The 2027 National Science Foundation Budget Request proposes to eliminate the Social, Behavioral, and Economic (SBE) Directorate, which funds social science research.1 So far, all signs suggest it means to follow through on this proposal (the NSF’s SBE Directorate has made essentially no research awards so far this year, which is highly atypical). We care a lot about R&D at the Abundance and Growth Fund, but let’s set aside for a minute the question of whether the NSF should be supporting social science research.2 What I find interesting about this whole episode is that we’ve been here before, a little over 40 years ago.

Reagan Revolution

Let’s go back to 1981, shortly after Ronald Reagan took office. The following account of the social science fights of the first Reagan administration are based on a few histories of the period that I read earlier this year.3 The Reagan administration ultimately ended up cutting inflation-adjusted non-defense R&D by a third by 1983, but it was especially hostile to the social sciences. In the FY1982 President’s Budget, the administration called for a sharp reduction in spending on social science research, more severe than proposed for the overall NSF.

The Executive Branch wasn’t an outlier though; Congress was also skeptical. In the same year, it passed a rescission package reducing the appropriated funds for social science at the NSF by $10mn, out of a total allocation of $33mn (Congress eventually restored $1mn). The next year, the Reagan administration proposed cutting the NSF social science budget from the post-rescission level of $24mn by an additional $14mn, but Congress split the difference and appropriated funds equivalent to another $6.4mn cut. Funding bottomed out in 1982 and then began to slowly recover. But the social sciences would not attain the inflation-adjusted level of funding they had enjoyed in 1980 until 1996.

While the scale of these cuts caught the social sciences off guard, they didn’t come out of nowhere. Support for social science research at the NSF had always been more tenuous than other fields: some draft legislation founding the NSF had even banned the social sciences from receiving funding, though an outright ban wasn’t in the legislation that ultimately established the NSF. Decades later, in the lead up to the 1981 cuts, multiple bills had been proposed that cut social science funding at the NSF (one passed the House, but not the Senate, in 1979). Social science research was also the frequent subject of ridicule, for example via Senator Proxmire’s “Golden Fleece” awards, which kicked off in 1975 with a criticism of an NSF-funded social science study on why people fall in love. Perhaps as a consequence of all this doubt, funding for the social sciences had been growing at a significantly slower rate than other sciences at the NSF leading up to the 1980s.

Make America Great Again

Fast forward 44 years to 2025, and the second Trump administration had just come into office. It also arrived highly skeptical of federal support for R&D; President Trump’s FY2026 budget proposed a 20% cut to overall R&D, with much larger cuts for non-defense R&D (57% at NSF, 40% at NIH4). This time, however, Congress was not on board with the proposed cuts. The normal appropriations process for discretionary spending (like R&D) is subject to the filibuster, which means any large cuts to NSF and NIH would have had to garner supermajorities to pass. This meant a large cut through the normal appropriations process was pretty unlikely.

That said, it was possible Congress might pass large cuts at the NSF and NIH with a rescission package instead. Rescissions are a mechanism for the executive branch to seek approval not to spend appropriated funds, and as noted above, Congress passed one in 1981. Through much of 2025, the administration’s spending at NIH and NSF was below trend (see figure below); a large pot of unspent funds near the end of the year could potentially be used as a pretext for rescissions, which unlike normal appropriations, can be passed by a simple majority.5

However, that isn’t what ultimately happened. In the second half of the summer of 2025, spending at NSF and NIH accelerated and ended the year on trend or above (see figure). No rescission packages targeting R&D were proposed. And the eventual appropriations from Congress cut the budget of the NSF by 3.4% (roughly $300mn), rather than 57%. The budget appropriated for NIH actually increased.

The social sciences, however, fared worse. As under the Reagan administration, one can see some indications that support for the social sciences has been less robust than for the rest of the sciences for many years. SBE as we know it today was formed in 1991, following years of advocacy from social science associations. But since then, support has gradually weakened, relative to the overall NSF Research and Research Activities Budget. While it made up 5% of research spending in 1995, that has drifted down to 4% at the time the Trump administration took office.

In 2025 though, support plunged. At SBE, spending in the first half of the year was on trend. But spending in the second half of the year failed to keep up with normal trends and the budget for the social sciences ended the year significantly below trend.6 And five months into the new year, new spending is close to zero.

What happens next?

Perhaps the past can give some clues as to how things might unfold in the future this time around. In response to the 1981 cuts, various disciplinary associations of social sciences founded the Consortium of Social Science Associations, an organization that had been previously proposed but never formally established. This new organization, COSSA, followed a pretty standard playbook to influence the US Federal budget: it hired staff to lobby Congress; mobilized social scientists to visit, telephone, and send letters to their representatives; focused on winning over members of Congress with large universities in their district; and targeted both Democrats and Republicans as part of a message that funding for the social sciences was not a partisan issue. The size of the proposed cuts (the 1982 proposal was a 75% cut relative to what the previous administration had proposed) also galvanized significant media coverage.

The timeline is consistent with this response working, at least a bit. COSSA would have had more time to develop and execute its strategy ahead of the 1982 budget battles than the 1981 rescission fight, and the budget cuts in 1982 were indeed smaller in absolute terms and as a share of the administration’s proposal, than the 1981 cuts. That said, if the campaign moderated the proposed cuts, it could not stop them. According to a contemporaneous article by James Zuiches, the partial reversal of the cuts that began after 1983 owed less to anything specific the social sciences did, and more to a renewed interest by the Reagan administration in research in general. Social science benefitted from the halo around all research (though even so, its growth rate remained below the growth rate of other sciences at NSF).

COSSA is still active today, but it’s less clear what Congress can do in 2026. The explanatory text for the latest NSF appropriations package already specified “No directorate shall receive more than a 5 percent reduction relative to the fiscal year 2024 enacted level.” However, while the overall budget of the NSF is set by Congress, the executive branch has considerably more latitude in how that funding is allocated (see my colleague Jordan’s earlier blog post about this). The preceding explanatory text, for example, does not have statutory force (though such statements usually guide agency actions).7

Zooming out, is there some future where the social sciences can enjoy the levels of political support more common in the life sciences and hard sciences? That’s an interesting question, and one I’ll come back to in a follow-up to this post.

Note: there will be no “What we’re reading” blog post from the Abundance and Growth Fund this week, as a large share of the team is traveling.

Hope you’ve had a great week! Here’s what we’ve been reading:

1. Our scientific ecosystem often prizes outputs that, themselves, don’t necessarily drive scientific progress, but instead are useful proxies for the more intangible processes that do. Three reads this week have me thinking about what happens if AI gets good at creating these outputs before (or instead of) getting good at the things they’ve been proxying for. First is David Bessis’s fascinating essay on AI in math. He discusses, among many other things, the idea that theorem-proving has historically served as a legible demonstration of underlying conceptual innovation, because solving major open problems typically required first building a new framework that made the solution tractable and expressible; AI may break that coupling, producing correct but unintelligible proofs and capturing social rewards without the accompanying distillation and canonization that allow the field to build on the new knowledge. Second is Engzell and Wilmers’ recent preprint, “The Paper Factory,” which, though different in goal and tone, describes a related dynamic in social science. The authors develop a multi-agent LLM workflow that automates most of the steps of creating a publishable empirical paper, but struggles with the intangibles that form the foundation of meaningful outputs, like problem selection and judgment. For a more speculative take on the topic, read Ted Chiang’s prescient short story “Catching crumbs from the table,” published in 2000 in Nature, which imagines human scientists reduced to interpreting the incomprehensible discoveries of superintelligent successors. — Jordan Dworkin

2. The US is richer than Europe. This is not especially controversial: residents of just about every European country, save for tax havens like Luxembourg and the petro-state of Norway, lag the US in median income. But is Europe falling further behind the US, or keeping up? Paul Krugman says “keeping up”; Luis and Pieter Garicano say “falling behind.” Some of their debate is technical and about what the proper purchasing power parity (PPP) metric to use when comparing living standards between the US and Europe. But the biggest question in the debate is how to think about US dominance in tech. The five most valuable companies on Earth (NVIDIA, Google, Apple, Microsoft, and Amazon) are all American tech firms, and the smallest of them is nearly five times more valuable than the most valuable European tech firm (ASML). To Krugman, this is basically irrelevant to living standards; Europeans can still use those American firms’ products, often (as with Google) for free, so the physical location of the firms isn’t so important. To the Garicanos, this pattern is evidence of a fundamental lack of dynamism in Europe that is already hurting residents and will hurt them more as AI scales up. — Dylan Matthews

3. Alan Durning at Sightline has a new report arguing that the climate movement is underrating one strategy for reducing climate change: apartment buildings. Apartment buildings have lower carbon emissions than single-family homes for several reasons. They are more energy efficient; more likely to be heated by electricity; and when sited near offices, shops, and public transit, let people get around more on foot, by bike, and by public transit instead of driving. But perhaps just as important is the fact that housing policy is tractable. Building more apartments doesn’t require new government spending, nor asking people to make hard tradeoffs. Private developers want to build apartment buildings, and people want to live in them, if only they are allowed. We’ve made a lot of progress in the last year to enable building more apartment buildings, but there is a long way to go. — Matt Clancy

4. Matt Yglesias dropped an avowedly contentious take on energy abundance, arguing that “conventional environmentalism” is overly focused on efficiency and missing the upside of truly abundant energy generation. Yglesias argues that “too cheap to meter” energy would not only make efficiency arguments irrelevant, but unlock vast new frontiers in energy-constrained areas like synthetic fuels, vertical farming, and lab-grown animal proteins. Leaving aside the political positioning of energy efficiency for the moment, I found this a helpful provocation on what we should and shouldn’t expect from more generation capacity. While more cheap energy is clearly a good idea, and we're sympathetic to many of the conclusions here (permitting reform and interregional transmission would be important wins), my read is that we still risk missing a step by focusing too closely on energy prices alone as a driver of innovation. For some sectors, the cost of energy probably is a binding constraint (synfuels, desalination, potentially some green industrial materials), so it follows that cheaper energy would lead to growth there. For others — including cultured meat and potentially vertical farms, but also next-gen aviation — there are still major materials science/biotech bottlenecks (or physical constraints) to be addressed. It’s not clear to what degree even too-cheap-to-meter energy would actually induce the innovations to make those technologies commercially viable; at least some of the literature on directed technical change implies that energy prices can shift R&D budgets within the energy sector, but we’re (to my knowledge!) missing an empirical through-line from energy prices to R&D in complementary fields. If we don’t know how strong the link between energy price and complementary innovations is, it’s worth asking whether driving energy prices as close to zero as possible is the most cost-effective way to induce those innovations. That said, there are plenty of good reasons to build more cheap energy — not least that growing demand has made ever tighter supply-side constraints an increasingly real possibility even for “conventional” growth. — Willow Latham-Proenca

5. New York took two big steps on housing permitting this week. After weeks of late-budget deadlock, Governor Hochul’s SEQRA modernization is reportedly final with bill text coming next week. The deal reportedly exempts NYC housing projects up to 500 units in medium/high-density districts and 250 in low-density areas, with smaller carveouts upstate. On the city side, the mayor launched the SPEED Task Force report. Originally scheduled to arrive after a final Albany budget, the report is explicit that its headline pre-certification cut — slashing ULURP rezoning pre-cert from two years to six months — is contingent on the state SEQRA carveout actually landing. Beyond SEQRA-enabled reform, SPEED promises: five months trimmed from new-construction permitting, five months from office-to-residential conversion permitting and lease-up, and a ground-up rewrite of the affordable housing lottery that takes median time-to-move-in from 210 days to under 100. All told, the report projects two years off the timeline for projects requiring rezoning and eight months off all affordable housing — backed by $14M in the executive budget and ~96 new agency hires where capacity actually binds the expected flood of streamlined housing permit applications. With Evan Soltas’s LA permitting work implying each year saved is likely worth ~8% in construction costs, that’s the stacked feasibility shift needed to help turn theoretical zoned capacity into actual homes. — Alex Armlovich

6. FDA Commissioner Marty Makary resigned this week, just over a year in the role. It seemed sudden from the outside, but was reportedly a long time coming internally, with months of friction with RFK Jr. Apparently, the final straw was his reluctance to approve flavored vapes. I find the chaos worrying, and although Makary announced some promising ideas, like streamlining clinical trials, Bayesian analysis, and reducing animal testing, as STAT news describes, he also oversaw the departure of experienced career scientists, more politicized approval decisions, and eroding evidentiary standards. — Saloni Dattani

7. One reason that clinical trials are slow, expensive, and wasteful is that the data they generate are locked in siloed systems and manually transcribed. Recently, the FDA announced a pilot project to fix this, which would create “Real Time Clinical Trials”. The idea is to let regulators see trial data in real time, as it’s collected, potentially compressing the dead time between study phases. But as Adam Kroetsch writes in a new post, the announcement was muddled; speakers discussed topics like rural access and site burden, which made it unclear what was actually being proposed. His take is that the idea is genuinely promising, but will be difficult to deliver at scale, and requires the FDA to go much further. He recommends they define how they actually use live data, build interoperability standards, and give sponsors explicit regulatory cover to abandon unnecessary verification practices. — Saloni Dattani

Here are a few other highlights and announcements from our team and grantees:

• Alex Armlovich published “Tokyo land is still >$85 million an acre” looking at what happens to land values when a megacity actually achieves housing abundance. The short answer: rents fall, but land values don’t, which matters for the political economy of getting zoning reform passed.

• NSF officially launched the $1.5 billion X-Labs initiative, funding independent research teams with large, flexible block grants to tackle scientific challenges outside traditional university structures. The initiative draws on ideas from across the science policy community, including the Institute for Progress, whose X-Labs proposal last August laid out a framework for this kind of funding model.

• The Foundation for American Innovation is hosting the Energy Imperatives Summit on June 9-10 in Washington, DC, a two-day forum on energy policy featuring speakers from government, industry, and finance.

Megacity YIMBYism lowers rents but not land values—plus who wins, who loses, and why most mortgage-holders shouldn’t worry.

When you zoom out on American politics, one pattern that snaps into focus is how much local elected officials focus on preserving, even boosting, home values. It’s a reasonable political instinct. Roughly two-thirds of American adults live in owner-occupied housing, and for most of them the home is by far their largest asset. The threat that finally building enough housing to satisfy pent-up demand would crash widely-held asset values is taken seriously across the spectrum—by populists who promise rising prices to homeowners, by mortgage-market watchers who remember 2008, and by political-economy theorists in the William Fischel tradition who treat homeowner profit as the prime mover of American zoning.

That fear is mostly wrong, but in an interesting way. Good public policy should indeed raise total land values: insofar as land markets are efficient at capturing all the “goods” and “bads” about living in a particular location, policymakers and society at large should want high land prices because they proxy for good wages and high quality of life.

Thus the right question isn’t whether YIMBYism would lower the user cost of housing—that’s the whole point of the YIMBY movement and the unanimous upshot of the academic urban economics literature—but whether it would lower the value of land overall. The answer will almost certainly turn out to be no, because higher land prices are substantially severable from, and can coincide with, lower structure prices. And Tokyo, the world’s largest city and the only one that features anything like a realistic best-case version of housing abundance at megacity scale, is the cleanest place to see it.

The world’s largest YIMBY metropolis still has more than $85M-an-acre dirt

Tokyo is the world’s largest single metropolitan labor market. Greater Tokyo holds about 37M people, and it’s still growing as Japan’s countryside empties out and reforests. As a comparison, Tokyo first surpassed NYC in population in the early 1960s; after decades of steady growth, its nearly twice the size of metropolitan NYC’s 19.3M residents.

By the standards of Anglosphere megacities—New York, London, Toronto, LA—Tokyo housing is famously cheap. Builders can put up small-lot single-family homes, midrises, microapartments, and single room occupancy-style shared housing units with ease and in large volumes. (High-rises are not allowed by-right everywhere, so this isn’t a pure laissez-faire experiment, just the closest real-world example.) If “Tokyo regulation” is what real-world YIMBY victory looks like in a global megacity, it’s the best dataset we have.

So what are Tokyo’s land prices?

Prices for land range from well over $100M per acre in the urban core (in 2024 USD, PPP-adjusted) to just under $20M per acre on the periphery of Tokyo proper’s 23 wards. Whether one uses PPP (~$154M/acre) or market conversion rates (~$90M/acre) is not particularly important to the thesis: The point is that land prices are extremely high, and demonstrate a typical metropolitan bid-rent gradient from the core to the periphery.1

The famous (relative) cheapness of Tokyo housing is not a story about cheap land. A one-acre detached house in central Tokyo would cost more than $100 million in dirt before you broke ground. The land is pricey but the structures are cheap. Admittedly, Tokyo’s rents and prices are not as cheap per square foot as buildings in the US Sunbelt’s midsize cities, but cheap by the standards of any 10-million-plus Anglosphere metro area. Tokyo built its way to relative affordability without ending up with low land values, and the values themselves look reasonable for a productive, agglomerated megacity that simply didn’t artificially restrict its own supply.

Land prices and structure prices are related but different things

Standard urban economics tells us that wages and amenities accessible from a location capitalize into the price of land at that location. The harder practical upshot, which Ed Glaeser and his coauthors have spent their careers teaching, is that whether high land prices translate into high structure prices is a function of land-use regulation.

High land prices are a price signal. They tell builders to economize on land per unit by stacking up more structure—more floors, smaller footprints, higher floor area ratios per unit of land. In a liberally regulated environment, the bid-rent gradient for land can be extremely steep, while the bid-rent gradient for structures is much shallower.2 Structure costs do rise with height—the U.S. model building code introduces real cost steps as a structure grows in density. A build starts needing tuned mass dampers and bespoke engineering somewhere past 50 stories—but those increases should be shallower than the underlying land-price gradient if regulation allows.3

As a useful sanity check on what land prices are really capitalizing: Consider tightly zoned waterfront cities, where the going price (and waitlist) for houseboat moorings—physically not land at all—behaves like part of the residential property market. The product being priced is not actually dirt! It’s locational access to the bundle of public and private goods available at that point on the map. Upzoning lets builders amortize that location access cost over more buildable square feet. It does not, and could not, reduce the value of the access itself as long as the net external congestion costs of growth don’t exceed the net external amenity and wage-agglomeration benefits of growth.

When policymakers upzone widely with by-right permitting across a high-demand metro, two things should happen at once. Measured per-acre (like farmland), land value rises, because the parcel has been granted a valuable option to host more buildable area. Land value measured like NYC-area developable land per buildable square foot falls, because that higher per-acre value is being amortized over much more floor area.

