Can you hear that? It’s the sound of attempted climate intervention bans and harsh regulations against research failing across the country. Twenty-nine states attempted to curtail research or deployment of geoengineering, solar radiation modification, and/or weather modification by introducing 70 ban bills this past legislative session. But it didn’t work. Only one passed into law – and only one succeeding is a big win! 

These bills mostly attempted to prohibit geoengineering, also known as “climate stabilization” or “climate interventions,” which refer to a broad set of emerging strategies designed to deliberately modify Earth’s systems in an effort to stave off the worst impacts of climate change. Examples of climate interventions include cloud seeding (in use since the 1940s) and solar radiation modification (SRM), which is still largely theoretical. 

Climate intervention (CI) ban bills put the cart before the horse. They are attempting to ban techniques that have not been thoroughly studied or even deployed, without regard to the potential benefits that these techniques may bring globally – or the unintended consequences of banning relevant research.

Science shows that climate impacts are getting worse and that there is a real possibility of passing critical climate “tipping points,” such as sea level rise, Atlantic meridional overturning circulation (AMOC) collapse, and irreversible melting of glaciers, before the mitigation strategies we have in place have sufficient impact. It may therefore be critical to pair mitigation and the continued clean energy transition with climate interventions to avoid or limit the consequences of overshoot and reduce tipping point risk. This could save countless lives from extreme weather and reduce irreparable harm to our climate systems. But premature ban bills would take this option off the table and keep us from accessing a valuable hazard response tool. 

Misinformation in the Driver’s Seat 

Three states have succeeded in banning CI research and deployment: Tennessee, Louisiana, and Florida. Florida’s bill, for example, prohibits the injection, release, or dispersion of chemicals or substances into the atmosphere within the state “for the express purpose of affecting the temperature, weather, climate, or intensity of sunlight.” The state touts the bill as successful in stopping CI activity, even though CI activity wasn’t happening in the first place. Florida could achieve the same track record if it passed a bill banning rocks from floating.

Why ban something that’s not happening? At the most basic level, these bills are driven by misinformation. Climate interventions, specifically stratospheric aerosol injection (SAI), have been wrongly conflated with chemtrail conspiracy theories – the false claim that airplane emissions known as contrails (not chemtrails) are comprised of toxic chemicals that hurt human health…and serve as a form of government-orchestrated weather and mind control… (which sounds like a great plot for a science fiction movie, but is far from reality of what’s happening here on Earth). This argument has resonated with the Make America Healthy Again (MAHA) movement, adherents of which are also strong supporters of many of the state ban bills. (I don’t need to point out the irony here, but I will, that geoengineering research is exploring ways that we can reduce the health impacts of heat, wildfires, and other climate driven threats.) This vocal faction also influenced the Clear Skies Act, federal legislation introduced by Rep. Majorie Taylor Green last year to criminalize CI and weather modification research. 

Even though the Clear Skies Act didn’t become law, it is a worrying example of how public policy from the state level all the way up to the federal level is being influenced and informed by conspiracy theories instead of fact. This can become a self-perpetuating cycle, in which policy predicated on misinformation ends up giving increased visibility and credibility to that misinformation, which in turn helps it spread further. 

Far Flung Impacts

TLDR: research bans have real consequences for public health and American innovation.

The public health sector suffers from research bans because we need experiments and other research in order to accurately characterize and guard against adverse health effects of proposed CI techniques. This includes potential impacts to humans as well as the systems humans depend on, like agriculture, where some research suggests that disrupting weather patterns can affect crop yields. On the flip side, there is other evidence that the stabilization of climate temperatures will create more ideal growing conditions for certain crops and with the lowering of temperatures, comes the decrease in heat related illness and deaths. The reality is, without additional field research, we won’t know what to expect, if the adverse consequences of CI outweighs the benefits, and how to minimize ill effects to the greatest extent possible.

Research bans also impact American innovation. I know you may be thinking, why should I care about that? It’s because stifling science in any domain creates a chilling effect, can introduce knowledge gaps, and can hinder the development of new and potentially beneficial technologies. Imagine if we had banned research into vaccines because of the contingent of anti-vaxxers that exist. We wouldn’t have saved 154 million lives worldwide. The U.S. also needs to keep pace with the scientific advances and innovations of other nations so we can verify new approaches, technologies, and research outcomes that advance our own national goals and priorities. AKA – if another country is able to harness the benefits of climate interventions, we will want to know if the same benefits are replicable here. 

Walking hand in hand with the necessity of research are the frameworks that inform its design. Research frameworks need to be grounded in transparency, accountability, and legitimacy. How else do you expect the public to support a large-scale climate intervention? It only works if the researchers can tell the public exactly what they are going to do, detail how they plan to do it, and give impacted communities a seat at the table to give critical feedback (and even veto power) along the way. This will be evermore important as laboratory research evolves into outdoor experiments. 

What Experiments Have Actually Happened

Currently, most of what we know about CI comes from computer models, not field research or outdoor experiments. SRM 360, a nonprofit organization that supports informed and evidence-based discussion of SRM, tracks completed, cancelled, and upcoming outdoor experiments related to CI from 2008 to 2028. The grand total? A whopping…14, only three of which have been completed. So it’s not that there’s a lot of real-world CI experiments going on, counter to what the ban bills would have you believe. It’s that a couple of experimenters haven’t done a great job of communicating their plans.

Consider an experiment that was planned in the Bay Area city of Alameda a couple of years ago. Researchers at the University of Washington were planning to test marine cloud brightening (MCB), a technique that involves spraying sea water particles into the air to brighten clouds to aid in blocking the sun’s rays (Australia has had success experimenting with this approach to reduce coral bleaching). The city council unanimously voted no against the experiment – not because they outright opposed the concept, but because the researchers took the “ask for forgiveness instead of permission” approach by not disclosing the experiment to the public or to local leaders well in advance. Another similar incident arose from Make Sunsets, a start-up that has experimented with small-scale applications of SAI, also without gaining public support. The public had questions, and they deserve answers, but banning research keeps us completely out of the loop.

These examples demonstrate the need for regulation surrounding transparent oversight (instead of outright bans). Having a blanket ban risks losing valuable information and advances into modeling, laboratory research, and small-scale outdoor experiments needed to resolve the questions and uncertainties of climate interventions. Outdoor experiments especially are an important tool to understand the most pressing research questions related to CI and how it could mitigate the worst impacts of climate change, but the experiments need to have sufficient guardrails built in. 

With outdoor experiments, scientists can test what computer models predict will happen, rigorously assessing in real-world conditions both the potential benefits of CI and what it would take to deploy at scale, as well as potential adverse consequences and strategies for avoiding them. These experiments will need to be done carefully, with transparent oversight and consultation with affected communities. Experiments that violate good research and engagement practices should be shut down. We do that in plenty of scientific disciplines. But responsible experiments should be allowed to proceed and we should reap the benefits of that responsible research. The right policies can make that happen. 

Venturing into the Unknown 

In an ideal world, we would not have state or federal climate intervention research bans. We would have affirmative policies and protections for researchers to be able to research climate interventions and solar geoengineering technologies. We would have researchers and institutions that would not shy away from revealing the good, the bad, and the ugly of these technologies and doing so responsibly. That world does not yet exist, but FAS believes it is worth building. Creating that world and believing that robust research is needed to answer the tough questions, alongside the creation of forward-thinking governance structures grounded in transparency and legitimacy will take time. But stopping bad policy and premature bans is the floor, not the ceiling. The path forward is building a research environment where good science can thrive, governed by frameworks worthy of the problems we are trying to solve.

This piece is the first of a series of conversations hosted by FAS CEO Daniel Correa aimed at highlighting issues ripe for deeper discussion, and bringing together thinkers with valuable perspectives on how to drive positive change in public policy. The following transcript has been lightly edited for concision and clarity.

Daniel Correa, FAS CEO: Thank you both so much for joining me for this conversation. Seemay, I’m going to start with you. So you’ve practiced science. You’ve taught and now you are running several organizations that have as their mission a desire to encourage more experimentation, and to provide more funding and support for ambitious, scientific and useful breakthroughs. At the same time, you’ve done a decent amount of writing about how research institutions that perform and support science need to be more experimental.

So I would actually just like to start there. Why is the point about experimental, at the institutional level, so important?

Seemay Chou, co-founder of Arcadia Science and Astera Institute: For a really long time in the U.S., we’ve had just one main type of [research] institution. It’s very monolithic. And I think experimentation is important for a couple of reasons. One is that we don’t actually know: what are the right solutions? And the opposite of what people do is not necessarily correct.  Also, I would argue that there’s not one solution. We need to learn a lot more about the heterogeneity of solutions that could exist that are fit for purpose. It’s very difficult to even explore that space, without just trying things. And what does it mean to do an experiment?

Is there some level of uncertainty where it could fail? Number one, that framing allows you to try many more things that in each case don’t have to hedge as much. Because if you do a bunch of things that hedge, you actually don’t shoot yourself into any new space to actually test some new hypothesis.

There’s so much we don’t know still, because of how few things we’ve tried. 

You have to go far out enough [from institutions] where people actually behave differently, and do something differently, and that creates a level of risk for everybody involved. And then you have to find the kinds of funding sources where they’re okay if it doesn’t go well.

So when are the moments in history where [experimentation] might be the most important? I would argue right now. This is the most important time to do it because the technological and sociological contexts in which we established these institutions has completely blown up. 

Correa: This very much resonates with me. I would love an example of what it looks like to do meaningful experimentation within scientific institutions.

Chou: Why don’t I just draw from my own example? But I’ll caveat it by saying the reason I will draw from my own examples is because I know the most about them. It is not because I think that they’ve fully succeeded, because I think that we’ve gone through a bunch of iterations where most of the experiments that got initiated in the last decade, we don’t actually have enough information to know if they’re full success.

Correa: Caveat accepted.

Chou: At Arcadia, one hypothesis we’re trying to test is that we think that there’s a whole lot of basic science that could be happening in the upper right hand quadrant of Pasteur’s quadrant, which is like a combination between basic and applied.

That is actually why we have set Arcadia up as a for-profit. If we have enough capital and different investment metrics, it’s basically self-funded to want to see a return on investment way later – like we can give it at least a decade. That’s very different from how capital usually flows today in the for-profit sphere.

Then the question is, can we see a win-win here that’s actually both good for science? Because you see more money coming in from the for-profit space, you see better connection with for-profit outcomes, like product outcomes that have true utility in the world. 

It’s very different from what you see in current institutions. It’s not quite “company” and not quite “academia”. There’s no “Oh, let’s start out as a nonprofit and then if it goes well, we’ll become a for profit,” right?

We have to go for it. This is the thesis. We’re going to have to make sure we find the right capital that’s aligned for it, and then we can really try it. And the jury’s still out on whether we’re going to succeed. But what I can tell you is that, I think we would have behaved very differently if we hadn’t done it exactly in this way.  We would have recruited a very different set of people, the outcomes would have been different in terms of the activities that we’re doing and the behaviors we’re seeing. 

Pasteur’s quadrant represents scientific research that aims to improve fundamental uderstanding, while having immediate use for society

If you talk to actual academic scientists, their ultimate deliverable right now is a journal article. That deliverable drives a whole lot of behaviors and activities that happen, because that is the incentive.  Besides money, there’s journal articles and prestige. 

And so that was a test at Arcadia: no more journal articles.  It’s not even an option. How would that fundamentally shake up how people are approaching their science? Incentives alone may not change people – but the most important thing this did was filter for the right people who are willing to approach things differently.

If you look at my track record, I’m not like an open science warrior.  Before this, I was definitely publishing in journals. I knew from my own experience and the scientists around me that this was extremely influential to how we did things, sometimes subconsciously.

So [at Arcadia] we took that off the table, and actually it was remarkable. It was surprising to me the degree to which it changed the game. Even more so than funding – this was number one.

For one, the thing that was the simplest to remove was the ranked authorship hierarchy  that creates an incredibly zero-sum prestige game that really changes the way you think about your cost-benefit analysis of where you’re going to put your time. You want to make sure you own things, you want to make sure you only work on things where you have the potential to be a higher up author. 

There’s no ranked order; it is just listed, alphabetically. Overnight, every single scientist at Arcadia told me that was the biggest game changer for them, because then when they thought about working together as a team – it was truly as a team. 

And I’ve seen this over and over even with other things we funded outside of Arcadia. When I take [journal publishing] off the table, people then actually have to go back to the drawing board and think, ‘All right, like, what should we do then?’ Because if that’s not part of the calculus, they literally come back and propose something entirely different. That’s maybe the strongest piece of evidence I have. 

And I do want to clarify: I don’t think these are the types of experiments government should take the first step on because government has a huge responsibility to think about lasting infrastructure. This is where I feel like philanthropy or private public partnerships are really helpful to be like, ‘What are the things that we need to try out for the first time?’

There’s a bunch of de-risking that should happen to develop conviction and tactical strategies and infrastructure and all that before you ask the entire ecosystem to do it. 

Correa: You put a lot on the table.  So let me just synthesize a little bit. So, institutional innovations and institutional experimentation – that is actually downstream of a broader exercise that you described, which is finding intervention points in the entire ecosystem of what it would take to change the system. I think it’s relevant to how federal agencies should think about their missions and their work.

Because the condition you put on publication in journals is born out of an experiment you wanted to run on an insight about what was wrong with the system broadly. Then you also built an institution that was itself designed to be experimental as an intervention. And these things go together, but they come from a common systems level, thinking about what’s needed to drive change.

Chou: Yes, that’s absolutely right. What is worth building incentives around? You really need to identify those ‘first principles’. And then the closer you can stick to those ‘first principles, the better off you’ll be.

Correa: Loren, I want you to react. Are our federal institutions capable of this type of experimentation?

Loren DeJonge Schulman, FAS Director of Government Capacity: I think the answer is: maybe. 

The simplest question is, ‘What do you want federal institutions to be there for?’ They could do this, but is that actually the role that we want them to play? 

And that’s very related to something that I often think as people are talking about federal innovation or reform, no matter what it is: ‘Who is your customer? What are you actually trying to do? And who are you trying to do it for?’

Because odds are, as you’re thinking about incentives and trying to manage them, you’re not actually thinking about who else is going to come in and mess with the dynamics in your ecosystem no matter what kind of incentives you try to build.

For federal science institutions, you’ve got a lot of customers, and it’s often hard to consider what those incentives are to serve each. It’s something where we need to make more active choices and fewer tacit  choices. You’ve got Congress, you’ve got the scientific community, you’ve got the public – hopefully, the public matters! And then you have industry that you’re trying to actually keep alive, and you have universities.

But we are not usually explicit about the master that we’re trying to serve most; or we try to serve them all, and serve all of them badly. And as a consequence, you end up designing incentives around things that are either more convenient or match the master of the time. 

The other challenge I see in federal institutions is so often we turn to: “We’ll bring in a new leader that will make everything better.”  Which is not a wrong choice short term, but all the experimentation that you are trying to drive with that new leadership change will probably end when they leave, unless you’ve actually rearchitected the incentives and the understanding of what you’re trying to innovate around – change management. 

I think that your point on budget is an interesting one, because most of the people outside of the federal space tend to think of there being a lot more funding in the federal government to invest in things than there actually is. Or, if they’re right about the amount, they’re wrong about the flexibility.

So much of [federal funding] is already sort of pre-baked. So the amount of resources and flexibility you have to do new tiny things goes into a smaller and smaller and smaller box. And with that comes all of the administrative burden requirements that we lump on top, and in government that’s often, “Well, since you’re wanting to innovate in this particular way, can you also do it also in this region of the country, with these particular innovators in mind ,and with also these sorts of outcomes?”

 [Public servants] tend to get to the end of that process and feel like, “I’m not really innovating, I’m just sitting here treading water in place,” even if it’s all very, extremely well-intentioned. 

Loren DeJonge Schulman speaking at the Data Policy Institute launch party

That’s where I think talent ends up being like the final constraint on innovation. The ways that we bring people in and out of the federal space, and particularly in the science space, is all very well-intentioned in terms of particular rotator programs, and particular civil service programs. But none of which allows you to accumulate the kind of knowledge you need in order to drive change consistently and sustainably, and also makes it so these people are incentivized to do the least innovative thing possible. 

All that said I believe the federal government can also be a really good vehicle for carving a space that no one else could before – and so I’ll guess I put it back to you two, should I be thinking about this in a drastically different way – that federal government should be carving a space no one else can or that the federal government should be the slightly conservative infrastructure behind the scenes?

Correa: I have thoughts on this, but I’d love to hear from [Seemay Chou].

Chou: The thing you said about talent really resonates with me. 

But the thing I have found most useful in, like, any of my interactions with government so far with our experiments is thinking of the government in one way as my customer. So I’m thinking, “What do I need to test to de-risk this [institutional experiment]?”

In some cases, the government has, actually, enormous power to sort of anoint something as potentially acceptable. Like preprints is a good example of that, right? Like when the government started basically paying attention and changing certain language and policy to basically say, ‘we accept this,’ that can move the Overton window. That is actually really helpful. 

Your comment about talent is very related to that. What I have found in every arena that I’ve worked in is that there is no training people how to be risk-tolerant.

If you want to do the crazy experiments, you need to find crazy people, because otherwise it’s going to continue coming towards the middle. And the people who are least crazy are the ladder climbers and so institutionalist. So now, when I hire for stuff like this, the more radical of a thing that we’re trying, the more I push on this with people – I’m trying to get a sense of them innately.

Do they have a stomach for this? Because otherwise, they’re not the right person to lead this. And this is part of the reason why we have such a sharp line on the open science stuff about being outside journals. It’s a sharp line that only a maniac would cross, and that’s the right kind of maniac for me.

