The first issue of Make: Magazine had contributions from science fiction writers Bruce Sterling and Cory Doctorow. The catalogers build a bridge between the genre and the broader Scientific Project, as Annalee Newitz calls it. The added romance turns a quiet technical hobby into a future-shaping exploration. 

Finding & Explaining New Ideas

There’s a good passage from Kurt Vonnegut’s Bluebeard novel where the main character, a painter named Rabo Karabekian, recounts an instructive lesson he received from another character, a WWII vet named Paul Slazinger: 

Slazinger claims to have learned from history that most people cannot open their minds to new ideas unless a mind-opening team with a peculiar membership goes to work on them. Otherwise, life will go on exactly as before, no matter how painful, unrealistic, unjust, ludicrous, or downright dumb that life may be.

The team must consist of three sorts of specialists, he says. Otherwise the revolution, whether in politics or the arts or the sciences or whatever, is sure to fail.

The rarest of these specialists, he says, is an authentic genius — a person capable of having seemingly good ideas not in general circulation. “A genius working alone,” he says, “is invariably ignored as a lunatic.”

The second sort of specialist is a lot easier to find: a highly intelligent citizen in good standing in his or her community, who understands and admires the fresh ideas of the genius, and who testifies that the genius is far from mad. “A person like this working alone,” says Slazinger, “can only yearn loud for changes, but fail to say what their shapes should be.”

The third sort of specialist is a person who can explain everything, no matter how complicated, to the satisfaction of most people, no matter how stupid or pigheaded they may be. “He will say almost anything in order to be interesting and exciting,” says Slazinger. “Working alone, depending solely on his own shallow ideas, he would be regarded as being as full of shit as a Christmas turkey.”

Good catalogers are explainers and genius spotters. They have the power to set new ideas in motion. Within a nascent scene, acknowledgment from a cataloger can put a project or person on the map. The encouragement can also fill someone with pride and embolden them to take on bigger challenges. Tim Berners-Lee, in his memoir Weaving the Web, tells a story about Dougherty’s influence during the early days of the World Wide Web: 

"Dale Dougherty of O'Reilly Associates, who had gathered the early Web creators at the first Wizards workshop and other meetings, saw a third alternative. After one session at the conference, a bunch of us adjourned to a local pub. As we were sitting around on stools nursing our beer glasses, Dale started telling everyone that, in essence, the SGML community was passe and that HTML would end up stronger. He felt we didn't have to accept the SGML world wholesale, or ignore it. Quietly, with a smile, Dale began saying, "We can change it." He kept repeating the phrase, like a mantra. "We can change it." 

Catalogers are hero-makers and not hero-worshippers. There's a distinct tone to their writing: interested, encouraging, and daring. They stand for the upstarts and the outsiders. When they do lean on famous figures—Gernsback including an interview with Thomas Edison, for example—it was to add a flavor of aspiration, not to idolize. Whytoff wrote that Gernsback’s mission was to “enable the public to contribute to the making of things rather than allow them to be overwhelmed by the perversity of things.”

Most catalogers share the sentiment. The target audience, both then and now, seems to be an imagined at-home experimenter in want of new tools, or more leverage, for their own adventures. The narratives they shape are always in-progress, ending with an ellipsis that begs a reader to write their own name into the story: “What happens next is up to you…”

They sing the siren call of the scene.

An Evolving Format

It seems the right way to document an emerging scene is over time, often using a periodical format that draws heavily from the readership for stories, ideas, and contributions. So while there are only a few books on amateur technology scenes, there is wonderful writing scattershot across the editorial pages of these magazines, catalogs, and web forums.1 

Like everything else, cataloging has moved online. What used to be a magazine is now a forum or Discord server to engage the community. Chris Anderson wrote about the important decision to allow DIYDrones to grow in feral directions, dictated by the users: 

That distinction—a site created as a community, not a one-man news and information site like a blog—turned out to make all the difference. Like all good social networks, every participant—not just the creator—has access to the full range of authoring tools. Along with the usual commenting, they can compose their own blog posts, start discussions, upload videos and pictures, create profile pages, and send messages. Community members can be made moderators, encouraging good behavior and discouraging bad. Open to anyone who chose to participate, the site was soon full of people trading ideas and reports of their own projects and research.