These move in opposite directions, and neither one alone is “the land price” in the sense political conversation usually means. This distinction dissolves a lot of the political-economy panic around housing abundance. Again, land prices quoted the way NYC land brokers quote them, on a “Zoning Square Feet” or “Buildable Square Feet” basis, will fall even as the total value of land is at least stable or rises in the metro area being upzoned.

The Homevoter Hypothesis Can’t Explain Everything

The leading academic framework for explaining American NIMBYism is Dartmouth economist Bill Fischel’s pairing of fiscal zoning and the homevoter hypothesis.

The fiscal-zoning argument is that single-family-only zoning and large minimum lot sizes are profit maximizing tools for managing the local tax base: By excluding apartments and small houses, exclusive suburbs effectively “faregate” the municipal border, restricting admission to households whose property taxes will exceed their consumption of local public services like schools. Local public goods become club goods, and the club charges admission at the municipal border rather than the schoolhouse door. Instead of hosting private for-profit schools, these suburbs act as private for-profit neighborhoods with an ostensibly “public” school inside.4

The homevoter hypothesis is the political mechanism: Homeowners turn out in local elections to defend the asset values that this fiscal arrangement protects.

Fischel’s homevoter hypothesis rests on two claims: that single-family zoning maximizes the asset value of incumbent owners, and that those owners (the electorally pivotal median voter in most US jurisdictions) turn out to defend it.5 The second half holds up in the suburban cases Fischel built the theory around. The first half breaks in the cases that matter most for national productivity: the high-demand cores of America’s largest cities.

Tokyo’s land and property market conditions show why: Tokyo combines metropolitan housing abundance with land prices that remain high, not low. The two facts coexist because the relevant scarcity isn’t housing supply; it’s centrally-located land. A homeowner sitting on a single-family parcel in a high-demand Tokyo ward holds a portfolio that is overwhelmingly land. Upzoning her parcel raises the value of that land, sometimes dramatically, because the parcel can now carry dozens of units rather than one. The option to redevelop is worth more than the right to preserve neighborhood character. Restrictive zoning, in this regime, is asset-destructive for a land-rich homeowner in a high-demand area.6

If American single-family homeowners in the cores and first-ring suburbs of New York, San Francisco, Boston, or LA were maximizing the dollar-denominated asset values Fischel says they care about, they would be voting to become like Tokyo landowners — to unlock the redevelopment option on their parcels. They are not. Whatever they’re maximizing, it isn’t profit.7 (See Agenda for Abundant Housing for a full-length argument.)

That’s not a fatal blow to the homevoter framework if we’re willing to relax the “objective function” of voters from strictly defined profit-maximization. As any good undergraduate economics professor will remind a student who discovers people valuing non-pecuniary interests: “Firms maximize profit. People maximize utility.” This is of course the beginning, not the end, of the question: It is the task of the other social sciences and humanities to help economists figure out what it is that people see as utility-maximizing or otherwise in their best all-things-considered interests, when consumers are not behaving in a way that maximizes apparent pocketbook dollars and cents.

Amenity preferences, socioeconomic and positional status concerns, and outright economic confusion all seem relevant — Clayton Nall and co-authors find that a slight majority of Americans believe new supply raises prices.8 But it does mean the rational-asset-defense story isn’t sufficient for the high-demand core cases…which is to say, for the cases where land-use reform would do the most national good. Maximizing “Baby Boomer suburb aesthetics” in neighborhood character is, in the cores of the most important metropolitan labor markets, a goal in direct tension with homeowner profit maximization–and all Americans, homeowner and renter alike, are collectively paying a tremendous price.

The bottom line

Tokyo is the closest thing we have to a real-world YIMBY megacity, and Tokyo land is still very expensive. Tokyo builds at a Sunbelt city pace–around 2% per year–far higher than other global megacities. Cheap housing and expensive land are the very likely fruits of successful housing abundance in America’s superstar cities, and also in most of the desirable areas of our other cities.

Aggregate land values should rise with wage and amenity access under abundance, even as the geographic distribution of land values will change. Per-unit prices will fall. Land-rich owners will profit. Structure-rich owners will likely take a haircut. All renters win. And, importantly, the mortgage market will not break provided we don’t time a supply rollout to coincide with a recession.

Finally, the political-economy theory that homeowner asset defense explains American zoning has to reckon with the fact that, as Tokyo’s high post-Abundance land values indicate, the assets in question would be uplifted, not bankrupted. The narrow profit-maximizing excuse for NIMBYism does not apply to the homeowners with strong redevelopment options in the metro areas where YIMBYism can do the most good.

Happy Friday! Here’s what caught our attention this week:

1. “Are we kind of being pricks?,” asked a resident at Marblehead’s annual Town Meeting this week in a video that went viral (full disclosure: I live here and serve on the Housing Committee). After four votes over three years, the town approved a zoning overlay to comply with the state’s MBTA Communities Act, this time by concentrating the required multifamily capacity on a private golf course, where it’s all but certain no new housing will be built. Marblehead isn’t alone. A Boston Foundation report from earlier this year sorts municipal responses into three categories: towns that exceeded requirements, towns that achieved incremental reform, and towns whose zoning will yield little or no new housing. The law has put nearly 7,000 units into the pipeline across 34 communities and is the most effective state zoning policy Massachusetts has passed in decades. But its flexibility was a deliberate tradeoff: ask too much and the whole thing collapses politically, ask too little and you get compliance plans built around golf courses. The law lets towns put their zoning capacity on land where nothing is likely to get built, which is why it matters when some guy stands up at a microphone and says what’s actually happening. — Nisha Austin

2. Our existing processes for how we decide what is allowed when there is disagreement are a key reason its hard to build things in the USA today. Over the last week, Marc Dunkelman and Alex Mechanick wrote an interesting pair of substacks grappling with this question. Dunkelman is concerned that abundance will require empowering someone to make decisions that not everyone will agree with (a decision he thinks not everyone will agree with!). He argues housing reforms have been successful in large part because they have implicitly selected someone to make a decision, in this case the property owner who decides whether or not to build more densely on land they own. But he points out the same trick won’t work with infrastructure that spans many parcels, like transmission or rail lines. In those cases, a centralized authority will need to make decisions about whose interests win out, and empowering a central authority to make those decisions runs counter to some liberal sensibilities. Meanwhile, Alex Mechanick asks how it can be simultaneously true that the government faces so many veto points that it cannot enact its decisions, but also is so unconstrained by process that it risks autocracy. The resolution for Mechanick is that many procedural checks are badly designed; they don’t constrain in cases where they ought to, and in other cases create delays, uncertainties, and costs, without offering much in the way of benefits. I’m inclined to think the kinds of procedural checks on government are the way we, in fact, make decisions about how to balance competing interests. As I see it, a key focus of the abundance agenda is that (1) these processes don’t work that well and (2) they implicitly put too little weight on the virtues of long-run growth, by making it easy for incumbents to veto projects whose net benefits are large and positive. — Matt Clancy

3. Two recent pro-housing champion advocates make the case that zoning reform is working. First, Nolan Gray of California YIMBY goes deep on the California data in “Where Are All the Cranes?,” documenting how ADU permits have produced nearly 150,000 new units, the state density bonus law is facilitating thousands more, and the signature CEQA and transit-oriented zoning reforms are less than a year old. Then this week Michael Andersen of Sightline Institute argues the picture outside California is just as encouraging: states across the country have only recently passed meaningful reforms and the results are beginning to show. Both pieces spiritually or explicitly respond to Derek Thompson, Marc Dunkelman, and Ezra Klein’s recent podcast episode reflecting on how the political landscape has changed in the year after Abundance was released. In that podcast, they worry that the vibes around abundance are stronger than the outcomes. For our part, we’re far more sanguine. While zoning isn’t everything, its hard to build anything if its not allowed and the evidence that significant and clean reform leads to construction continues to pile up. — Alex Armlovich

4. Oliver Kim has a detailed review of a new book on Singapore’s public housing system that complicates the YIMBY movement’s frequent invocation of Singapore as a model. The country houses 76 percent of the population in high-rise public units called HDBs with 99-year land leases, creating a version of the Georgist’s political economy problem: the state is the residual claimant of those structures when the land leases expire. Eventually the government must retake possession and let the terminal value of leasehold HDB homes hit zero. That’s hard to reconcile with residents’ intuitive sense of property ownership. The other challenge is more structural: because Singapore is already mostly high-rise and homeownership-focused, upzoning can’t unlock land value the way it does in American superstar cities dominated by single-family homes. In contexts with large potential density changes, like the US, allowing a developer to build an apartment building where a single-family house stood creates meaningful land value uplift that helps pay for the transition. In Singapore, unless the government increases allowable lot coverage ratios, there’s little additional value to generate from redeveloping existing HDB blocks. — Alex Armlovich

5. I’ve been thinking a lot about the relationship between energy and growth lately, which is less clear-cut than you might expect. I’ll be writing more about this soon, but this set of papers on energy and development does a nice job illustrating part of why it’s so complicated. Colmer, Lagakos and Shu, in a paper revised and published last month, find that improving productivity in the energy sector might not actually affect GDP growth very much, casting doubt on the idea that energy is a critical “weak link” driving development outcomes. However, a 2020 paper by Fried and Lagakos (yes, same coauthor) shows why the energy/growth link might still be an important driver of productivity, even if it’s more complicated than more generation > more growth; they model long-run general equilibrium effects of eliminating power outages at about 20% of GDP for a sample of 5 African countries. Together, these imply that growth effects might be asymmetrical – resources wasted on expensive self-generation, and the high cost of entry this creates for new firms, might be more important as a drag on growth than new generation capacity is as an accelerant. — Willow Latham-Proenca

6. Loudoun County, Virginia is notable for a few things. It’s the richest county in America by median household income, significantly richer than runners-up in Silicon Valley; it houses Dulles Airport and is thus the exurban area DC residents find ourselves schlepping to most often. But its most important role is as the capital of the internet. Due to proximity to the Defense Department, northern Virginia has long been home to a disproportionate share of the ARPAnet and then internet’s core infrastructure, and it still houses 13 percent of the world’s data centers today, with Loudoun and the town of Ashburn specifically at the epicenter. Judge Glock of the Manhattan Institute has an excellent piece explaining what this has meant for Loudoun as a county. In the 2027 budget, the county projects that it will get a whopping 45 percent of its revenue from data centers. For residents, that has meant lower property tax rates, lots of investment in schools and roads, and general, well, abundance. Data centers are not generally popular with voters, but they’re definitely good for Loudoun County. — Dylan Matthews

7. Drug development fails 95% of the time, making biotech a brutal investment environment. So how does the industry keep the money flowing? Abhishaike Mahajan, also known as Owl Posting, has a great post on the financial engineering tricks that have emerged to make this survivable. The examples range from hub-and-spoke holding companies, which pool uncorrelated drug programs so that one big win can offset many failures, to synthetic royalties — manufactured financial claims on future drug revenue that let biotechs raise cash without diluting shareholders or taking on debt. Priority Review Vouchers also get a mention — tradable tickets that speed up FDA review, originally designed to incentivize neglected disease drugs. As I’ve often thought, he mentions that while they reward getting a drug approved, they don’t reward whether it actually gets manufactured or reaches patients affordably. — Saloni Dattani

8. Many leading science funders have a stated goal of supporting ambitious, “high-risk, high-reward” research, or at least specific programs dedicated to that cause — to give a few examples: “ARIA was built to… go after ideas that may seem far-fetched, but could unlock world-changing capabilities,” “[ARPA-H] provides leadership for high-risk, high-reward biomedical and health research,” “[VolkswagenStiftung] supports groundbreaking and risky research ideas,” and “NSF’s commitment to fund high-risk, high-reward ideas strengthens the U.S. economy...” At the same time, there is relatively widespread concern in the metascience community that science is becoming too risk-averse, conservative, and incremental, and there is growing research evidence to back up the case. What gives? Part of the answer is institutional: Franzoni and Stephan argue that standard review processes, regardless of stated goals, are not structured to properly evaluate risk; and Azoulay and Greenblatt find that risky NIH grants are indeed renewed at lower rates, especially for novel research areas and new investigators. But incentives for/against risk are also social and individual: risk aversion can be rational at the level of the individual scientist, even if it’s collectively suboptimal. So is the scientific enterprise too risk-averse? This week Johns Hopkins hosted a debate on exactly this question, with Tyler Cowen and Brandon Ogbunu arguing for, and Kate Biberdorf and Sethuraman Panchanathan arguing against. In the end, the “too risk-averse” side won more converts among audience members, but both sides agreed that expanding support for science is one of the most important levers the enterprise could pull to increase innovation. — Jordan Dworkin

Shoutouts and a few other things worth mentioning:

• Alex Armlovich joined Jamie Rubin on Vital City’s After Hours podcast to discuss the downsides of a rent freeze and what happens to buildings when operating costs outpace revenue.

• Matt Clancy published an outline of the Abundance and Growth fund’s plans for what kinds of work to fund in 2026, titled How do you get Abundance and Growth?.

• Saloni Dattani joined Ben Southwood on the Works in Progress podcast to discuss how to speed up clinical trials, covering everything from Eroom’s law to why pharma companies are moving early trials to Australia, with Ruxandra Teslo as a guest.

• Ruxandra also got a shoutout on the Ezra Klein show about her work on Clinical Trial Abundance.

• Chris Snyder was interviewed on Freakonomics, where he discussed the Market Shaping Accelerator, a program he co-directs that designs incentives to spur private-sector innovation in areas where commercial returns alone fall short of social need.

• Progress Ireland played a role in Ireland’s new planning exemption for backyard modular homes.

• The Centre for British Progress is looking for someone to work on clinical trials policy

• Inclusive Abundance put out a request for policy proposals as part of their Abundance Agenda, looking for bold federal policy ideas across housing, energy, health, and governance.

• The Build America Caucus’s April newsletter from Rep. Josh Harder covered the caucus’s bipartisan work on housing, infrastructure, and permitting, and featured a couple of our posts.

What happened to growth? Part of the answer is policy. When there is insufficient weight put on the long-run growth effects of a policy, we make bad tradeoffs and pursue better policies with insufficient urgency. We think better policies are possible and there is an opportunity for philanthropy to help get us there.

Our primary goal at the Abundance and Growth Fund is promoting broadly shared economic growth—reducing the cost of living by increasing the supply of goods and services—via better policy. We’re a three-year fund with a $120 million commitment. In this post I want to give a bird’s eye view of the kind of work we’re supporting in 2026.

Policy areas

There are a lot of different policy areas that bear on growth and the cost of living, but we’re starting with five areas that we think are important and tractable: innovation, energy, clinical trials, housing, and state capacity.

Our first area is innovation. Technological progress, drawing on scientific research, is the foundation of economic growth. But government funding for science has not significantly evolved for decades. Spending has been fixed at 0.4% of GDP since the 1970s, despite a large academic literature documenting that federal funding for research has a large impact on productivity growth (it probably pays for itself via increased tax revenue, over a long enough time horizon). Meanwhile, even though the returns to science are high, we think they can be even higher: the science funding ecosystem has not evolved significantly for decades and is starting to show its age. Across a variety of different indicators (Nobel prizes, citations, patents), the impact per dollar of recent science seems to have fallen. In our grantmaking to support increasing innovation, we break it down into three pieces: talent, resources, and productivity. Talent refers to the people doing the innovation, resources to the funding and other support they have, and productivity is how well our talent is able to use resources (including AI) to drive innovation.

Our second area is energy. For the average person to benefit from innovation, new technologies need to actually be deployed at scale. But building new energy infrastructure - whether solar, wind, geothermal, or nuclear power - requires navigating a government approval process that has become dramatically more onerous over time. For example, in the 1970s, environmental reviews were a few dozen pages on average; the average is now above 1700 pages, and they take years to complete (more here). This constraint on the supply of energy is likely to worsen over time as demand for electricity from electric vehicles and data centers grows. Our energy program focuses on policy reforms to make it easier to get new energy technologies built in the real world. Specifically, we’re supporting efforts to bring about federal policy reforms to streamline energy permitting, both in the near term and over the longer run, and to support similar reforms in the states.

Next comes clinical trials. New drugs face a similar dynamic as energy; breakthroughs in biomedical science can lead to better health, but only if people can access them. The cost of getting a drug to market has been doubling roughly every nine years. Much of this cost is associated with the costs of running clinical trials, which have slowed over time from roughly six years in the 1970s to 8-9 years in the 2010s. As with energy, we’re again interested in supporting national policies that reform the clinical trials system to make it faster and less expensive, without compromising safety. But we’re also interested in increasing transparency around the data used in clinical trials, as a way to speed up research on drug efficacy and new applications (especially as AI potentially lowers the cost of analyzing that data).

Our fourth area is housing. Most people spend roughly a third of their income on housing - the single largest expenditure category - so if we want to create broadly shared economic growth, creating abundant housing is a prerequisite. Alas, since the 1960s, many local governments have increasingly restricted the ability to build more housing in the places people most want to live, whether via zoning, excessively restrictive building codes, parking requirements, or any number of other policies developed by the 20th century “growth control” movement. The result is a growing gap between construction costs and home prices in high-income metros, with real consequences for broadly shared growth: migration to high-wage cities has reversed since the 1980s, and lower-wage workers are now worse off if they move to expensive metros once housing costs are factored in. We support work to change these policies to increase the supply of housing. While we have historically focused on the most supply-constrained big cities, this year we want to look at state and regional reforms in jurisdictions that might be more tractable to work in before the wedge between prices and costs gets too large.

Finally, cutting across all of the above is the capacity of the government itself to perform its functions well, which we call state capacity. Just as government policies can impede the supply of energy, medicine, and housing, so too can it impede its own function. For example, policies designed to prevent biases in hiring decisions can mean subject matter experts who best understand what is needed for a role are excluded from key parts of the hiring process. In other cases, policies designed to reduce paperwork increase it. More broadly, Americans are not happy with how well the government works: for decades, between half and two-thirds of Americans have believed the government is generally ‘wasteful and inefficient.’ This matters for growth in two ways: government policy affects the economy and the government is itself a major economic player (US federal government spending is 14% of GDP, even after setting aside social security and paying interest on the debt). To increase the ability of the government to execute, we support reform policies that cut across all of government. Two areas we are especially interested in are rules around civil service hiring and government procurement, which jointly determine whether government objectives are carried out by government employees or contractors who are positioned to do the work well.

Most of our focus is on the United States, but we are also interested in these policy areas in other high income countries,1 and have made some grants to support work abroad (mostly in Europe).