Correa: I’m imagining three categories of experimentation for government and for research institutions.  I think one is where we started, which is government itself is not actually doing anything experimental at all. Things are being de-risked elsewhere. So I think a good example of this is the way in which the philanthropic community directs new funding models or new vectors of research.

Focused research organizations (FROs) are a good example – de-risked by philanthropic dollars, as a model for team-based time-bound science that now government is adding to its portfolio, at least at a modest size. An earlier example of that was, the work that HHMI (Howard Hughes Medical Institute) did to fund people, not projects, [to the point] where we now see NIH adding it to the portfolio.

We can quibble about whether those are the right level of adoption amid the portfolio in the government. But you can see that this move where someone else experiments, gets some success, and government can go take it and say, ‘Hey, this thing benefits from scale that only government can deliver.’ That is a move. 

Category two is explicit experimental function. So we’re talking now about things like a metascience unit at the National Science Foundation. There are antecedents to this in government through the Evidence Act, that gave agencies a mandate to ask questions and to evaluate whether the answers and have them inform the way that they work, and other movements before that.

There’s a piece of that that’s deeply relevant to the state of the field. And what we understand about how to fund breakthrough science and how to organize it and what works. We actually don’t know enough.

And then the third piece is where you want to empower talented people to be the living embodiment of a vibrant, experimental approach to shaping a field. Our institutions on the research side are not organized this way. 

It’s a very different structure, talent model, and level of empowerment that would get us to that.  You don’t even need to call it experimentation. It just looks like empowering field leaders and tackling bottlenecks as a substantial part of what government research does.

DeJonge Schulman: The thing that struck me most immediately as you were talking is the explicit experimental functions that government can create space for: the metascience unit, the Evidence Act, the ARPAs, and things like that. Government needs to get better at being able to provide the behind the scenes infrastructure for that kind of work, and to acknowledge that sometimes it’s going to start a new team, a new organization, a new ARPA, whatever it is, and that’s going to last for like three years.

Not necessarily because it was a bad idea, but because momentum will fade – the right leader is not there. You may have the right moment in time for a thing, and it doesn’t need to be forever. But what you need to get good at is starting it, and making sure that whatever incentive you’re trying, you’re getting that perfect, whether that be the get-the-money-out-the-door function, which is hard in government, or the hiring function that gets you the right kind of people in quickly. 

If I said, “I’m going to go start a totally new experimental research institution in government,” people might say, “Okay, cool. That’s going to be like ten years from now,” because it just takes forever to do.

Let’s get better at doing the shorter-term stuff with the acknowledgment that it may be shorter term. 

Chou: There has to be an on and off rate. Otherwise there’s not really innovation. I have often found that the startup space does way better than the nonprofit space: if you talk to founders, they’re not that nervous if their first company fails, because that just happens.

Then they found a new company or find a new job. There’s more of a market around that. So none of it’s that scary. The metascience stuff would benefit from that, and it makes sense why the government would benefit from it too.

Things fail and things also expire as the world changes. 

Correa: It’s not like this is a novel thing that only science is grappling with. We sit here and talk about Vannevar Bush – that was 75 years ago, but think about how long the Department of Interior has been around. Everyone’s grappling with aging institutions and mechanisms and trying to fit them to a changing mission.

Loren, are there any examples outside of our research institutions that we should be grabbing on to, for how our federal reasons from some situations can do this better?

DeJonge Schulman: Some of the newer institutions that have kicked off in government have done reasonably well: U.S. Digital Service, Consumer Financial Protection Bureau and a few others. They have tried to find the systems and authorities that made the most sense for them to operate, as opposed to just accepting whatever authority was handed over to them.

So whether that be around the amount that you could pay people, how quickly you can hire them, the way you do procurement, your enforcement power – one key thing is being able to mine the authorities that are available to you. You hire a bunch of genius lawyers who are ‘yes’ people, ‘yes-and’ people, to find what is available for you to actually go out and do. You either build those into the system that you’re trying to create, or be as flexible as you can in order to adopt them in some way.

Something else these sorts of organizations have done really well is be really purposeful about learning and adapting. In the federal space, that’s a really nice-to-have phrase. But what I mean by that is that there’s a cyclical moment in those organizations where they’re saying, “Hey, we set up this process that really sucked for everybody. Let’s redo it, not just on an every-year basis, but iterate every couple of weeks, every week or so.” Check on things that you have power over in those moments, as opposed to putting them off. 

You can’t just rely on your superstar celebrity leader being the person who takes you over the innovation curve. You’ve probably got to have something like them at a key stage, but you also have to build up the cadre of, like, the most boring administrative functions you can think of who make their vision happen. They’ve got to be your champions, too.

When Seemay was talking about, ‘you need the crazy people,’ – I think you also need the crazy people who are building the safe space for that crazy person. I don’t mean ‘safe space’ like I’m going to limit you, but let me give you the infrastructure around you to make sure you succeed. So that they are always there to kind of have an internal absorption rate of the crazy [innovators] that come in, and so that they are not maybe safe, but like they are not likely to totally go off the rails.

The ‘point five’ to that last point is having really, really, really strong recruitment pipelines. Federal government doesn’t invest in that. They just wait for people to apply. Having and investing in people whose job it is to recruit and find the weirdest and most amazing possible talent for you is something that would get us so much gain in the federal space.

Correa: And so you could imagine something coming into focus that is a more experimental, agile form of federal research institution. Can you all help me understand what the core components of an agenda like that might be in your mind? Where do we start?

Chou: There are existing examples to draw on outside of government. I think a lot about pharma because I’m in biotech. There’s a whole bunch of innovation labs within companies like Johnson and Johnson, and Pfizer. They hire the right people, with guardrails.

Biochemist and microbiologist Seemay Chou showing off ticks in her UCSF lab

But they’re really trying to figure out how to increase the surface area and touch points between them and the startup ecosystem. It can be anything from them going out and literally funding startups, to just advising them. 

[Eli] Lilly is doing this with the new Lilly Tunelab. They provide basically the [AI-driven drug development] models and the data, or they provide the models and the infrastructure. And then outside folks come in from all over, whether it’s startups, academia, and sometimes they can have access to the models, but they at least introduce data. It’s a win-win. It’s also a great way to ‘date’ and see who’s out there, doing what. 

Is there something like that that the government can help with, through data infrastructure or something like that, that actually creates like an awesome scaffold to have people trying lots of different things that [government] may not even need to directly fund, but that draws people in that are innovating in different arenas and for different problems to kind of learn from that?

DeJonge Schulman: To build on that, something that we talked a lot about in the tech talent space is that for a while – actually probably still – government would hire incredible technology talent into the federal government, and they would be brought in and it would take three months to get them an actual working computer, or the printer would never work.

Then it took ten minutes to load up the software in the morning to get them to log in. And the analogy we use for that was, ‘you’re bringing in World Cup players and asking them to play on a concrete field at an elementary school.’  Don’t even think the mission is going to be possible until you’re willing to invest in whatever that playing field is.

I think some of the other obvious [elements for success]: independence of some kind, whatever that means when you’re operating in a political context because it’s always going to be political in some way. And figuring out ways to have failure tolerance, and being really clear upfront all the time in every possible conversation about what that means, not only with Congress and the appropriators, but also with leaders, and the people that are doing your hiring for you.  

We talked about talent, we’ve talked about hiring authority a whole lot. But we should also be really clear on not just the kinds of folks you want to bring in and how long you want to have them there, and how you want to grow them – but what a healthy organization looks like to you. How do you want people to feel at the end of the day? Do they have what they need to succeed? We’re not thinking about that a lot in the federal space. It’s just more, ‘Here’s your cube. Show up.’

We would have a far more productive, and often experimental, federal government if we were more focused on the health of the organization – not just how a few people feel about it, but how you know you’re actually doing the right work and are actually building that environment around it.

Correa: Going back to where we started the conversation, there’s a north star for what the National Science Foundation should be doing, and what its role in the research system is or can be. And this is like the bleeding edge of pushing the frontier, and there are a vast number of ways to effectuate that, but there are some common sense things that can get us closer to what it might look like to do that really well. 

A couple of examples that I can think of are: in the global development context, for a long time, there’s there’s a nonprofit organization called UnlockAid. Back in the days of USAID, this nonprofit was both a source of programmatic ideas and accountability for actual evidence and ROI of what USAID was doing. 

There’s no ‘Unlock NSF’, for example, where it is like a friendly partner on the outside that has capacity and expertise in how to think about how to make what you’re doing even better, given that some of those functions are hard to endure in government.

That strikes me as  a little bit similar to the models that Seemay was just describing. By the same token, an aligned, philanthropic foundation like what DOE (Department of Energy) has and a bunch of other research institutions have. NSF doesn’t have one where you can imagine resources being deployed in a really aligned way.

In addition to some of the core attributes that we would want to include, just to continue to pull this thread for the National Science Foundation: it is up for a reauthorization just next year. So there’s an actual opportunity to kind of interrogate the DNA of the institution at a moment.

As Seemay pointed out, we are in one of these founding moments for federal institutions, by choice or not, where you can actually pull a number of different levers that could shape how an organization like that functions [and make sure that it’s] rooted in a more experimental approach. 

Chou: One thing that really strikes me is just how difficult it is to move that process in conversation, because it’s getting really conflated with political polarization in this moment. The way I think about an institution like NSF, I actually feel like the shift in the technological landscape is actually bigger, even though the political landscape is louder.

If we can try and move past some of the political polarization to actually talk about what needs to happen, because there needs to be a lot of change, actually. And it’s kind of a once in a lifetime opportunity for that. 

Correa: I think that’s why ‘experimental’ is the right Northstar. It’s not in the political sense. But like in reality, as a metric, an agile, self-learning institution is one that is equal to the challenge that our science endeavors present.

What excites me is that it’s very tempting to be very discouraged, and say, ‘Oh, we’ve got these archaic institutions that are calcified and you could never change them.’ But I think we’re in the middle of a technological revolution that will upend lots of things, and does provide a window.

But we have to be really intentional about building that experimental capability into the institutions, and it gives me hope. 

DeJonge Schulman: I think this NSF moment is both good unto itself, but also it’s a good moment for us to think about how to do this on any number of levels with federal institutions. One of the things that is true of every federal institution is there’s just a ton of mythology built up around it, some of it kind of close to true, and some of it maybe not as much true.

There’s probably a lot of mythology at NSF around what is the right kind of instrument mix. What is it that we actually know about? What works in terms of funding mechanics and getting money out of the door, not just at NSF, but in the overall R&D enterprise. We know a little bit about that, and we have a lot of biases about it.

But that’s an example of something where we really need to interrogate our mythology around NSF, and around similar institutions before we go in and say, here’s where this has to be. Something that often happens when Congress gets a chance to relook at something – their instinct is, ‘What do I want to protect and keep, and constrain and keep in a box?’

We need to do as much as possible to get [Congress] to think about what they’re trying to empower and incentivize overall. That’s a much better platform to start from. But it’s harder. It requires looking at questions like: ‘What slowed us down before that we had wanted to do? What is it that we always want to be true about this institution? What is it we would want a chance to be able to revisit on a regular basis as the science changes?’

Because reauthorizations may end up taking forever because of how Congress works. They just delay things and kick it down the road. It’s something where there’s a lot of trust and mistrust that is resident in this space right now.

Rethinking how public accountability, public feedback, public participation in science and design work. It doesn’t have to be a radical rethinking, but at least opening the door to that is really important, as is thinking about what the public wants to know about boundaries within R&D. As we look at artificial intelligence, like in health care, or look at quantum, or biosecurity, any number of issues where there’s a lot of fear in this space and there doesn’t necessarily have to be. There is an opportunity to have a dialogue, and we miss those chances when we don’t take this moment to say, ‘How might this work better? 

Correa: I think it’s a great place to leave it. Thank you both.

The Civic Research Agenda is a culmination of several years of study, partnerships, and intelligence gathering that is the first comprehensive reporting on the priority research needs of American cities and counties. It considers the demand and supply of research: what are the research needs of local governments, and how can research outputs improve to “supply” or provide answers to better serve that audience?  

The priority research needs for U.S. local governments are the following:

• Housing
• Economic Development
• Human Services
• Climate and Energy 
• Transportation
• Community Engagement
• Service Delivery

Beyond any specific policy domain, local governments expressed the desire for support from the research community in three overarching areas: 1) evaluation; how can the research community measure and provide evidence that a policy intervention has achieved desired (or negative) impacts; 2) efficiency; how can the research community help local governments do more with less; and 3) data generation; how can the research community create and provide access to useful data that do not currently exist. 

This report also focuses on the ecosystem itself; what are the current perceptions, barriers, and recommendations that can inform and improve how local governments and universities work together? Findings show that issues include:

• Perceptions: Incorrect perceptions are stalling trust between these institutions. University faculty and staff fear being seen as too intellectual and disconnected. Local government staff fear being seen as not intellectual enough. 
• Barriers: The importance of relationship building and lack of networking: there are clear structural discovery systems on either side. Both are operating informally, and often, only by individual relationships. This report also provides evidence that the research demand/supply ecosystem is deeply relationship-dependent, creating high barriers for newcomers, inequity of access, and bottlenecks around well-connected individuals.
• Recommendations: The best way for research outcomes and publications to become actionable are:
  • Make the research and findings as specific to a jurisdiction as possible
  • Reduce paywalls 
  • Provide executive summaries with no technical jargon

• Provide peer-city or peer-county comparative analysis
• Go beyond observation and make recommendations

Finally, this report provides specific recommendations for local governments and universities to improve and grow the research-to-impact pipeline for one simple purpose: make research actionable, understandable, and accessible to communities across the country. 

The singular recommendation that can strengthen the research-to-impact pipeline is this: research should have an audience that lives outside of the peer-to-peer review system. 

Breakthrough technologies are emerging rapidly throughout U.S. transportation systems, from AI-enabled traffic management pilots by state DOTs to the continued expansion of automated vehicle (AV) testing and deployment. Yet the institutions responsible for researching, testing, and deploying these innovations were largely designed for a different era, with funding and governance structures organized around distinct transportation modes, limiting their ability to integrate cross-cutting, system-level technologies.

Over the past few years, the Federation of American Scientists (FAS) has engaged hundreds of local governments, researchers, industry leaders, and transportation experts to better understand where the most pressing transportation R&D gaps lie. These insights informed FAS’ recent recommendations  to the Department of Transportation’s (DOT) as it shapes its Research and Development (R&D) Strategic Plan. 

Talking with hundreds of people, so many kept saying the same thing: the biggest barriers to transportation innovation are not purely technical – they are structural. Innovation is happening across the ecosystem, but it is often fragmented, slow to deploy, and poorly coordinated across jurisdictions. Addressing structural barriers will require a more coordinated national approach to transportation innovation, including fully funding the Advanced Research Projects Agency-Infrastructure (ARPA-I), strengthening DOT’s role as a national convener, and investing in regional research networks that can bridge the gap between research and real-world deployment.

Infrastructure & Innovation Are Moving at Different Speeds

Our national transportation infrastructure was designed for station wagons, not the innovations of today and those yet to come. Roadways, signals, and transit systems were built around relatively predictable patterns of vehicle ownership and travel behavior. However, today, cities are navigating shared mobility, micromobility devices, automated vehicles, and digitally connected transportation systems operating simultaneously. 

At the same time, many promising technologies already exist that could improve transportation systems. Advanced sensing tools, AI-enabled traffic management systems, and connected infrastructure platforms have the potential to improve safety, reduce congestion, and enhance system resilience. Advanced construction methods and materials are being developed to the point where efforts like ARPA-I’s eXceptional Bridges through Innovative Design and Groundbreaking Engineering (X-BRIDGE) program can realistically set out to answer the question: how can we deliver bridges at half the cost, in half the time, and with twice the lifespan?

The challenge? Deployment. Traditional procurement and financing models are often designed for long-term infrastructure projects rather than rapidly evolving digital technologies. Even when solutions are available, local governments may struggle to evaluate, pilot, and scale them. These challenges are particularly pronounced for smaller jurisdictions with limited technical capacity.

Emerging Technologies Raise New Research Questions

Innovation moves quickly, research and validation do not. 

Think of the automated vehicles piloted in a major metropolitan near you (we’ve seen them drop off some folks at FAS HQ). Demonstrating their safety relative to human drivers requires robust evaluation methods and standardized testing frameworks. Researchers must also better understand how automated systems will integrate with transit networks, emergency response operations, and existing road users.

Meanwhile, intangible data is becoming the backbone of modern transportation systems. Connected vehicles, smart infrastructure, and real-time mobility services all depend on the ability to collect and share large volumes of data. Sounds great, but transportation data ecosystems remain fragmented across jurisdictions and operators that limit interoperability and coordination.

Those we’ve spoken to have emphasized ensuring transportation innovation benefits communities of all sizes. Many emerging technologies are first piloted in large metropolitan areas, leaving smaller cities and rural communities with fewer opportunities to participate in early deployments. Accessibility considerations, including ensuring new mobility systems work for people with disabilities, must also remain central to transportation innovation efforts. 

Together, these challenges highlight the need for a more coordinated approach to transportation research, development, and deployment.

ARPA-I: Building a Stronger Transportation Innovation Ecosystem

Addressing these challenges will require a more integrated national transportation R&D strategy – one that combines breakthrough research, regional experimentation, and strong federal coordination.

How can we do it? Congress should fully fund and support ARPA-I. ARPA-I was designed to support high-risk, high-reward research capable of addressing systemic infrastructure challenges. Its milestone-driven model allows researchers to test ambitious ideas quickly and refine them through rapid iteration. This approach is particularly well suited to emerging areas such as digital twins for infrastructure systems, AI-enabled safety technologies, and advanced construction methods.

At the same time, DOT must continue strengthening its role as a national convener for transportation innovation. Federal leadership ensures that lessons learned from pilot programs are shared across jurisdictions, that data standards remain interoperable, and that research investments align with real-world operational needs.