Even though it was open to all, Anderson played a heavy curatorial role in managing the site. He was the most frequent contributor, pointing out new efforts and developments. And he always inspired the community to consider the larger context of their tinkering. Like Gernsback, Anderson would frequently inject the community with imaginative new ideas, whether through highlighting the absurd new projects, like the TacoCopter, or by referencing the sci-fi interpretations of drone futures, like Kill Decision by Daniel Suarez.

But even as digital platforms have made the cataloging infrastructure more accessible, the amount of genuinely new and exciting scenes seems steady state. Or worse: David Chapman’s thesis—laid out in his “Geeks, MOPs, and Sociopaths” essay — is that subcultures have mostly stopped working as an engine of cultural creativity. How could that be?

There are several possible explanations. One could be the VC-ification of amateur scenes. As venture capitalists have recognized the pattern of new companies emerging from these scenes, they’re sending their scouts in earlier than ever — funding anything with even a whiff of potential. The premature adulation messes with the scene dynamics. It forces the players to start looking for big customers, instead of playing for each other.2

Another explanation is that cataloging isn’t celebrated as a unique skill. Going unnamed has caused it to go unrecognized and underdeveloped. If that’s the case, it should be an easy fix. Just name it. We already celebrate these folks, but giving them a common title might encourage more to try.

Cataloging done well is a peculiar sort of success. It's impossible to measure through financial metrics or by the accolades that accrue to famous scientists. But the catalogers are revered, and the esteem is enduring. They become the “hero’s hero”, as the journalist Carole Cadwalladr referred to Brand. Gernsback didn’t win the award, he became it.

The catalogers know something important that most of the startup hype obscures: the purest joy in the technology world is not a big funding round or other high achievements. It’s the simple pleasure of a curious mind and the camaraderie that develops in an amateur technology scene on the verge of something new — a mixture of possibility, personal power, and shared passion.

It seems that every generation requires someone to dust off the old cataloger playbook in order to rediscover and retell this timeless story.

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Josh Greenberg, the author of the amateur tech classic From Betamax to Blockbuster, suggested I package up my research scraps into the occasional post. I liked the idea. Let me know what you think of the format.

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~~~
The “Crazy Money” Hypothesis

Occasionally a pot of money will become available to support wild, off-the-beaten-path ideas. These funds can be a result of a new initiative from an inexperienced philanthropist, a windfall to an organization, or a new funding mechanism like Kickstarter’s early days. Crazy money can spur unusual outcomes.

These are finite anomalies — brief moments where everything new is emboldened. Both bad and brilliant projects finally get a chance to sprout and grow. 

These pots are not endless, though. The bold philanthropist eventually hires experienced program officers to put a rigorous process in place. The new angel investor gets burned on a bad bet and tightens their pockets. Thousands of Kickstarter backers are left hanging on a flamed-out bust. The party always ends. Sometimes quickly and sometimes slowly. Sometimes quietly and sometimes dramatically.

The flip side is true, too. Once a funding stream proves consistent, the already-established organizations will adapt to try and absorb as much as possible. Like water to the ocean, it’s patterned and predictable. 

I don’t know if there is a way around this. For both funders and doers, it seems a constant battle to stay eccentric enough to do something interesting. Groups like DARPA and venture capitalists (the good ones, anyway) talk openly about this natural regression to the mean. Many have tried to put guard rails up to prevent it. The only workable solution for these groups seems to be adding fresh blood; bring in new deciders with smarts, enthusiasm, and not quite enough experience to know better. 

The other option is just to start over; spin up a new foundation, start another company, or create a new government agency without all the cultural baggage. This can work, too, but it’s a big administrative burden to get such a simple-seeming outcome. 