Strategy areas

Within those domains, we support a lot of different kinds of work - anything we think will improve the prospects of a sufficiently good outcome. It can be a bit hard to describe all the kinds of work we do concisely, but one way to organize it is on a continuum from research to fieldbuilding to practice.

On one end of the spectrum is research. Sometimes, it’s not clear what the best policy even is, or the evidence base for a policy is too weak for us (or policymakers) to be sure it’s a good idea. In that case, we may fund academic research to try and build a better evidence base about the tradeoffs of different policies. We’ve made several grants like this related to improving our understanding of what kinds of policy changes can improve science (for example, we are co-funding a series of grants with the UK Department for Science, Innovation and Technology on this topic), and are looking at opportunities to support research on what kinds of permitting reform would have the biggest impact on building energy infrastructure. But as a group focused mostly on policy change, we usually think about supporting research with the objective of shedding light on a specific policy question, rather than open-ended exploratory research.2

Once we have some clarity around what policies will be most effective, we often support fieldbuilding; work that spreads ideas and grows the network of people who care about them. For example, we were founding investors in The Argument, in part because we didn’t see a publication looking at contemporary policies and politics with an abundance frame. We also have a program to support experts part-time to write accessible living literature reviews. Other work focuses on the people who are excited by ideas. For example, we support some of the major conferences in these policy areas (YIMBYTown, Metascience, the Roots of Progress, the Abundance conference) as well as some membership organizations that help people interested in these topics learn more and actively participate. This process can take time; for example, while our housing policy work has recently racked up major wins, we have been supporting YIMBYTown since 2016.

Finally, when there is an ecosystem of people and a solid evidence base on policy design, it’s time to support practice. That can take a lot of different shapes as well. In some cases, we help launch organizations that directly do important work, such as the International Student Resource Center, which provides authoritative immigration information to international students in the USA; Pilot City, which organizes workshops where city agencies pitch research projects that will help them achieve local government goals to academics, and vice versa; and research accelerators like Brains and BiTS. In other cases, our grantees generate and provide information that is directly useful to policymakers. For example, we have supported modeling work on the budget impacts of high skilled immigration reform, or the climate impacts of permitting reform. And, of course, other organizations we have supported directly advocate for specific policies. These might include DC-based think tanks (such as the Institute for Progress), or regional groups that focus on their state or local government (such as California YIMBY, Open New York, or the Sightline Institute).

What’s next

We think innovation, energy, clinical trials, housing, and state capacity cover some of the most important and tractable policy areas for promoting broadly shared growth, but there is a lot more we could do. Part of that will come down to how our budget changes over time; it isn’t fixed and we are seeking additional funding.3 But part will also be the outcome of research into new potential policy areas.

Three new areas we’re particularly interested in looking at are transportation, healthcare, and restrictions on labor choices. Transportation infrastructure facilitates access to the productivity benefits of major cities, but it is also bedeviled by many of the same permitting and approval processes that restrict the supply of energy and housing. Health is about delivery of medical care even more so than the supply of new medical treatments, but the supply of doctors and other health services is also constrained by specific policy choices. Meanwhile, productivity growth rises when people can more flexibly change careers to pursue what they think suits their skills, but occupational licensing regimes and non-competes restrict labor choices.

Meanwhile, even within our initial set of five areas, I expect our plans will change; research turns into clear policy goals, fieldbuilding leads to mature ecosystems, and policies eventually get passed. And we’ll also learn from what’s working and what isn’t, and like everyone else, adapt to a changing world. But for 2026, that’s what the Abundance and Growth Fund will be up to.

Matt Clancy: The British Industrial Revolution in Global Perspective. Why did the Industrial Revolution happen in the UK and not somewhere else in Europe? Allen argues it’s down to economics. It’s only worth doing all the work to develop a steam engine if you expect people will want to buy it, and that will only happen (initially) in places where the cost of labor is expensive and the cost of coal is cheap. The UK qualifies, but most of the rest of Europe didn’t. Allen’s book was the first serious work I read about innovation and it changed my whole career. Not because of Allen’s specific argument, which I’ve subsequently been convinced wasn’t a big part of the story. Instead, it was the notion that technological progress - which I already believed was the big lever of rising material prosperity in human history - had causes which could be both understood and influenced. Eighteen years later, I’m still working on that.

Dylan Matthews: The Land of Too Much: American Abundance and the Paradox of Poverty. My first obsession in public policy was the problem of poverty in the rich world: why, in a nation as wealthy as the United States, do people still have to endure homelessness and hunger? It’s a particularly big question for the US specifically, where poverty rates (especially if defined in “relative” terms) are significantly higher than in peer countries. The most provocative and mind-expanding explanation comes from this 2012 book by sociologist Monica Prasad. America's unusual level of poverty is ultimately the result, she claims, of our outrageous levels of agricultural productivity in the late 19th century. That surge in production led to an agrarian populist movement which demanded not a strong safety net but extensive access to credit. Loans and government redistribution are both ways that people are able to spend money they don't immediately have access to, and the US chose to embrace debt over social insurance, with far-reaching consequences to this day. The value to me, though, is less in the argument’s specifics than in its ability to demonstrate the importance of seemingly ancient policy decisions in shaping the structure of American society today. That suggested that setting up better policy basics now, the way AGF’s grantees seek to, could prove massively high-value.

Jordan Dworkin: As Science Evolves, How Can Science Policy? Coming up in science, my peers and I enjoyed critiquing the institutions in which we worked, and occasionally used our computational skills to probe and quantify the scientific ecosystem. But that exercise often stopped short of attempting to fully understand the structures and incentives that produced the inefficiencies we critiqued, or identifying the paths that might be charted out of them. In the mid-2010s, the work of a growing economics-of-science community helped me start to bridge that gap. This 2011 NBER Innovation Policy and the Economy chapter by Ben Jones was particularly influential. Building on his then-recent “burden of knowledge” theory, Ben laid out the evidence that knowledge accumulation was leading to increased specialization, more extended training, and an emerging dominance of team science. But he also took the important step of grappling with how science policy would need to rethink grant mechanisms, evaluation systems, and incentive designs to adapt to the changing enterprise. There was a lot of that grappling in the roughly three-year period surrounding his piece; in hindsight, 2011-2013 saw the publication of many now-classic ideas that shape metascience today.

Saloni Dattani: I don’t have a foundational text that drew me to clinical trial reform. I learnt about the topic during the Covid-19 pandemic. It was surprising how efficient some pandemic clinical trials were and, despite it being an emergency situation, it seemed to me that a lot of these processes could have been applied more widely. The RECOVERY trial, for example, tested around a dozen drugs within two years and quickly identified treatments estimated to have saved over a million lives. The COVID-19 vaccine trials ran much faster than usual, thanks to parallelized phases, rolling regulatory reviews, Operation Warp Speed, and other reasons I outlined in a piece forecasting when the vaccines would arrive. Some pieces I found memorable from the time were a book chapter on the history of clinical trials in the US and how AIDS activists reshaped the FDA, and an episode with Martin Landray, who co-led the RECOVERY trial. I’d also read Stuart Ritchie’s book Science Fictions, on bias and poor research practices in science, and Ben Goldacre’s Bad Pharma. Both made me wonder about ways to improve scientific rigor while limiting bureaucracy, which I wrote about in a piece later on.

Willow Latham-Proenca: Political Order and Political Decay: From the Industrial Revolution to the Globalization of Democracy. I frequently argue that Francis Fukuyama is an under-acknowledged intellectual godfather to the abundance movement. He coined the term “vetocracy” to describe America’s dysfunctional approach to building things back in 2013, and has written some of the most cogent descriptions of American institutional decay (as well as some of the most elegant defenses of modern liberal democracy) in the decade since. His classic Political Order duo is not just a fascinating tour of societies throughout history but a convincing argument on a universal framework of good governance. This was the first thing I read that really brought the tension between state capacity and the rule of law to life for me – the idea that the norms and institutions that allow the state to function effectively (think the Chinese bureaucracy throughout history, or the Ottoman devshirme) and those that prevent state overreach (the medieval English court system…or the US regulatory system) aren’t just items on a “good government” checklist, but self-sustaining leviathans that require active competition to avoid overgrowth.

Alex Armlovich: How Skyscrapers Can Save The City (2011). Ed Glaeser introduced almost everything YIMBYism is today in this Atlantic feature published years before the first modern YIMBY grassroots groups were even founded. This single paragraph’s math, zoning, and even building code comments are still the core of the YIMBY analysis:

Building up is more costly, especially when elevators start getting involved. And erecting a skyscraper in New York City involves additional costs (site preparation, legal fees, a fancy architect) that can push the price even higher. But many of these are fixed costs that don’t increase with the height of the building. In fact, once you’ve reached the seventh floor or so, building up has its own economic logic, since those fixed costs can be spread over more apartments. Just as the cost of a big factory can be covered by a sufficiently large production run, the cost of site preparation and a hotshot architect can be covered by building up. The actual marginal cost of adding an extra square foot of living space at the top of a skyscraper in New York is typically less than $400. Prices do rise substantially in ultra-tall buildings—say, over 50 stories—but for ordinary skyscrapers, it doesn’t cost more than $500,000 to put up a nice 1,200-square-foot apartment. The land costs something, but in a 40-story building with one 1,200-square-foot unit per floor, each unit is using only 30 square feet of Manhattan—less than a thousandth of an acre. At those heights, the land costs become pretty small. If there were no restrictions on new construction, then prices would eventually come down to somewhere near construction costs, about $500,000 for a new apartment. That’s a lot more than the $210,000 that it costs to put up a 2,500-square-foot house in Houston—but a lot less than the $1 million or more that such an apartment often costs in Manhattan.

Nisha Austin: Around the time when I started getting into ‘Abundance’ (in the way we all think about it here), my mother-in-law gave me Robin Wall Kimmerer’s The Serviceberry. I think she was trying to piece together exactly what I was so excited about and landed on a bestseller about abundance in nature, reciprocity, and community (things I am admittedly also excited about!). Not quite a book about permitting reform or building state capacity, but it turned out to be more relevant than I expected. It named something I’d been circling: that abundance is relational, that all flourishing is mutual, and that wealth means having enough to share. The other book I keep coming back to is Doris Kearns Goodwin’s Team of Rivals and the issues Lincoln grappled with: What do we owe the extraordinary institutions we inherited? How do we hold them together through profound disagreement? How do we keep building when the whole project feels fragile? All of these questions are still relevant. Ultimately, I came to this work from a lot of different directions, but the thread connecting all of it is stewardship of the places, institutions, and systems that let people flourish. Kimmerer reminds me that abundance only matters if it’s shared, while Lincoln reminds me that the answer is never settled. Every generation has to show up and do the work.

Hope your week is going well! Here's what caught our attention:

1. There are a lot more loose vibes than specific predictions when it comes to AI’s economic impacts these days, which is why I was grateful to see Metaculus, Renaissance Philanthropy, and the Schultz Family Foundation collaborate on a forecasting project on this topic. The Labor Automation Forecasting Hub collects the whole Metaculus community’s predictions on everything from the outlook of specific jobs (nursing seems like it’ll continue to do well) to the outlook of specific US states (Washington state looks like it’ll do okay, despite Microsoft and Amazon’s vulnerability to tech layoffs.) I expect the forecasts to be wrong a lot, but I appreciate the clarity with which they’re being made. — Dylan Matthews

2. As AI drives down the time & effort cost required to produce scientific grant proposals, funders are likely to be flooded with eligible, reasonable-seeming submissions that strain an already overburdened review system. It seems like we are starting to see the effects. Last week, the European Research Council (ERC) announced stricter application rules for 2027, not mentioning AI specifically but citing a “rapidly increasing number of applications.” The new policy increases the amount of time that unsuccessful applicants must wait before submitting another proposal (you cannot submit in 2027 if you received a “B” score on a proposal in the past two years, or a “C” score in the past three) and limits researchers to one application per year. Last summer, the NIH instituted a less strict but similarly motivated rule capping annual applications at six per investigator. Caps are one option for re-imposing application costs on researchers and encouraging self-screening, but they are a blunt instrument. Some other proposals for dealing with the influx include distributed peer review (which is being piloted by the UK Metascience Unit, and can produce more targeted application costs by scaling an applicant’s review requirements to the number of applications they submitted), tiered review (which could be a valuable tool, especially if combined with soft caps, but also runs the risk of increasing the number of applications by lowering the first-stage requirements), and AI-driven review (which is interesting and should be studied, though I remain wary of turning science into this cartoon). Figuring out the optimal solution is an open, and important, metascientific question. — Jordan Dworkin

3. In the interest of getting our fair share of aesthetic controversy, we’ve been reading Zane Kasher’s job market paper on wind farm construction and the effect on local residents. While the paper focuses on implications for taxing wind farms - Kashner finds taxes actually help wind farms get built in all but the most sparsely-populated areas, since it’s easier for developers to compensate locals for the downside - he also finds a 12% average drop in home prices within 3 miles of a wind farm. Since most auditory effects are within 2 miles, we wondered what the academic literature has to say about the aesthetics of wind farms. Beyond the entangled moral and political judgments that influence perception, there’s a few interesting takeaways. First, aesthetic responses vary a lot by geography (Spain and Sweden seem not to mind wind farms; Korea and Germany are less enthused on the aesthetics). Interestingly, some of that variation might trace back to the literal background - Molnarova et al. 2012 and Lothian 2008 both find that people like the look of wind farms better in degraded landscapes than in more scenic ones (an interesting wrinkle for the land-value externalities in Kashner’s wind paper, if more expensive homes cluster in baseline-prettier environments). Offshore, the question is much simpler: distance from shore dominates everything else (Ladenburg 2009; Cranmer et al. 2022 find indifference sets in beyond ~10 nautical miles). Finally, blinking aviation obstruction lights are a big - but pretty cheaply avoidable - driver of annoyance (aircraft-detection lighting systems can keep turbines dark ~95% of the night, and costs are estimated at just $1-2m for a single wind farm). — Willow Latham-Proenca

4. Our friends at the Centre for British Progress (with Labour Together) are out with a new paper on street votes—a mechanism that lets residents of a given street collectively hire an architect, agree on an upzoning plan, and vote to allow new housing, with value capture flowing to the consenting homeowners. The bottom-up mechanism design is interesting precisely because it doesn’t require preemption: you can imagine it mattering most in jurisdictions where state overrides have proven elusive, like Long Island in New York. In a galaxy brain sense, the most successful “street vote” of all time may be Senakw in Vancouver, where the Squamish Nation—exercising sovereign planning authority over ~10 acres near downtown—voted for thousands of rental units that will house tribal members and generate billions in revenue. They proved that single family zoning in high-opportunity areas is often NOT profit-maximizing, contra the simplest “pop” version of the Homevoter Hypothesis. With a tribal voting mechanism to allow it, the Squamish Nation’s people voted for abundance. UK Street Votes won’t be allowed to unlock as much density as Senakw, but Senakw shows how the incentives and voting could play out. — Alex Armlovich

5. Vincent Rollet’s MIT job market paper on zoning and the dynamics of urban redevelopment — which won Best Student Paper at the 2025 European UEA meeting and lands him at Stanford and then UChicago — is getting attention from actual city planning staff beyond academia. Rollet built a parcel-level panel of 833,000 NYC lots and estimates a dynamic spatial equilibrium model that captures something the static literature misses: redevelopment is slow, lumpy, and driven by fixed costs that rise sharply with existing building size. The fact that planners are paying attention matters: this is the kind of structural model that can discipline a rezoning’s projected supply impact in ways the usual back-of-envelope feasibility studies cannot. — Alex Armlovich

6. On a new episode of the Works in Progress podcast, Ruxandra Teslo, Ben Southwood and I chatted about everything you wanted to know about clinical trial reform — from why ethics reviews are so inefficient to how Australia has made it simpler and faster to run earlier stage trials. We also discuss our wishlist for policy reforms and some of the worst ideas we’ve heard. — Saloni Dattani

7. Separately, Niko McCarty and I wrote a round-up of news you might have missed in biotech and medicine. For example, did you know that organ donations have become vastly more efficient because of improvements in preserving organs from deceased donors? That semaglutide failed to slow the progression of Alzheimer’s in two large trials? Or that a transformative new drug to treat late stage pancreatic cancer succeeded in phase 3 trials, and will likely be expanded for earlier treatment and for other cancers too? Now you do! There’s much more in our post. — Saloni Dattani

8. And Matt is on holiday this week!

In grantee and team news:

• The Roots of Progress 2026 conference has been announced. Coefficient Giving is a sponsor and the conference brings together people across the progress and abundance movements. Applications are open through May 31.

• Progress Ireland’s Seán Keyes shared new ideas for Irish housing and infrastructure policy.

• Andrew Stern of Open New York has been hired as press secretary for NYC’s Department of Housing Preservation & Development.

• And in case you missed it, we published a digest of 79 things we read about abundance, a look back at the key readings and announcements from January through March.

Over the first quarter of 2026, we published eleven “What we’re reading” roundups covering research and policy developments across housing, energy, science funding, clinical trials, and more. This digest contains condensed highlights from those posts, organized by theme. Click the linked dates for full details.

