Venture Capital Has Lessons for Government and Philanthropy

tackling structural and bureaucratic rot

by Aishwarya Khanduja (Analogue Group) and Stuart Buck (Good Science Project)

Most basic science in the US is funded either by government or philanthropy, which collectively donate over $100 billion every year. Traditional government funding offers three things private investors usually can’t match.

• Democratic accountability: agencies like NIH and NSF have deployed billions of dollars guided by public interest rather than profit.

• Massive scale: government has created scientific infrastructure that required collective action at scale, such as particle accelerators, the Hubble Space Telescope, and national laboratories.

• Long-term thinking: Agencies maintain decades-long research programs whose benefits emerge only over generations, such as (D)ARPA funding the R&D in the 1960s that eventually became the Internet.

Philanthropy has its pluses as well. The Gates Foundation could pour billions into malaria research because of the effect on global health, not because it would generate returns.

Government and Philanthropy Have Serious Structural Rot

Despite their historical successes, both traditional government and philanthropic funding have developed structural pathologies that actively hold back scientific progress. We think they should do more to imitate the VC model of funding that has enabled the startup scene in Silicon Valley and elsewhere.¹

Government’s Bureaucratic Nightmare

To paint a picture of what traditional government funding looks like: imagine that you are a brilliant researcher with an idea that could transform your field, if it works. You spend six months writing a grant proposal that can stretch to over 100 pages in total, in which you need to predict the next five years, specify your methods in detail, and provide preliminary data proving your idea works (even though you need the grant money to generate that data in the first place).

You submit this proposal to NIH or NSF and wait for many months while your proposal is reviewed by scientists who are often your intellectual competitors. As is well-documented, review committees tend to favor safe, incremental projects over truly new ideas that might fail.

By the time you get the funding (if at all) and execute the research plan, you might learn that your first idea wasn’t quite right and that an even better approach might work (*Marcia McNutt told one of us that this is what regularly occurred when she was a practicing scientist). But if you try to amend the grant, you will need to navigate the federal bureaucracy once again.

Imagine if entrepreneurs—from laundromat owners to startups—had to go to the Federal Reserve to get financing for their business. Our economy would be at a standstill if that was the process. But that is how we treat science. With such centralized funding opportunities in science, it’s no wonder the process is suboptimal.²

Program officers face perverse incentives too. If a major federal initiative funds a bunch of failed ideas, Congress might hold a hearing. But if a given grant succeeds brilliantly, Congress doesn’t hold a ceremony to give the program officer a medal. Agency bureaucrats get paid the same salary either way, with no upside for identifying breakthroughs and serious downside for visible failures.

Consider what happened with mRNA vaccine research. Scientists like Katalin Karikó spent decades struggling to get NIH funding for mRNA research. Grant reviewers thought it was too risky. Karikó’s university demoted her more than once, ultimately driving her to move to BioNTech, where she had the freedom to pursue the work that ultimately enabled the COVID vaccines. DARPA did fund mRNA vaccine research in the early 2010s, but the then-director Arati Prahabhakar told one of us that people at NIH said she was crazy for sponsoring such research.³

Philanthropy Is Little Better

Donor preferences can be arbitrary and fashion-driven. For example, a tech billionaire might get interested in longevity research, and therefore that field gets overfunded while equally important areas languish. When a foundation’s priorities shift because they hired a new President with different interests, valuable research programs get orphaned. Moreover, many foundations have become bureaucracies that mirror the government’s worst features: credentialism, reporting requirements, etc.

Another problem is that of coordinating multiple funders. Imagine you’re a researcher who needs $2 million to complete an important project. You get a $500K grant from Foundation A, $300K from Foundation B, and $400K from Foundation C, and the rest of the money from NIH. Now you have to spend a huge amount of time managing four different sets of reporting requirements in which you pretend to have spent each pot of (completely fungible!) money on a very specific list of tasks, and navigating four different program officers’ expectations. Each funder wants credit for your success but none wants to fully fund the work.

Indeed, the reporting requirements from traditional funders are often as nonsensical as going to Best Buy to get a new laptop, and then demanding to see a receipt stating that your money was spent on the letters A-M on the keyboard, on the RAM but not the internal processor, and also that Best Buy was only allowed to spend 10% of the overall bill on rent and utilities for the store.

