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.

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?