More Abundance

1. Shawn Regan, writing for The Ecomodernist, catalogs ways the conservation and abundance movements share goals — permitting that slows infrastructure also hampers conservation. (February 20)

2. In “Transportation for the Abundant Society,” Shill and Levine argue policy should focus on accessibility to valuable places, not just mobility. (February 20)

3. Abundance NY’s Abundance Agenda sets ten-year goals: halve cost overruns, build 500K homes, double bus speeds, with detailed policy mechanisms throughout. (March 13)

4. Daniel Stid points out there’s a mismatch between how much governing happens at the state level and the level of civil infrastructure (think tanks, policy support) that operates there. (January 8)

5. Ashish Jha argues we’ve been too focused on the demand side of healthcare. The supply-side questions matter too: are we letting enough hospitals get built? Are we licensing enough physicians? (January 30)

6. Childcare costs are hard to reduce because labor is such a big part of the story. One easy fix: more states should let daycares operate above the ground floor. (February 13)

7. Alexander Kustov finds public support for skilled immigration is remarkably robust, even on the right. So why do we have so many restrictions? The vocal minority is concentrated where it has the most influence. (February 27)

8. George Borjas argued H-1B workers earn 16% less than comparable natives. EIG’s new analysis says the comparison isn’t apples-to-apples; when corrected, the gap falls to 7.5% and shrinks further with adjustments. (February 27)

9. The Paperwork Reduction Act of 1980 probably wins the prize for “most Kafkaesque US federal law.” Posting a voluntary government survey requires a 20-page document with annotated screenshots. (January 23)

Housing

10. Anti-growth zoning and stagnant construction productivity may be the same problem: an NBER paper finds the post-1970 productivity collapse coincides with downzoning, and growth controls directly limit economies of scale. (January 23)

11. Does rent control make tenants into NIMBYs or make them more open to growth? A critique of a Berlin study digs into why the literature still can’t say for sure. (January 30)

12. California’s local impact fees consume a troublingly high share of federal LIHTC subsidy: a new Terner report finds $300M/year in fees against $550M/year in new awards. (January 30)

13. David Card and coauthors find coastal superstar cities pay a 10-18% wage premium — but those gains are mostly eaten up by higher costs of living, especially housing. (February 6)

14. Evan Soltas and Jonathan Gruber find that permit approval raises land prices by 50% in LA — a clever way to measure the cost of permitting burdens on housing. (February 27)

15. Tokyo houses 38 million people with remarkably affordable rents. The US-Japan Foundation explores what American cities can learn from Japan’s approach. (February 27)

16. New housing construction moderates older “Class C” rents even more than newer “Class A” prices — YIMBYism in high-rent regions is a powerful antipoverty tool. (February 20)

17. The Senate passed the bipartisan 21st Century ROAD to Housing Act 89-10. A late investor ban threatened build-to-rent, but a HUD Code exemption may preserve the pipeline. (March 13)

18. We asked Michael Wiebe to review an influential paper on high-tech clusters and housing. He found serious problems, prompting us to downgrade our innovation-effects estimate. (March 20)

19. Brian Potter examines the elusive cost savings of prefab housing. The benefits tend to be schedule and quality, not price, and it’s not a substitute for zoning reform. (March 20)

20. Jerusalem Demsas writes about how urban divestment is a much larger problem in the US than gentrification, but compositional drivers crowd out the statistical reality. (March 20)

21. Austin, TX: supply finally caught up to demand and rents are back to pre-pandemic levels. Pew’s new analysis shows what happens when permitting actually works. (March 27)

Energy

22. Zeke Hausfather makes the case that making clean energy cheap is more tractable than making fossil fuels expensive, and that the 1.5C target has become a political snare. (January 23)

23. State-level electricity price increases vary widely: California and the Northeast are driving the national pattern, but other regions may be catching up. (January 30)

24. Why can’t we build enough electricity supply? Macey and Kiesling argue the regulatory system gives incumbents perverse incentives to keep supply constrained. (February 6)

25. The EPA repealed the endangerment finding. Philip Rossetti has a helpful take on the legal battles ahead, and a new working paper finds this type of climate policy uncertainty depresses investment, R&D, output, and raises prices. (February 13)

26. Wildfire smoke contributed to roughly 24,000 US deaths per year from 2006-2020, with no safe threshold. Proactive fuel management reduces severity 62-72% — if regulators let it happen. (February 20)

27. Solar is popular among Trump voters: 70% support when panels are American-made. But broader GOP support may be trending down — reform windows matter. (February 27)

28. Eliminating constraints on moving energy would have saved $3-5B in generation costs in 2023, and $6-7B in 2022 when gas prices spiked. Coastal incumbents benefit from the status quo. (March 6)

29. Samuel Roland tours the byzantine landscape of state-level geothermal regulation: mismatched frameworks, unclear water law, and legal risk that can kill project financing. (March 6)

30. Federal permitting talks have restarted in the Senate. Alex Trembath argues the need for bipartisan compromise is an opportunity, not a drawback. (March 13)

31. NEPA never explicitly provides for private lawsuits, but it became a litigation machine. Samuel Roland offers a deep dive into how that happened. (January 30)

Innovation Policy

32. Congress preserved science budgets for FY26: the Metaculus forecasters we commissioned got it roughly right. But Nature documents real damage: ~8,000 grants frozen, 20% staff losses at science agencies. (January 23, January 30)

33. The Institute for Progress responded to OSTP’s RFI on accelerating science with recommendations including fast grants, program officer empowerment, and agency metascience units. (January 8)

34. The Market Shaping Accelerator released a new guide for sizing prizes and advance market commitments, with an interactive calculator for estimating incentive sizes. (January 23)

35. Hundreds of new uses for off-patent drugs may go undeveloped because nobody has incentive to run the trials. Nicholas Reville proposes an NIH High-Leverage Trials program. (January 30)

36. 66% of new or revised federal research requirements since 1991 were issued in the last 10 years, with 2025 showing the biggest jump. COGR’s new report shows admin burden on researchers is rising fast. (February 6)

37. AI makes good science easier but also makes junk papers easier to churn out. Oliver Hanney writes about what comes next: curation, academic influencers, living documents. (February 6)

38. GABRIEL, a new open-source AI framework, finds tech adoption lags have compressed from ~50 years in the 1800s to ~5 years today. R&D may pay off faster than we assume. (February 20)

39. A good back-and-forth on NIH funding: Khanduja and Buck push VC-style practices for funders; former NIGMS director Berg responds that R01s already work more flexibly than critics think. (February 27)

40. State-level science funding is growing: Massachusetts proposed $400M, New York is eyeing a $6B biomedical institute, and Texas voters already approved $3B for dementia research. (February 27)

41. AlphaFold boosted basic protein research 15-35% on previously unsolved structures, but hasn’t yet shifted early-stage drug R&D. Hill and Stein’s new paper finds structure was a bottleneck for science, not drug discovery. (March 20)

42. Does MAGA actually want American science to win? Ari Shulman argues the critique of status quo science is broadly on point, but the policy conclusions won’t make America stronger. (March 20)

43. Astera Institute announced Radial: up to $500M over a decade to experiment with how life sciences research is organized, funded, and shared. Plus an essay competition on systemic bottlenecks. (March 13)

44. SocSci-Repro-Bench tested AI agents on reproducing 54 papers. Claude fully reproduced 78% accurately; OpenAI’s Codex got 35.8%. The results weren’t memorized, the models did the work from scratch. (March 13)

45. Alvin Djajadikerta explores whether AI-for-science might enhance prediction within current frameworks while weakening our capacity for true paradigm shifts. (March 27)

Clinical Trials

46. The FDA released draft guidance on Bayesian methods in clinical trials. But as Witold Wieçek notes, it reads like Bayesianism with frequentist guardrails. (January 23)

47. How much is a faster clinical trial worth? Frank Lichtenberg estimated ~4,200 life-years lost per drug if post-1990 medicines hadn’t been available. Philipson et al. found PDUFA sped up approvals by 6-7%, generating $14-31B in surplus. (March 27)

48. NIH dropped the “BESH“ clinical trial classification — good news for neuroscientists who had to treat simple EEG studies as clinical trials since 2014. (February 6)

Adam Kroetsch wrote extensively on trials this quarter:

49. The FDA announced a shift from requiring two clinical trials to one. Adam points out this has been moving in this direction since 1997 as most drugs are already approved on a single trial. He puts the FDA’s trust woes in historical context: how did the agency earn its trusted status in the first place? A reminder that the world is awful, much better, and can be much better still. (January 8)

50. His take on the FDA’s new Bayesian guidance explains how the guidance has been a long time coming and should help translate subjective judgment into a more transparent framework. (February 20)

51. He argues we need to pay attention to the boring stuff like the unglamorous work of setting up trials, recruiting patients, and collecting data. (March 13)

52. Adam also launched the Clinical Trials Efficiency Project. (February 6)

Ruxandra Teslo covered surrogate endpoints, regulatory uncertainty, and IRB reform:

53. A good surrogate endpoint can save years of trial time; a bad one can be misleading. She is launching a series on getting more good ones. (January 8)

54. IRBs are meant to protect research participants, but fragmented standards create needless delays. She makes the case for reform: let researchers choose any compliant external IRB. (January 30)

55. The FDA refused to review Moderna’s flu vaccine trial, then reversed course. She argues regulatory uncertainty can be worse than strict standards. (February 20)

56. She argued that AI alone won’t speed up clinical trials and the real bottlenecks are regulatory and operational, not technical. (March 6)

Europe

57. A father-son duo offers competing explanations for Europe’s innovation gap: Luis Garicano credits Sweden’s angel investor tax incentives; Pieter blames restrictive labor laws. (February 20)

58. Rasheed Griffith argues the EU is “the world’s most underrated libertarian project,” doing more to break down barriers between nation-states than most recognize. (January 8)

59. Progress Ireland’s Seán Keyes lists ideas for more housing, infrastructure, and energy in Ireland, from joint ventures to pre-approved “pattern book” building designs. (January 8)

60. EU Commission staffers argue that EA community members should seek employment at the Commission; in Europe, working inside government can be a uniquely powerful route to impact. (February 13)

Team Announcements

61. We launched the Pop-Up Journal Initiative with the Sloan Foundation. The NBER will host the first journal on the “Griliches Question” about R&D returns. (February 27)

62. We published a directory of Living Literature Reviews, continuously updated research syntheses by expert authors across abundance-related topics. (March 13)

63. Jordan Dworkin made the case that replication is underrated. (January 23)

64. Jordan and Stuart Buck debated the ROI of funding replications over at The Good Science Project. (January 30)

65. Saloni Dattani published The Case for Sharing Clinical Trial Data. (February 13)

66. Dylan Matthews published “What Gilded Age America and 1960s police can teach us about state capacity“. (February 27)

67. Saloni wrote on the history of clinical trial reforms: how randomization, preregistration, and results reporting went from radical to standard practice. (March 6)

68. Jordan published “Who Will Program-Manage the Program Managers?“. (March 27)

69. Dylan launched a substack. (January 8)

70. Saloni wrote about The Golden Age of Vaccine Development in Works in Progress. (January 8)

71. Saloni published a tour of medical breakthroughs of 2025 on her substack. (January 8)

72. Saloni and several grantees launched Clinical Trials Abundance, a joint blog publishing weekly on making trials more efficient. (February 13)

Grantee Announcements

73. Ben Schifman discusses the NEPA litigation “doom loop” and its potential fixes in a new long-form piece at IFP. (March 6)

74. Open NY stood alongside Governor Hochul and Mayor Adams to support modernizing environmental review. UK Research and Innovation opened their second round of metascience grants. (February 13)

75. Renaissance Philanthropy launched Pilot City, connecting cities with local academic expertise through matchmaking events. (February 13)

76. Sightline Institute’s “funded inclusionary zoning“ model inspired a bill that just passed the Oregon Senate. (February 27)

77. Speculative Technologies announced the 2026 Brains Fellows. Renaissance Philanthropy launched the BiTS Americas cohort training scientists to lead large-scale research. (March 13)

78. Progress Ireland published on how Brussels can help with Galway’s housing problems using funding conditionality tied to housing production. (March 13)

79. Greater Greater Washington published their take on DC’s draft Future Land Use Map — only 15K units by 2050, banking on leftover development sites. The draft drew mass protest. (March 27)

1. A new Urban Institute piece asks, what happens to housing production after “big upzonings” in strong housing markets? It turns out, a lot. Top economics journals have long considered case studies of upzoning too trivial to publish–it is obvious that lifting a quota on housing production in locations where homes sell for more than the marginal supply cost of homes will result in new housing production. But earlier peer-reviewed research in planning journals had focused on small upzonings that produce small changes–a corollary to our last blog post about the need for feasibility-oriented large upzonings. This new paper begins to fill the case study gap on large upzonings with by-right permitting in strong markets. — Alex Armlovich

2. Larry Katz, known today as a legendary labor economist, had a precocious side quest in urban economics as an incredibly incisive observer of the downzoning crisis that hit the Bay Area in the 1970s. His Commencement speech on housing at Berkeley recently did rounds on Twitter, but his 1981 article on how growth control regulations in San Francisco drove home prices from near the national median to the highest in the country, in just a decade, goes into even more detail (screenshots there, original is paywalled). Beyond Katz’s direct intellectual contribution, this hauntingly-contemporary time capsule is an important rebuttal to recent and troubling claims that housing underproduction in high-demand US cities only began after single family mortgage underwriting tightened in 2008. It’s important to understand the superstar city zoning crisis, and the roots of the YIMBY academic literature, both date back decades before 2008. YIMBYs should be proud to stand on the shoulders of giants, but also humbled to know simply having the correct answer is not enough to win alone. — Alex Armlovich

3. Sticking with housing, the burden of the housing shortage is falling disproportionately on new buyers and renters, according to a piece last month by Jess Remington. Prior to 2022, new homeowners and existing homeowners paid a similar share of their income to housing costs (~22% for new homeowners, ~20% for existing ones). But since then, the gap has rapidly grown, with new homeowners now paying 26% of income for homes. Meanwhile, renters who recently moved are paying a record high share of their income to rent - 33%. With the typical age of a new buyer slightly over 40, millennials are especially exposed to this trend - possibly that helps explain why they are the generation with the highest levels of economic anxiety. — Matt Clancy

4. The President’s Budget for fiscal year 2027 was released earlier this month - while it’s just the Administration’s proposal (actual appropriations will be enacted by Congress later this year), it’s a worthwhile barometer of the Administration’s goals. For the Department of Energy (DOE), the proposal is a stark cut, representing an 11% reduction from enacted FY26 levels for civilian energy programs. As expected, the proposal aligns with the fossil fuel-oriented restructuring that started last year, including $15 billion in Infrastructure Investment and Jobs Act rescissions alongside large transfers to baseload programs and major cuts to most renewable energy offices (worth noting that transfers of this scale would require additional cancellations of obligated awards - it’s not clear where this stands). Former DOE staff at the DOE Alumni Network published a helpful summary that highlights some of the less-expected pieces - for example, the budget proposes rescissions to:

   1. the same grid accounts that funded DOE’s recent SPARK solicitation

   2. accounts supporting hydropower facilities that the same budget proposes targeting with the Baseload Power program

   3. critical minerals processing and recycling programs that the administration announced a new solicitation for last month. — Willow Latham-Proenca

5. Beyond the budget request, DOE has also been slow-walking grants it already committed to (a similar pause is now in place at NOAA) as well as slowing the pace of the new solicitations and award obligations that keep appropriated funds moving. — Willow Latham-Proenca

6. In the Endless Frontier, Vannevar Bush famously posited that “scientific progress… results from the free play of free intellects, working on subjects of their own choice, in a manner dictated by their curiosity.” In a characteristically generative piece, Ben Reinhardt suggests that the reign of this framework, which has shaped science policy and practice since WWII, was a historical anomaly that is nearing its end. In its place, Ben calls for researchers to think more deeply about who their customers are, and why those customers would want to “buy” their science. This is not simply a call for shifting research up the TRL scale; reasons for buying research can include not just health or profit, but “wonder, status, guilt, and existential dread” – what’s important is figuring out what it is you’re selling and to whom. This mindset applies not only at the level of research agendas, but also research ecosystems, and Ben highlights the need for the independent research ecosystem (think coordinated research programs, FROs, BBNs) to more carefully define and cultivate its own value proposition for the set of potential customers. Realistic options for what independent research could offer its customers seem to include: superior quality, clear ownership over outcomes, and status; the first seems most compelling to me (and seemingly Ben), so let’s do what we can to prove that one out. — Jordan Dworkin

7. One thing I’m not reading this week: your writing about metascience policy. If you’d like to fix that, Emergent Ventures just announced a new tranche of grants for (1) metascience policy entrepreneurs and (2) science & metascience communicators. If you’re interested, you can apply through their general portal. — Jordan Dworkin

8. My friend Lauren Gilbert has launched a new magazine called In Development, aiming to be the New Yorker (or Works in Progress?) for global development. I enjoyed reading the inaugural piece by Paul Niehaus, co-founder of GiveDirectly, on the rise of cash transfers as a form of foreign aid. From the piece, I learnt that a dollar is worth roughly 250 times more to someone living in extreme poverty compared to the average American, mostly because of diminishing marginal utility: at low incomes an extra dollar goes toward basic necessities like food and shelter, while at high incomes it tends to go toward discretionary spending or savings. Another was that Harvard’s IRB nearly killed GiveDirectly’s first RCT on the grounds that giving people money might harm them; they had to argue transfers were safe in order to study whether they were safe. And finally, it was interesting to think about how, while most research on foreign assistance was focused on showing donors how to achieve their priorities, research on cash transfers helps donors understand recipients’ spending and priorities – the most important ones being housing, food and business investment. The idea that people in extreme poverty would spend money they received on alcohol and tobacco instead of basic necessities was in fact unfounded. – Saloni Dattani

9. Eroom’s Law (the reverse of “Moore’s Law”), for those of us who don’t spend all our days thinking about pharmaceutical innovation, is a term describing the historical trend that the R&D cost of developing a new drug seems to have gone up a lot over the past sixty or so years. Interestingly, the “Law” flattened out since 2010, but it’s merely stagnated, not reversed. There’s a lot of optimism in the biotech world that AI could turn Eroom’s Law in reverse. Investor Elliot Hershberg has a thoughtful essay arguing that the Law emerged partially because only a few large pharma companies are currently able to take promising research leads and commercialize them. That means innovation is essentially “rate-limited” by those firms’ ability to move drugs through the pipeline. What we need are regulatory changes that make it easier for small biotech firms to work through that pipeline themselves. This is one of many reasons we’ve been prioritizing clinical trial abundance here at AGF. — Dylan Matthews

10. I’ve been thinking a lot about AI lately, and how it relates to the kind of work our team does to try to chip away at bottlenecks to economic growth. One key question here is whether the economy of the 2030s and beyond is going to be, well, recognizable: are most people going to be working? Will they earn enough to meet their basic needs? Will humans be economically necessary anymore? One of the better contributions I’ve seen to this debate is a new post from UChicago’s Alex Imas on the rise of “relational work.” Some stuff, like food, people spend less of their income on as they get richer. Other stuff they spend more on, and this stuff, Imas argues, tends to include irreducibly human elements. Present-day billionaires, for instance, spend more than normal people on just about everything, but especially more on services: spas, waitstaff, private chefs, personal trainers, etc. It’s a compelling case that human labor will still matter and the economy will remain somewhat recognizable, though still very, very different from what we’re used to. — Dylan Matthews

We also wanted to share some updates from our grantees and team:

• It’s a good week for evidence that the YIMBY playbook is working. Besides the piece Alex highlighted in our first item above, Nolan Gray looks at the evidence that the policy changes pushed by California YIMBY (one of our long-time grantees) are working.

• In the current political moment, how can environmentalists best achieve their climate goals? Aliya Haq of Clean Economy Project at Harvard Belfer Center (a recent grantee) recently gave a talk (video) highlighting the importance of moving from obstruction to meeting clean energy demand, making clean energy faster and more affordable, and creating credible and verifiable measurements.

• Earlier this week, Jordan Dworkin blogged here about a new website he has created, ScienceSpending.org, to track how well US science funders are spending down their budgets.