That would be unworkable. When you buy something from Best Buy or anywhere else, you just want to know if the overall product or experience is worth the cost, and you couldn’t care less how the seller allocated all of its internal expenditures to rent, employee hours, materials, marketing, electricity, etc. Traditional funders should start caring about the ultimate results (and only that) from their grants, and stop requiring such nonsensical intermediate reporting from their grantees.

Why Venture Capital’s Structure Works

Betting on People Over Proposals

When a VC funds a scientist-turned-entrepreneur, they’re betting on that person’s ability to figure things out, not on the correctness of a Soviet-style five-year plan.

When Ginkgo Bioworks originally pitched VCs on building an “organism foundry” to engineer microbes, they didn’t have a detailed five-year plan for exactly which organisms they’d engineer and which applications would succeed. Their investors bet on the team’s ability to navigate toward valuable applications.

Over the years, Ginkgo has pivoted multiple times. Early on they focused on manufacturing specialty chemicals. Then they shifted toward fragrance and flavor compounds. They’ve worked on probiotics, therapeutics, COVID testing, detection of biological engineering, and most recently, a partnership with OpenAI to do autonomous lab experiments.

The typical NIH grant would have locked them into whatever they proposed initially, but venture capital allowed them the flexibility to change as needed. In no event would VC firms demand to see a year 5 report showing how a company’s officials spent thousands of hours of time on the exact same activities they had predicted in year 1.

We need the same attitude in traditional science funding. As former NIH official Mike Lauer recently said, “the very nature of science is that you don’t know what you’re going to be doing over the next year, for sure not five years.”

Speed vs. Bureaucracy

In early 2020, Moderna identified the Covid virus sequence and designed an mRNA vaccine candidate in 48 hours. They were in clinical trials within weeks, and had a working vaccine in under a year.

This happened not just because VC and private companies were involved, but because the government component (Operation Warp Speed) took place entirely outside the normal NIH mechanisms that would have taken the first year just to do peer review and start sending the initial checks.

Similarly, in 2021, Benchling (lab software for biotech) raised additional venture capital within two weeks. A government-funded research project would have waited for the next grant cycle, which might not come for another year. By the time additional funding arrived, the moment would have passed.

There have been occasional efforts in government and philanthropy to fund scientists quickly with minimal bureaucracy, such as Fast Grants and NSF’s rapid response grants for COVID research. But those occasions are far too rare. Both government and philanthropy need more of these flexible and speedy mechanisms.

Skin in the Game Can Create Better Incentives

Venture capitalists invest their own money and their firm’s capital. When a16z’s Marc Andreessen and Ben Horowitz backed Coinbase at a $100 million valuation, they were making a personal bet. If Coinbase failed, they lost their investment. When it went public with nearly a $100 billion valuation, they made massive returns that benefited their firm.

By contrast, government program officers invest taxpayer money. If their funded projects fail, they lose nothing personally except the possibility of public criticism. But if their funded projects succeed brilliantly, they get nothing beyond their salary. This is true even in the case of SBIR (small business) grants that NIH makes every year: neither NIH nor the personnel involved have any equity stake in any of the early-stage companies they fund. Perhaps unsurprisingly, NIH has been criticized for allowing people with no expertise in biotech or industry to make most of the funding decisions, and for having “little idea whether the SBIR program is efficient for the institution.”

For traditional science funding, the incentives are clear: avoid criticism, don’t take chances, and err towards funding established researchers at prestigious institutions doing incremental work.

Both philanthropy and government need to think about creating a set of incentives for program officers to personally capture some of the upside if a scientific grant results in a huge breakthrough. In some cases, that might be a small personal equity stake in a company, but in many cases, that breakthrough might not be a commercial company at all. In those cases, we should think about offering large financial awards, e.g., “This program officer funded early CRISPR research in 2005, and in honor of her foresight, she will receive a $1 million bonus.”