Finally, investing in regional transportation research networks can help bridge the gap between research and deployment. Regional Centers of Excellence that connect universities, public agencies, industry partners, and nonprofit organizations can provide environments for collaborative experimentation, workforce development, and technology transfer. These networks would mean that small jurisdictions have opportunities to participate in innovation efforts.

Turning Research Insights into Action

The insights gathered from local governments, researchers, and industry leaders make one thing clear: the U.S. does not lack ideas for improving its transportation system. What it needs is a research ecosystem capable of turning those ideas into deployed solutions.

Fully funding ARPA-I, strengthening DOT’s innovation capacity, and investing in regional research networks would create a coordinated pipeline for transportation innovation. Congress can make this possible. Sustained appropriations for ARPA-I will ensure the agency can pursue high-risk, high-reward research programs that address systemic infrastructure challenges. Lawmakers can also support transportation innovation by directing resources toward regional research partnerships, Centers of Excellence, and workforce development initiatives that help state and local governments and manage emerging technologies.

Congress should also consider policies that modernize procurement and financing pathways for emerging transportation technologies, support interoperable data standards across jurisdictions, and provide targeted technical assistance to state and local agencies implementing advanced infrastructure systems. These steps would bridge the gap between research and deployment, particularly for smaller jurisdictions that often lack the resources to evaluate and implement new technologies.

Taken together, these actions would allow the U.S. to accelerate transportation breakthroughs while ensuring that innovations reach communities across the country. Building the transportation systems of the future will require more than new technologies, it will require building the institutions, partnerships, and policy frameworks needed to bring those technologies to life

American investment in science is incredibly productive. Yet, it is losing trust with the public, being seen as misaligned with American priorities and very expensive. To increase the real and perceived benefit of research funding, funding agencies should develop challenge goals for their extramural research programs focused on the impact portion of their mission. For example, the NIH could adopt one goal per institute or center “to enhance health, lengthen life, and reduce illness and disability”; NSF could adopt one goal per directorate “to advance the national health, prosperity and welfare; [or] to secure the national defense”. Asking research agencies to consider person-level or economic impacts in advance helps the American people see the value of federal research funding, and encourages funders to approach the problem holistically, from basic to applied research. For almost every problem there are different scientific questions that will yield benefit over multiple time scales and insight from multiple disciplines. 

This plan has three elements: 

1. Focus some agency funding on measurable mission impacts 
2. Fund multiple timescales as part of a single plan
3. Institutionalize the impact funding process across science funders

For example, if NIH wanted to reduce the burden of Major Depression, it could invest in a shorter time frame to learn how to better deliver evidence-based care to everyone who needs it. At the same time, it can invest in midrange work to develop and test new models and medications, and in the decades-long work required to understand how the exosome influences mood disorders. A simple way to implement this approach would be to build on the processes developed by the Government Performance Results Act (GPRA), which already requires goal setting and reporting, though proposals could be worked into any strategic planning process through a variety of administrative mechanisms.

Challenge and Opportunity

In 1945, Vannevar Bush called science the ‘endless frontier’, and argued funding scientific research is fundamental to the obligations of American government. He wrote “without scientific progress no amount of achievement in other directions can insure our health, prosperity, and security as a nation in the modern world”. The legacy of this report is that health, prosperity, and security feature prominently in the missions of most federal research agencies (see Table 1). However, in this century we have begun to drift from his focus on the impacts of science. We have the strange situation where our enterprise is both incredibly productive, and losing trust with the public, viewed as out of touch or misaligned with American priorities. This memo  proposes a simple solution to address this issue for federal funding agencies like NIH and NSF that largely focus on extramural investigator-initiated research. These are research programs where the funding agency signals interest in specific topics and teams of scientists submit their research plans addressing those topics. The agency then funds a subset of those plans with input from external scientific reviewers.

This funding approach is incredibly productive. For example, NIH funds most of the pipeline for the emerging bioeconomy, which accounts for 5.1% of our GDP. From 2010 to 2016, every one of the 210 new entities approved by the FDA had some NIH funding. And yet, there appears to be a disconnect between our funding strategy and the public interest focus of the Endless Frontier operationalized through our federal science agency missions for investigator initiated research. 

A fundamental driver of this disconnect might be a slight misalignment of the incentives of academic scientists, who are rewarded for novelty and scientific impact, with the broader public interest. Our federal agencies are highly attuned to scientific leaders, and place equal or even greater weight on innovation (novelty plus scientific impact) than real world impact. For example, NSF review criteria place equal weight on intellectual merit (‘advance knowledge’) and broader impacts (‘benefit society and contribute to the achievement of specific, desired societal outcomes’). NIH’s impact score of new applications is an ‘assessment of the likelihood for the project to exert a sustained, powerful influence on the research field(s) involved’ [emphasis mine], which is only part of the agency’s mission. The practical implications of this sustained focus away from the impact portion of agencies missions become apparent in figure 1, showing tremendous spending in health research unrelated to a key public interest measure like lifespan, especially when compared to other nations’ health research spending. 

Perhaps the realization that the federal research investment is not strongly linked to their mission impact is one reason why American science has been slowly losing public trust over time. Among the people of 68 nations ranking the integrity of scientists, Americans ranked scientists 7^th highest, whereas we ranked scientists 16^th highest in our estimation of them acting in the public interest. And this is despite the fact that the American investment in science is many times higher than the 15 nations who rated scientists more highly on public interest. A more accurate description of our 21^st century federal science enterprise might be the ‘timeless frontier’, where our science agencies pursue cycles of funding year in and year out, with their functional goal being scientific changes and their primary measure of success being projects funded. Advancing the economy, health, national defense, etc., are almost incidental benefits to our process measures. 

We can do better. In 2024, the National Academy of Medicine called out the lack of high level coordination in research funding. In 2025, the administration has been making drastic cuts and dramatic changes to goals and processes of federal research funding, and the ultimate outcome of these changes is unclear. In the face of this change, Drs. Victor Dzau and Keith Yamamoto, staunch champions of our federal science programs, are calling for “a coherent strategy […] to sustain and coordinate the unrivaled strengths of government-funded research and ensure that its benefits reach all Americans”.

We can build on the incredible success of the federal science enterprise – inarguably the most productive science enterprise in all history. The primary source of American scientific strength is scale. American funding agencies are usually the largest funders in their space. I will highlight some challenges of the current approach and suggest improvements to yield even more impactful approaches more closely aligned with the public interest.

The primary federal funding strategy is broad diversification, where our agencies fund every high scoring application in a topic space (see FAQs). Further, federal science agencies pay little attention to when they expect to see a fundamental impact arising from their research portfolio. For example, a centrally directed program like the Human Genome Project can lead to breakthrough treatments decades later, but in the meantime, other research that generates improvements on faster timescales could have been coordinated, such as developing conventional drug treatments, or research to optimize quality and delivery of existing treatment. 

And yet, the breadth and complexity of broad diversification makes it easy to cherry pick successes. This is a strategic issue, and is bigger than the project selection issues highlighted in the earlier discussion about review criteria. When research funding agencies make their pitch for federal dollars they highlight a handful of successes over tens of thousands of projects funded over many years. They ignore failures, the time when investments were made, and time to benefit. With the goals and metrics we have in place, it is simply too hard to summarize progress in any other way. 

Overly diversified science funding supports both good Congressional testimony and bad strategy. If your problem happens to fall into a unicorn space of success, there is a lot to celebrate. But most problems do not, and we experience inconsistent returns. We need to define the success of research funding more precisely, in advance, and in ways that more obviously align with the public interest. 

Plan of Action 

If we tweak our funding strategy to focus on societal impacts, we can move to a more impactful science enterprise, and help regain public support for science funding. We can focus federal research funding on effective answers to difficult problems demanding both urgency and short term improvements, and fundamental discoveries that may take decades to realize. My solution and implementation actions for agencies, and potentially Congress, are described below. 

Recommendation 1. Focus some agency funding on measurable mission impacts. 

We should empower our science agencies to step away from broad diversification as the predominant funding strategy, and pursue measurable mission impacts with specific time horizons. It can be a challenge for funders to step away from process measures (e.g. projects or consortia funded) and focus on actual changes in mission impact.

Ideally, these specific impacts would be broken into measurable goals that would be selected through a participatory process that includes scientific experts, people with lived experience of the issue, and potential partner agencies. I recommend each agency division (e.g. an NSF Directorate) allocate a percentage of their budget to these mission impact strategies. Further, to avoid strategic errors that can arise from overwhelming power of federal funding to shape the direction of scientific fields, these high level funding plans should be as impact focused as possible, and avoid steering funding to one scientific theory or discipline over another. 

Recommendation 2. Fund multiple timescales as part of a single plan. 

Research funders need to balance their investment portfolios not only across problem areas, but over time. Complex challenges will often require funding different aspects of the solution on different timelines in parallel as part of a larger plan. Balancing time as well as spending allows for a more robust portfolio of funding that draws from a broader array of scientific disciplines and institutions. 

Note, this approach means starting lines of research that may not lead to ultimate impact for decades. This approach might seem strange given our relatively short budget cycles, but is very common in science, where projects like the Human Genome initiative, the Brain Initiative, or the National Nanotechnology Initiative, have all exceeded a single budget cycle and will take years to realize their full impact. These kinds of efforts require milestones to ensure they stay on track over time. 

Recommendation 3. Institutionalize the impact funding process across science funders.

Our research enterprise has become oriented around investigator-initiated, project-based awards. Alternative funding strategies, such as the DARPA model, are viewed as anomalies that must require completely different governance and procedures. These differences in goals are unnecessary. A consistent focus on impacts and strategy in funding across agencies will help the scientific community become more aware of the time to benefit of research, help underscore the value of research investment to the American public, and help research agencies collaborate among themselves and with their partner agencies (e.g. NIH collaborates more closely with CMS, FDA, etc.). 

In short, institutionalizing this process can lead to greater accountability and recognition for our science enterprise. This structure allows our funders to report to the public progress on specific goals on predetermined and preannounced timelines, rather than having to comb through tens of thousands of independent funding decisions and competing strategies to find case studies to highlight. In this way, expected and unexpected scientific results, and even operational challenges, can be discussed within an impact framework that clearly ties to the agency mission and public interest.

Example of Planning using  an Impact Focus

Here is an example of a mission impact goal Reducing the Burden of Major Depressive Disorder that could be put forth by the National Institute of Mental Health (NIMH), and the process to develop it.

Commence Inclusive Planning: NIMH brings together experts from academia, clinical care, industry, people impacted by depression, and FDA and CMS to develop measures, timelines and funding strategies. 

Develop Specific Impact Measures: These should  reflect the agency’s impact portion of their mission. For example, NIH’s mission impact of “enhance health, lengthen life, and reduce illness and disability” requires measuring impact on human beings. Example measurement targets could include:

• Reduced incidence of Major Depressive Disorder 
• Increased productivity (e.g. days worked) of people living with Major Depressive Disorder
• Reduced suicide rates

Fund Multiple Time Scales: Designate time scales in parallel as part of a comprehensive strategy. These different plans would involve different disciplines, funding mechanisms, and private sector and government partners. Examples of plans working at different timescales to support the same goal and measures could include:

• 10 year plan: Increase utilization of evidence based care
• 15 year plan: Develop and implement new treatments
• 30 year plan: Determine how the exposome causes and prevents depression, and how can be changed
• It is likely that NIMH has already obligated funds to projects that support one of these plans, though they may need additional work to ensure that those projects can directly tie to the specific plan measures. 

Implementation Strategies for Impact Goals

Each federal funding agency could allocate a percentage of their budget to these and other  impact goals. The exact amount would depend on the current funding approach of each agency. As this proposal calls for more direct focus on agency mission, and not a change in mission, it is likely that a significant percentage of the agency’s current budget already supports an impact goal on one or more of its time scales. 

For an agency heavily weighted towards project based funding of small investigator teams, like NIH, I would recommend starting with a goal of 20% of their budgets set towards impact spending and consider increases over time. Other agencies with different funding models may want to start in a different place. Further, I would recommend different goals and targeted funds for each major administrative unit, such as an institute or directorate. 

All federal funders already engage in some form of strategic and budget planning, and most also have formal structures for engaging stakeholders into those planning decisions. Therefore, each agency already has sufficient authorities and structures to implement this proposal. However, it is likely that these impact goals will require collaboration across agencies, and that could be difficult for agencies to efficiently conduct by themselves. 

Additional support to make this change could come from Congressional Report language as part of the budget process, through interagency leadership from the White House Office of Science and Technology, or through the Office of Management and Budget. For example, the Government Performance Results Act (GPRA) already requires agency goal setting, reporting and supports cross agency priority goals. That planning process could easily be adapted to this more specific impact focus for research funding agencies, and reporting on those goals could be incorporated into routine reporting of agency activities. 

Conclusion 

We are living through a massive disruption in federal research funding, and as of the fall of 2025, it is not clear what future federal research funding will look like. We have an opportunity to focus the incredibly productive federal research enterprise around the central reasons why Americans invest in it. We can meet Bush’s challenge of the Endless Frontier simply by clearly defining the benefits the American people want to see, and explicitly setting plans, timing and money to make that happen. 

We can call our shots and focus our science funding around impacts, not spending. And we can set our goals with enough emotional resonance and depth to capture both the interests of the average American, and the needs of scientists from different disciplines and types of institutions. We already have the legal authorities in place to adopt these techniques, we just need the will.

Through partnership with the Doris Duke Foundation, FAS is advancing a vision for healthcare innovation that centers safety, equity, and effectiveness in artificial intelligence. Inspired by work from the Social Science Research Council (SSRC) and Arizona State University (ASU) symposiums, this memo explores new research models such as large-scale behavioral “megastudies” and how they can transform our understanding of what drives healthier choices for longer lives. Through policy entrepreneurship FAS engages with key actors in government, research, academia and industry. These recommendations align with ongoing efforts to integrate human-centered design, data interoperability, and evidence-based decision-making into health innovation.

By shifting funding from small underpowered randomized control trials to large field experiments in which many different treatments are tested synchronously in a large population using the same objective measure of success, so-called megastudies can start to drive people toward healthier lifestyles. Megastudies will allow us to more quickly determine what works, in whom, and when for health-related behavioral interventions, saving tremendous dollars over traditional randomized controlled trial (RCT) approaches because of the scalability. But doing so requires the government to back the establishment of a research platform that sits on top of a large, diverse cohort of people with deep demographic data. 

Challenge and Opportunity

According to the National Research Council, almost half of premature deaths (< 86 years of age) are caused by behavioral factors. Poor diet, high blood pressure, sedentary lifestyle, obesity, and tobacco use are the primary causes of early death for most of these people. Yet, despite studying these factors for decades, we know surprisingly little about what can be done to turn these unhealthy behaviors into healthier ones. This has not been due to a lack of effort. Thousands of randomized controlled trials intended to uncover messaging and incentives that can be used to steer people towards healthier behaviors have failed to yield impactful steps that can be broadly deployed to drive behavioral change across our diverse population. For sure, changing human behavior through such mechanisms is controversial, and difficult. Nonetheless studying how to bend behavior should be a national imperative if we are to extend healthspan and address the declining lifespan of Americans at scale.

Limitations of RCTs

Traditional randomized controlled trials (RCTs), which usually test a single intervention, are often underpowered, and expensive, and short-lived, limiting their utility even though RCTs remain the gold standard for determining the validity of behavioral economics studies. In addition, because the diversity of our population in terms of biology, and culture are severely limiting factors for study design, RCTs are often conducted on narrow, well-defined populations. What works for a 24-year-old female African American attorney in Los Angeles may not be effective for a 68-year-old male white fisherman living in Mississippi. Overcoming such noise in the system means either limiting the population you are examining through demographics, or deploying raw power of numbers of study participants that can allow post study stratification and hypothesis development. It also means that health data alone is not enough. Such studies require deep personal demographic data to be combined with health data, and wearables. In essence, we need a very clear picture of the lives of participants to properly identify interventions that work and apply them appropriately post-study on broader populations. Similarly, testing a single intervention means that you cannot be sure that it is the most cost-effective or impactful intervention for a desired outcome. This further limits the ability to deploy RCTs at scale. Finally, the data sometimes implies spurious associations. Therefore, preregistration of endpoints, interventions, and analysis of such studies will make for solid evidence development even if the most tantalizing outcomes come from sifting through the data later to develop new hypotheses that can be further tested. 

Value of Megastudies

Professors Angela Duckworth and Katherine Milkman, at the University of Pennsylvania, have proposed an expansion of the use of megastudies to gain deeper behavioral insights from larger populations. In essence, megastudies are “massive field experiments in which many different treatments are tested synchronously in a large sample using a common objective outcome.” This sort of paradigm allows independent researchers to develop interventions to test in parallel against other teams. Participants are randomly assigned across a large cohort to determine the most impactful and cost-effective interventions. In essence, the teams are competing against each other to develop the most effective and practical interventions on the same population for the same measurable outcome. 

Using this paradigm, we can rapidly assess interventions, accelerate scientific progress by saving time and money, all while making more appropriate comparisons to bend behavior towards healthier lifestyles. Due to the large sample sizes involved and deep knowledge of the demographics of participants, megastudies allow for the noise that is normal in a broad population that normally necessitates narrowing the demographics of participants. Further, post study analysis allows for rich hypothesis generation on what interventions are likely to work in more narrow populations. This enables tailored messaging and incentives to the individual. A centralized entity managing the population data reduces costs and makes it easier to try a more diverse set of risk-tolerant interventions. A centralized entity also opens the door to smaller labs to participate in studies. Finally, the participants in these megastudies are normally part of ongoing health interactions through a large cohort study or directly through care providers. Thus, they benefit directly from participation and tailored messages and incentives. Additionally, dataset scale allows for longer term study design because of the reduction in overall costs. This enables study designers to determine if their interventions work well over a longer period of time or if the impact of interventions wane and need to be adjusted.