• Congratulations to Saloni Dattani for her Ted Talk earlier this week!

  

The past year and a half has been turbulent for US science. In FY25, the administration proposed cutting NIH by roughly 40% and NSF by more than half, froze and terminated grants at multiple agencies, cut staff across agencies, and pursued a range of policy changes that created uncertainty across the research community.1

In August 2025, against that backdrop, we commissioned professional forecasters to predict the next three years of US science funding. They were asked two questions. The first was about appropriations: how much money would Congress give NIH and NSF? Despite the turmoil, the forecasters predicted relative stability, with both agencies expected to land near their FY25 levels.

They were right. In early 2026, Congress passed bipartisan spending bills for FY26 that broadly rejected the proposed cuts. NIH received $48.7 billion, a $415 million increase over FY25, and NSF received $8.8 billion, a slight decrease from FY25 but more than double the administration’s request.

The forecasters were also asked a second question: how much money would these agencies actually spend? Here, they again predicted relative stability. But on this question, the jury is still out.

How federal money moves (or doesn’t)

For our purposes, the federal budgeting process has three key steps. First, Congress authorizes the use of federal resources for certain activities, then it appropriates specific dollar amounts to agencies, then the agencies obligate those funds for their appropriated purpose.

These numbers do not always align. It is not uncommon, for example, for Congress to authorize funding that it never actually appropriates.2 But the deviation that is rarer, and more relevant today, is a gap between appropriation and obligation. In a typical year, this gap is modest and uninteresting; agencies usually spend what they are given at a roughly predictable pace over the course of the year, and communicate to Congress the reasons for any differences between appropriations and obligations.

But the past two years have not been typical. Across the federal government, agencies have been slowing grant-making, terminating awards, and losing the staff capacity needed to process and manage spending.

In FY25, NIH spending slowed dramatically starting in February and into the spring, finally picking up in July thanks to pressure from advocates and members of Congress who pushed back against an attempt by the administration to freeze funding. NIH’s situation was particularly visible, with analysts and media outlets keeping a close eye on its spending rate. But other agencies were not so lucky. It wasn’t until the end of the fiscal year that it became clear that USDA had been struggling to make grants, and ARPA-E ended the fiscal year having obligated less than half of what it was appropriated.3

Tracking science spending

Despite strong appropriations, we’re now several months into FY26 and the same dynamics are playing out again. To ensure agencies are in a position to execute on their mission, policymakers will need to keep a close eye on spending; but information on grantmaking and obligations is fractured across budget execution reports, USAspending, and agency-specific databases.

To support this effort, I built ScienceSpending.org. This site pulls together up-to-date data on spending at five major agencies’ science functions to show whether spending is on pace relative to appropriations and historical trends.4

The site compares each agency’s current spending to spending rates in past years, and provides rates of new grantmaking, all award-making (i.e. including non-competitive continuations or modifications of previous years’ grants), and obligations (the broadest category of spending, including not only award-making, but personnel, admin, etc).

At time of writing, the NSF’s situation is the most concerning. NSF is making new awards roughly 70% slower than typical, and even its more general rates of getting money out of the door (both “all awards” and “obligations”) are almost 60% behind pace.5 In FY25 NIH was able to close a similarly large gap, in part by increasing its use of forward funding;6 NSF has less room to use that lever this year, as it already uses up-front funding for the majority of its grants.

NIH is also significantly behind pace in FY26, spending money on new grants roughly 50% slower than it typically has; it is also 29% behind on all award-making, and 16% behind on all obligations. Notably, these topline numbers hide substantial heterogeneity across institutes. The National Cancer Institute, for example, is even farther behind pace than the agency as a whole, having spent 79% less on new grants, and 43% less on grantmaking overall, than is typical at this point in the fiscal year. The National Institute of General Medical Sciences, meanwhile, is near its normal pace.7

Other agencies are in a better position. DOE’s science spending is a bit behind pace overall, with the Office of Science’s lack of grantmaking8 being balanced out by ARPA-E’s increased spending after having spent well under its appropriation in FY25. NASA is slightly behind its typical pace, and USDA is roughly on track.

There are, of course, legitimate reasons why an agency might be above or below its historical pace in any given month. Government shutdowns delay processing; new policies require revised procedures; large coordinated funding programs can shift the curve. But having insight into patterns within and across agencies can be helpful for spotting troubling or hopeful trends, and understanding when and where attention is needed.

In most years, this kind of tracking would be an esoteric exercise. I hope it will be again soon. Until then, thanks for keeping an eye on it.

1. Sam Peltzman has an alarming new paper about American happiness. Since 1972, the General Social Survey (GSS) has asked a random sample of Americans how happy they were, and in the blue line of the figure above I’ve plotted average happiness by assigning numerical values to the possible answers. Peltzman documents a big negative trend break starting around Covid-19, which has persisted into at least 2024, well after the abatement of the worst of the pandemic. I was curious if this was some fluke in the GSS, so in the figure above, I added US data on self-reported life satisfaction from Gallup’s World Happiness Report. Gallup only has data from 2011 to 2025, and the two series do not exhibit the same year-by-year fluctuations. Nonetheless, both show American well-being remains near unusually low values, at least circa 2024 and 2025. — Matt Clancy

2. When I’m arguing to more AI-pilled friends that the tech’s diffusion through the economy might be slower than they think, one of my go-to examples is “doctors still use fax machines.” But another good one, that I just learned from global development expert Todd Moss, is “31 percent of the World Bank’s policy reports are never downloaded, but the bank still writes them.” It’s not just the World Bank, though; NGOs and think tanks across DC and the world are putting out myriad reports, dozens if not hundreds of pages long, every month, despite compelling evidence that very few people read them. The biggest problem with this, as Moss says, is that it’s a tragic waste of talent: “The opportunity cost to busy people – who should be world-leading experts on vital social, economic, and political issues – is actually mind-boggling. Imagine the social good such smart capable people could do with all the time not spent on long reports.”— Dylan Matthews

3. Everyone’s talking about building codes! Though we have not yet fixed most of the obvious growth control regulations, like zoning, permitting, & procedural review laws, housers are also looking ahead to the more obscure rules that set breakeven construction costs, especially the building code. Though YIMBYs have been looking into building codes since 2020, and scholars long ago archived express regulatory intent to use building codes to shadowban apartments in the 20th century, YIMBYs didn’t have a building code agenda until the 2021 breakout of “single stair” reform popularized by Eliason, Speckert, & Smith, and more recently elevator reform, plumbing, and more. California YIMBY just summarized an excellent UCLA Lewis Center report on how the building code writing process works and how we got where we are now. — Alex Armlovich

4. Our AGF post on the “Zoned Capacity Illusion” got a warm reception, with shoutouts from NYC’s Deputy Mayor during Mayor Mamdani’s Reddit AMA on housing; and from Matt Yglesias on why YIMBYs need to center zoning feasibility in addition to incrementalist zoning strategies. In high-rent cities, where construction costs for hi-rise fireproof construction will pencil if allowed, Missing Massive is an essential tool for unlocking what Jane Jacobs called “cataclysmic money”, a.k.a. an upzoning for large multifamily, in the most attractive central locations, that pencils out immediately instead of decades from now. — Alex Armlovich

5. The National Science Foundation’s 2027 budget request makes for some high variance reading. Two things that have me excited are the proposal for an NSF metascience unit to “champion agency-wide efforts to explore non-traditional R&D funding mechanisms and drive evidence-based optimizations to the R&D

   portfolio” and the request for $50mn to the NSF Tech Labs (a number I hope grows). A metascience unit at the NSF is the kind of thing metascience geeks have dreamt of for many years. But two things that have me the opposite of excited are the proposal to eliminate the Social, Behavioral, and Economic Sciences Directorate and cut overall funding to the NSF by a 55%. Fortunately, appropriations are set by Congress, and last year they largely ignored the proposal for a similar cut (this tweet by IFP’s Matt Esche provides a lot of useful context for how to interpret this proposal). My hope is that we land in a world where we avoid the bad parts of the proposal and retain the good bits. — Matt Clancy

6. The legal fight over the NIH’s proposed 15% indirect cost cap is over (for now). STAT reports that the administration let the Supreme Court petition deadline pass without filing, ending 14 months of litigation. The courts found that NIH violated congressional appropriations language in trying to unilaterally replace negotiated rates. But indirect cost reform isn’t off the table; the administration may still attempt changes through OMB’s Uniform Guidance, and Congress is looking at alternatives like the FAIR model. If you want to understand the substance, two pieces are worth your time. This week, writing for the Good Science Project, Jeremy Berg published a detailed explainer that walks through how indirect cost (IDC) rates are negotiated, what they cover, and common misunderstandings. And over the summer, Pierre Azoulay, Daniel Gross, and Bhaven Sampat wrote a policy brief for IFP (adapted from their NBER working paper) unpacking the history of IDCs, the details of their calculation and implementation, and the pros and cons of proposed reforms. A key takeaway from both is that the negotiated IDC rate and the percent of total funding that universities take home for indirect costs differ significantly, primarily because (a) not all direct costs or grant types are eligible for IDC funding, and (b) the numbers use different denominators. For example, a 50% negotiated IDC rate applied to a grant with $1mil of direct costs, $250k of which are exempt from indirects, would yield: a total indirect cost of 50% * ($1mil - $250k) = $375k, for an effective IDC rate of $375k / $1mil = 37.5%, and a total indirect cost share for the university of $375k / ($1mil + $375k) = 27%. If that’s not clear, don’t worry, the administration conflated the total indirect share and the negotiated rate in their original 15% cap announcement too. — Jordan Dworkin

7. Niko McCarty, founder of Asimov Press and now a fellow at Astera Institute, recently ran a bounty to surface new ideas to reduce the cost of lab experiments in biology. I found his reflections really interesting. He received far more interest than he expected (430 submissions in total), and though he expected to award prizes to only 4, he ended up awarding 20. Some ideas I found interesting were: “a protein printer fabricated using DNA origami” and a way to run protein synthesis reactions on a gel filtration column. He explains that the micro-grants (<$5000 each) aren’t likely to push the projects forward materially, but might still do so by providing a vote of confidence in them. — Saloni Dattani

8. And we hope Willow is enjoying some beach reads while on holiday this week!

Some updates from our team and grantees:

• Matt Clancy published A Little Progress Is Worth a Trillion Dollars this week, making the case for why even modest improvements in economic growth have enormous value over time. The post is part of a suite of related projects: a web tool where you can adjust assumptions and see how different growth scenarios play out, and an interactive explainer walking through the model step by step.

• The Institute for Progress published a new analysis of Section 901 of the ROAD to Housing Act, examining how the Build-to-Rent provisions would affect rental housing supply and what the exemptions mean for innovative construction methods.

• Ben Holland joined David Roberts on the Volts podcast to discuss why climate funders should be investing in housing policy - arguing that reducing housing costs in climate-vulnerable regions and enabling dense, transit-oriented development are critical climate strategies that philanthropy has largely overlooked.

• Clinical trials today are highly bureaucratic, expensive, and time-consuming. Most trials fail to recruit enough participants to answer their questions at all. A great new post by Adam Kroetsch explains how things got this way.

For example, if our discoveries and inventions improve standards of living by 1 percent a year, children will by adulthood be 35 percent better off than their parents. If they improve livelihoods at 3 percent a year, those same children will grow up to be about 2.5 times better off. Whether viewed in terms of large or small improvements, progress matters a lot.

This is a common rhetorical strategy to justify why we ought to care about faster progress, and it’s one that I agree with: the magic of compounding means small differences in growth rates, sustained over time, add up.

But the case for caring about progress doesn’t rely only on the magic of compounding interest rates. Even small one-off changes in the rate of broadly shared progress are worth huge sums.

Ballparking the value of a small boost to progress

Let’s illustrate this by estimating the value of a relatively small one-time increase in the rate of technological progress. Economists like myself generally assume increases in living standards are brought about by technological progress, so let’s consider the impact of a one-time increase in the rate of per-capita economic growth from 2% to 2.1% in the United States (2% is the long-run average). After one year of faster growth, assume we drop back down to the usual 2% per year. Going from 2% to 2.1% would be a 5% relative increase in the rate of growth. What would that be worth?

To ballpark it, consider that US GDP per capita is around $90,000. Growing by 2.1% instead of 2% means this increases by an extra 0.1pp, or $90 per person. Shared over 340 million Americans, that works out to $30.6 billion.

But that’s only in the first year. Even though we’re imagining a one-off change in the rate of progress, that doesn’t mean the benefits are one and done. Technological progress is a cumulative process, where new technologies are built off older ones. If we grow by an extra 0.1%, in the next year we’ll be working off a slightly more advanced technological base from then on. So even if we drop back down to the historical average rate of 2% per year thereafter, we’ll still be 0.1% richer than we otherwise would have been. It’s like a runner who briefly sprints before reverting to their former pace. Even after they are no longer sprinting, they remain ahead of where they would have been, had they merely maintained a steady pace.

To keep things simple, let’s assume the current cohort of Americans enjoys an additional $90 per year for the rest of their working lives and not worry about discounting (we’ll come back to that). The average American is about 40 today, so if they retire at 65, that’s 25 years of earning another $90 per year. Ninety dollars a year, over 25 years, across 340 million people is $765 billion.

Another important benefit flowing from technological progress is health. In the USA, life expectancy has been on a long-run upward trajectory, increasing by about 0.1 years per year for several decades.1 If we think that is mostly driven by technological (in this case biomedical) progress, then an increase in the rate of progress should also have some health benefits. Just as we increased the rate of economic growth by 5% (going from 2 to 2.1%), let’s increase the annual gains to life expectancy by 5%: that means during our year of accelerated progress, we’ll add 0.105 years to life expectancy instead of 0.1. That’s an increase of life expectancy of 0.005 years, or 1.8 days.

To put a crass dollar value on that, let’s turn to the Value of a Statistical Life Year.2 The US Department of Health and Human Services has a range of estimates of the Value of a Statistical Life year, ranging from $282,000 to $1.5 million (see Table 3). The conservative lower bound will work fine for us. If we assume an extra year of progress yields an extra 0.005 years of life expectancy, this is valued at $1,410 per person. Across 340 million Americans, that’s $479 billion.

Add the long-run income and health benefits up and the upshot is this: the health and income benefits of a 5% one-time boost to the annual rate of technological progress are in the ballpark of $1.2 trillion.

Leaving the ballpark

The above calculation is illustrative, but takes a lot of shortcuts. It ignores changes in population. It assumes economic benefits are $90 per year, which is not correct if we assume growth is exponential. It ignores the impact on people not born today, who might still benefit from technological progress. And it doesn’t properly discount for benefits that arrive in the more distant future.

To try and capture all that, I’ve built a simple economic model that incorporates all this and more, and with the help of Claude code I’ve written a web tool you can play around with to value different kinds of interventions to the rate of progress. Under my default settings, this more realistic model also values the above policy change at $1.2 trillion. If you are interested in using this tool, read the explainer here. We’ve also put the underlying code up here as a commented python file, in case you want to build your own version. Just give the code to a frontier LLM and ask it to modify it to incorporate any changes you want to make.

What is an ocean but a multitude of drops?

The important takeaway from this exercise is that even tiny increases in broadly shared progress are almost inconceivably beneficial. Mechanically, this is because we’re assuming the kind of progress we care about is shared by many people over many years.3 The import of this fact can be hard to grasp intuitively. We’re all individuals, but this is a situation where focusing on an individual experience can give us the wrong intuitions. As individuals, if we think of what it would be like to be 0.1% richer and live an extra 1.8 days, it seems good but hardly something to get passionate about. But aggregated across the generations, the import is qualitatively different. The extra income cumulates to hundreds of billions; the extra days add up to more than a million years.

If we take the conclusions of this post seriously, then we should be willing to put a lot of effort into raising the rate of progress. And I see that as precisely what we, and the broader abundance and progress studies movements, are trying to do.