We need better ways to keep things weird.

~~~

~~~

“Most innovation comes from amateurs, who are free to be radical, and from scientists in academia, who are free to follow their curiosity. But then there’s a gap. It’s hard to develop radical ideas into something broadly practical, because commercial money and government money are obliged to be conservative, and academic money is limited to discovery. The best money for pursuing really radical ideas into experimental use comes from individual philanthropists (foundations tend to avoid risk).”

-Stewart Brand, Whole Earth Discipline

I recently went back looking for this quote, which I first read in 2009, and found the amateur bit underlined and starred. I suspect this paragraph may be the origin of this decades-long odyssey of researching and experimenting with amateur technology scenes.

~~~
Venkat Rao, who is ringleading a sort of amateur protocol scene this summer, just wrote about his research management philosophy:

Laissez-faire management, mindful presence, context-sensitivity, and porous boundaries. That’s the current best formula for how you can be both methodologically anarchic and usefully institutionalized at the same time.

We’re in a boom time for new institutional experiments in science and research, which should translate into diverse, new ideas about research management. Rao’s philosophy — and the last point about porosity, in particular — stands out as unique. This is not common practice, but I think he’s on to something.

Either way, the Summer of Protocols is worth keeping an eye on.

Suppose a shopkeeper approaches you with a problem. 

Their business is growing every year. All the top line numbers are increasing—sales are up, prices have been adjusted to match inflation, and they’ve thoughtfully expanded their product offerings. Yet somehow, the business seems to be making less in profits. 

The first question you would ask: what are the costs? 

Without a clear picture of costs, it would be impossible to reasonably suggest how to improve the business. This is a current conundrum for science. 

There is a widening discussion among scientists, administrators, and policymakers about how to improve the institution of science. The question has inspired conferences, new organizational experiments, and an emerging discipline—metascience—which is turning the scientific method back on itself. There is real, pragmatic pressure to do science better.

The value of scientific research is notoriously hard to measure. We know how much we're spending on science. For example, the U.S. federal government spends roughly $100 billion on basic research every year. We also know what’s emerging from that investment: the papers, patents, and trained scientists. But it’s still difficult to accurately measure those returns. The best attempts are relative. For example, Patrick Collison and Michael Nielsen argued in The Atlantic that the returns to scientific investment seem to be slowing.

As with the shopkeeper, closer scrutiny of scientific costs could yield opportunities for improvement.

The most visible discussion of scientific costs has been a debate about administrative overhead, but an equally important question has been hiding in plain sight: why are scientific tools so expensive?

Direct vs. Indirect

For any individual scientific grant, the budget is divided into two categories: direct and indirect costs. The direct costs include researcher salaries and equipment. The indirect costs cover overhead: the university and organizational administration, facilities, and support. 

For the past few decades, the debate has been about rising indirect costs. Philanthropic funders began to push back against university overheads that had drifted ever higher. It had become common—and still is—to see more than 50% indirect rates on top of the direct costs listed in the grant. Some private funding organizations, like the Bill & Melinda Gates Foundation, pushed back and instituted policies to limit those amounts. Operating non-profits countered with a campaign—"The Overhead Myth"—attempting to explain the value and importance of good non-profit management.

The administrative cost issue is thorny, and certainly deeper and more complex than I can convey here. But the debate has overshadowed a more promising frontier for inquiry: tools. 

Science & Manufacturing

Direct costs dictate research directions.

For personnel costs, the influence is straightforward. Grant availability can push more principal investigators to undertake a problem or enable the hiring of additional post-doctoral researchers in a lab.

The costs of tools are just as important, but much more difficult to pin down. Scientific equipment is an essential part of discovery. New technologies are enablers of new ideas and perspectives, and vice versa. Throughout history and across disciplines—from telescopes to microscopes, synchronized clocks to automated genomic sequencers—technology sets the pace for knowledge and insight. It's personal for scientists, too. Access to cutting-edge tools can make or break careers by enabling priority in experimentation and, in turn, earlier publication. 