Funding and Operational Challenges

But this kind of “apples to apples” comparison has serious drawbacks that have prevented megastudies from being used routinely in science despite their inherent advantage. First, megastudies require access to a large standing cohort of study participants that will remain in the cohort long term. Ideally, the organizer of such studies should be vested in having positive outcomes. Here, larger insurance companies are poor targets for organizing. Similarly, they have to be efficient, thus, government run cohorts, which tend to be highly bureaucratic, expensive, and inefficient are not ideal. Not everything need go through a committee. (Looking at you, All of Us at NIH and Million Veterans Program at the VA). 

Companies like third party administrators of healthcare plans might be an ideal organizing body, but so can companies that aim to lower healthcare costs as a means of generating revenue through cost savings. These companies tend to have access to much deeper data than traditional cohorts run by government and academic institutions and could leverage that data for better stratifying participants and results. However, if the goal of government and philanthropic research efforts is to improve outcomes, then they should open the aperture on available funds to stand up a persistent cohort that can be used by many researchers rather than continuing the one-off paradigm, which in the end is far more expensive and inefficient. Finally, we do not imply that all intervention types should be run through megastudies. They are an essential, albeit underutilized tool in the arsenal, but not a silver bullet for testing behavioral interventions.

Fear of Unauthorized Data Access or Misuse 

There is substantial risk when bringing together such deep personal data on a large population of people. While companies compile deep data all the time, it is unusual to do so for research purposes and will, for sure, raise some eyebrows, as has been the case for large studies like the aforementioned All of Us and the Million Veteran’s Program. 

Patients fear misuse of their data, inaccurate recommendations, and biased algorithms—especially among historically marginalized populations. Patients must trust that their data is being used for good, not for marketing purposes and determining their insurance rates. 

Icons © 2024 by Jae Deasigner is licensed under CC BY 4.0

Need for Data Interoperability

Many healthcare and community systems operate in data silos and data integration is a perennial challenge in healthcare. Patient-generated data from wearables, apps, or remote sensors often do not integrate with electronic health record data or demographic data gathered from elsewhere, limiting the precision and personalization of behavior-change interventions. This lack of interoperability undermines both provider engagement and user benefit. Data fragmentation and poor usability requires designing cloud-based data connectors and integration, creating shared feedback dashboards linking self-generated data to provider workflows, and creating and promoting policies that move towards interoperability. In short, given the constantly evolving data integration challenge and lack of real standards for data formats and integration requirements, a dedicated and persistent effort will have to be made to ensure that data can be seamlessly integrated if we are to draw value from combining data from many sources for each patient.

Additional Barriers

One of the largest barriers to using behavioral economics is that some rural, tribal, low-income, and older adults face access barriers. These could include device affordability, broadband coverage, and other usability and digital literacy limitations. Megastudies are not generally designed to bridge this gap leaving a significant limitation of applicability for these populations. Complicating matters, these populations also happen to have significant and specific health challenges unique to their cohorts. As the use of behavioral economic levers are developed, these communities are in danger of being left behind, further exacerbating health disparities. Nonetheless, insight into how to reach these populations can be gained for individuals in these populations that do have access to technology platforms. Communications will have to be tailored accordingly. 

External motivators have been consistently shown to be the essential drivers of behavioral change. But motivation to sustain a behavior change and continue using technology often wanes over time. Embedding intrinsic-value rewards and workplace incentives may not be enough. Therefore, external motivations likely have to be adjusted over time in a dynamic system to ensure that adjustments to the behavior of the individual can be rooted in evidence. Indeed, study of the dynamic nature of driving behavioral change will be necessary due to the likelihood of waning influence of static messaging. By designing reward systems that tie personal values and workplace wellness programs sustained engagement through social incentives and tailored nudges may keep users engaged.

Plan of Action 

By enabling a private sector entity to create a research platform using patient data combined with deep demographic data, and an ethical framework for access and use, we can create a platform for megastudies. This would  allow the rapid testing of behavioral interventions that steer people towards healthier lifestyles, saving money, accelerating progress, and better understanding what works, in whom, and when for changing human behavior. 

This could have been done through either the All of Us program or Million Veterans program or a different large cohort study, but neither program has the deep demographic and lifestyle data required to stratify their population. Both are mired in bureaucratic lethargy that is common in large scale government programs. Health insurance companies and third-party administrators of health insurance can gather such data, be nimbler, create a platform for communicating directly with patients, coordinate with their clinical providers. But one could argue that neither entity has a real incentive to bend behavior and encourage healthy lifestyles. Simply put, that is not their business.

Recommendation 1. Issue a directive to agencies to invest in the development of a megastudy platform for health behavioral economics studies.

The White House of HHS Secretary should direct the NIH or ARPA-H to develop a plan for funding the creation of a behavioral economics megastudy platform. The directive should include details on the ethical and technical framework requirements as well as directions for development of oversight of the platform once it is created. The platform should be required to establish a sustainability plan as part of the application for a contract to create the megastudy platform. 

Recommendation 2. Government should fund the establishment of a megastudy platform.

ARPA-H and/or DARPA should develop a program to establish a broad research platform in the private sector that will allow for megastudies to be conducted. Then research teams can, in parallel, test dozens of behavioral interventions on populations and access patient data. This platform should have required ethical rules and be grounded in data sovereignty that allows patients to opt out of participation and having their data shared.

Data sovereignty is one solution to the trust challenge. Simply put, data sovereignty means that patients have access to the data on themselves (without having to pay a fee that physicians’ offices now routinely charge for access) and control over who sees and keeps that data. So, if at any time, a participant changes their mind, they can get their data and force anyone in possession of that data to delete it (with notable exceptions, like their healthcare providers). Patients would have ultimate control of their data in a ‘trust-less’ way that they never need to surrender, going well past the rather weak privacy provisions of HIPAA, so there is no question that they are in charge.

We suggest that using blockchain and token systems for data transfer would certainly be appropriate. Data held in a federated network to limit the danger of a breach would also be appropriate. 

Recommendation 3. The NIH should fund behavioral economics megastudies using the platform. 

Once the megastudy platform(s) are established, the NIH should make dedicated funds available for researchers to test for behavioral interventions using the platform to decrease costs, increase study longevity, and improve speed and efficiency for behavioral economics studies on behavioral health interventions. 

Conclusion

Randomized controlled trials have been the gold standard for behavioral research but are not well suited for health behavioral interventions on a broad and diverse population because of the required number of participants, typical narrow population, recruiting challenges, and cost. Yet, there is an urgent need to encourage and incentivize d health related behaviors to make Americans healthier. Simply put, we cannot start to grow healthspan and lifespan unless we change behaviors towards healthier choices and habits. When the U.S. government funds the establishment of a platform for testing hundreds of behavioral interventions on a large diverse population, we will start to better understand the interventions that will have an efficient and lasting impact on health behavior. Doing so requires private sector cooperation and strict ethical rules to ensure public trust.

This memo produced as part of Strengthening Pathways to Disease Prevention and Improved Health Outcomes.

The transition to a clean energy future and diversified sources of energy requires a fundamental shift in how we produce and consume energy across all sectors of the U.S. economy. The transportation sector, a sector that heavily relies on fossil-based energy, stands out not only because it is the sector that releases the most carbon into the atmosphere, but also for its progress in adopting next-generation technologies when it comes to new technologies and fuel alternatives. 

Over the past several years, the federal government has made concerted efforts to support clean energy innovation in transportation, both for on-road and off-road. Particularly, in hard-to-electrify transportation sub-sectors, there has been added focus such as through the Sustainable Aviation Fuel (SAF) Grand Challenge. These efforts have enabled a wave of biotechnology-driven solutions to move from research labs to commercial markets, such as LanzaJets alcohol-to-jet technology in producing SAF. From renewable fuels to bio-based feedstocks, biotechnologies are enabling the replacement of fossil-derived energy sources and contributing to a more sustainable, secure, and diversified energy system. 

SAF in particular has gained traction, enabled in part by public investment and interagency coordination, like the SAF Grand Challenge Roadmap. This increased federal attention demonstrated how strategic federal action, paired with demand signals from government, targeted incentives, and industry buy-in, can create the conditions needed to accelerate biotechnology adoption.

To better understand the factors driving this progress, FAS conducted a landscape analysis at the federal and regional level of biotechnology innovation within the clean energy sector, complemented by interviews with key stakeholders. Several policy mechanisms, public-private partnerships, and investment strategies were identified that were enablers of advanced SAF adoption and production and similar technologies. By identifying the enabling conditions that supported biotechnology’s uptake and commercialization, we aim to inform future efforts on how to accelerate other sectors that utilize biotechnologies and overall, strengthen the U.S. bioeconomy.

Key Findings & Recommendations

An analysis of the federal clean energy landscape reveals several critical insights that are vital for advancing the development and deployment of biotechnologies. Federal and regional strategies are central to driving innovation and facilitating the transition of biotechnologies from research to commercialization. The following key findings and actionable recommendations address the challenges and opportunities in accelerating this transition.

Federal Level Key Findings & Recommendations

The federal government plays a pivotal role in guiding market signals and investment toward national priorities. In the clean energy sector, decarbonizing aviation has emerged as a strategic objective, with SAF serving as a critical lever. Federal initiatives such as the SAF Grand Challenge, the SAF Roadmap, and the SAF Metrics Dashboard have helped to elevate SAF within national climate priorities and enabled greater interagency coordination. These mechanisms not only track progress but also communicate federal commitment. Still, despite these efforts, current SAF production remains far below target levels, with capacity largely concentrated in HEFA, a pathway with constrained feedstock availability and limited scalability. 

This production gap reflects deeper structural challenges, many of which parallel broader issues across the clean-energy biotech interface. One of the main challenges is the fragmented, short-duration policy incentives currently in use. Tax credits like 40B and 45Z, while important, lack the longevity and clarity required to unlock large-scale, long-term private investment. The absence of binding fuel mandates further undermines market certainty. These policy gaps limit the ability of the clean energy sector to serve as a sustained demand signal for emerging biotechnologies and slow the transition from pilot to commercial scale. 

Importantly, these challenges point to a broader opportunity: SAF as a test case for how the clean energy sector can serve as a driver of biotechnology uptake. Promising biotechnologies, such as alcohol-to-jet and power-to-liquid, are currently stalled by high capital costs, uncertain regulatory pathways, and a lack of coordinated federal support. Addressing these bottlenecks through aligned incentives, technology-neutral mandates, and harmonized accounting frameworks could not only accelerate SAF deployment but also establish a broader policy blueprint for scaling biotechnology across other clean energy applications.

To alleviate some of the challenges identified, the federal government should:

Extend & Clarify Incentives

While tax incentives such as the 45Z Clean Fuel Production Credit offer a promising framework to accelerate low-carbon fuel deployment, current design and implementation challenges limit their impact, particularly for emerging bio-based and synthetic fuels. To fully unlock the climate and market potential of these incentives, Congress and relevant agencies should take the following steps:

• Congress should amend the 45Z tax credit structure to differentiate between fuel types, such as SAF, e-fuels, biofuels, and renewable diesel, based on life cycle CI and production pathways. This would better reflect technology-specific costs and accelerate deployment across multiple clean fuel markets, adding specificity as to how to utilize and earn the credits based on the type of fuel. 
• Congress should extend the duration of the 45Z credit and other clean-fuel related incentives to provide long-term policy certainty. Multi-year extensions with a defined minimum value floor would reduce investment risk and enable financing of capital-intensive projects.
• Congress and the Department of Treasury should clarify eligibility to ensure inclusion of co-processing methods and hybrid production systems, which are currently in regulatory gray areas. This would ensure broader participation by innovative fuel producers.
• Federal agencies, including the Department of Energy (DOE), Department of Transportation (DOT), and the Department of Defense, should be directed to enter into long-term (more than 10 years) procurement agreements for low-carbon fuels, including electrofuels and SAF. These offtake mechanisms would complement tax incentives and send strong market signals to producers and investors.

Scale Biotech Commercialization Support

The clean energy transition depends in part on the successful commercialization of enabling biotechnologies, ranging from advanced biofuels to bio-based carbon capture, SAF and biomanufacturing platforms that reduce industrial emissions. Recent or proposed funding cuts to clean energy programs risk stalling this progress and undermining U.S. competitiveness in the bioeconomy. 

To accelerate biotechnology deployment and bridge the gap between lab-scale innovation and commercial-scale production, Congress should take the following actions:

• Authorize and appropriate expanded funding to the DOE, particularly through Bioenergy Technologies Office (BETO) and to the Department of Agriculture (USDA) to support pilot, demonstration, and first-of-a-kind commercial scale projects that enable biotechnology applications across clean energy sectors.
• Direct and fund the DOE Loan Programs Office to establish a dedicated loan guarantee program focused on biotechnology commercialization, targeting platforms that can be integrated into the energy system, such as bio-based fuels, bioproducts, carbon utilization technologies, and electrification-enabling materials.
• Encourage DOE and USDA to enter into long-term offtake agreements or structured purchasing mechanisms with qualified bioenergy and biomanufacturing companies. These agreements would help de-risk early commercial projects, crowd in private investment, and provide market certainty during the critical scale-up phase. 
• Strengthen public-private coordination mechanisms, such as cross-sector working groups or interagency task forces, to align commercialization support with industry needs, improve program targeting, and reduce time-to-market for promising technologies. 

Design and Promote Next-Gen Biofuel Policies

To accelerate the deployment of low-carbon fuels and enable innovation in next-generation bioenergy technologies, Congress and relevant agencies should take the following actions:

• Congress should direct the Environmental Protection Agency (EPA) to modernize the Renewable Fuel Standard by incorporating life cycle carbon intensity as a core metric, moving beyond volume-based mandates. Legislative authority could also support the development of a national Low Carbon Fuel Standard, modeled on successful state-level programs to drive demand for fuels with demonstrable climate benefits.
• EPA should update its emissions accounting framework to reflect the latest science on life cycle greenhouse gas (GHG) emissions, enabling more accurate assessment of advanced biofuels and synthetic fuels. 
• DOE should expand R&D and demonstration funding for biofuel pathways that meet stringent carbon performance thresholds, with an emphasis on scalability and compatibility.

Regional Level Key Findings & Recommendations

Regional strengths continue to serve as foundational drivers of clean energy innovation, with localized assets shaping the pace and direction of technology development. Federal designations, such as the Economic Development Administration (EDA) Tech Hub program (Tech Hub), have proven catalytic. These initiatives enable regions to unlock state-level co-investment, attract private capital, and align workforce training programs with local industry needs. Early signs suggest that the Tech Hub framework is helping to seed innovation ecosystems where they are most needed, but long-term impact will depend on sustained funding support and continued regional coordination. 

Workforce readiness and enabling infrastructure remain critical differentiators. Regions with deep and committed involvement from major research universities, national labs, or advanced manufacturing clusters are better positioned to scale innovation from prototype to deployment. Real-world testbeds provide environments for stress-testing technologies and accelerating regulatory and market readiness, reinforcing the importance of place-based strategies in federal innovation planning. 

At the same time, private investment in clean energy and enabling biotechnologies remains crucial to developing and scaling innovative technologies. High capital costs, regulatory uncertainty, and limited early-stage demand signals continue to inhibit market entry, especially in geographies with less mature innovation ecosystems. Addressing these barriers through coordinated federal procurement, long-term incentives, and regional capacity-building will be essential to supporting growth in regions with strong assets to develop industry clusters that could yield clean energy benefits. 

To accomplish this, the federal government and regional governments should: 

Strengthen Regional Workforce Pipelines

A skilled and regionally distributed workforce is essential to realizing the full economic and technological potential of clean energy investments, particularly as they intersect with the bioeconomy. While federal funding is accelerating deployment through initiatives such as the IRA and DOE programs, workforce gaps, especially outside major innovation hubs, pose barriers to implementation. Addressing these gaps through targeted education, training, and talent retention efforts will be critical to ensuring that clean energy projects deliver durable, regionally inclusive economic growth. To this end:

• Federal agencies like the Department of Education and National Science Foundation should explore expanding support for STEM programs at community colleges and Minority Serving Institutions, with a focus on biosciences, engineering, and agricultural technologies relevant to the clean energy transition.
• Federally supported training and reskilling programs tailored to regional clean energy and biomanufacturing workforce needs could benefit new and existing cross-sector partnerships between state workforce agencies and regional employers. 
• State and local governments should consider implementing talent retention strategies, including local hiring incentives, relocation support, and career placement services, to ensure that skilled workers remain in and contribute to regional clean energy ecosystems.

Strengthen Regional Infrastructure and Foster Cross-Sector Collaboration

Robust regional infrastructure and cross-sector collaboration are essential to accelerating the deployment of clean energy technologies that leverage advancements in biotechnology and manufacturing. Strategic investments in shared facilities, modernized logistics, and coordinated innovation ecosystems will strengthen supply chain resilience and improve technology transfer across sectors. Facilitating access to R&D infrastructure, particularly for small and mid-sized enterprises, will ensure that innovation is not limited to large firms or major metropolitan areas. To support these outcomes: 

• Federal support for regional testbeds, prototyping sites, and grid modernization labs, coordinated by agencies such as DOE and EDA,  would support the demonstration and scaling of biologically enabled clean energy technologies.
• State and local governments, in coordination with federal agencies including DOT and DOC, explore investment in logistics infrastructure to enhance supply chain reliability and support distributed manufacturing.
• States should consider creating or expanding the use of innovation voucher programs that allow small and mid-sized enterprises to access national lab facilities, pilot-scale infrastructure, and technical expertise, fostering cross-sector collaboration between clean energy, biotech, and advanced manufacturing firms. 