Happy Friday! Here’s what caught our attention this week:

1. Industrial Policy for Housing Policy is a new blueprint for tackling the housing crisis from Arpit Gupta and Steve Teles. One bit I particularly liked was the five-part diagnosis of why factory-built housing has failed to really take off in a sustainable way: (1) housing’s boom-and-bust cycle makes it hard for large investments into factory production to survive lean times, especially because (2) the government has not traditionally moved in to smooth fluctuations; (3) a patchwork of different regulations makes it hard for learning by doing to get as much traction, (4) it’s expensive to transport housing components away from factories, and (5) it’s hard to assemble lots of little (separate) parcels into a contiguous area that can support dense new buildings. The diagnosis suggests several possible fixes and Gupta and Teles have a bunch of other ideas about how to overcome these barriers. — Matt Clancy

2. Why America is so much better at immigration than Europe is a fascinating new piece by Kelsey Piper and Alexander Kustov (who also writes the great Popular by Design substack on immigration). Piper and Kustov point to a number of factors that have led to better outcomes and more public support for immigration in the United States (I know, right?), not least of which is America’s flexible labor market. When immigrants can get to work, they are more likely to integrate, impose less of a fiscal burden, and less likely to engage in crime. That, in turn, leads to more public support for immigration. Indeed, Piper and Kustov argue that the turn in public opinion against immigration in the USA coincides with shifts that made it harder for immigrants in the US to work! The labor market is only one part of the argument Piper and Kustov make, but it’s highly relevant to our thinking about policy work we could support in Europe. — Matt Clancy

3. We at AGF don’t work as directly on AI issues as many of our peers at Coefficient Giving, but you can’t really think about the medium to long-run state of the US and world economies without thinking seriously about AI. I found a big new paper from the Forecasting Research Institute (summary thread here) asking AI experts, economists, “superforecasters,” and the general public to predict AI’s effects on the economy by 2030 and 2050 very helpful in calibrating my own views. Perhaps the most surprising thing to me is that the four groups weren’t terribly far apart from each other. In a world of rapid AI progress, economists expect 3.5 percent GDP growth in 2050; AI experts expect 5.3 percent. This pales next to forecasts of double-digit annual economic growth caused by transformative AI that more optimistic commentators have made. For more, see Matt, Alex, and my follow-up posts on the survey. — Dylan Matthews

4. This week, there were two very different pieces on reforming how we fund science. In City Journal, Michael Gibson (former VP of Grants at the Thiel Foundation) argues that Jim O’Neill’s nomination as NSF director is a chance to shake up the agency’s approach to talent identification and grant design. His specific proposals will be familiar to anyone who follows metascience, and include partial lotteries, scout programs, open access, and indirect cost caps. Meanwhile, in PNAS, Harvey Fineberg (former president of the Gordon and Betty Moore Foundation) echoes the need to reinvigorate the U.S. scientific ecosystem, but presents a more macro vision for getting there: sustained increases in federal science funding, networked innovation clusters, state-level funding programs, and a focus on domestic and global talent. Notably, both emphasize the need for increased support of younger scientists, but otherwise their priorities diverge. — Jordan Dworkin

5. However you want to reform science funding, structural shake-ups require that there are functioning agencies to reform; to keep tabs on how things are going on that front, Grant Witness is now tracking NIH and NSF funding curves in real time. — Jordan Dworkin

6. It’s a big week for electricity price data - the Lawrence Berkeley National Laboratory updated its Retail Electricity Price Trends and Drivers with 2025 data and new analysis, and a Heatmap/MIT/CleanEcon collaboration launched an Electricity Price Hub with electricity bill and price data down to the utility level. Both illustrate how fragmented price patterns continue to resist a coherent story: the most dramatic price increases continue to be geography-specific, like outsize generation costs where PJM load growth has hit supply constraints. LBNL’s update does flag a few trends that will likely continue to shape prices going forward: investor-owned utilities requested their largest revenue increases since the 1980s in 2025 (and regulatory approval rates for such increases have gone up significantly since 2020, particularly in New England and the Southeast); and equipment prices increases for transmission and distribution inputs continue to far outpace inflation. — Willow Latham-Proenca

7. Research in action: Why permitting reform matters. Evan Soltas translated his recent LA permitted reform paper, that we featured a few weeks ago, into a localized estimate of the value of permitting reform in NYC. Each year of permitting time saved in NY is equivalent to an 8% drop in construction costs. With state SEQRA reform and local NYC streamlining, 2.5 years of promised permitting time savings could save quite a bit. Chris Elmendorf further notes the Governor & State Senate would need to prioritize SEQRA in budget negotiations with a still-recalcitrant Assembly to get clean reform over the finish line — Alex Armlovich

8. We might finally get a vaccine against Lyme disease! Maybe. Hopefully? Unfortunately, the confidence intervals in the phase three trial were really wide: after four doses, its efficacy was estimated at 73.2%, with a 95% confidence interval of 15.8% to 93.2%. (The trial’s primary endpoint was to surpass a lower bound of 20% efficacy, which it failed to do.) That huge uncertainty is probably because Lyme disease is so rare that you’d need a huge sample size to reach a precise estimate with a standard trial design. This trial had over 12,000 participants, but it seems even that wasn’t enough for more precision. But also… we actually had a Lyme vaccine in the ‘90s! And then it was withdrawn after a lack of public demand for it. (Not because people don’t care about Lyme disease, but because of fears that the vaccine caused arthritis, even though no connection was found by FDA analysis and later research…) If that sounds incredibly frustrating, that’s probably because it is. Maybe we’ll have better luck this time. — Saloni Dattani

We also wanted to share a few updates from our team and grantees:

• Our grantee Witold Więcek co-authored a perspective in JAMA on the FDA’s new Bayesian statistics guidance for clinical trials. The piece argues that Bayesian methods can improve both trial design and regulatory decisions by formally incorporating prior information from related studies.

• DC’s City Council passed single stair reform on first reading, which our grantee Greater Greater Washington has long supported. If it clears second reading and the mayor’s signature, the reform will unlock small multifamily-zoned sites where lot assembly for double-loaded stairs is difficult or impossible - particularly valuable given DC’s history of allowing creative exterior stair workarounds.

• The Institute for Progress released a new report on Department of Labor wage proposals that affect H-1B visa eligibility, analyzing how different wage ranking methodologies would impact skilled immigration flows.

• Alex Armlovich published Zoned Capacity Is Like an Artificial Organ Donor Registry, exploring how cities create the illusion of housing supply through theoretical zoned capacity that often fails to translates into actual construction.

• And if you’re looking for abundance-related job opportunities, the Abundance Network maintains a job board with openings across housing, energy, infrastructure, and innovation policy organizations.

Zoned capacity is the gap between what land use regulators allow to be built on a parcel and what’s already been built. The volume of unbuilt zoned capacity gets treated, in popular conversation, as a bank account with a fungible dollar balance that planners can exchange for housing, unit by unit without diminishing returns. The discourse engages as if the next dollar in the balance is just like the last dollar. It doesn’t work like that!

Instead, a better metaphor for zoned capacity is an artificial, regulatory oil deposit.

Unlike bank accounts, oil is not perfectly fungible. Oil deposits vary in grade and extraction difficulty. As the easy deposits deplete, producers move to harder, more expensive sites1. Just so, zoned capacity varies enormously across parcels in quality, quantity, and location. And after years of building out the best sites allowed by regulators in a city, the zoned capacity left over is not like the zoned capacity you started with: it’s the leftover sites that are more expensive or less desirable to work with. All else equal, permitting rates in a city should slowly decline and rents should face gradually increasing pressure as a city’s cheapest and easiest development sites get built out and only the more expensive, and/or less desirable, parcels remain.

With oil, the size of a deposit is set by geology. With zoned capacity, the volume that can be “mined” by developers is set by regulators - importantly, this means regulators can make more of it. Eliminating or substantially relaxing this artificial zoning cap to legalize an abundant supply of housing is the entire premise of the YIMBY movement.

However, the US Geological Survey distinguishes between contingent oil “resources”—oil that physically exists in the ground—and “proved reserves”—the amount that is economically and technically recoverable. “Proved reserves” are thus a hybrid engineering & economic concept: At current prices with current technology, how much can you actually produce? If prices spike or technology improves, proved reserves increase even though nothing physical has changed underground. Urban planning has analogues: what the oilmen call technically recoverable “resources” and economically feasible “proved reserves” for oil, urban planners call “zoned capacity” and economically buildable “soft sites” for new housing.

Zoned capacity, as a raw “resource”, is an invisible cap over every zoned plot in a city. But the usable “reserve” citywide is just a small fraction: it’s the capacity that has a realistic probability of becoming housing in any reasonable near term forecast period. It depends on the size and shape of an individual lot, the condition and value of what’s already built, interest rates, construction costs, the land-value-to-structure-value ratio, neighborhood demand expectations, and more. Change any of those and you could change the usable reserve.

Once one understands this difference between theoretical “zoned capacity” and economically feasible “soft site” estimates, one can begin to understand why California’s state-mandated zoned capacity targets have struggled for so long; why Washington, DC’s profoundly confused land use planning process has produced such a terrible first Comprehensive Plan draft; and why NYC’s “City of Yes” reform for 82,000 new homes over 15 years sounded so small compared to other reforms that purport to legalize huge numbers of units.2

Why do finance-focused and regulation-focused commentators differ on what “drives” permitting?

For any given level of zoned capacity, financial conditions and the macroeconomy dominate timeseries variation in permitting within a city and in the national headline average. “Housing is the business cycle”.3 If you’re looking at a time series of permits in a single city or nationwide, the business cycle is doing much of the timeseries work.4

But financial conditions do not explain massive and persistent cross-sectional variation across cities at any given point in time. At any macroeconomic moment—boom or bust—some cities permit vastly more housing per capita, and as a percentage of the existing housing stock, than others. That variation between cities is overwhelmingly the result of binding growth control regulations within the commutable zone of a labor market. Cities and neighborhoods with reserves that widely exceed existing built densities by at least double the existing floor area “by-right” permit far more than cities with thin or fake capacity, permitted on a discretionary project by project basis, regardless of macro conditions.5

The housing crisis is not a crisis of theoretical zoned capacity on paper. There is “enough” theoretical capacity on paper in some expensive cities like DC—but this is about as relevant as the observation that there is theoretically enough deuterium in the ocean to solve all of humanity’s energy needs. The crisis is one of usable, proved reserves of soft sites: large increments of development rights on ripe sites, in high-demand locations, where the economics of teardown and redevelopment actually pencil. As we will discuss, DC’s comprehensive plan just demonstrated in real time how the failure to understand this distinction leads directly to policy complacency. If you want housing this decade, you need proved reserves–buildable soft sites!–not theoretical resources stranded far from any realistic near term use.

Why this all matters: Thinly spread zoned capacity is mostly stranded capacity

Proponents of incremental development often note, accurately, that NYC is a “3-story city”. If one could instantly and frictionlessly turn every 3-story building into a 5-story building, the housing shortage would be over for now. The problem is it does not make economic sense to tear down most 3-story multifamily buildings only to build a 5-story building. The average successful redevelopment in NYC is 3.4 times larger in floor area than the building that came before it. Thinly spread zoned capacity—generally meaning any zoned capacity that doesn’t allow a proposed building to at least double in floor area from its existing use6—is functionally trapped. It is uncommon to tear down a 3-story multifamily building to build a 5-story building, unless the 3-story building already happens to be in end-of-life condition; adds significant floor area beyond the immediately demolished site; or constitutes a rare exception, insofar as even buildings with a 1% annual probability of redevelopment do eventually get redeveloped.7

Vincent Rollet’s groundbreaking job market paper at MIT studied every parcel-level redevelopment in New York City from 2004 to 2022. He found that, on average, new buildings in NYC are 3.4 times larger than the building they replaced. The average torn-down building had a floor area ratio (FAR) of about 1 (i.e., a two-story building covering half the lot). To achieve even a ~15% chance of redevelopment over the nearly 20 year sample period, a parcel needed permission to build at least two extra floor area ratio points’ worth of square footage on top of the existing use. In simplified terms that means unless a typical property owner could add roughly 3 to 4 stories beyond the existing typical one-or-two-story building torn down in Vincent’s dataset, there was not a significant probability of teardown and redevelopment. The near-term production effects of upzoning are almost entirely concentrated on parcels with low existing density and high allowed capacity in high-priced neighborhoods. Upzoning parcels in the cheapest areas yields almost nothing—the reward for adding floorspace is too low to cover the large fixed costs of demolition and reconstruction.

To be sure, America’s oldest big cities are full of squat, unsprinklered, highly flammable wood-framed tenements reaching the end of their design life. Under current zoning, these get gut-rehabs without adding any new homes, but under incrementalist zoning, they’d likely add a story or two once the structure deteriorates enough to merit redevelopment. The incremental development advocates consider this century-long slow growth process a feature, not a bug. Outside the nation’s largest megacities (and the small but extremely high-wage “superstar cities” like San Francisco, Boston, & Seattle) slow growth might be enough.8 But in the cities with the largest housing backlogs, a human lifetime is far too long to wait for housing abundance.

Another key thing about the incrementalist “three story city becoming a five story city” vision that doesn’t get enough attention: the sheer physical disruption of redeveloping a city’s entire landmass within a single business cycle, by a small increment on each lot, would be staggering. As former Director of Planning in DC, Harriet Tregoning, astutely observed amid circa-2013 debates over lifting DC’s height cap: Adequate housing production at modest Paris heights would entail rapid Paris-scaled demolition and physical transformation of wide swathes of DC.

Even stipulating for argument’s sake that it would be economically feasible to quickly tear down and rebuild three-story apartment buildings to grow them by 1 or 2 stories each without invoking eminent domain, it would be a disruptive and politically troubled undertaking.9 We are almost lucky that regulatory approaches intending voluntary, wide-area lowrise intensification have low redevelopment probabilities on any one parcel, because if development were feasible widely, we’d have a political maelstrom of universal construction noise and tenant displacement on every lot in every block of the city.10

To unlock feasible housing supply in the highest-demand metropolitan areas now with minimum disruption, one must lift caps on density near downtowns and near rail transit altogether, allowing the important housing typology known as “Missing Massive”.11 Along frequent bus corridors and other high-resource locations, rezone to allow at least the building code’s “high-rise” threshold—usually 75 feet in most US states and cities—at which construction costs begin to spike as cheap light wood framing is no longer allowed. Everywhere else can be zoned for the incrementalist “gentle density” that will glacially produce new housing over the next century as existing buildings wear out and need major repair or replacement. Having said all this as a necessary corrective to pure regulatory incrementalists, it is nonetheless true that, in the highest-land-value metro areas with the biggest housing production backlogs, there’s no tradeoff or tension between legalizing rapid Missing Massive near transit and downtowns and incrementalist gentle density elsewhere. In the half dozen coastal superstar metros targeted by Coefficient Giving’s housing reform portfolio to date, both strategies will be required if we are to address the accumulated scale of housing underproduction through voluntary bottom-up action alone, without resorting to the coercive state-led eminent domain and urban renewal tools of Robert Moses or Baron Haussmann.

Zoned capacity confusion is doing real damage right now

This past week, DC’s Office of Planning released its draft Future Land Use Map (FLUM) for DC 2050, the city’s first comprehensive plan rewrite in nearly 20 years. Their reasoning was laid out plainly: DC has about 325,000 households today. The current comprehensive plan allows for 445,000. They estimate the city needs capacity for 460,000 by 2050 to keep prices from rising faster than inflation.12 So the proposed plan adds capacity for just 15,000 more households—a rounding error on a quarter-century planning horizon—because they figure it’s already almost there.

This is a huge disappointment. The Office of Planning’s 445,000 figure is a theoretical resource, not a proved reserve of buildable sites…worse still, it comprises the leftover “rump” of the last comprehensive plan, whose most feasible sites have disproportionately already been built out. It counts every parcel in the District where zoning technically permits more than what currently exists—regardless of whether the existing building is a rent-stabilized apartment complex that will never be torn down, a recently renovated rowhouse whose owner has zero interest in selling, or a parcel where the gap between allowed and existing density is so small that no builder would ever bother. Even California has recently begun forcing cities to stop the policy of simply “rolling over” planned sites that were not ultimately rezoned or built from one planning cycle to the next in full knowledge that these sites will not yield any housing production without further regulatory relief. DC’s planners shamelessly rolled forward all existing planned capacity without interrogation.

Nobody at DC’s Office of Planning (OP) did—or at least nobody published—a soft site analysis asking: of that 120,000-unit gap between 325,000 existing households and 445,000 theoretical capacity, how much sits on parcels where the economics of teardown and redevelopment actually pencil?

If the Office of Planning ran that analysis, the answer would almost certainly be that a large share of their 120,000-unit theoretical surplus is vaporware–or, returning to our petroleum engineering metaphor, “contingent resources” that are not realistically deliverable anytime soon, anywhere close to current prices. That means the actual shortfall between usable reserves and the 460,000 target is not 15,000 units! And because the city is planning on the assumption that it has nearly enough capacity already, it is not poised to create the deep, transit-adjacent and high-opportunity upzonings that would actually generate the housing equivalent of feasible “proved reserves”.

The confusion over zoned capacity becomes self-reinforcing: claim adequate capacity, do nothing, watch housing costs rise, blame everything except the zoning.

The zoned capacity problem is not limited to DC. California has the same problem on a massive scale. California’s Regional Housing Needs Allocation (RHNA) housing element law advises jurisdictions to zone for 15%–30% more capacity than their housing target, as a buffer. Since the housing element law’s inception in 1969, California’s cities have met RHNA nominal zoned-capacity mandates by upzoning land that is literally underwater, designating City Hall as a place likely to be demolished for new housing to be built, and counting narrow strips of abandoned railroad tracks and polluted brownfields.13 Though DC may not actively be gaming their zoned capacity estimates, their shockingly unambitious Office of Planning proposal’s 30% zoned capacity “buffer” above the existing number of built homes is still very Californian: A buffer of that size has historically been more than sufficient to give a city a passing grade under California’s benighted RHNA framework.

California’s preemption of local land use law yields a principal-agent battle between state government principals and local government agents, which entails a hostile intergovernmental dynamic in which cities try to game their way out of state law. But even outside California’s RHNA gaming dynamics, zoned capacity does not (and cannot ever!) have any fixed ratio or simple linear relationship with realistic development potential. Yes, the relationship gets directionally worse when a city is actively gaming the numbers to break state law, but even innocent attempts to measure zoned capacity—like DC’s—can’t distinguish between thinly spread development rights on already-built land versus thickly mapped, fully usable development rights on ripe underbuilt parcels. The ratio of theoretical zoned capacity to economically buildable soft sites must inherently (all else equal) decline over time as a result of increasing marginal cost or decreasing marginal desirability of the remaining sites left over after the good ones have been used up.

Environmental Review in NYC: The Rare Case of Tallying Soft Sites, not Zoned Capacity

New York City’s Department of City Planning has a forecasting framework for environmental review of rezonings called the “Reasonable Worst Case Development Scenario.” (The name itself reflects the anti-housing disposition of the people who devised NYC’s regulations. The “worst case” they’re worried about is the scenario where the most housing gets built.)

Rather than forecasting development from some concocted flat percentage of maximum theoretical zoned capacity, DCP does a lot-level analysis of “soft sites”: parcels that have a plausible likelihood of actually being redeveloped within a 10- to 15-year forecast period. To be a soft site, a lot generally has to be at least 50% underbuilt relative to its maximum allowable floor area ratio, and at least 5,000 square feet, among other things.14

Among the identified parcels, DCP sorts them into “projected” sites (likely to develop) and “potential” sites (could develop, probably won’t). Only the projected sites count toward the density analysis that determines whether a rezoning triggers mitigation requirements. This is NYC’s most rigorous attempt to convert raw zoned capacity into a realistic forecast of how many homes will actually get built—and even this method routinely misses the mark in both directions, historically underestimating production in the strongest markets and overestimating the weakest markets.

Housing watchers may notice that NYC’s recent citywide upzoning, called City of Yes, is forecast to unlock only 82,000 new homes as a headline result. That sounds small compared to other reported actions in smaller cities like San Francisco’s “Family Zoning Plan”–coincidentally also headlined with 82,000 new homes–or Columbus, Ohio’s “Zone In” upzoning at 88,000 units. The answer is partly that, yes, NYC’s City of Yes is much weaker than a true response to the housing crisis needs to be. But the bigger answer is that San Francisco and Columbus are reporting out theoretical zoned capacity figures, not feasible soft site estimates like NYC. San Francisco’s plan is likely to produce less than 20,000 soft site units over 20 years; Columbus does not have a soft site feasibility estimate.

The upshot: In evaluating housing production outlooks for particular reform scenarios across jurisdictions, compare apples to apples. New York City does not produce a theoretical zoned capacity estimate for rezonings like City of Yes, but if it did, the number would be enormous. Alternatively, if other cities reported soft sites like NYC instead of on-paper zoned capacity, their topline numbers would be smaller too.

What is to be done?

What planners in places like DC and California both need is to forecast feasible development the way Vincent Rollet’s earlier-described paper does: parcel by parcel, accounting for existing density, structure value, lot size, neighborhood prices, and the fixed costs of demolition. Not “how much does zoning theoretically allow” but “how much is actually plausible to get built, where, and by when.”