As with personnel costs, tool costs dictate research directions, whether that’s determining the size of a telescope to build or deciding what kind of mice to use. Cost is the driving factor in deciding which equipment a lab will buy, share, or just leave on their wishlist. Relatedly, costs affect the pace of discovery. For example, the dropping costs of genetic sequencing have created an explosion of new research. When costs go down, we see a direct correlation with scientific output as well as industrial and commercial applications.

Analyzing the cost of tools is harder than just looking up prices on Amazon. The metascientists have approached the issue, but haven’t directly engaged. 

Kanjun Qui and Michael Nielsen laid out a metascience vision and perspective for how to improve the social processes of science. It took them two years of research to capture their important argument: we’ve only explored a small fraction of the possible arrangements for doing science. Even amongst their expansive scientific world-building, tools only got a footnote:

"It's striking that the builder of the first telescope is not remembered by most scientists, but Galileo is. The usual view is: Galileo made the scientific discoveries, but the toolbuilder did not. But they did enable discovery. This is an early example of a pattern that persists to this day. It's beyond the scope of this essay to delve deeper, but fascinating to think upon."

Paula Stephen, a leading science economist and author of the book How Economics Shapes Science, came to a similar cliff. Stephan dedicates an entire chapter to tools and materials, but there's a missing analysis of why they cost so much. Stephen points out that "despite the important role that equipment plays in research, little is known about the degree of competition in the market for equipment." 

That sums up the scientific attitude towards tool-building: forgotten footnotes. 

I have a pet theory about why scientific instruments remain expensive and exquisite. And why this arrangement has settled into a "don't ask too many questions" equilibrium. It's twofold. 

First, scientists never have to care. Scientists write grants and simply include the cost of equipment in the budget. The grant is met by review committees with a binary "yes" or "no" answer. They get no points for trying to reduce costs. The lack of downward cost pressure on behalf of the scientists – the end users, in this case – means manufacturers don't try to compete on price. 

The second half of my theory is more subtle: a lack of exposure. Most scientists aren't exposed to the realities and possibilities of manufacturing. It's generally not part of the curriculum in higher education and it's difficult knowledge to acquire outside of direct experience or vocational training. The flip side is also true. The folks who understand the tools of mass production and the realities of global supply chains rarely consider the plight of the scientist. And when they do consider, they don't like what they find: small markets, convoluted purchasing processes, and customers who demand almost constant modification and customization. 

It's worth clarifying that many scientists do build tools. In fact, some of the most incredible machines on (and off) earth are built by scientists: the James Webb Telescope, CERN, and the IceCube Neutrino Observatory. But rarely do scientists build tools using modern manufacturing techniques that would allow them to reap the economic benefits of scale. In some cases, the reasons are clear. It's hard to justify needing more than one neutrino observatory. I suppose humanity could aspire to dozens of Thirty Meter Telescopes, but through sharing and cooperation amongst scientific communities, it’s reasonable to hope for just one (or a few). In these cases, scientists coordinate their research interests to design, fund, and build the tools that will advance the entire field.1

Even on smaller scales, scientists are constantly rigging their experiments with makeshift equipment given tight budgets and field realities. If they have a specific question they want to ask, they will go to extreme lengths to hack together a device to capture the right experimental data. I've seen this firsthand on countless occasions in oceanographic research settings. These impressive MacGyver-like machines and setups prove what’s possible.

However, there’s another level (or several). Making one tool is impressive, but designing and manufacturing thousands is an altogether different challenge. There’s a reason that most projects don’t take the leap. For any individual scientist, their career progression depends on their publication record. The investment of time and money into manufacturing doesn’t make sense, even though taking the extra step—making a given tool available for others—helps raise the tide for everyone. 

In the microbial sampling expedition kit described above, Adi Gajigan is using the PlanktoScope tool developed by the Prakash Lab—a Raspberry Pi-based device developed to automate plankton sampling and identification. Manu Prakash and his team are among a small group of scientists who understand the leverage of affordable tools. They have developed and implemented a philosophy of “frugal science” which aims for radical accessibility of both the tools of science and the camaraderie of scientific exploration. The Foldscope, an origami paper microscope, is perhaps their most famous invention. 