Attract and De-Risk Private Capital

Attracting and de-risking private capital is critical for scaling clean energy and biotechnology innovations. By offering targeted financial mechanisms and leveraging federal visibility, governments can reduce the financial uncertainties that often deter private investment. Effective strategies, such as state-backed loan guarantees and co-investment models, can help bridge funding gaps while strategic partnerships with philanthropic and venture capital entities can unlock additional resources for emerging technologies. To this end: 

• State governments, in collaboration with the federal agencies such as DOE and Treasury, should consider implementing state-backed loan guarantees and co-investment models to attract private capital into high-risk clean energy and biotech projects. 
• Federal agencies like EDA, DOE, and the SBA should explore additional programs and partnerships to attract philanthropic and venture capital to emerging clean energy technologies, particularly in underserved regions.
• Federal agencies should increase efforts to facilitate connecting early-stage companies with potential investors, using federal initiatives to build investor confidence and reduce perceived risks in the clean energy sector.

Cross-Cutting Key Findings

The successful deployment of federal clean energy and biotechnology initiatives, such as the SAF Grand Challenge, relies heavily on the capacity of regional ecosystems and the private sector to absorb and implement national goals. Many regions, particularly those outside established innovation hubs, lack the infrastructure, resources, and technical expertise to effectively utilize federal funding. As a result, the impact of national policies is often limited, and the full potential of federal investments goes unrealized in certain areas.

Federal programs often take a one-size-fits-all approach, overlooking regional variability in feedstocks, industrial bases and cost structures. Programs like tax credits and life cycle analysis models can unintentionally disadvantage regions with different economic contexts, creating disparities in access to federal incentives. This lack of regional customization prevents certain areas from fully benefiting from national clean energy and biotech initiatives. 

The diffusion of innovation in clean energy and biotechnology remains concentrated in a few key regions, leaving others underutilized. Despite robust federal R&D investments, commercialization and scaling of innovations are primarily concentrated in regions with established infrastructure, hindering the broader geographic spread of these technologies. In addition, workforce development efforts across federal and regional programs are fragmented, creating misalignments in talent pipelines and further limiting the ability of local industries to leverage available resources effectively. The absence of a unified system for tracking key metrics, such as SAF production and emissions reductions, makes it difficult to coordinate efforts or assess progress consistently across regions. To address this, the federal and regional governments should:

Create a Federal–Regional Clean Energy Deployment Compact

A Federal-Regional Clean Energy Deployment Compact is critical for aligning federal clean energy initiatives with the unique capabilities and needs of regional ecosystems. By establishing formal mechanisms, such as intergovernmental councils and regional liaisons, federal programs can be more effectively tailored to local conditions. These mechanisms will ensure two-way communication between federal agencies and regional stakeholders, fostering a collaborative approach that adapts to evolving technological, economic, and environmental conditions. In addition, treating regional tech hubs and initiatives as testbeds for new policy tools, such as performance-based incentives or carbon standards, will allow for innovative solutions to be tested locally before scaling them nationally, ensuring that policies are effective and contextually relevant across diverse regions. To this end:

• The White House Office of Science and Technology Policy (OSTP), in collaboration with the DOE and EPA, should establish formal intergovernmental councils or regional liaisons to facilitate ongoing dialogue between federal agencies and regional stakeholders. These councils would focus on aligning federal clean energy initiatives with regional needs, ensuring that local priorities, such as feedstock availability or infrastructure readiness, are considered in policy design.
• The DOE and EPA should treat tech hubs and regional clean energy initiatives as testbeds for policy innovation. These regions can pilot performance-based incentives, carbon standards, and other policy tools to assess their effectiveness before scaling them nationally. Successful models developed in these testbeds should be expanded to other regions, with lessons learned shared across state and local governments.
• Regional universities and innovation hubs should collaborate with federal and state agencies to develop pilot programs that test new policy tools and technologies. These institutions can serve as incubators for innovative clean energy solutions, providing valuable data on what policies work best in specific regional contexts.

Build a National Innovation-to-Deployment Pipeline

Creating a seamless innovation-to-deployment pipeline is essential for scaling clean energy technologies and ensuring that regional ecosystems can fully participate in national clean energy transitions. By linking DOE national labs, Tech Hubs, and regional consortia into a coordinated network, the U.S. can support the full life cycle of innovation, from early-stage R&D to commercialization and deployment, across diverse geographies. Additionally, co-developing curricula and training programs between federal agencies, regional tech hubs, and industry partners will ensure that talent pipelines are closely aligned with the evolving needs of the clean energy sector, providing the skilled workforce necessary to implement and scale innovations effectively. To accomplish this the:

• DOE should facilitate the creation of a national network that connects federal labs, regional tech hubs and innovation consortia. This network would provide a clear pathway for the transition of technologies from research to commercialization, ensuring that innovations can be deployed across different regions based on local needs and capacities. 
• Regional Tech Hubs, in partnership with local universities and research institutions, should be integrated into the pipeline to provide localized innovation support and commercialization expertise. These hubs can act as nodes in the broader network, offering the infrastructure and expertise necessary for scaling up clean energy technologies.

Develop a Shared Metrics and Monitoring Platform

A centralized dashboard for tracking key metrics related to clean energy and biotechnology initiatives is crucial for guiding investment and policy decisions. By integrating federal and regional data can provide a comprehensive, real-time view of progress across the country. This shared platform would enable better coordination among federal, state, and local agencies, ensuring that resources are allocated efficiently and that policy decisions are informed by accurate, up-to-date data. Moreover, a unified system would allow for more effective tracking of regional performance, enabling tailored solutions and based on localized needs and challenges. To this end:

• The DOE, in partnership with the EPA, should lead the development of a centralized dashboard that integrates existing federal and regional data on SAF production, emissions reductions, workforce needs, and infrastructure gaps. This platform should be publicly accessible, allowing stakeholders at all levels to monitor progress and identify opportunities for improvement.
• State and local governments should contribute relevant data from regional initiatives, including workforce training programs, infrastructure development projects, and emissions reductions efforts. These contributions would help ensure that the platform reflects the full range of activities across different regions, providing a more accurate picture of national progress.
  The Department of Labor and the DOC should integrate workforce development and industrial capacity data into the platform. This would include information on training programs, regional workforce readiness, and skills gaps, helping policymakers align talent development efforts with regional needs.
  The DOT should ensure that transportation infrastructure data, particularly related to SAF production and distribution networks, is included in the platform. This would provide a comprehensive view of the supply chain and infrastructure readiness necessary to scale clean energy technologies across regions.

The clean energy sector, and specifically SAF, highlights both the promise and the persistent challenges of scaling biotechnologies, reflecting broader issues, such as fragmented regulation, limited commercialization support, and misaligned incentives that hinder the deployment of advanced biotechnologies. Overcoming these systemic barriers requires coordinated, long-term policies including performance-based incentives, and procurement mechanisms that reduce investment risk and free up capital. SAF should be seen not as a standalone initiative but as a model for integrating biotechnology into industrial and energy strategy, supported by a robust innovation pipeline, expanded infrastructure, and shared metrics to guide progress. With sustained federal leadership and strategic alignment, the bioeconomy can become a key pillar of a low-carbon, resilient energy future.

The bioeconomy is a pivotal economic sector driving national growth, technological innovation, and global competitiveness. However, the biotechnology innovation and biomanufacturing sector faces significant challenges, particularly in scaling technologies and overcoming long development timelines that don’t align with short-term return expectations from investors. These extended timelines and the inherent risks involved lead to funding gaps that hinder the successful commercialization of technologies and bio-based products. If obstacles like the ‘Valleys of Death, a lack of capital at crucial development junctures, that companies and technology struggle to overcome are not addressed, this could result in economic stagnation and the U.S. losing its competitive edge in the global bioeconomy.

Government programs like SBIR and STTR lessen the financial gap inherent in the U.S. bioeconomy, but existing financial mechanisms have proven insufficient to fully de-risk the sector and attract the necessary private investment. In FY24, the National Defense Authorization Act established the Office of Strategic Capital within the Department of Defense to provide financial and technical support for its 31 ‘Covered Technology Categories’, which includes biotechnology and biomanufacturing. To address the challenges associated with de-risking biotechnology and biomanufacturing within the U.S. bioeconomy, the Office of Strategic Capital within the Department of Defense should house a Bioeconomy Finance Program. This program would offer tailored financial incentives such as loans, tax credits, and volume guarantees, targeting both short-term and long-term scale-up needs in biomanufacturing and biotechnology. 

By providing these essential funding mechanisms, the Bioeconomy Finance Program will reduce the risks inherent in biotechnology innovation, encouraging more private sector investment. In parallel, states and regions across the country should develop regional specific strategies, like investing in necessary infrastructure, and fostering public-private partnerships, to complement the federal government’s initiatives to de-risk the sector. Together, these coordinated efforts will create a sustainable, competitive bioeconomy that supports economic growth, and strengthens U.S. national security.

Challenge & Opportunity

The U.S. bioeconomy encompasses economic activity derived from the life sciences, particularly in biotechnology and biomanufacturing. The sector plays an important role in driving national growth and innovation. Given its broad reach across industries, impact on  job creation, potential for technological advancements, and requirement for global  competitiveness, the U.S. bioeconomy is a critical sector for U.S. policymakers to support. With continued development and growth, the U.S. bioeconomy promises not only economic benefits, but also strengthens national security, health outcomes, and environmental sustainability for the country.

Ongoing advancements in biotechnology, including artificial intelligence and automation, have accelerated the growth of the bioeconomy, making the sector both globally competitive and an important domestic economic sector. In 2023, the U.S. bioeconomy supported nearly 644,000 domestic jobs, contributed $210 billion to the GDP, and generated $49 billion in wages. Biomanufactured products within the bioeconomy span multiple categories (Figure 1). Growth here will drive future economic development and address societal challenges, making the bioeconomy  a key priority for government investment and strategic focus.

Figure 1. Bioeconomy Valorization Cascade

Biomanufactured products span a wide range of categories, from pharmaceuticals and chemicals, which require small volumes of biomass but yield high-value products, to energy and heat, which require larger volumes of biomass but result in lower-value products. Additionally, there are common infrastructure synergies, bioprocesses, and complementary input-output relationships that facilitate a circular bioeconomy within bioproduct manufacturing. Source: https://edepot.wur.nl/407896

An important driving force for the U.S. bioeconomy is biotechnology and biomanufacturing innovation. However, bringing biotechnologies to market requires substantial investment, capital, and most importantly, time. Unlike other technology sectors which see returns on investment within a short period of time, often, there is a misalignment between scientific and capitalistic expectations. Many biotechnology based companies rely on venture capital, a form of private equity investments, to finance their operations. However, venture capitalists (VCs) typically operate on short return on investment timelines, which may not align with the longer development cycles characteristic of the biotechnology sector (Figure 2). Additionally, the need for large-scale and the high capital expenditures (CAPEX) required for commercially profitable production, along with the low-profit margins in high-volume commodity production, create further barriers to obtaining investment. While this misalignment is not universal, it remains a challenge for many biotech startups.

The U.S. government has implemented several programs to address the financing void that often arises during the biotechnology innovation process. These include the Small Business Innovation Research (SBIR) and Small Business Technology Transfer (STTR) programs, which provide phased funding across all Technology Readiness Levels (TRLs); the DOE Loan Program Office, which offers debt financing for energy-related innovations; the DOE Office of Clean Energy Demonstrations which provides funding for demonstration-scale projects that provide proof of concept; and the newly established Office of Strategic Capital (OSC) within the DOD (as outlined in the FY24 National Defense Authorization Act), which is tasked with issuing loans and loan guarantees to stimulate private investment in critical technologies. An example is the office’s new Equipment Loan Financing through OSC’s Credit Program.

Figure 2. Representative Biotechnology & Non-Biotechnology Development Cycle Timeline

Biotechnology development timelines typically take around ~10+ years to complete and reach the market due to longer R&D and Demonstration & Scale-Up phases, while non-biotechnology development timelines are generally much shorter, averaging around ~5+ years.

While these efforts are important, they are insufficient on their own to de-risk the sector to the degree which is needed to realize the full potential of the U.S. bioeconomy. To effectively support the biotechnology innovation pipeline at critical stages, the government must explore and implement additional financial mechanisms that attract more private investment and mitigate the inherent risks associated with biotechnology innovation. Building on existing resources like the Regional Technology and Innovation Hubs, NSF Regional Innovation Engines, and Manufacturing USA Institutes, help stimulate private sector investment and are crucial for strengthening the nation’s economic competitiveness. 

The newly established Office of Strategic Capital (OSC) within the DOD is well-positioned to enhance resilience in critical sectors for national security, including biotechnology and biomanufacturing, through large-scale investments. Biotechnology and biomanufacturing inherently require significant CAPEX, expenses related to the purchase, upgrade, or maintenance of physical assets. This requires substantial amounts of strategic and concessional capital to de-risk and accelerate the biomanufacturing process. By creating, implementing, and leveraging various financial incentives and resources, the Office of Strategic Capital can help build the robust infrastructure necessary for private sector engagement.

To achieve this, the U.S. government should create the Bioeconomy Finance Program (BFP) within the OSC, specifically tasked with enabling and de-risking the biotechnology and biomanufacturing sectors through financial incentives and programs. The BFP should focus on different levels of funding based on the time required to scale, addressing potential ‘Valleys of Death’ that occur during the biomanufacturing and biotechnology innovation process. These funding levels would target short-term (1-2 years) scale-up hurdles to accelerate the biotechnology and biomanufacturing process, as well as long-term (3-5 years) scale-up challenges, providing transformative funding mechanisms that could either make or break entire sectors.

In addition to the federal programs within the BFP to de-risk the sector, states and regions must also make substantial investments and collaborate with federal efforts to accelerate biomanufacturing and biotechnology ecosystems within their own areas. While the federal government can provide a top-down strategy, regional efforts are critical for supporting the sector with bottom-up strategies that complement and align with federal investments and programs, ultimately enabling a sustainable and competitive biotechnology and biomanufacturing industry regionally. To facilitate this, regions should develop and implement state-wide investment initiatives like resource analysis, infrastructure programs, and a cohesive, long-term strategy focused on public-private partnerships. The federal government can encourage these regional efforts by ensuring continued funding for biotechnology hubs and creating additional opportunities for federal investment in the future.

Plan of Action

To strengthen and increase the competitiveness of the U.S. bioeconomy, a coordinated approach is needed that combines federal leadership with state-level action. This includes establishing a dedicated Bioeconomy Finance Program within the Office of Strategic Capital to create targeted financial mechanisms, such as loan programs, tax incentives, and volume guarantees. Additionally, states must be empowered to support commercial-scale biomanufacturing and infrastructure development, leveraging tech hubs, cross-regional partnerships, and building public-private partnerships to build capacity and foster innovation nationwide.

Recommendation 1. Establish and Fund a Bioeconomy Finance Program

Congress, in the next National Defense Authorization Act, should codify the Office of Strategic Capital (OSC) within DOD and authorize the creation of a Bioeconomy Finance Program (BFP) within the OSC to provide centralized federal structure for addressing financial gaps in the bioeconomy, thereby increasing productivity and competitiveness globally. In 2024, Congress expanded the OSCs mission to offer financial and technical support to entities within its 31 ‘Covered Technology Categories,’ including biotechnology and biomanufacturing. Additionally, in order to build resilience in the sector and maintain a competitive advantage globally while also strengthening national security, these substantial expenditures should be housed within the OSC. Establishing the BFP within the OSC at the DOD would allow for a targeted focus on these critical sectors, ensuring long-term stability and resilience against political shifts. 

The DOD and OSC should leverage its own funding as well as its existing partnership with the Small Business Administration to direct $1 billion to set up the BFP to create and implement initiatives aimed at de-risking the U.S. bioeconomy. The Bioeconomy Finance Program should work closely with relevant federal agencies, such as the DOE, Department of Agriculture (USDA), and the Department of Commerce (DOC), to ensure a long-term cohesive strategy for financing bioeconomy innovation and biomanufacturing capacity.

Recommendation 2. Task the Bioeconomy Finance Program with Key Initiatives

A key element of the OSC’s mission and investment strategy is to provide financial incentives and support to entities within its 31 ‘Core Technology Categories’. By having BFP design and manage these financial initiatives for the biotechnology and biomanufacturing sectors, the OSC can leverage lessons from similar programs, such as the DOE’s loan program, to address the unique needs of these critical industries, which are essential for national security and economic growth.

Currently, the OSC has launched a credit program for equipment financing. While this is a necessary first step in fulfilling the office’s mission, the program is open to all 31 ‘Core Technology Categories’, resulting in broad, dilutive funding. To accelerate the bioeconomy and reduce risks in biotechnology and biomanufacturing, it is crucial to allocate resources specifically to these sectors. Therefore, BFP should take the lead in several key financial initiatives to support the growth of the bioeconomy, including:

Loan Programs

The BFP should develop specific biotechnology enabling loan programs, in addition to the new equipment loan financing program run by the OSC. These loan programs should be modeled after those in the DOE LPO, focusing on biomanufacturing scale-up, technology transfer, and overcoming financing gaps that hinder commercialization.

Example loan programs:

• DOE Title 17 Clean Energy Financing Program
• USDA Business & Industry Loan Guarantee
• Solar Foods EU Grant/Loan

Tax Incentives

The BFP office should create tax incentives tailored to the bioeconomy, such as, transferable investment  and production tax credits. For example, the 45V tax credit for production of clean hydrogen could serve as a model for similar incentives aimed at other bioproducts.

