Algae as Analogy in an Age of Disruption and Transition Complexity
This piece developed in concert with the Moonshots and Metascience event made possible by the Alfred P. Sloan Foundation.
Algae is making plenty of headlines lately with the recent drama at the Lincoln Memorial reflecting pool. But this is far from the first time that algae has seized the spotlight. When I was a graduate student at the University of Washington (UW) in the late 1990s, I biked daily along the Burke-Gilman trail from my house in Seattle’s Wallingford neighborhood to campus. As a recent transplant from New York City, the blue waters of Lake Washington on sunny days never failed to amaze me. But the stories of Lake Washington’s not-so-distant “Lake Stinko” past were the stuff of legend. A few decades prior Lake Washington had a giant pollution problem — cloudy, dirty, and smelly, fouled with 20 million gallons of wastewater pumped into it each day, which prompted algae outbreaks and then algae die-offs.
At UW I was inspired by professors Tommy and Yvette Edmondson, then emeriti faculty and local heroes whose work on ecological tipping point theory and Lake Washington offered living proof that systems could rapidly undergo dramatic transitions between different ecological states. And sometimes, they could snap back to a pre-transition state. Their research led directly to the creation of Metro, the Seattle agency that diverted sewage effluent away from the lake, enabling Lake Washington’s dramatic recovery.
This history is top of mind because complex systems – from ecological to political to socio-technical – rarely change the way we expect. They can absorb pressure for years, even decades, then bifurcate, reorganizing rapidly around a new equilibrium that can look nothing like the old one. What happens after the transition has a lot to do with how resilient the system is to begin with.
It is perhaps unsurprising then, that this current moment of disruption and transition in the U.S. scientific enterprise is making me think deeply again about tipping points and state changes, beginning with the question of how resilient was this system to begin with? Even prior to this current moment of acute disruption, the U.S. scientific enterprise has been showing early warning signs of instability for years — research becoming measurably less disruptive, grant success rates falling, administrative burden consuming nearly half of researcher time, alternative funding models proliferating at the margins (e.g. FROs, Fast Grants, etc), because the core systems were not sufficiently responsive.
Many of our research institutions were built for a scientific, technological, and geopolitical environment that no longer exists. That environment was the one Vannevar Bush designed for in 1945: a single Cold War rival, science conducted by individual investigators in small academic labs, and a public trust in science near its historical peak. Eighty years later, the competition spans AI, biotech, and advanced manufacturing at once; research is large-scale and computational; and that trust is being actively renegotiated. The architecture hasn’t kept pace.They were optimized for incentives and stakeholders that have changed and have lacked the flexibility to adapt.
The acute perturbation over the past year, massive cuts to federal grants and large-scale erosion of the federal workforce, has rapidly exacerbated an instability that was already there, and is forcing the system into a rapid and uncertain transition. Though Congress ultimately rejected the administration’s deepest proposed cuts to NIH and NSF, the agencies tasked with running the system never recovered the staff needed to operate it: as of April 2026, NSF had awarded only about a fifth of its typical grant volume for the fiscal year, and NIH about half.
Metascience Separates Progress from Noise
Meanwhile, the growth of artificial intelligence (AI) applied to science is layering rapid, untested acceleration on top of institutional turbulence, with promises of massive scientific breakthroughs with fewer guardrails. For example, in October 2025, OpenAI CEO Sam Altman announced the company is internally tracking toward an intern-level AI research assistant by September 2026 and a fully autonomous “legitimate AI researcher” by 2028, on the same day OpenAI completed its shift away from nonprofit governance constraints. The institutional capacity to evaluate, govern, and correct what such systems produce is not advancing on anything close to that timeline. Acceleration without the institutional capacity to evaluate what is working, course-correct when necessary, and build knowledge that outlasts any single initiative will create noise, not progress. What we do next needs to be grounded in evidence. This is where metascience (the science of science) is poised to play an outsized role – by actively treating this moment of disruption as a large-scale systems-level experiment and learning as we go. This will only work though, if we can grow the field of metascience and position it for success.
In our recent FAS workshop, “Metascience for Moonshots and Moonshots for Metascience” we grappled with two distinct questions. The first proved more tractable than the second. It proved relatively straightforward to contend with the idea that metascientific approaches should be applied to help drive and evaluate progress toward ambitious goals with clear finish lines; for example, a climate-related goal to fly a zero-emission passenger plane from Washington to London within 10 years and health-related goal to build a national initiative to increase healthspan by 5% over 10 years – these are goals that can be formally constructed by working backward from a defined outcome. Metascience expertise embedded within moonshot efforts can help us assess the progress and to set achievable timelines, as well as build evidence along the way to enable learning and adjust strategy to improve output and efficiency.