I lived through a similar experience. We started OpenROV in 2011 as two friends and less than a few thousand dollars between the both of us. Our goal was a small underwater remotely operated vehicle (ROV) that we could send to depths of 100m. At the time, a capable device with the specs we wanted cost north of $20,000. We started where we could: off-the-shelf parts and access to the tools at our local makerspace. Our timing was good, as new single-board microcontrollers and microcomputers like the Beaglebone and Raspberry Pi were just becoming available. 

Fast forward through a Kickstarter project, a startup company, and a vibrant amateur community of builders; the world is much different. Multiple companies and projects started and grew off that initial momentum. The result is an expanded (and more affordable) marine robotics industry. Today you can find capable devices that meet our initial specs on Amazon from a number of companies, and many for less than $1,000. Scientists have used this new class of vehicles to discover new deep-water kelp forests, monitor invasive tunicates, and beyond. From the perspective of our humble garage beginnings, this is mission accomplished.

We were not alone in the marine robotics effort, or in the broader effort to democratize the tools of science. There are now hundreds of other projects who have connected the dots between low-cost components, the growing capabilities of the digital manufacturing suite (3D printers, laser-cutters, etc), and a desire for some unaffordable piece of scientific equipment. Ariel Waldman wrote a report for the White House Office of Science and Technology about the nascent trend of building low-cost scientific tools in 2013. Ten years later, there are battle-tested entrepreneurs (like us) who've weathered the gauntlet of manufacturing as well as a vibrant community of new builders who are prototyping the next wave of devices and mechanisms. 

There's currently no database of these efforts, but they span the sciences.2 The idea has grown tremendously in terms of participation and technical capability since Waldman's initial report. The Gathering of Open Science Hardware (GOSH) has been a nexus point for this global community, hosting both events and virtual discourse. GOSH was a hybrid outgrowth of two other movements: open hardware and open science. 

The open-source hardware strategy—releasing plans online to allow others to reproduce and remix a given electronic or mechanical design – was developed more than a decade ago by hardware hackers who wanted to recreate the culture and momentum of open-source software in the physical world. Real-world friction and manufacturing realities have held the movement back from catching up to their digital counterparts, but there are success stories like the Arduino microcontroller which has accelerated embedded systems prototypes around the world. 

Open science has been a decades-long push by scientific activists towards making the results and process of science more transparent and available for public review. The open science community has made steady progress towards open-access publications, the inclusion of source data, and, most recently, the adoption of registered reports. 

GOSH adopted both the tactics and goals of its parent movements. They encourage the sharing and easy replication of equipment designs and lobby for institutional adoption of their methods. From the GOSH perspective, truly open science demands open hardware – replication at every step of an experiment, right down to the tooling. So far, open tools have meant cheaper tools because they’ve been made with mostly off-the-shelf components and digitally fabricated designs. The popularity of the Openflexure microscope is an example of open science hardware gone right. 

Frugal Science, GOSH, and their corresponding projects are often inspired by idealism, but they’re sustained through economics. They are disruptive innovations in the true Christensen sense of the term: an initially-harder-to-use product that finds a niche community of users, steadily increasing in competitive performance with their commercial counterparts. We should cheer for continued momentum. In fact, we should do more than cheer: we should incentivize.

Bridging the Gap 

We know the economics of science can change with big money efforts. The Human Genome Project (HGP) kicked off in 1990 with the goal of sequencing the human genome. The program did much more than its stated goal of creating a map of the first human reference genome. 

The budget—$3.8 billion over the 13-year length of the program—was a massive public investment in the biological sciences. The HGP kickstarted a revolution in technology development that continues to this day. Initially undertaken using Sanger sequencing methods, new techniques, and strategies were employed to speed up the process. The Celera team used a shotgun sequencing approach. Like every human achievement, doing something first proves it can be done, but the following efforts show how it can be done better.