There is a growing consensus among housing expert practitioners that RHNA-style housing targets should upgrade from zoned capacity to soft site forecasts. This is helpful not only to start focusing on feasibility instead of symbolic zoning wins, but also to avoid the fear-mongering associated with theoretical zoned capacities in the millions of units.15

The first step is for planners to start thinking in terms of proved reserves rather than contingent resources. Until they do, they will keep producing comprehensive plans that look adequate on paper and fail in practice.

Hope you’ve had a great week! Here’s what caught our attention:

1. The very best biology magazine in the world, Asimov Press, announced they’re winding down (for now). I’ve been an advisor to them since before they started, and I’m very sad to hear the news, even though I’m looking forward to what the team behind it – Niko McCarty and Xander Balwit – are doing next. At Asimov, they published some of the deepest and most thoughtful writing on biology and actually made it feel enchanting – so unlike the textbook descriptions most people encounter at school. It’s the kind of writing I’ve tried to achieve in my own work. They covered topics across the history of technology, biological curiosities, ideas for science policy reform, and visual explainers of how molecular processes work. Here are some of my favourite pieces: Origins of the lab mouse, Making the micropipette, Where’s the synthetic blood?, Before they hatch, Driving Toward Nanopores, and you know what, I’ll even include a piece of my own, Measuring the Black Death. At the end of their farewell post, they leave open the possibility that it might be revived in the future; I really hope it happens one day! — Saloni Dattani

2. How about one more from Asimov before they go: this week Alvin Djajadikerta published “Designing AI for Disruptive Science.” In it, he explores the possibility that (current approaches to) AI-for-science might enhance our ability to predict within current frameworks while simultaneously weakening our capacity to identify and shift into entirely new paradigms. He lays out histories of important paradigm shifts, discusses some interesting papers on technology-based scientific narrowing (arising from both AI and, interestingly, the internet), and closes with a call for more and better study of the mechanisms of science – perhaps using AI agents as a metascientific model organism for better understanding the effects of different institutional and incentive designs (I, for one, would immediately subscribe to a science-focused AI village-style experiment). It’s a thoughtful and thought provoking piece, and well worth the read. — Jordan Dworkin

3. I’ve been revisiting some older papers that might shed light on the benefits of faster clinical trials. Let’s start with a short 2007 paper by Frank Lichtenberg, which looked at the correlations between deaths from various diseases over time, and the share of medical prescriptions for that disease relying on newer medicines. Lichtenberg found that mortality was lower for a given disease when more of its prescriptions relied on newer medicines, and he used this to back out an estimate of the consequences for US health if those new prescriptions had not been available. For example, in 2003, he estimates there would be roughly 1.6 million fewer life-years if drugs invented since 1990 had not been available. With roughly 380 new molecular entities approved between 1990 and 2002, that works out to 4,200 life years lost, on average, per drug: i.e., if a drug invented after 1990 had not been available during 2003. Just one year later, Philipson et al. (2008) looked at the impact of the Prescription Drug User Fee Act of 1992. Philipson and coauthors argue the act sped up drug approvals by 6-7%, and generated a surplus (the gap between what consumers would have been willing to pay and the cost of producing medicine) equal to $14-31 billion. — Matt Clancy

4. In most product markets, transitory supply and demand shocks can spike prices, but as long as there’s open entry to the market allowing elastic supply, prices come back down near marginal supply costs. This is rare in American housing markets because infill supply responses are usually illegal under local growth control laws–but not, per Pew’s new analysis, in Austin, Texas. Austin enjoyed among the highest permitting rates in the country for several years; as in-migration has slowed somewhat, supply finally caught up to demand and rents are back down to pre-pandemic levels in nominal terms and down significantly in real terms and as a share of incomes. Austin’s biggest zoning reforms actually came during the boom, allowing Austin to keep permitting at pre-2019 rates in 2025 despite substantially lower real rents. That said, once rents fall to near construction costs plus a competitive gross margin, development will slow substantially as it has in Minneapolis (where median market rate rents are now at federally subsidized LIHTC levels). — Alex Armlovich

5. More progressives in the housing space are coming out against the Senate’s build-to-rent ban. The growing concern is that the ban could reduce rental housing supply when affordability remains a pressing issue. It’s a tough situation and we hope all stakeholders come to a pro-housing solution with sustainable politics. — Alex Armlovich

6. “You will be hard-pressed to find a true admirer of Excel,” writes David Oks (who I first noticed as one of the Gravel Teens and is now at a16z, truly an ideological evolution for our time). But Oks is, if not an admirer, then surely a believer that Excel and predecessors like VisiCalc and Lotus 1-2-3 are inventions of deep historical importance. The most intriguing part of his argument is the claim that the rise of finance in the 1980s, particularly leveraged buyouts, was driven in large measure by the dawn of the spreadsheet. Before, “analyzing a single company would take weeks”; now it might take a few hours, and your model of that company could be amended in seconds. I’m not fully convinced (is the spreadsheet a driver of financialization or did financialization provide a larger market that spurred better spreadsheets?) but it’s an intriguing case study of the ways in which a particular technology’s diffusion can help reshape the economy, or perhaps solidify a reshaping already occurring. — Dylan Matthews

7. The nation’s largest offshore wind farm to date, the Coastal Virginia Offshore Wind project - which clocks in at 2.6 GW - sent its first commercial power to the grid this week (it joins Rhode Island’s Revolution Wind, which sent its first electrons onto the New England grid earlier this month). In stranger wind news, Interior announced a ~$928 million settlement with TotalEnergies to surrender two Atlantic wind leases. Jake Bittle & Rebecca Egan McCarthy have a pragmatic take on the deal - the payment mostly reimburses lease fees TotalEnergies paid in 2022, and the “redirected” oil and gas investments (Rio Grande LNG in South Texas and conventional production in the Gulf of Mexico) were already committed. Neither offshore lease was under active development or even near to completing federal permitting, and the deal doesn’t preclude future re-auction. Encouragingly, permitting reform negotiations in the Senate have apparently continued undisturbed. — Willow Latham-Proenca

Meanwhile, Jordan Dworkin published Who Will Program-Manage the Program Managers? this week on this blog, exploring how science funding agencies can build capacity to support ambitious, unconventional research programs. If you’re interested in this space, ARIA (the UK’s Advanced Research and Invention Agency) is currently hiring.

Greater Greater Washington published their take on DC's draft Future Land Use Map. The plan would add only 15,000 housing units by 2050 and confuses theoretical "zoned capacity" with economically feasible development sites. By carrying over sites from the previous Future Land Use Map, DC's Office of Planning is banking on lower-quality development opportunities left after the best sites have already been built. The silver lining: the draft was bad enough to draw mass public protest and condemnations from both leading mayoral candidates.

The Irish government is seeking volunteers for their Clinical Trials Advisory Council. If you have relevant expertise and interest in shaping European regulatory frameworks, consider applying.

This is a relatively normal week for an ARPA1 program manager. But few people who walk into this role have received any formal preparation for it.

A few weeks ago in our “What we’re reading this week”, we highlighted initiatives from two grantees — Speculative Technologies’ Brains Accelerator, and Renaissance Philanthropy’s Big if True Science (BiTS) Accelerator — that are trying to change that. These programs identify ambitious scientists with ideas for coordinated research programs,2 work with them over a few months to refine their scientific vision and planned approach for program implementation, and then help them find a role with (or funding from) federal agencies, philanthropies, or research incubators that are seeking talent.

So, essentially, programs to improve the quality of program managers’ management.

This seems like a pretty meta thing to fund, even for a program that specializes in metascience. But we can decompose the theory of these efforts’ value into four more tangible claims:

1. There are efficiency gains to be had in how we manage and allocate scientific resources.

2. Coordinated research programs are a high-leverage context for intervening in R&D resource allocation.

3. Graduates of training and accelerator programs are likely to make higher quality management and resource allocation decisions than they3 would have otherwise.

4. Graduates of these programs are reasonably likely to actually end up in charge of substantial, high-leverage R&D resources.

Are there efficiency gains to be had in scientific resource allocation?

If you’re tempted to just say “yes” and move along, stick with me. It’s true that there’s been a lot of conversation about the inefficiencies of our modern scientific ecosystem: a lack of risk-taking, homogeneity in the structure of scientific institutions, biases in what gets published, etc. These dynamics suggest that Things Could Be Better^TM. But they don’t necessarily suggest that changes in management and resource allocation, within existing scientific frameworks and institutions, could make things better.

Let’s look at two papers that provide some evidence that this is likely to be the case.

The first is Azoulay, Graff Zivin, and Manso (2011), which compares scientists funded by the Howard Hughes Medical Institute (HHMI) — which provides long-term, unrestricted support to individual researchers — to comparable NIH-funded scientists. They find that HHMI-funded scientists produce considerably more high-impact work, suggesting the mechanism of resource allocation that is used by funders can meaningfully affect the amount and quality of scientific output, even without changing the total quantity of resources.

The second is a new paper by Bertolotti, Myers, and Tham (2025), which takes a more holistic view, ambitiously attempting to measure the overall misallocation of resources as a function of productivity across the U.S. scientific landscape. They develop a survey-based method for estimating individual researchers’ productivity by using hypothetical salary-for-{resources/time} tradeoffs, and find that the distribution of scientific productivity is extremely skewed: the 90% percentile researcher is roughly 30 times more productive than the 10th percentile. Importantly, they show that current resource allocations don’t track this distribution well. Their counterfactuals suggest that more efficient allocation of existing resources could produce the same increase in scientific output as raising the federal science budget by billions of dollars.

It’s incredibly difficult, however, to predict the impact and potential outcomes of resource allocation from observables. That means that the judgement, taste, and expertise of people making allocation decisions is critical, and any mechanism that helps improve the quality of those decisions can have very large returns.

Are coordinated research programs uniquely high-leverage?

In terms of leverage for improving R&D resource allocation, ARPA-style agencies (and similar coordinated research programs) are a unique opportunity.

Azoulay, Fuchs, Goldstein, and Kearney (2019) lay out the core features of the ARPA model, highlighting in particular the importance of the empowered program manager (a.k.a. program director). PMs identify technology directions, create programs, select performers, assemble and reshape research teams, and actively manage portfolios. As they put it, “hiring talented program staff with a penchant for exploration is pivotal to the success of ARPA‑like programs.” Goldstein and Kearney (2020) provide a view into what this active management looks like in practice. Using confidential ARPA-E program data, they show that program managers routinely modify the terms of projects, reallocate resources, and make go/no-go calls throughout a program’s lifecycle.

This reliance on individual, skilled decision-makers makes these agencies high-tractability targets for improving decision quality. At NIH, allocation decisions are distributed across thousands of reviewers and study sections. At DARPA, roughly 100 program managers control $3-4 billion per year, meaning that improving one PM’s judgement potentially affects tens of millions of dollars in R&D spending. And because that spending is largely discretionary rather than process-driven, training and improved judgement can more easily flow to decisions and outcomes.

So coordinated research programs are great places to intervene in terms of tractability. What about in terms of the impact of the scientific outcomes? Anecdotally, they’re great on this as well! If you’re one of the loyal innovation policy stans following this blog, you can probably rattle off a few of DARPA’s historical wins (the internet! autonomous vehicles! GPS!). But you can do something similar for NSF (also the internet! MRI! gravitational waves! AI!), so how can we actually tell how per-dollar outcomes compare?

Quantitatively, it’s difficult. Getting apples-to-apples comparisons across institutional designs is not straightforward, and much of the excellent research on the topic of ARPAs is historical and qualitative rather than empirical.

Fortunately, in 2018, Anna Goldstein and Venkatesh Narayanamurti published a study comparing patenting and publication outcomes from ARPA-E grantees to those from the DOE’s Office of Energy Efficiency and Renewable Energy (EERE) and Office of Science (OS), which fund “applied research, development, demonstration and deployment activities” and “basic research programs”, respectively. Across a range of outcomes, they find that ARPA-E grants:

• lead to more technological output per dollar than EERE or OS (e.g. number of patents, at least one cited patent);

• lead to more scientific output than EERE, but are roughly on par with OS (e.g. number of publications, at least one highly cited publication);

• and are substantially more likely to produce both technological and scientific output than EERE or OS (e.g. at least one patent and at least one publication).4

A back-of-the-envelope synthesis of the paper’s results across comparison office, performer type, and outcome suggests that ARPA-E generated roughly 3x as much observable paper-and-patent output per dollar as traditional DOE grantmaking.5 Importantly, this includes a selection effect: at the time, ARPA-E’s budget was significantly smaller than either comparator agency, and as a result it could be more selective. Depending on how you frame the counterfactual, this could be a feature or a bug of the comparison, but in any case, a conservative adjustment comparing only within the same budget window of top-performing grants still finds a 1.3x output advantage for ARPA-E.6 With the necessary caveats about papers and patents, these findings are noteworthy in that they suggest that the model itself (even controlling for performer type and selection) is meaningful.

Does training actually help?

Here the evidence is more sparse. We don’t have a great sense of how effective these types of programs will be at improving eventual resource allocation and use.7 But we do have some broader evidence on management training that is consistently positive, and some spillover benefits from programs like these that suggest additional value above and beyond generic training.

The first place to look is the broader management training literature. A classic paper here is Bloom et al. (2013), which randomly provided management consulting to Indian textile firms, and found that it raised productivity by 17% in the first year, with effects persisting and in some cases growing over time. Giorcelli (2019) finds lasting benefits of management training on firm performance in a very different setting (Italian firms sending managers to train with US companies under the Marshall Plan’s Productivity Program), and Giorcelli (2024) finds that a WWII-era MBA-style program had significant benefits for individual performance and career outcomes. Meta-analyses (e.g. Busso, Park, and Irazoque, 2023) find that management training has productivity benefits in the 5-10% range.

A somewhat tangential literature on venture capital and private equity funds looks at the returns to experience rather than training. For example, the “first-fund” penalty in PE/VC seems to be on the order of 10% with respect to performance (Kaplan and Schoar, 2005), and more experienced VCs are better able to identify and invest in unproven talent (Gompers, Kovner, Lerner, and Scharfstein, 2006). To the extent that training can compress some of the learning curve, giving first-time PMs exposure to the tacit knowledge, strategies, and networks that typically only accumulate through experience, these benefits may apply.

The link to our context is admittedly tenuous (textile firms, postwar Italian manufacturers, and VC funds are not ARPA programs), but this literature is helpful for calibrating reasonable effect sizes; if we observed a similar 5-10% improvement in our context, it would be as if graduates’ $50m ARPA programs got an extra $3+ million-worth of scientific outcomes for essentially free.8

And importantly, accelerators don’t just train, they also select and place. PM roles are incredibly unique within the scientific ecosystem, are often difficult to hire for, and are high risk if the wrong person is selected. As a result, the identification and matchmaking that these types of programs provide can be a high-leverage activity in itself. This may be an even more important factor than management training if the literature is to be believed; Burks, Cowgill, Hoffman, and Housman (2015) study referral hiring, and find that referred workers in high-tech roles produce 19% more citation-weighted patents than non-referred workers.

While the state of the evidence on this question is currently middling, there seems to be growing interest in it. The Scientific Labs Management Project, for example, is applying the World Management Survey methodology to scientific labs, aiming to systematically measure the quality of organizational practices as a first step toward identifying where management training could help most. Results aren’t out yet, but the project’s existence signals recognition of scientific management as an under-studied bottleneck. Until then, we think the circumstantial case is relatively strong, and would be excited to see more rigorous research and evaluation.

Will graduates be put in charge?

Early signs point to yes! From the first BRAINS cohort of 16 fellows in 2024, several have already taken steps towards research leadership roles: two are now running Focused Research Organizations (FROs), another is leading a program at a philanthropic foundation, and others have entered hiring pipelines at various ARPA-style agencies. In aggregate, Speculative Technologies reports that first-cohort fellows have collectively secured more than $70 million in committed funding from philanthropists and governments. The program has since expanded to a 2025 AI-focused cohort and a 2026 cohort.

On the BiTS side, the program has scaled rapidly since its late 2024 announcement. Renaissance Philanthropy has now launched several cohorts in direct partnership with government innovation agencies: a UK cohort in partnership with ARIA,9 an EU cohort supported by SPRIND, and a Japan cohort as part of the Cabinet Office’s International Research Program. Several BiTS fellows are now in, or in the pipeline for, PM roles at ARIA and SPRIN-D, and others are in the process of setting up coordinated research programs outside of government.

The long-term vision for these efforts is even more ambitious than the impacts discussed above; ideally, these programs would raise the ambition of the scientific ecosystem, bringing more philanthropic resources into frontier science and inspiring the creation of new ARPA-style initiatives. Ben Reinhardt, CEO of Speculative Technologies, put it well, saying, “Over time, we’re hoping to do for ARPA programs, FROs, and other coordinated research programs what YC and other accelerators did for startups. Starting and working at startups went from an obscure pursuit… to a normalized (if still high-variance) path for people all over the world.” We don’t know yet whether that vision will be realized, but the early trajectory is exciting.

Happy spring! Here’s what caught our attention this week:

1. Ryan Hill and Carolyn Stein have a new working paper on how AlphaFold has affected protein structure research in the five years since its release. They ask three primary questions:

   1. Did experimental (i.e., non-computational) structure determination decline?

   2. Did basic research shift toward proteins that previously had no structural information?

   3. Did early-stage drug development start targeting those newly solved proteins?

   Pre-register your guesses for each, before reading item #3 for a discussion of what Hill and Stein found. — Jordan Dworkin

2. While you’re thinking about that, let’s stay with science. Does MAGA Actually Want American Science to Win? is the latest from Ari Shulman, writing in The New Atlantis. Shulman argues that MAGA’s critique of the failings of status quo American science are broadly on point (and part of a tradition that predates President Trump), but that the policy conclusions it draws from that critique will not make America stronger. Dramatic cuts to research budgets are not enough (I would add that restrictions to skilled immigration don’t help either). The positive vision for science that Shulman grapples most with is a revitalization of scientific ideals championed by NIH Director Jay Bhattacharya, where there are no taboos, no appeals to authority, and free debate carries us to truth. My take? The deep challenge of organizing science is that only specialized knowledge can assess the quality of scientific work.1 That means peer approval is a fundamental part of the scientific process, and from there it’s not far from appeals to authority and deferring to experts. It’s tough! But that’s not to say the status quo was optimal and things can’t be better. Indeed, I’m a fan of some of the metascience innovation we’ve seen under this administration, from the NSF tech labs proposal to the streamlining of research bureaucracy. — Matt Clancy

3. Got your guesses about Alphafold ready? Here’s what Hill and Stein found.

   1. Did experimental (i.e., non-computational) structure determination decline? The rate of experimental structure determination is unchanged, and the relevant papers are still being published in top journals; AlphaFold predictions are complementing rather than replacing experimental research (though Hill & Stein note that this lack of substitution isn’t necessarily efficient, or permanent).