Example tax incentives:

• The Inflation Reduction Act’s transferable tax credits are the gold standard for this category.
  • 45X Advanced Manufacturing Production Credit
  • 45U Nuclear Power Production Tax Credit
  • 45Z Clean Fuel Production Credit

Volume Guarantees & Procurement Support

To mitigate risks in biomanufacturing, the office should establish volume guarantees for various bioproducts, offering financial assurance to manufacturers and encouraging private sector investment. An initial assessment should be conducted to identify which bioproducts are best suited for such guarantees. Additionally, the office should explore the possibility of procurement programs to increase government demand for bio-based products, further incentivizing industry growth and innovation. This effort should be undertaken in coordination with the USDA’s BioPreferred Program to minimize redundancy and to create a cohesive procurement strategy. In addition, the BFP should look to the procurement innovations promoted by the Office of Federal Procurement Policy to find solutions for forward funding to create a functioning market.

Example Volume Guarantees & Procurement Support:

• Heavy Forging Press Infrastructure Lease Agreement
• NASA and USAF buying Fairchild semiconductors in advance of needing them, and overbought performance
• Advance Market Commitments
• Joint Venture Partnerships
• Other Transaction Authorities

Recommendation 3. Develop Pipeline Programs to Address Financial and Time Horizon Needs

Utilizing the key initiatives highlighted above, the BFP should create a two-tiered financial mechanisms pipeline and program to address both the short-term and long-term financial needs. The different financial levels could potentially include:

• Level 1 – Short Term Scale-Up (1-2 years) Programs 
  • Subsidized cost of electricity and other utilities (waste, wastewater treatment, natural gas, energy, etc.)
  • Funding for demonstration-scale projects and early-stage engineering development. Similar to the DOEs Office of Clean Energy Demonstrations or the DODs’ Defense Industrial Base Consortium round one $1-2M engineering grants)
  • Tax holidays for corporate taxes and property taxes
  • Allowing accelerated depreciation to reduce tax liabilities
  • Land grants or subsidies for manufacturing assets
  • Fast-track permitting and site preparation to avoid long waits
  • Labor and workforce subsidies
  • Removal of export duties on products created in the U.S. and shipped overseas

• Level 2 – Long Term Scale-Up (3-5 years) Programs
  • Large-scale transferable tax credits (either production or investment tax credits) for manufacturing. Similar to the tax credits seen in the Inflation Reduction Act for clean energy.
  • Large-scale manufacturing grants
  • Large-scale, low-interest manufacturing loans and loan guarantees
  • Government procurement contracts or commitment for offtake, such as partial/full volume guarantees
  • Government direct or indirect equity investments in biomanufacturing and biotechnology innovations

Recommendation 4.  State-Level Initiatives, Infrastructure Development, and Public-Private Partnerships

While federal efforts are crucial, a bottom-up approach is needed to support biomanufacturing and the bioeconomy at the state level. The federal government can support these regional activities by providing targeted funding, policy guidance, and financial incentives that align with regional priorities, ensuring a coordinated effort toward industry growth. States should be encouraged to complement federal initiatives by developing programs that support commercial-scale biomanufacturing. Key actions include:

• State-Level Bioeconomy Resource Analysis: Each state and region should conduct their own analysis to understand the bioeconomy resources at their disposal and determine what relevant resources they would need to establish or strengthen state or regional bioeconomies. Identifying  these resources will help the nation understand its true bioeconomic potential by understanding where certain biomass is contained, what facilities are available and needed to develop an economically sustainable bioeconomy, and create data to better understand the economic return on investment.
  • Once the analysis is completed, States should collaborate with federal agencies like the DOE, DOC, and Economic Development Administration (EDA) to create and apply for specialized grants for commercial-scale biomanufacturing facilities based off of these analyses. Grants should prioritize non-pharmaceutical biomanufacturing to expand the scope of bioeconomy growth beyond traditional sectors. 

• Utility Infrastructure Grants: Another critical area is the creation of utility infrastructure needed to support biomanufacturing, such as wastewater treatment and electricity infrastructure. States should receive targeted funding for these infrastructure projects, which are essential for scaling up production. States should take these targeted funds and establish their own granting mechanism to build necessary, regional infrastructure that is needed long-term to support the U.S. bioeconomy.

• Tech Hub Partnerships: States should leverage existing tech hubs to serve as centers for innovation in bioeconomy technologies. These hubs, which are already positioned in regions with high technological readiness, can be incentivized to partner with other regions that may not yet have robust tech ecosystems. The goal is to create a collaborative, cross-regional network that fosters knowledge-sharing and builds capacity across the country.

• Foster Public-Private Partnerships (PPP): To ensure the success and sustainability of these initiatives, states should actively foster PPPs that bring together government, industry leaders, and academic institutions. These partnerships can help align private sector investment with public goals, enhance resource sharing, and accelerate the commercialization of bioeconomy technologies. By engaging in collaborative R&D, sharing infrastructure costs, and co-developing new biotechnologies, PPPs will play a crucial role in driving innovation and economic growth in the bioeconomy sector. In addition to fostering PPPs, regions should proactively work on creating models that enable these partnerships to become self-sustaining, helping to mitigate potential financial pitfalls if partners drop out of the partnership. By not only creating PPPs, but also ensuring they become fully independent over time, the associated risks with PPPs decrease significantly.

By addressing these steps at both the federal and state levels, the U.S. can create a robust, scalable framework for financing biomanufacturing and the broader bioeconomy, supporting the transition from early-stage innovation to commercial success and ensuring long-term economic competitiveness. A good example of how this approach works is the DOE Loan Program Office, which collaborates with state energy financing institutions. This partnership has successfully supported various projects by leveraging both federal and state resources to accelerate innovation and drive economic growth. This model makes sense for biomanufacturing and biotechnology within the BFP in the OSC, as it ensures coordination between federal and state efforts, de-risks the sector, and facilitates the scaling of transformative technologies.

Conclusion

Biotechnology innovation and biomanufacturing are critical components of the U.S. bioeconomy which drives innovation, economic growth, and global competitiveness, but these sectors face significant challenges due to the misalignment of development timelines and investment cycles. The sector’s inherent risks and long development processes create funding gaps, hindering the commercialization of vital biotechnologies and products. These challenges, including the ‘Valleys of Death,’ could stifle innovation, slow down progress, and result in the U.S. losing its global leadership in biotechnology if left unaddressed.

To overcome these obstacles, a coordinated and comprehensive approach to de-risk the sector is necessary. The establishment of the Bioeconomy Finance Program (BFP)  within the DOD’s Office of Strategic Capital (OSC) offers a robust solution by providing targeted financial incentives, such as loans, tax credits, and volume guarantees, designed to de-risk the sector and attract private investment. These financial mechanisms would address both short-term and long-term scale-up needs, helping to bridge funding gaps and accelerate the transition from innovation to commercialization. Furthermore, building on existing government resources, alongside fostering state-level initiatives such as infrastructure development, and public-private partnerships, will create a holistic ecosystem that supports biotechnology and biomanufacturing at every stage and will substantially de-risk the sector. By empowering regions to develop their own bioeconomy strategies and leverage local federal government programs, like the EDA Tech Hubs, the U.S. can create a sustainable, scalable framework for growth. By taking these steps, the U.S. can strengthen both its economic position but also lead the world in development of transformative biotechnologies.

The U.S. should create a new non-governmental Innovation Accelerator modeled after the successful In-Q-Tel program to invest in small and mid-cap companies creating technologies that address critical needs of the United States. Doing so would directly address the bottleneck in our innovation pipeline that limits innovative companies from bringing their products to market. 

Challenge and Opportunity 

While the federal government funds basic, early-stage R&D, it leaves product development and commercialization to the private sector. This paradigm has created a so-called innovation Valley of Death: a lack of capital support for the transition to early commercialization, and one that  stalls economic growth for many innovation-driven sectors. The U.S. currently leads the world in the formation of companies, but the limitations on capital sources artificially restrict growth. For example, the U.S. currently leads the world in biotechnology and biomedical innovation. The U.S. market alone is worth $600B, and is projected to exceed $1.5 trillion.  However, international rivals  are catching up: China is projected to close the biotechnology innovation gap in 2028. The U.S. must act quickly to protect its lead.

Typically, early and mid-stage innovations are too immature for private capital investors because they present an outsized risk. In addition, private capital tends to be more conservative in rough economic times, which further dries up the innovation pipeline. Investment “fads” tend to starve other fields of capital investment for potentially years at a time So, though the U.S. government provides significant early-stage discovery funding for innovation through its various agencies, the grant lifecycle is such that after the creation and initial development of new technologies, there are few mechanisms for continued support to drive products to market. 

It is this period – after R&D but before commercial demonstration – that creates a substantial bottleneck for entrepreneurs where their work is too advanced for the usual government research and development grant funding but not developed enough to draw private investment. Existing SBIR and STTR grant programs that the government provides for this purpose are typically too small to significantly advance such innovations, while the application process is too cumbersome and slow to draw the interests of many companies. As a result, small businesses created around these technologies often fail because of funding challenges, rather than any faults of the innovations they are developing. 

The federal government, therefore, has an opportunity to make the path from lab to market smoother by establishing a mechanism for supporting smaller companies developing innovative products that will substantially improve the lives of Americans. A new U.S. Innovation Accelerator will provide R&D funding to promising companies to accelerate innovations critical to the U.S. by de-risking them as they move toward private sector funding and commercialization. 

Creating the U.S. Innovation Accelerator 

We propose creating a new federally guided entity modeled on In-Q-Tel, the government funded not-for-profit venture capital firm that invests in companies that are developing technologies that can be used by intelligence agencies. Similar to In-Q-Tel, the U.S. Innovation Accelerator would operate independently of the government, but leverage federal investments in research and development to ensure that promising new technologies make it to market. 

By having the organization live outside of the government, it will be able to pay staff a wage that is commensurate with their experience and draw top talent interested in driving innovation across the R&D spectrum. The organization would invest in the development of technology companies, and would partner with private capital sources to help develop critical technologies that are too risky for private capital entities to fund on their own. Such capital would allow innovation to flourish. In exchange, the organization could establish requirements for keeping such companies, and their manufacturing operations, in the U.S. for some period after receiving public funding (10 years, for example) to prevent the offshoring of technologies that are developed with public dollars. The agency would use a variety of funding vehicles to support companies to best match their needs and increase their chances of success. 

Scope

The new U.S. Innovation Accelerator could be established as a sector-specific entity, (for example as biotechnology and healthcare-focused fund), or it could include a series of portfolios that invest in companies across the innovation spectrum. Both approaches have merits worth exploring: a narrower biomedical fund would have the benefit of quickly deploying capital to accelerate key areas of strategic U.S. interest while proving the concept and setting the stage to expand to other sectors of the economy; alternatively, if a larger pool of funding is available initially, a broader investment portfolio would allow for targeted investments across sectors ranging from biotechnology and agriculture to advanced materials and energy.  

Sources of Capital 

The U.S. Innovation Accelerator can be funded in several ways to create a robust investment vehicle for advancing biotechnology and healthcare innovation. Two potential models include a publicly-funded revolving fund, similar to In-Q-Tel, while the other would draw capital from retirement and pension funds providing a return on investment to voluntary investors. 

Appropriations driven revolving fund. Like In-Q-Tel, Congress could kick start the Innovation Accelerator though direct appropriations. This annual investment could be curtailed and repaid to the treasury once the fund starts to realize returns on the investments it makes.

Thrift Savings Plan allocations. The federal employee retirement savings plan, the Thrift Savings Plan, holds approximately $700 billion in assets across various investment funds. By allowing for voluntary investment allocation in the Innovation Fund by federal employees, even a small percentage of TSP assets could provide billions in initial capital. This allocation would be structured as part of the TSP’s broader investment strategy, through the creation of a new specialized SBF fund option for participants.

U.S. State & Public Pension Plans. State and local government pension plans hold assets totaling roughly $6.25 trillion. The Innovation Fund could work with state pension administrators to create investment vehicles that align with their risk-return profiles and support both financial and social impact goals. These would be made available to plan participants in a similar manner to the TSP or through more traditional allocation. 

Reforming the SBIR/STTR Programs. The SBIR and STTR programs represent 3.2% of the total Federal R&D budget for 11 agencies, but struggle to attract suitable applicants. This is not because there is a lack of need in early-stage innovation. Typically, these grants are judged and awarded by program managers that have little or no private sector experience, take too long from application to award, and provide insufficient funds for many companies to consider them. Those dollars could instead be allocated to the Innovation Accelerator program, and invested in more promising small businesses through a streamlined program that creates a revolving fund through returns on initial investment that can be then reinvested in additional promising companies. The program now uses ceilings for different phases of SBIR grants. These phases are artificial, and do not reflect the reality of the needs of different types of companies and thus should be eliminated and replaced with needs-based funding. USG agencies can issue technology priority guidance to the U.S. Innovation Accelerator and completely off-load the burden of having to run multiple SBIR programs. 

Part of the proposed US Sovereign Wealth Fund. In February of this year, President Trump issued an Executive Order directing the Secretaries of Commerce and Treasury to develop plans for the creation of a sovereign wealth fund. The plan will include recommendations for funding mechanisms, investment strategies, fund structure, and a governance model. Such funds exist across many countries as a mechanism for amplifying the financial return on the nation’s assets and to leverage those returns for strategic benefit and economic growth. We propose that the U.S. Innovation Accelerator falls squarely in the remit of a sovereign fund and that the fund could serve as a sustainable source of capital to fund the development of innovative companies and products that address critical national challenges and directly benefit Americans in tangible ways. 

Structure and operations

The Innovation Accelerator program will be structured similar to a lean private venture capital entity, with oversight from the U.S. government to inform strategic deployment of capital towards innovative companies that address unmet national needs. As an  independent, non-profit organization or public benefit corporation (PBC), overhead can be kept low, and it can be guided by a small entrepreneurial Board of Directors representing innovative industries and investment professionals to ensure that the organization stays on mission. Further, the organization should collaborate with federal agencies to identify areas of national need and ensure that promising companies that originate from other federal research and development programs will have the capital necessary to bring their innovations to market, thus ensuring a stable innovation pipeline and addressing a longstanding bottleneck that has driven American companies to seek foreign capital or to offshore their operations. 

The professional investment team would include expertise in a broad set of domains and with a proven track record of commercial success. The organization would have a high degree of autonomy but maintain alignment with national technology priorities and competitive strategy. Transparency and accountability will be paramount and include constant full public accounting of all investments and strategy. 

The primary objective of the Innovation Accelerator will be to deliver game changing innovations that generate exceptional returns on investment while supporting the development of strategically important technologies. The U.S. Innovation Accelerator will also insulate domestic innovation from the delays and inefficiency caused by the private sector funding cycle.

Conclusion

The U.S. Innovation Accelerator would address a critical gap in the current U.S. innovation pipeline that was created as an artifact of the way we fund research. Most public dollars are dedicated to early-stage research but development and commercialization are normally left to the private sector, which is vulnerable to macroeconomic trends that can stall innovation for years. The U.S. Innovation Accelerator would open up that bottleneck by driving innovation and economic growth while addressing critical national needs. Because the U.S. Innovation Accelerator would exist outside of the federal government, it can be created without an act of Congress. The President could direct his administration through an executive action to develop plans and create the U.S. Innovation Accelerator as either part of the sovereign fund he has proposed or independent of that action. However, to get it initially funded and backed by the U.S. government, (see funding mechanisms above), Congress would have to appropriate dollars through an existing federal agency. Part of the charter for establishing the U.S. Innovation Accelerator could be repayment of the initial investments back to the U.S. Treasury from fund returns.

The U.S. healthcare system produces 5 million tons of waste annually, or approximately 29 pounds per hospital bed daily. Roughly 80 percent of the healthcare industry’s carbon footprint comes from the production, transportation, use, and disposal of single-use devices (SUDs), which are pervasive in the hospital. Notably, 95% of the environmental impact of single-use medical products results from the production of those products. 

While the Food and Drug Administration (FDA) oversees new devices being brought to market, it is up to the manufacturer to determine whether a device will be marketed as single-use or multiple-use. Manufacturers have a financial incentive to market devices for “single-use” or “disposable” as marketing a device as reusable requires expensive cleaning validations.

In order to decrease healthcare waste and environmental impact, FDA leads on identifying reusable devices that can be safely reprocessed and incentivizing manufacturers to test the reprocessing of their device. This will require the FDA to strengthen its management of single-use and reusable device labeling. Further, the Veterans Health Administration, the nation’s largest healthcare system, should reverse the prohibition on reprocessed SUDs and become a national leader in the reprocessing of medical devices.

Challenge and Opportunity

While healthcare institutions are embracing decarbonization and waste reduction plans, they cannot do this effectively without addressing the enormous impact of single-use devices (SUDs). The majority of research literature concludes that SUDs are associated with higher levels of environmental impact than reusable products. 

FDA regulations governing SUD reprocessing make it extremely challenging for hospitals to reprocess low-risk SUDs, which is inconsistent with the FDA’s “least burdensome provisions.” The FDA requires hospitals or commercial SUD reprocessing facilities to act as the device’s manufacturer, meaning they must follow the FDA’s rules for medical device manufacturers’ requirements and take on the associated liabilities. Hospitals are not keen to take on the liability of a manufacturer, yet commercial reprocessors do not offer many lower-risk devices that can be reprocessed. 

As a result, hospitals and clinics are no longer willing to sterilize SUDs through methods like autoclaving even despite documentation showing that sterilization is safe and precedent showing that similar devices have been safely sterilized and reused for many years without adverse events. Many devices, including pessaries for pelvic organ prolapse and titanium phacoemulsification tips for cataract surgery, can be safely reprocessed in their clinical use. These products, given their risk profile, need not be subject to the FDA’s full medical device manufacturer requirements.  

Further, manufacturers are incentivized to bring SUDs to market quicker than those that may be reprocessed. Manufacturers often market devices as single-use solely because the original manufacturer chose not to conduct expensive cleaning and sterilization validations, not because such cleaning and sterilization validations cannot be done. FDA regulations that govern SUDs should be better tailored to each device so that clinicians on the frontlines can provide appropriate and environmentally sustainable health care. 