But unlike curing cancer, sequencing the human genome, or landing on the moon, defining a moonshot for metascience resists a clearly defined finish line. Through the course of the workshop, the group came to a consensus that the specific moonshot framing did not hold well. There was no argument that the field of metascience needs to grow and should play a role in meeting this moment of transition in the scientific enterprise, but it proved difficult to define a “moonshot for metascience.” Rather, metascience lends itself to a different kind of ambitious mission.
Accelerator versus Transformer Missions
Economist Mariana Mazucatto distinguishes between “accelerator missions” that are hard, expensive, and coordinated, but can ultimately be defined by a single verifiable achievement. These are traditional moonshots. In contrast, she outlines a different class of “transformer missions,” which lack a specific finish line, but succeed by changing what the system itself is capable of doing. Her prime example of a transformer mission is Germany’s Energiewende, the country’s decades-long commitment to shifting from fossil fuels to renewable energy. Framed around a clear political direction but designed to stimulate bottom-up innovation across multiple sectors, it had no single endpoint, no moment at which Germany could declare the transition complete. Instead, the investment built the regulatory, market, and institutional infrastructure that made the direction of travel self-sustaining across governments, industries, and generations. The goal was transformation, not achievement. By this articulation, metascience is a transformer mission. Its aim is not to solve a single problem but to build the capacity that makes the entire science system better at learning from evidence — and to embed that capacity inside the accelerator missions already underway.
Over the waning weeks of 2025, the White House Office of Science and Technology Policy (OSTP) issued an ambitious Request for Information (RFI), asking for public input on how to “Accelerate the American Scientific Enterprise.” The RFI called for a comprehensive assessment of how the federal government prioritizes and structures scientific research, including a specific question on the role of metascience. FAS submitted a comprehensive response. Many organizations called for evidence-based reform of federal grantmaking, with near-universal agreement on the need to reduce administrative burden, reform peer review, and build metascience capacity inside federal agencies. But notably absent is any proposal to build metascience as a discipline in its own right and equip it to measure and evaluate this scientific enterprise in transition.
Metascience Will Be Central to Transformative Change
Metascience cannot stop the rapid disruption and changes in the scientific enterprise underway, but it can help decide where it ends up. That means doing a few concrete things now, while the system is still in motion, before it settles into a new normal.
- Treat what’s already happening now as THE experiment. The Genesis Mission, NSF’s new X-Labs, and other major initiatives launching right now are, in effect, live tests of new ways to fund and organize science, whether or not anyone designed them that way. Metascience doesn’t need to wait for a dedicated initiative of its own; it can study what’s already underway and feed lessons back before these efforts harden into permanent practice. But that only works with real rigor and intention, building in the comparisons and honest evaluation needed to learn and generate evidence.
- Build the basic data and record-keeping first. Before you can run real experiments on what works in funding science, agencies need to publish their data in usable, machine-readable form, with consistent standards so grants can be compared across agencies, and pre-registration so results can’t be quietly reshaped after the fact.This will be the only honest way to tell whether any reform actually worked.
- Build the field, starting with the people. Right now, a growing number of people want metascience’s findings, but almost nobody is funding what it takes for the field to actually exist over time — training programs, places to publish, a professional community. Training a researcher takes years, so anyone who wants to be doing serious metascience work by 2035 needs to start now, in the next year or two, whether through a Ph.D. or another path into the field.
- Give the people doing this work real say, not just a seat at the table. If metascience teams inside agencies like NSF, NIH, and DOE can only offer advice, their work tends to get quietly absorbed into business as usual. They need a direct line to the people who decide what gets funded, real ability to change how grants are evaluated, protection from being slowly watered down over time, and enough independence to report findings honestly even when those findings are unflattering to the agency that funds them.
Complex systems in transition do not suddenly pause. And unlike Lake Washington, the transition of the U.S. scientific enterprise will almost certainly not restore to a prior equilibrium once acute pressures are removed. We need the field of metascience to be ready to guide the system into a state that is more responsive, more trustworthy, and fit for purpose in a world that looks nothing like the one the current system was built for. That is metascience’s “moonshot.”
We’ve identified the key ingredients of successful moonshots that meet the moment, and developed recommendations about what future efforts can and should look like.
Complex systems – from ecological to political to socio-technical – rarely change the way we expect.
Federal data is a diverse ecosystem with well over 500,000 datasets – including those tackling Alzheimer’s disease and related dementias (ADRD).
This is a tremendous opportunity to redefine what people expect from government, and in doing so, inspire cities across the country to raise their own ambitions. We are excited to see this initiative lead the way and look forward to cheering your success.