A variety of new approaches came to market soon after the HGP, now known collectively as Next Generation Sequencing (NGS) technologies. A race between technologies and performance has pushed the costs of sequencing down precipitously. The latest announcement from Illumina promises a $200 price to read a person's entire genetic code, with their eventual target of $100 within sight. 

The financial outcomes of the HGP investment are clear and phenomenal. It kick-started the genomics industry. As early as 2010, the program had reportedly created nearly $800 billion in economic output, and, in 2010 alone, the federal tax revenues were estimated to be roughly equivalent to the entire cost of the program. 

The effects of the HGP have been profound for science, medicine, and industry – from providing insights on disease treatments to unlocking new understanding about the natural world. The program made civilizational progress. Despite this success, there are still too few people working on the frontiers of scientific tooling. We could be making this kind of progress in many directions and at multiple scales. And we have the meta tools to accelerate the trend.

One method is Focused Research Organizations (FROs), a new type of non-profit research organization dedicated to advancing specific and time-bound challenges in a given scientific field. You can imagine them as miniature HGPs – focused teams and efforts with budgets of tens of millions of dollars instead of billions. The idea was proposed by Adam Marblestone and Sam Rodriques in 2020 and has gained significant momentum in a short period of time. Since their white paper was published, numerous FROs have spun up to address various scientific bottlenecks: new model organisms, brain mapping, and many more to come.

More FROs are a welcome development, as are the open-source prototypes of scientific equipment. Ultimately, the long-term impact of these tools is dependent on economic tailwinds to sustain their momentum. And, even there, we have levers to pull.

Making Scientific Markets

There is an emerging subset of applied economics called "market shaping" which uses new mechanisms to solve problems and fix inefficiencies. The most popular idea has been Advanced Market Commitments, a philanthropic purchase order designed to incentivize producers to deliver at price points that can reach underserved markets. The idea was proposed by the economist Michael Kremer and made famous by the Bill and Melinda Gates Foundation’s billion-dollar bet on GAVI. More recently, a version of the idea was used to speed up the development and distribution of COVID-19 vaccines. 

I've made the argument that AMCs should be used to spur more affordable scientific equipment: 

The model stands in stark contrast to the existing tools of philanthropy, which have traditionally operated by distributing grants, awarding prizes, and recently “impact” investing into purpose-aligned companies. The AMC spurs action by imitating one of the most powerful actors in the capitalist system: the customer. The bigger the purchase order, the faster companies will line up to serve. 

It should be emphasized that AMCs are not silver bullets. They were only part of the COVID-19 playbook. They work as a powerful force to ensure production is stable, distribution is equitable, and companies have a financial incentive to produce. But they are only effective as part of a bigger strategy. 

Another part of that scientific tool strategy could be Equipment Supply Shocks: 

An Equipment Supply Shock (ESS) is a targeted increase in the availability of (normally) expensive or hard-to-access tools with the anticipation that new users will discover productive new uses. 

I've seen this work with scientific tools before, with us and others. We used the technique to support hundreds of early-career scientists and conservation teams through the donation of our underwater drones. I’m still getting messages about new discoveries and scientific careers that were boosted by that donation. The Foldscope team did something similar: giving out thousands of their origami microscopes to curious minds around the world.3

Market shaping takes the systemic problems head-on. The mechanisms work specifically to bridge the gaps between research and development—between prototype and product—by smoothing out the cost curves of mass production. That doesn't mean that more traditional kinds of support wouldn't be effective. Grants to develop new tools would be most welcome. The only focused effort I know of is the Tool Foundry accelerator funded by Schmidt Futures and the Moore Foundation. More kickstarts are needed.

Awareness is a big part of the equation. The GOSH community in particular has built a strong foundation of knowledge sharing and mutual support. So far, the open science hardware trend has mostly been an amateur technology effort, but it's worth expanding.

More scientists, policymakers, and funders should care about the intersection of science and manufacturing. We should hope for better, cheaper, and more accessible scientific tools. The examples have already helped to transform disciplines—even society—in surprising and beneficial ways. 

The first question we should be asking to make science better: what are the costs?