Moreover, being a good scientist is a distinct skillset from being a good science funder.⁴ Traditional science funding almost universally puts scientists in charge of distributing funding, whether through peer review panels or program officer stints at the NSF. We do almost nothing to identify, promote, or celebrate the skills and careers of science funders.

In VC-world, by contrast, investors and entrepreneurs occupy quite different roles. Entrepreneurs can become successful investors, of course, but the two categories still have separate incentives, identities, and career trajectories. Famously, journalists (such as Mike Moritz) and people with other backgrounds have thrived in the investor role.

We wouldn’t expect that top athletes and top coaches are always going to be the same people. Occasionally those two groups overlap, but some of the best athletes made mediocre coaches, while some of the best coaches were never superstar athletes themselves. Whether in athletics or VC, we know that performing is different from funding or coaching. Traditional science funders need to recognize this basic principle.

Risk Tolerance: The Asymmetry of Success

Benchmark led an early $11m round in Uber, and by the time of the IPO, its stake became worth over $7 billion. That one investment returned their entire fund multiple times over. The fact that they also invested in companies that failed completely doesn’t matter: one massive success compensates for many failures.

This is how venture capital is supposed to work. VCs expect 70% of investments to return nothing, 20% to return modest multiples, and 10% to return enough to make the fund profitable. Peter Thiel’s $500,000 investment in Facebook for 10.2% of the company became worth over $1 billion when he sold his shares (and would be worth far more today). The asymmetry is the point.

The federal government has trouble operating this way. Imagine a congressional hearing where it comes out that 80% of NIH-funded projects produced nothing. That would be considered a political scandal, and NIH officials would be brought before congressional committees to explain why they were responsible for so much government waste.

The public saw this firsthand when the company Solyndra failed. The Department of Energy had made a $535 million loan guarantee to this solar panel company. When Solyndra went bankrupt, it became a massive political scandal. Even though the DOE’s overall clean energy loan portfolio actually returned a profit to taxpayers, one visible failure dominated the narrative.

Philanthropic funders face similar pressure. Foundations want to tell compelling success stories. Boards want to see impact. A portfolio where most grants “fail” feels like poor stewardship, even if a few successes are transformative.

We need a change in political culture here. Members of Congress should stop highlighting individual scientific grants that they think are ridiculous or that failed. To the contrary, they should start demanding that half of all NIH projects “fail”! After all, if successful outcomes can be predicted in advance for all NIH projects, then why bother to do the research in the first place? We already know the answer! A 100% success rate means that the government is only funding marginal projects, or that the results are being exaggerated, or both.

Networks and Support Beyond Money

When Stripe started out, the YC ecosystem provided not just startup capital, but constant advice, connections to successful founders who had solved similar problems, access to potential customers and partners, help with recruiting, and a network of many alumni. Collison said this:

“Sometimes the issues are actually huge, that you are trying to close this round, this big deal, some recalcitrant investor, this stressful thing happening or it’s I don’t know how to talk to this customer, or whatever it is. I really kind of spans the gamut. I think Paul and the other partners are really good at getting that. They have done companies and they have seen this happen literally thousands of times in other companies and so whether the issue is closing this round, doing this big deal, this acquisition or whatever. Or it’s just well, how should the signup button work? They have pretty good advice. That has made a big difference to Stripe.”

By contrast, government and philanthropic grants usually provide little support other than money. We have no equivalent of the YC ecosystem in government-funded or philanthropy-funded science. We could do better.

Learning from Failure Quickly

The venture ecosystem has developed more systematic mechanisms for capturing and sharing knowledge. Industry conferences bring investors together to share their lessons learned, LP meetings create accountability for articulating what worked and why, and successful VCs increasingly write publicly about their successes and failures.

Even if much of this public knowledge is stagecraft in some sense, it is still more transparent than the government and philanthropic programs that operate on decades-long feedback cycles. Program evaluations happen years after decisions were made, by which point the people who made those decisions have moved to different roles and the context has changed so much that lessons are hard to extract.

We need more rapid feedback cycles and evaluation with regard to traditional science funding.

Venture Capital Has Real Problems

We are not claiming venture capital is perfect. It has serious structural issues that limit what it can support.