Reprocessed devices cost 25 to 40% less. Thus, the use of reprocessed SUDs can reduce costs in hospitals significantly — about $465 million in 2023. Per the Association of Medical Device Reprocessors, if the reprocessing practices of the top 10% performing hospitals were maximized across all hospitals that use reprocessed devices, U.S. hospitals could have saved an additional $2.28 billion that same year. Indeed, enabling and encouraging the use of reprocessed SUDs can also yield significant cost reductions without compromising patient care. 

Plan of Action

As the FDA began regulating SUD reprocessing in 2000, it is imperative that the FDA take the lead on creating a clear, streamlined process for clearing or approving reusable devices in order to ensure the safety and efficacy of reprocessed devices. These recommendations would permit healthcare systems to reprocess and reuse medical devices without fear of noncompliance by the Joint Commission or Centers for Medicare and Medicaid Services that reply on FDA regulations. Further, the nation’s largest healthcare system, the Veterans Health Administration, should become a leader in medical device reprocessing, and lead on showcasing the standard of practice for sustainable healthcare.

1. FDA should publish a list of SUDs that have a proven track record of safe reprocessing to empower hospitals to reduce waste, costs, and environmental impact without compromising patient safety. The FDA should change the labels of single-use devices to multi-use when reuse by hospitals is possible and validated via clinical studies, as the “single-use” label has promoted the mistaken belief that SUDs cannot be safely reprocessed. Per the FDA, the single-use label simply means a given device has not undergone the original equipment manufacturer (OEM) validation tests necessary to label a device “reusable.” The label does not mean the device cannot be cleared for reprocessing. 

2. In order to help governments and healthcare systems prioritize the environmental and cost benefits of reusable devices over SUDs, FDA should incentivize applications of reusable or commercially reprocessable devices, such as through expediting review. The FDA can also incentivize use of reprocessed devices through payments to hospitals for meeting reprocessing benchmarks. 

3. The FDA should not subject low-risk devices that can be safely reprocessed for clinical use to full device manufacturer requirements. The FDA should further encourage healthcare procurement staff by creating an accessible database of devices cleared for reprocessing and alerting healthcare systems about regulated reprocessing options. In doing so, the FDA can help reduce the burden on hospitals in reprocessing low-risk SUDs and encourage healthcare systems to sterilize SUDs through methods like autoclaving. 

4. As the only major health system in the U.S. to prohibit the use of reprocessed SUDs, the U.S. Veterans Health Administration should reverse its prohibition as soon as possible. This prohibition likely remains because of outdated determinations of risks, which comes at major costs for the environment and Americans. Doing so would be consistent with the FDA’s conclusions that reprocessed SUDs are safe and effective.  

5. FDA should recommend that manufacturers publicly report the materials used in the composition of devices so that end-users can more easily compare products and determine the environmental impact of devices. As explained by AMDR, some Original Equipment Manufacturer (OEM) practices discourage or fully prevent the use of reprocessed devices. It is imperative that the FDA vigorously track and impede these practices. Not only will requiring public reporting device composition help healthcare buyers make more informed decisions, it will also help promote a more circular economy that uplifts sustainability efforts. 

Conclusion

To decrease costs, waste, and environmental impact, the healthcare sector urgently needs to increase its use of reusable devices. One of the largest barriers is FDA requirements that result in needlessly stringent requirements of hospitals, hindering the adoption of less wasteful, less costly reprocessed devices.

FDA’s critical role in medical device labeling, clearing, or approving more devices as reusable, has down market implications and influences many other regulatory and oversight bodies, including the Centers for Medicare & Medicaid Services (CMS), the Association for the Advancement of Medical Instrumentation (AAMI), the Joint Commission, hospitals, health care offices, and health care providers. It is essential for the FDA to step up and take the lead in revising the device reprocessing pipeline. 

This action-ready policy memo is part of Day One 2025 — our effort to bring forward bold policy ideas, grounded in science and evidence, that can tackle the country’s biggest challenges and bring us closer to the prosperous, equitable and safe future that we all hope for whoever takes office in 2025 and beyond.

PLEASE NOTE (February 2025): Since publication several government websites have been taken offline. We apologize for any broken links to once accessible public data.

The advent of artificial intelligence (AI) has revolutionized business practices, enabling companies to process vast amounts of data and automate complex tasks in ways previously unimaginable. However, while AI has gained much praise for its capabilities, it has also raised various antitrust concerns. Among the most pressing is the potential for AI to be used in an anticompetitive manner. This includes algorithms that facilitate price-fixing, predatory pricing, and discriminatory pricing (harming the consumer market), as well as those which enable the manipulation of wages and worker mobility (harming the labor market). More troubling perhaps is the fact that the overwhelming majority of the AI landscape is controlled by just a few market players. These tech giants—some of the world’s most powerful corporations—have established a near-monopoly over the development and deployment of AI. Their dominance over necessary infrastructure and resources makes it increasingly challenging for smaller firms to compete.

While the antitrust enforcement agencies—the FTC and DOJ—have recently begun to investigate these issues, they are likely only scratching the surface. The covert and complex nature of AI makes it difficult to detect when it is being used in an anticompetitive manner. To ensure that business practices remain competitive in the era of AI, the enforcement agencies must be adequately equipped with the appropriate strategies and resources. The best way to achieve this is to (1) require the disclosure of AI technologies during the merger-review process and (2) reinforce the enforcement agencies’ technical strategy in assessing and mitigating anticompetitive AI practices.

Challenge & Opportunity

Since the late 1970s, antitrust enforcement has been in decline, in part due to a more relaxed antitrust approach put forth by the Chicago school of economics. Both the budgets and the number of full-time employees at the enforcement agencies have steadily decreased, while the volume of permitted mergers and acquisitions has risen (see Figure 1). This resource gap has limited the ability of the agencies to effectively oversee and regulate anticompetitive practices.

Figure 1. Merger Enforcement vs. Total Filings

Source: Department of Justice Antitrust Division Operations

Changing attitudes surrounding big business, as well as recent shifts in leadership at the enforcement agencies—most notably President Biden’s appointment of Lina Khan to FTC Chair—have signaled a more aggressive approach to antitrust law. But even with this renewed focus, the agencies are still not operating at their full potential. 

This landscape provides a significant opportunity to make some much-needed changes. Two areas for improvement stand out. First, agencies can make use of the merger review process to aid in the detection of anticompetitive AI practices. In particular, the agencies should be on the look-out for algorithms that facilitate price-fixing, where competitors use AI to monitor and adjust prices automatically, covertly allowing for tacit collusion; predatory pricing algorithms, which enable firms to undercut competitors only to later raise prices once dominance is achieved; and dynamic pricing algorithms, which allow firms to discriminate against different consumer groups, resulting in price disparities that may distort market competition. On the labor side, agencies should screen for wage-fixing algorithms and other data-driven hiring practices that may suppress wages and limit job mobility. Requiring companies to disclose the use of such AI technologies during merger assessments would allow regulators to examine and identify problematic practices early on. This is especially useful for flagging companies with a history of anticompetitive behavior or those involved in large transactions, where the use of AI could have the strongest anticompetitive effects.

Second, agencies can use AI to combat AI. Research has demonstrated that AI can be more effective in detecting anticompetitive behavior than other traditional methods. Leveraging such technology could transform enforcement capabilities by allowing agencies to cover more ground despite limited resources. While increasing funding for these agencies would be requisite, AI nonetheless provides a cost-effective solution, enhancing efficiency in detecting anticompetitive practices, without requiring massive budget increases.

The success of these recommendations hinges on the enforcement agencies employing technologists who have a deep understanding of AI. Their knowledge on algorithm functionality, the latest insights in AI, and the interplay between big data and anticompetitive behavior is instrumental. A detailed discussion of the need for AI expertise is covered in the following section.

Plan Of Action

Recommendation 1. Require Disclosure of AI Technologies During Merger-Review.

Currently, there is no formal requirement in the merger review process that mandates the reporting of AI technologies. This lack of transparency allows companies to withhold critical information that may help agencies determine potential anticompetitive effects. To effectively safeguard competition, it is essential that the FTC and DOJ have full visibility of businesses’ technologies, particularly those that may impact market dynamics. While the agencies can request information on certain technologies further in the review process, typically during the second request phase, a formalized reporting requirement would provide a more proactive approach. Such an approach would be beneficial for several reasons. First, it would enable the agencies to identify anticompetitive technologies they might have otherwise overlooked. Second, an early assessment would allow the agencies to detect and mitigate risk upfront, rather than having to address it post-merger or further along in the merger review process, when remedies may be more difficult to enforce. This is particularly applicable with regard to deep integrations that often occur between digital products post-merger. For instance, the merger of Instagram and Facebook complicated the FTC’s subsequent efforts to challenge Meta. As Dmitry Borodaenko, a former Facebook engineer, explained: 

“Instagram is no longer viable outside of Facebook’s infrastructure. Over the course of six years, they integrated deeply… Undoing this would not be a simple task—it would take years, not just the click of a button.”

Lastly, given the rapidly evolving nature of AI, this requirement would help the agencies identify trends and better determine which technologies are harmful to competition, under what circumstances, and in which industries. Insights gained from one sector could inform investigations in other sectors, where similar technologies are being deployed. For example, the DOJ recently filed suit against RealPage, a property management software company, for allegedly using price-fixing algorithms to coordinate rent increases among competing landlords. The case is the first of its kind, as there had not been any previous lawsuit addressing price-fixing in the rental market. With this insight, however, if the agencies detect similar algorithms during the merger review process, they would be better equipped to intervene and prevent such practices.

There are several ways the government could implement this recommendation. To start, The FTC and DOJ should issue interpretive guidelines specifying that anticompetitive effects stemming from AI technologies are within the purview of the Hart-Scott-Rodino (HSR) Act, and that accordingly, such technologies should be disclosed in the pre-merger notification process. In particular, the agencies should instruct companies to report detailed descriptions of all AI technologies in use, how they might change post-merger, and their potential impact on competition metrics (e.g., price, market share). This would serve as a key step in signaling to companies that AI considerations are integral during merger review. Building on this, Congress could pass legislation mandating AI disclosures, thereby formalizing the requirement. Ultimately, in a future round of HSR revisions, the agencies could incorporate this mandate as a binding rule within the pre-merger framework. To avoid unnecessary burden on businesses, reporting should only be required when AI plays a significant role in the company’s operations or is expected to post-merger. What constitutes a ‘significant role’ should be left to the discretion of the agencies but could include AI systems central to core functions such as pricing, customer targeting, wage-setting, or automation of critical processes.

Recommendation 2. Reinforce the FTC and DOJ’s Technical Strategy in Assessing and Mitigating Anticompetitive AI Practices.

Strengthening the agencies’ ability to address AI requires two actions: integrating computational antitrust strategies and increasing technical expertise. A wave of recent research has highlighted AI as a powerful tool in helping detect anticompetitive behavior. For instance, scholars at the Stanford Computational Antitrust Project have demonstrated that methods such as machine learning, natural language processing, and network analysis can assist with tasks, ranging from uncovering collusion between firms to distinguishing digital markets. While the DOJ has already partnered with the Project, the FTC could benefit by pursuing a similar collaboration. More broadly, the agencies should deepen their technical expertise by expanding workshops and training with AI academic leaders. Doing so would not only provide them with access to the most sophisticated techniques in the field, but would also help bridge the gap between academic research and real-world implementation. Examples may include the use of machine learning algorithms to identify price-fixing and wage-setting; sentiment analysis, topic modeling, and other natural language processing tools to detect intention to collude in firm communications; or reverse-engineering algorithms to predict outcomes of AI-driven market manipulation. 

Leveraging such computational strategies would enable regulators to analyze complex market data more effectively, enhancing the efficiency and precision of antitrust investigations. Given AI’s immense power, only a small—but highly skilled—team is needed to make significant progress. For instance, the UK’s Competition and Markets Authority (CMA) recently stood up a Data, Technology and Analytics unit, whereby they implement machine learning strategies to investigate various antitrust matters. For the U.S. agencies to facilitate this, the DOJ and FTC should hire more ML/AI experts, data scientists, and technologists, who could serve several key functions. First, they could conduct research on the most effective methods for detecting collusion and anticompetitive behavior in both digital and non-digital markets. Second, based on such research, they could guide the implementation of selected AI solutions in investigations and policy development. Third, they could perform assessments of AI technologies, evaluating the potential risks and benefits of AI applications in specific markets and companies. These assessments would be particularly useful during merger review, as previously discussed in Recommendation 1. Finally, they could help establish guidelines for transparency and accountability, ensuring the responsible and ethical use of AI both within the agencies and across the markets they regulate.

To formalize this recommendation, the President should submit a budget proposal to Congress requesting increased funding for the FTC and DOJ to (1) hire technology/AI experts and (2) provide necessary training for other selected employees on AI algorithms and datasets. The FTC may separately consider using its 6(b) subpoena powers to conduct a comprehensive study of the AI industry or of the use of AI practices more generally (e.g., to set prices or wages). Finally, the agencies should strive to foster collaboration between each other (e.g., establishing a Joint DOJ-FTC Computational Task Force), as well as with those in academia and the private sector, ​​to ensure that enforcement strategies remain at the cutting edge of AI advancements.

Conclusion

The nation is in the midst of an AI revolution, and with it comes new avenues for anticompetitive behavior. As it stands, the antitrust enforcement agencies lack the necessary tools to adequately address this growing threat.

However, this environment also presents a pivotal opportunity for modernization. By requiring the disclosure of AI technologies during the merger review process, and by reinforcing the technical strategy at the FTC and DOJ, the antitrust agencies can strengthen their ability to detect and prevent anticompetitive practices. Leveraging the expertise of technologists in enforcement efforts can enhance the agencies’ capacity to monitor levels of competition in markets, as well as allow them to identify patterns between certain technologies and violations of antitrust.

Given the rapid pace of AI advancement, a proactive effort triumphs over a reactive one. Detecting antitrust violations early allows agencies to save both time and resources. To protect consumers, workers, and the economy more broadly, it is imperative that the FTC and DOJ adapt their enforcement strategies to meet the complexities of the AI era.

This action-ready policy memo is part of Day One 2025 — our effort to bring forward bold policy ideas, grounded in science and evidence, that can tackle the country’s biggest challenges and bring us closer to the prosperous, equitable and safe future that we all hope for whoever takes office in 2025 and beyond.

PLEASE NOTE (February 2025): Since publication several government websites have been taken offline. We apologize for any broken links to once accessible public data.

The labeling-only 505(b)(2) NDA pathway for non-manufacturers to seek FDA approval

Repurposing generic drugs as new treatments for life-threatening diseases is an exciting yet largely overlooked opportunity due to a lack of market-driven incentives. The low profit margins for generic drugs mean that pharmaceutical companies rarely invest in research, regulatory efforts, and marketing for new uses. Nonprofit organizations and other non-commercial non-manufacturers are increasing efforts to repurpose widely available generic drugs and rapidly expand affordable treatment options for patients. However, these non-manufacturers find it difficult to obtain regulatory approval in the U.S. They face significant challenges in using the existing approval pathways, specifically in: 1) providing the FDA with required chemistry, manufacturing, and controls (CMC) data, 2) providing the FDA with product samples, and 3) conducting post-marketing surveillance. Without a straightforward path for approval and updating drug labeling, non-manufacturers have relied on off-label use of repurposed drugs to drive uptake. This practice results in outdated labeling for generics and hinders widespread clinical adoption, limiting patient access to these potentially life-saving treatments. 

To encourage greater adoption of generic drugs in clinical practice – that is, to encourage the repurposing of these drugs – the FDA should implement a dedicated regulatory pathway for non-manufacturers to seek approval of new indications for repurposed generic drugs. A potential solution is an extension of the existing 505(b)(2) new drug application (NDA) approval pathway. This extension, the “labeling-only” 505(b)(2) NDA, would be a dedicated pathway for non-manufacturers to seek FDA approval of new indications for well-established small molecule drugs when multiple generic products are already available. The labeling-only 505(b)(2) pathway would be applicable for repurposing drugs for any disease. Creating a regulatory pathway for non-manufacturers would unlock access to innovative therapies and enable the public to benefit from the enormous potential of low-cost generic drugs.

Challenge and Opportunity

The opportunity for generic drug repurposing

On-patent, branded drugs are often unaffordable for Americans. Due to the high cost of care, 42% of patients in the U.S. exhaust their life savings within two years of a cancer diagnosis. Generic drug repurposing – the process of finding new uses for FDA-approved generic drugs – is a major opportunity to quickly create low-cost and accessible treatment options for many diseases. In oncology, hundreds of generic drugs approved for non-cancer uses have been tested as cancer treatments in published preclinical and clinical studies. 

The untapped potential for generic drug repurposing in cancer and other diseases is not being realized because of the lack of market incentives. Pharmaceutical companies are primarily focused on de novo drug development to create new molecular and chemical entities. Typically, pharmaceutical companies will invest in repurposing only when the drugs are protected by patents or statutory market exclusivities, or when modification to the drugs can create new patent protection or exclusivities (e.g., through new formulations, dosage forms, or routes of administration). Once patents and exclusivities expire, the introduction of generic drugs creates competition in the marketplace. Generics can be up to 80-85% less expensive than their branded counterparts, driving down overall drug prices. The steep decline in prices means that pharmaceutical companies have little motivation to invest in research and marketing for new uses of off-patent drugs, and this loss of interest often starts in the years preceding generic entry.