   2. Did basic research shift toward proteins that previously had no structural information? Basic research on previously unsolved proteins is up 15-35% relative to proteins with known structures, with initial shifts taking place in protein function research and more recent (and larger) shifts among expression, disease, and interaction studies.

   3. Did early-stage drug development start targeting those newly solved proteins?There is no corresponding increase in early-stage drug R&D on newly solved proteins vs. previously solved ones (though the authors note that private pharma work would not be captured here).

   Overall, it seems that structure was a meaningful bottleneck for basic science, but not the binding one for drug discovery. — Jordan Dworkin

4. Back in 2023, we asked Michael Wiebe to take a look at a 2021 article by Enrico Moretti titled “The Effect of High-Tech Clusters on the Productivity of Top Inventors.” We were interested because one of the inputs to our grantmaking decisions is a quantitative estimate of the social impact of a grant, and this paper was important for our estimate of how housing affects innovation. Unfortunately Wiebe found a lot of problems with the paper, prompting us to substantially downgrade the weight we put on the innovation effects of new housing. Other (non-innovation) benefits of housing now form a much larger part our estimate of its impact (though we’ll revise estimates again as new evidence emerges). Wiebe has now written up his conclusions on his substack, and a comment on the paper will appear in the journal in which it was originally published. — Matt Clancy

5. Brian Potter examines the elusive cost savings of the prefabricated home, and as someone now living through the third modular construction hype cycle of my career, the piece is a useful corrective. There’s a reason we keep coming back to off-site construction: HUD-code manufactured housing does deliver real savings in the US; the Sears “kit home” panelized model was a legacy success in the US; and contemporary international examples (especially Germany and Sweden) show factory-built housing can work at scale. But again and again, modular construction in the US has failed to sustainably deliver on its cost promises, with high-profile bankruptcies in the UK telling a similar story. The benefits tend to be schedule, predictability, and quality control rather than price. As we politely take part in the latest wave of enthusiasm, it’s worth being clear-eyed that this has been tried before, and is not a substitute for growth control reform. — Alex Armlovich

6. Separately, Jerusalem Demsas writes about how urban divestment is (still, as Pete Saunders reminds us) a much larger problem in the US than urban “gentrification”, but compositional & mimetic drivers of un-representative stylized facts crowd out the underlying statistical & material reality of neighborhood conditions in most American cities. — Alex Armlovich

7. It’s official: the Trump administration is suspending the Jones Act, which bans non-US flagged vessels from traveling between US ports, for sixty days in an attempt to keep gas prices low amid the Strait of Hormuz crisis. I doubt the suspension will do much toward that specific purpose, but it’s as good a time as any to think about the extreme costs that law imposes on the US economy. Colin Grabow, the premier Jones Act foe at the Cato Institute, highlights some oil/gas specific costs, like the fact that California gets fuel from the Bahamas as opposed to Gulf Coast US states because of a lack of tankers that can ship oil directly from Texas and Louisiana. — Dylan Matthews

8. One of the big challenges in permitting reform is that it’s really hard to forecast how changing one piece of the massive US legal/regulatory permitting system will play out. At a more micro-level, it’s also a headache for developers to try to figure out what environmental requirements their project actually triggers. Specifically, when contemplating a new project, developers do not know if the Clean Water Act applies until the Army Corps of Engineers completes an assessment. In a new working paper, Greenhill, Walker, and Shapiro train a deep learning model to evaluate whether Clean Water Act provisions apply to a given parcel, using Army Corps of Engineers determinations tunder several recent rulemakings. They find their model is 65 times better at identifying regulated sites than the leading geophysical approach. This helps in analyzing past regulatory shifts (the authors find Sackett deregulated roughly a third of all previously regulated waters), helps in generating high-quality projections of proposed regulations before they’re implemented, and could potentially help developers better predict if they’re actually subject to CWA restrictions. — Willow Latham-Proenca

Happy Friday the 13th (again!)! Here’s what we’ve been reading:

1. Last week, Abundance NY put out The Abundance Agenda, a roadmap to, well, Abundance for New Yorkers. I’m a fan of just how concrete the document is, both in terms of the big picture and the small. On the big picture, the agenda lays out some very specific ten-year goals: reduce capital project cost overruns and schedule delays by half, build 500,000 new homes, double average bus speeds city-wide. But it also zooms in to the small details that add up to big change: challenge-based procurement, pre-approved template plans for common building types, pre-emption of local authority to de-facto ban solar projects, and so on. This Abundance Agenda is tailored to New York - it has recommendations related to scaffolding and trash pickup - but it would be great to see more local orgs put together wide ranging roadmaps to abundance, tailored to their own local needs. — Matt Clancy

2. The Senate passed the bipartisan 21st Century ROAD to Housing Act 89–10 on Thursday — a landmark pro-supply package that streamlines housing regulation, creates new building incentives, and modernizes federal programs. A late addition requiring institutional investors with 350+ single-family homes to sell after seven years threatened to shut down build-to-rent construction entirely--but then, shortly after the bill passed, Senate staff revealed an industrial policy exemption for innovative HUD Code construction methodologies, including newly chassis-free CrossMods unlocked by the bill’s other reforms. If you like your single family BTR, you can keep it--as a stable pipeline of demand for innovative new factory built homes. To be sure, there are costs and risks to aggressive industrial policy & “picking winners” in new technology. — Alex Armlovich

3. Meanwhile, closer to home, Governor Hochul’s EXPRESS NY initiative is accepting submissions through April 3 from anyone who can identify a state regulation or practice that adds unnecessary burden — with a focus on housing, infrastructure, and small business. If you know a bad rule flying under the radar in New York, this is a real channel to flag it. — Alex Armlovich

4. Federal permitting negotiations have re-started in the Senate, following the first steps of an apparent thaw in solar permitting. Alex Trembath of the Breakthrough Institute has a timely piece in response, discussing the increasing partisanship of energy policy, reinforcing the importance of passing meaningful reform this Congress, and arguing that the current need for bipartisan compromise is an opportunity, not a drawback. — Willow Latham-Proenca

5. In a previous post, we noted that Congress largely preserved science budgets for FY26, rejecting large proposed cuts. Unfortunately appropriations are only step one. A few weeks ago Nature reported that OMB has been withholding authorization for agencies to spend their congressionally approved funding. As of the beginning of this month, NIH had not received approval to spend research money from the budget bill signed February 3, and was operating on leftover funds from last fall’s stopgap. As a result, they’ve issued only ~800 new and competing awards so far in FY26, versus the ~3000 that would be typical. NSF only got its funding authorized in late February, and is making grants at an even more glacial pace than NIH. — Jordan Dworkin

6. On a happier note: This week the Astera Institute announced Radial, a new division committing up to $500M over the next decade to experimenting with how life sciences research is organized, funded, and shared. Early projects include open datasets for drug discovery, new infrastructure for protein dynamics data, and a publishing platform prototype. Alongside the launch, they are running an essay competition asking working scientists to identify systemic bottlenecks in their own research and propose ways to test solutions. The judging panel includes both owner-of-the-blog Matt Clancy and friend-of-the-blog Jacob Trefethen, and we encourage you to submit your best/most ambitious ideas. — Jordan Dworkin

7. This year has seen AI agents break through in a serious way among social scientists I respect. So it was only a matter of time until social scientists designed an AI benchmark to see how good agents really are. Meysam Alizadeh, Mohsen Mosleh, Fabrizio Gilardi, and Joshua Tucker’s SocSci-Repro-Bench tested if Claude Code and Codex could reproduce 54 distinct papers. The most striking result to me is how different the models’ capabilities were: Claude could fully reproduce 78 percent of papers accurately, while Codex only got 35.8 percent. What’s more, the study found evidence the results were not memorized by the models; they were doing the work from scratch. Funny enough, OpenAI’s success rate here is similar to that of GPT 4/4o back when the Institute for Replication tested those models in 2024 (though it’s not clear to me if the replication tasks in the two papers are similarly challenging). — Dylan Matthews

8. Adam Kroetsch has a new post arguing that if we want to make trials more efficient, we need to pay attention to the boring stuff, in other words, the unglamorous work of clinical operations like setting up trials, recruiting patients, and collecting data. — Saloni Dattani

9. I’ve also been following the BioNTech announcement that co-founders Ugur Sahin and Özlem Türeci will leave the company by year-end to start a new biotech focused on next-generation mRNA therapeutics (their third startup). The stock dropped over 20%, partly because no successors were named, and partly because BioNTech is on the cusp of multiple late-stage cancer therapy readouts. — Saloni Dattani

Finally, some updates from our team and grantees:

We’re celebrating new cohorts for two research accelerator programs we support: Speculative Technologies announced the 2026 Brains Fellows, helping scientists plan, identify funding for, and execute ambitious technical research from electronic noses to bacteriophage therapies. Renaissance Philanthropy launched the Big if True Science (BiTS) Americas cohort, training scientists to design and lead large-scale research initiatives across plasma chemistry, biosecurity, developmental neuroscience, and more.

Progress Ireland published a piece by Fergus McCullough on how Brussels can help with Galway’s housing problems, proposing that the EU use funding conditionality to reward jurisdictions that actually increase housing supply, drawing on models like Canada’s Housing Accelerator Fund and the US ROAD to Housing Act.

We also published a directory of Living Literature Reviews this week: a collection of continuously updated research syntheses by expert authors on a range of topics. If you know of a review we’ve missed, let us know - it’s a living directory!

The number of academic papers doubles every 12 years. This wealth of new knowledge is exciting, but the pace of growth makes keeping up with the latest developments increasingly difficult, even for specialists. Living literature reviews are one response to this problem: continuously updated collections of accessible articles that synthesize academic research on a specific topic, written by a single expert who maintains a consistent voice and perspective over time.

We fund several living literature reviews through the Abundance and Growth Fund, and we’re aware of others doing similar work independently. This post is our attempt to collect them all in one place and to create a directory you can browse to find out if someone is writing a living literature review on a topic you care about.

We're not the only ones who think living literature reviews are valuable. VoxDev maintains a library of living literature reviews focused on development economics, covering topics from agricultural technology to taxation. Their model is a bit different from ours (team-authored, updated annually rather than continuously), but the goal is the same: making research accessible and keeping it current. In health and clinical research, the Living Evidence Atlas tracks hundreds of living evidence efforts worldwide.

We plan to keep this post updated over time. If you know of a living literature review we’ve missed, please let us know at abundanceandgrowth@coefficientgiving.org.

Current directory:

New Things Under the Sun by Matt Clancy. Social science research on science and innovation. How new ideas are generated, who generates them, and what helps or hinders the process. Topics: Science & Innovation, Economic Growth

The Patentist by Gaétan de Rassenfosse. Accessible, evidence-based essays synthesizing academic research on how patents shape innovation, competition, licensing, and litigation. Topic: Science & Innovation

AI Accountability Review by Nick Diakopoulos. Translating research to bridge the gap between knowledge and practice for AI policymakers, practitioners, and researchers. Topic: AI & Technology

Scaling in Human Societies by Michael Goff. How and why size matters, scaling patterns across institutions, cities, and systems. Topics: Economic Growth, Interdisciplinary Research

Bridging Boundaries by Rachel George. How institutions shape collaboration across fields, what barriers get in the way, and how emerging technologies like AI may help bridge disciplinary divides. Topics: Interdisciplinary Research, Science & Innovation

Lauren Policy: Migration Literature Review by Lauren Gilbert. Research on migration, progress, innovation, growth, and development. Topics: Labor & Immigration, Economic Growth

The Care Gap by Nalini Gulati and Vikas Dimble. Applying a gender lens to healthcare demand and supply in low- and middle-income countries, drawing on research across development economics, public policy, and behavioral science. Topic: Global Health & Development

Existential Crunch by Florian Jehn. An interdisciplinary look at societal collapse: what factors make it more likely, and what societies can do to be more resilient. Topic: Climate & Resilience

The Price of Power by Nikhil Kalyanpur. Synthesizing what we know about when money does and doesn’t mean power, across democracies and autocracies, in peace and war, from oligarchs to civil society. Topic: Governance & Political Economy

Monitoring Gene Drives by Felix Moronta Barrios. Research on gene drive technology and its implications. Topic: Biotechnology

Reskilled by James Ransom. From the aftermath of war to the AI frontier: how do you get people back into work when their jobs disappear? Topics: Labor & Immigration, Economic Growth

Building Abundance by Michael Wiebe. Research on housing and infrastructure policy. What gets built, what doesn’t, and why. Topics: Housing & Land Use, Infrastructure

Related projects

Here are a few related projects worth knowing about:

Generative AI for Economic Research by Anton Korinek. A Journal of Economic Literature article on how generative AI can assist economists, with regularly published supplemental updates tracking the latest capabilities as AI systems evolve. Topics: AI & Technology, Science & Innovation

Ghosts of Electricity by Alex Imas. Individual research syntheses on AI adoption and its economic effects, including posts on who uses AI and how and the impact of AI on productivity. Topics: AI & Technology, Economic Growth

Under Review by Seth Werfel & Devon Magliozzi. Research-driven policy analysis that synthesizes peer-reviewed evidence to evaluate whether popular policy ideas hold up. Posts are treated as living documents, updated as new evidence emerges. Topics: Housing & Land Use, Governance & Political Economy

Specialized Research Bibliographies by Ben Trettel. A curated directory of expert-maintained research bibliographies across fields. Less detailed than full literature reviews, but broader in coverage and a useful complement.

What are we missing?

This post is itself a living document. If you write or follow a living literature review that belongs here we’d like to hear about it. We’re agnostic about topic areas and interested in any regularly updated, publicly accessible research synthesis written by someone with deep expertise in the field. Drop us a line at abundanceandgrowth@coefficientgiving.org.

1. If policymakers in the US can agree on anything, it’s that they want more semiconductor plants (“fabs”) built in the US. The bipartisan CHIPS & Science Act promised hundreds of billions of dollars in federal subsidies toward this purpose, and President Biden even signed legislation exempting the factories from federal environmental reviews. Problem solved, right? Lol, of course not. Micron, a US-based chip company, is building a fab near Syracuse, NY, costing $100 billion and employing some 9,000 workers in an area that’s lost a lot of manufacturing jobs. Naturally, for this they are getting sued by a foundation/union-funded group, Jobs to Move America, for not doing sufficient review under New York’s environmental review statute. The best summary comes from the trade pub SemiAnalysis, which notes, “The project has already taken an absurd 1200 days between announcement and groundbreaking. Competitors overseas who began at the same time now have built and working fabs.” — Dylan Matthews

2. Last month we posted a Kiesling/Macey paper, which argued that perverse incentives for incumbents help keep energy supply constrained. A paper published last week by Dasom Ham, Owen Kay, and Catherine Hausman dovetails nicely with that post, providing more quantitative evidence for how those incentives are created. The authors find that eliminating some of the current constraints to moving energy would have reduced electricity generation costs in the continental US by roughly $3 - 5 billion in 2023 and $6 - 7 billion in 2022, when natural gas prices spiked. Better connections between energy supply (particularly in the renewables-rich interior of the country) and demand centers (primarily on the coast) would also help equalize prices, trimming revenues for coastal incumbent generators currently benefiting from their locational “advantage.” — Willow Latham-Proenca

3. When novel technologies get shoehorned into ill-fitting permitting systems, the results are rarely enviable. In a recent piece, Samuel Roland tours the byzantine landscape of state-level geothermal regulation. While some states consider geothermal a “surface estate” - where the landowner holds the rights - others allocate it as a mineral right to a separate - sometimes difficult to locate - entity. Attempts to map geothermal permitting onto existing technologies like oil & gas drilling have also led to permitting processes that don’t match actual risks for each project phase, and a dizzying patchwork of agency jurisdictions. To top it off, unclear application of western water law can create enough legal risk to kill project financing. The piece proposes several practical fixes, including a voluntary model code, statutory safe harbors for non-consumptive water systems, and risk-tiered permitting. — Willow Latham-Proenca

4. Around 23% of the terrestrial territory of the Netherlands is a protected area; to secure a permit to build near these areas, environmental review about nitrogen impacts are particularly important. The country used to use a system called the Programma Aanpak Stikstof to determine when the nitrogen impact of new construction was compliant with regulations, but a 2019 court ruling determined this program was invalid. Thus began the Dutch Nitrogen Crisis, which has constrained building in the country since. Inferring the impact of this policy change is hard because construction in the following years was probably impacted by covid-19; but a new paper makes some headway by comparing building rates across sites nearer and farther away from the protected areas. It finds the policy change reduced construction by 2.7 permits per quarter, against a mean of 41.4 permits per quarter. This paper is interesting in other ways too - see item #5 for more. — Matt Clancy

5. Another interesting thing about the paper discussed in item #4, about Dutch nitrogen regulation and residential building, is that it was written completely by AI! It’s part of the Autonomous Policy Evaluation Project (Project APE), an initiative from the Social Catalyst Lab at the University of Zurich. They’ve used AI to generate 241 papers so far, each evaluating the various impacts of some policy with real code and data pulled from official sources, that you can download from github to audit. The Dutch nitrogen paper has some serious issues - many of the figures and citations have been improperly processed and don’t show up correctly in the paper. But AI continues to improve. There’s been a lively debate playing out online about how the ability of AI to churn out papers could break peer review and the journal system; but I found project APE a good reminder of the potential upside: for us, transparent quantitative analysis on every policy for which data is available. — Matt Clancy

6. The main thing Alex has been reading this week is the text of the Senate Banking Committee housing reform bill, for which he is deep in the details. It’s encouraging to see bipartisan movement on federal housing policy, though there’s a lot to get right as it moves forward. And Jordan is on holiday. We’re excited to hear about his beach reads next week! — Nisha Austin

Here are a few other updates from our grantees and team:

• Ben Schifman discusses the NEPA litigation “doom loop” - and its potential fixes - in a recent long-form piece at IFP.

• Ruxandra Teslo argues that AI alone won’t speed up clinical trials. The real bottlenecks are regulatory and operational, not technical, and solving them requires institutional reform.

• Finally, Saloni wrote a new post on the history of clinical trial reforms and what we should do next. She discussed how practices like randomization, preregistration, and results reporting were once seen as radical, and why greater data transparency is the natural next step.