Venture capital inherently needs financial returns. This excludes important work with pure knowledge value or public good characteristics. Moreover, given the risk/reward tradeoff, the potential financial returns need to be quite high. In short, the need for unicorn-scale outcomes means VCs must reject many innovative ideas that might be valuable but won’t plausibly reach a billion-dollar scale.

Moreover, capital has grown more and more concentrated. The top 30 firms raised $49 billion recently versus $9.1 billion for 188 emerging firms. This concentration could create a bias toward established networks rather than towards outsiders like Katalin Kariko without elite credentials or warm introductions to brand-name VCs. New fund managers face pressure to raise $30 million or more, creating barriers to entry for talented but unproven investors.

Conclusion

Government and philanthropic funding remains essential for large-scale infrastructure, basic research without commercial paths, and pure public goods. Moreover, there are many reasons to think that we are creating a new category of R&D institutions that will replace much of traditional VC at some point.

But there are many lessons to learn from VC’s style and approach.

First, even imperfect incentives beat no incentives, let alone perverse incentives. The VC requirement for financial returns is often better than government and philanthropic structures that create zero personal incentive for identifying breakthroughs, and instead create strong cultural incentives to avoid visible failures. Traditional science funding should do more to create the right incentives for finding breakthroughs.

Second, and relatedly, a commercialization requirement imposes some productive discipline, in that venture-backed researchers must consider whether their work creates value for actual users. Traditional science funding should, at least in some fields, do more to sponsor scientific research that is connected to real-world firms (which was arguably the basis for Bell Labs’ success).

Third, the competitive nature of VC creates evolutionary pressure, in that firms that don’t adapt will eventually disappear as they are outcompeted by newer firms. By contrast, government funding evolves glacially, bound by appropriations processes and political constraints, while philanthropic funding evolves mainly through leadership changes that bring arbitrary changes in priorities. It’s admittedly difficult to re-create a competitive market in government or philanthropy, but we need more innovative thinking as to how to let old institutions die off and be replaced by newer and more successful ones.

Fourth, the heterogeneity of venture capital creates multiple pathways for researchers to find support. Different firms pursue different strategies, focus on different stages and sectors, etc. If one firm’s thesis doesn’t match your opportunity, another firm might be open to it.

Government programs have much less diversity as to ideas, structure, or approach. If you’re a biomedical researcher, for example, NIH is by far the biggest funder in the world. There are relatively few competitive options for finding any funding. Similarly, philanthropic funding may look diverse at a surface level, but is often fairly homogenous, as shown in the essay “The Case for Crazy Philanthropy.” We need more diversity at the institutional level. For example, imagine breaking up NIH into 10 separate organizations that compete with each other for who can find the most successful way to find cures for cancer and Alzheimer’s.

Fifth, NIH in particular has been criticized by the National Academies for running its small business program (SBIR) as if it’s a traditional research program, while not involving anyone who has biotech or industry experience. Moreover, NIH’s timeline and Phase I award process—which can result in an award of an average $327,388 after a process that “averages about 9 months” (p. 77)—is too cheap and slow to be of much use. Here is a place where NIH should literally hire people with actual VC and/or biotech experience, either as program directors or as entrepreneurs-in-residence, and let them completely overall the SBIR program.

In short, government and philanthropy should entertain bold reforms to bring VC ideas into their own work. Even when it comes to basic science, both government and philanthropy could come up hybrid approaches that mimic VC’s core advantages (backing people over proposals, speed, accountability through results, risk tolerance, skin in the game, and mechanisms for capturing non-financial value alongside return), while continuing to offer longer time horizons and a wider range of grant sizes.

Acknowledgments

Special thanks to David Lang, Reggie James, Christina Agapakis, Kristin Ellis, Sarah Drinkwater, and Yatu Espinosa for their thought partnership on this piece.

1. In the rest of this piece, we are critiquing traditional government funding at NIH, NSF, and similar agencies, NOT DARPA and its imitators in ARPA-E, etc.

2. Thanks to David Lang for this analogy.

3. Ironically, the same NIH that rejected Kariko’s grant proposals for decades threw billions at mRNA vaccines once the pandemic hit and the approach was already proven.

4. Thanks to David Lang for this extended point.