In theory, pharmaceutical companies could repurpose generics without changing the drugs and apply for method-of-use patents, which should provide exclusivity for new indications and the potential for higher pricing. However, due to substitution of generic drugs at the pharmacy level, method-of-use patents are of little to no practical value when there are already therapeutically equivalent products on the market. Pharmacists can dispense a generic version instead of the patent-protected drug product, even if the substituted generic does not have the specific indication in its labeling. Currently, nearly all U.S. states permit substitution by the pharmacy, and over a third have regulations that require generic substitution when available.

Nonprofits like Reboot Rx and other non-commercial non-manufacturers are therefore stepping in to advance the use of repurposed generic drugs across many diseases. Non-manufacturers, which do not manufacture or distribute drugs, aim to ensure there is substantial evidence for new indications of generic drugs and then advocate for their clinical use. Regulatory approval would accelerate adoption. However, even with substantial evidence to support regulatory review, non-manufacturers find it difficult or impossible to seek approval for new indications of generic drugs. There is no straightforward pathway to do so within the current U.S. regulatory framework without offering a specific, manufactured version of the drug. This challenge is not unique to the U.S.; recent efforts in the European Union (EU) have sought to address the regulatory gap. In the 2023 EU reform of pharmaceutical legislation, Article 48 is currently under review by the European Parliament as a potential solution to allow nonprofit entities to spearhead submissions for the approval of new indications for authorized medicinal products with the European Medicines Agency. To maximize the patient impact of generic drugs in America, non-manufacturers should be able to drive updates to FDA drug labeling, enabling widespread clinical adoption of repurposed drugs in a formal, predictable, and systematic manner.

The importance of FDA approval

Drugs that are FDA-approved can be prescribed for any indication not listed on the product labeling, often referred to as “off-label use”. Since non-manufacturers face significant challenges pursuing regulatory approval for new indications, they often must rely on advocating for off-label use of repurposed drugs.

While off-label use is widely accepted and helpful for specific circumstances, there are significant advantages to having FDA approval of new drug indications included in labeling. FDA drug labeling is intended to contain up-to-date information about drug products and ensures that the necessary conditions of use (including dosing, warnings, and precautions) are communicated for the specific indications. It is the primary authoritative source for making informed treatment decisions and is heavily valued by the medical community. Approval may increase the likelihood of uptake by clinical guidelines, pathways systems, and healthcare payers. Indications with FDA approval may generate greater awareness of the treatment options, leading to a broader and more rapid impact on clinical practice. 

Clinical practice guidelines are often the leading authority for prescribers and patients regarding off-label use. In oncology, for example, the National Comprehensive Cancer Network (NCCN) Guidelines are commonly used guidelines that include many off-label uses. However, guidelines do not exist for every disease and medical specialty, which can make it more difficult to gain acceptance for off-label uses. The Centers for Medicare and Medicaid Services (CMS) policy routinely covers off-label drug uses if they are listed in certain compendia. The NCCN Compendia, which is based on the NCCN Guidelines, is the only accepted compendium that is disease-specific.

Off-label use requires more effort from individual prescribers and patients to independently evaluate new drug data, thereby slowing uptake of the treatments. This can be especially difficult for community-based physicians, who need to remain up-to-date on new treatment options across many diseases. Off-label prescribing can also introduce medico-legal risks, such as malpractice. These burdens and risks limit off-label prescribing, even when there is supportive evidence for the new uses.

As new uses for generic drugs are discovered, it is crucial to update the labeling to ensure alignment with current clinical practice. Outdated generic drug labeling means that prescribers and patients may not have access to all the necessary information to understand the full risk-benefit profile. Americans deserve to have access to all effective treatment options – especially low-cost and widely available generic drugs that could help mitigate the financial toxicity and health inequities faced by many patients. For the public benefit, the FDA should support approaches that remove regulatory barriers for non-manufacturers and modernize drug labeling.

Existing pathways for manufacturers to obtain FDA approval 

The current FDA approval system is based on the idea that sponsors have discrete physical drug products. Traditionally, sponsors seeking FDA approval are pharmaceutical companies or drug manufacturers that intend to produce (or contract for production), distribute, and sell the finished drug product. For the purposes of FDA regulation, “drug” refers to a substance intended for use in the treatment or prevention of disease; “drug product” is the final dosage form that contains a drug substance and inactive ingredients made and sold by a specific manufacturer. One drug can be present on the market in multiple drug products. In the current regulatory framework, drug products are approved through one of the following:

• 505(b)(1) NDA: New drug products, including new molecular entities, are first submitted for FDA review through the 505(b)(1) new drug application (NDA) pathway. In this pathway, the sponsor either generates all necessary clinical data or owns the right of reference to the clinical data needed to support the NDA. FDA approval of an NDA establishes a reference listed drug (RLD), based on the FDA’s determinations of safety and effectiveness for the drug. 
• 505(b)(2) NDA: For new drug products or formulations utilizing or related to previously approved drugs or drug substances, the 505(b)(2) NDA pathway offers a streamlined alternative to the 505(b)(1) NDA. By allowing the sponsor to reference the FDA’s findings of safety and effectiveness for previously approved drugs and published literature, the sponsor can submit data they do not own or for which they do not have the right of reference, once any exclusivities expire.
• ANDA: Generic drug equivalents are approved through the 505(j) abbreviated NDA (ANDA) pathway. The RLD serves as the standard that other manufacturers may reference when seeking approval for generic versions of the drug. Upon approval, ANDA products receive a therapeutic equivalence designation in the FDA’s Orange Book, indicating the generic drug products are interchangeable for and substitutable with the RLD.
• BLA: Biological products are approved through the biologics license application (BLA). There is currently no provision analogous to section 505(b)(2) under the law governing the approval of biological products. While the FDA is authorized to approve biosimilars, including interchangeable biosimilars akin to generics, no current pathway permits the addition of new indications for use to a biosimilar product short of submitting a complete, original BLA with full preclinical and clinical data. Our proposed regulatory pathway is relevant for small molecule drugs and would not be applicable to biological products under current law.

Manufacturers can add new indications to their approved labeling without modifying the drug product through existing pathways. With supportive clinical evidence for the new indication, the NDA holder can file a supplemental NDA (sNDA), while an ANDA holder may submit a 505(b)(2) NDA as a supplement to their existing ANDA. As previously discussed, the drug product will likely be subject to pharmacy-level substitution with any available therapeutically equivalent generic. The marketing exclusivities that sponsors may receive from the FDA do not protect against this substitution. Therefore, these pathways are rarely, if ever, used by pharmaceutical companies when there are already multiple generic manufacturers of the product. 

Challenges for non-manufacturers in using existing pathways

Since manufacturers are not incentivized to seek regulatory approval for new indications, labeling changes are more likely to happen if driven by non-manufacturers. Yet non-manufacturers face significant challenges in utilizing the existing regulatory pathways. Sponsors must submit the following information for all NDAs for the FDA’s review: 1) clinical and nonclinical data on the safety and effectiveness of the drug for the proposed indication; 2) the proposed labeling; and 3) chemistry, manufacturing, and controls (CMC) data describing the methods of manufacturing and the controls to maintain the drug product’s quality. 

To submit and maintain NDAs, non-manufacturer sponsors would need to address the following challenges: 

1. Providing the FDA with required CMC data. NDA sponsors must provide CMC data for FDA review. Non-manufacturers would not produce physical drug products, and therefore they would not have information on the manufacturing process. 
2. Providing the FDA with product samples. If requested, NDA sponsors must have the drug products and other samples (e.g., drug substances or reference standards) available to support the FDA review process and must make available for inspection the facilities where the drug substances and drug products are manufactured. Non-manufacturers would not have physical drug products to provide as samples, the capabilities to produce them, or access to the facilities where they are made. 
3. Conducting post-marketing surveillance. Post-marketing responsibilities to maintain an NDA include conducting annual safety reporting and maintaining a toll-free number for the public to call with questions or concerns. Non-manufacturers, such as small nonprofits, may not have the bandwidth or resources to meet these requirements.

Within the current statutory framework, a non-manufacturer could sponsor a 505(b)(2) NDA to obtain approval of a new indication by partnering with a current manufacturer of the drug – either an NDA or ANDA holder. The manufacturer would help meet the technical requirements of the 505(b)(2) application that the non-manufacturer could not fulfill independently. Through this partnership, the non-manufacturer would acquire the CMC data and physical drug product samples from the manufacturer and rely on the manufacturer’s facilities to fulfill FDA inspection and quality requirements.

Once approved, the 505(b)(2) NDA would create a new drug product with indication-specific labeling, even though the product would be identical to an existing product under a previous NDA or ANDA. The 505(b)(2) NDA would then be tied to the specific manufacturer due to the use of their CMC data, and that manufacturer would be responsible for producing and distributing the drug product for the new indication.

As a practical matter, this pathway is rarely attainable. Manufacturers of marketed drug products, particularly generic drug manufacturers, lack the incentives needed to partner with non-manufacturers. Manufacturers may not want to provide their CMC data or samples because it may prompt FDA inspection of their facilities, require an update to their CMC information, or open the door to product liability risks. The existing incentive structure strongly discourages generic drug manufacturers from expending any additional resources on researching new uses or making any changes to their product labeling that would deviate from the original RLD product. 

Plan of Action 

To modernize drug labeling and enhance clinical adoption of generic drugs, the FDA should implement a dedicated regulatory pathway for non-manufacturers to seek approval of new indications for repurposed generic drugs. Ultimately, such a pathway would enable drug repurposing and be a crucial step toward equitable healthcare access for Americans. We propose a potential solution – a “labeling-only” 505(b)(2) NDA – as an extension of the existing 505(b)(2) approval pathway. 

Overview of the proposed labeling-only 505(b)(2) NDA pathway 

The labeling-only 505(b)(2) NDA would enable non-manufacturers to reference CMC information from previous FDA determinations and, when necessary, provide the FDA with samples of commercially available drug products. Through this approach, the new indication would not be tied to a specific drug product made by one manufacturer. There is no inherent necessity for a new indication of a generic drug to be exclusively linked to a single manufacturer or drug product when the FDA has already approved multiple therapeutically equivalent generic drugs. Any of these interchangeable drug products would be considered equally safe and effective for the new indication, and patients could receive any of these drug products due to pharmacy-level substitution. 

We describe non-manufacturer repurposing sponsors as entities that intend to submit or reference clinical data through a labeling-only 505(b)(2) NDA. This pathway is designed to expand the FDA-approved labeling of generic drugs for new indications, including those that may already be considered the standard of care. Non-manufacturers do not have the means to independently produce or distribute drug products. Instead, they intend to show that there is substantial evidence to support the new use through FDA approval, and then advocate for the indication in clinical practice. This evidence may be based on their research or research performed by other entities, including clinical trials and real-world data analyses.

The labeling-only 505(b)(2) NDA pathway helps address the three major challenges non-manufacturers face in pursuing regulatory approval. Through this pathway, non-manufacturers would be able to: 

1. Reference the FDA’s previous determinations on CMC data. Currently, a 505(b)(2) NDA can reference the FDA’s previous determinations of safety and effectiveness for an approved drug product. For eligible generic drugs, the labeling-only 505(b)(2) NDA would build on this practice by allowing non-manufacturer sponsors to reference the FDA’s previous determinations on any NDA or ANDA that the manufacturing process and CMC data are adequate to meet regulatory standards.
2. Provide the FDA with product samples using commercially available drug product samples. Currently, it is up to the discretion of the FDA whether or not to request samples in the review of an application. With the labeling-only 505(b)(2) NDA, non-manufacturers would provide the FDA with samples of commercially available products from generic manufacturers. Given that the FDA would have already evaluated the products and their bioequivalence to the RLD during the previous reviews, it is not expected that the FDA would need to re-examine the product at the level of requesting samples, except potentially to examine the packaging and physical presentation of the product for compatibility with the new indication and conditions of use. The facilities where the drugs are made would remain available for inspection, under the same terms and conditions as the existing, approved marketing applications. 
3. Manage post-marketing responsibilities. Since most post-marketing surveillance and adverse event reporting are drug product-specific, these obligations would continue to be the responsibility of the manufacturer of the physical drug product dispensed. With the labeling-only 505(b)(2) NDA, the non-manufacturer would not have product-specific obligations because they are not putting a new product into the marketplace. However, we anticipate the non-manufacturer would be responsible for the repurposed indication on their labeling, including but not limited to post-marketing surveillance as well as indication-specific adverse event reporting and reasonable follow-up.

Under the labeling-only 505(b)(2) NDA, the non-manufacturer sponsor would not introduce a new physical drug product into the market. The new labeling created by the approval would not expressly be associated with one specific product. The non-manufacturer’s labeling would refer to the drug by its established generic name. In that way, the non-manufacturer sponsor’s approval and labeling could be applicable to all equivalent versions of the drug product and would be available for patients to receive from their pharmacy in the same way that generic drugs are typically dispensed at the pharmacy. That is, with the benefit of pharmacy-level substitution, patients could receive any available, therapeutically equivalent drug products from any current manufacturer. 

Eligibility criteria

We envision the users of this pathway to be non-manufacturers that conduct drug repurposing research for the public benefit, including organizations like nonprofits and patient advocacy groups. The FDA should implement and enforce additional guardrails on eligibility to ensure that sponsors operate in good faith and cannot otherwise meet traditional NDA requirements. This process may include pre-submission meetings and reviews. The labeling-only 505(b)(2) NDA should be held to the FDA’s standard level of rigor and scrutiny of safety and effectiveness for the proposed indication during the review process.

The labeling-only 505(b)(2) would only be suitable for well-established, commercially available small molecule generic drugs, which can be identified as: 

1. Drugs with a U.S. Pharmacopeia and National Formulary (USP-NF) monograph. The USP-NF monograph system ensures the uniformity of available products on the market by setting a consensus minimum standard of identity, strength, quality, and purity among all marketed versions of a drug. It is expressly recognized in the Federal Food, Drug, and Cosmetics Act (FDCA). The USP-NF strives to have substance and product monographs for all FDA-approved drugs. USP-NF monographs for generics are commonly available because the drugs have been on the market for a long time and are typically produced by multiple manufacturers. Drug products in the U.S. market must conform to the standards in the USP-NF, when available, to avoid possible charges of adulteration and misbranding. 

By statute and regulation, the FDA already allows for NDAs and ANDAs to reference the USP-NF to satisfy some CMC requirements, such as for specifications of the drug substance. As an illustration of the acceptance of the USP-NF, clinical trial protocols requiring the use of background therapy or supportive care, as well as trials testing medical devices requiring the use of a drug product, often will specify that any available version of the drug product meeting USP-NF standards can be used. We propose that products without USP-NF monographs, including certain newer drugs and drugs with especially complicated manufacturing processes that are not conducive to standardization, would not be eligible for the labeling-only 505(b)(2) pathway.

2. Drugs with multiple A-rated, therapeutically equivalent products in the FDA Orange Book. The FDA does not regulate which specific products are dispensed or substituted for a given drug prescription. The listing of therapeutic equivalents in the Orange Book facilitates the seamless replacement of drug products from different manufacturers in clinical practice. Therapeutically equivalent drug products: i) have demonstrated bioequivalence to the RLD; ii) have the same strength, dosage form, and route of administration as the RLD; and iii) are labeled for the same conditions of use as the RLD. Therapeutic equivalents that meet these criteria are designated “A-rated” in the Orange Book. A-rated drug products are substitutable for any other version of that A-rated drug product, including the RLD itself. 

Implementation 

The labeling-only 505(b)(2) NDA pathway could be implemented through an FDA guidance document interpreting the current statute and regulations or through legislation that clarifies the FDA’s existing authority. Guidance documents contain the FDA’s interpretation of a given policy on regulatory issues. The FDA’s Center for Drug Evaluation and Research (CDER) could issue new guidance that allows for interpretation of the existing statute, thereby officially authorizing previous FDA determinations of acceptable CMC data to be referenceable for eligible generic drugs and adjusting drug sample requirements. Alternatively, the labeling-only 505(b)(2) could be enacted by Congress through a statutory change by incorporation into FDCA, which is up for reauthorization through the Prescription Drug User Fee Act (PDUFA) in 2027, or other congressional acts as appropriate. FDA guidance would be a faster pathway to adoption, while statutory authorization would offer additional safeguards for the continuance of the pathway long term.

The labeling-only 505(b)(2) NDA pathway could be funded through user fees, which are established by PDUFA for the cost to file and maintain NDAs. However, many nonprofit sponsors would not be able to afford the same user fees as for-profit pharmaceutical manufacturers. Relevant statutes will likely need to be updated to create a different fee schema for non-manufacturers who use the labeling-only 505(b)(2) pathway. In a similar spirit to reducing barriers to maintaining up-to-date labeling, the 2017 PDUFA update waived the fee for submitting an sNDA, which is how an existing RLD holder would update their labeling with new indication information.

Conclusion

Patients need new and affordable treatment options for diseases that have a devastating societal impact, and repurposing generic drugs can help address this need. Nonprofits and other non-manufacturers are driving these efforts forward due to a lack of interest from pharmaceutical companies. As momentum gains for generic drug repurposing, the U.S. regulatory system needs a pathway for non-manufacturers to seek FDA approval of new indications for existing generic drugs. Our proposed labeling-only 505(b)(2) NDA would eliminate undue administrative burden, enabling non-manufacturers to pursue FDA approval of new indications. It would allow the FDA to provide the public with the most up-to-date drug labeling, improving the ability of patients and physicians to make informed treatment decisions. This dedicated pathway would increase the availability of effective treatment options while reducing costs for the American healthcare system.

This action-ready policy memo is part of Day One 2025 — our effort to bring forward bold policy ideas, grounded in science and evidence, that can tackle the country’s biggest challenges and bring us closer to the prosperous, equitable and safe future that we all hope for whoever takes office in 2025 and beyond.

PLEASE NOTE (February 2025): Since publication several government websites have been taken offline. We apologize for any broken links to once accessible public data.