Thread by @RaianyRomanni

How could tiny breakthroughs in aging science change U.S. GDP and population growth?

What’s the economic value of making 41 the new 40, or 65 the new 60?

How many lives could we create or save if we could slow reproductive or brain aging by just 1 year? What would billions of healthier hours be worth to the economy, if we assume no change in the age of retirement?

I spent the last two years obsessing over the design, research, and execution of this project. The result is a book upcoming with Harvard University Press, a preprint, and—maybe your favorite part—an interactive simulation tool that lets you input your own timelines and assumptions for specific breakthroughs in aging bio, then see the ROI in terms of US population & GDP growth.

From @RickEcon and Jason DeBacker—the economists who co-developed the open-source, macro model that made this project possible—to extensive comments by @tylercowen, @sapinker, Richard Freeman, @NDHendrix, @ebudish, @elidourado, @geochurch, @jasoncrawford as well as interviews with 102 scientists (!) and countless iterations with award-winning designer Giorgia Lupi and the @pentagram team, we built something we hope will be a benchmark for how scientists, economists, designers, philosophers, entrepreneurs and storytellers can come together to paint, fund, and build different flourishing futures for our species.

I couldn’t be more excited to share this. It’s the start of an open and evolving project—the labor and product of love, obsession, and unrelenting care.

I hope you have fun playing with our simulation tool — and if you do, please share!

silverlinings.bio

Biological aging is the primary risk factor for most deaths, disability, and chronic illness on the planet.

It is also the primary risk factor for severe disease by infections like pneumonia and COVID-19.

The bad news is no clinical trial is underway to test if a drug can improve human aging.

Private markets have mostly produced a $200B supplements industry with a handful of bold biotechs in between.

Life expectancy doubled, but we age almost the same way we did in 300 BC.

The good news is biological aging is malleable.

Nature itself has produced mammals like the bowhead whale, who often live to be 200 without ever getting cancer or Alzheimer’s.

The American Lobster even becomes stronger and more fertile with age.

In a 2013 study, modifying two genes in C. elegans worms resulted in the equivalent of a 400-year-old human – in seemingly good health.

If we engineer a tiny fraction of this change in the biology of human aging, how could the U.S. economy and population look like?

We find reversing biological aging in the U.S. population by just a year would add roughly $408B/y to GDP—yielding $27T in net present value in the long run.

Over 5 years, this yearly sum yields roughly the equivalent of adding a Canada-sized economy to the U.S.

And we assume no change in the age of retirement.

We also find that slowing reproductive aging by a year would add roughly $9B/y to GDP, yielding $9.3T in net present value in the long run.

Advancements in ovarian aging that increase birth rates slightly are most valuable about 50 years in the future to GDP—once the newborns enabled by healthier pregnancies and lower age-related infertility reach peak income.

Biology isn’t the primary reason why people don’t have more kids, and it’s not the only reason why people stop working. But it’s one vastly overlooked factor in both decisions.

Healthier brains offer immediate returns to the economy.

While newborns typically take decades to produce more than they consume, healthier humans with fully grown brains means lower caregiving costs, lower death rates, and short-term productivity and labor supply gains.

We find reversing brain aging by a year would add $201B per year to U.S. GDP, yielding a similar $8.9T in net present value in the long run.

Investments in brain aging are also extraordinarily valuable in GDP per capita terms, since they make everyone better off.

But these goals aren’t at odds. We can create new humans while investing in existing ones–e.g., by extending the reproductive healthspan of would-be menopause patients.

This is the beauty of aging science: it could improve US productivity, mortality, and fertility rates at once.

In 2026, birth rates are fast declining, while our deaths have grown slower and costlier with time.

The demographic returns of health- and life-extending research have been largely overlooked.

But longevity—even in imperfect health—drove a stunning share of 20th-century GDP growth.

With natural and artificial intelligence, we could multiply these gains many times over.

How exactly would investments in aging bio pay off, if Social Security is so costly?

This is what labor productivity looks like for most humans. Our income is stunningly triangle-shaped.

We on avg. peak, income-wise, around age 58—then very predictably, our income falls (or we stop working) primarily as a result of how we age.

Changes in the biology of aging are unique in that they can extend our productive time at the peak of this triangle.

That’s like importing millions of highly experienced workers from a highly developed country: the US!

The key asterisk is we simulate chronologically—not biologically—older adults. At age 58, we typically add more to GDP in good health than we do at age 20.

This matters not just in the context of hospital bills, but also in people’s decision to supply labor. When our hourly income does not go down due to biological decline—ours or our loved ones’—many of us choose to work longer.

The economic model we use takes into account how some people love work, while others don’t. We simulate giving people the healthspan to choose.

In 2026, adults 65+ are the fastest-growing labor group in the economy. For all its woes, work often provides greater benefits than unemployment.

But every year, millions of Americans stop working because they or their parent or spouse receives an age-related diagnosis.

Today, some 40M Americans are unpaid caregivers to older adults.

Meanwhile, many older adults want to work but lack the cognitive and overall healthspan to do so productively.

Many of our most important discoveries come when expertise peaks—often when cognitive decline begins. If we could stay cognitively sharp longer, we’d likely see more discoveries.

For more on how aging bio could impact innovation, see the book! Spoiler: some mild negative effects.

These are our baseline results. We hope you add to them, critique them, evolve them—and have fun playing with our tool!

In short, the closest thing to an economic free lunch we can get is letting people work—and have babies—in good health when they want to.

In the short term, the gains from slower aging would far outweigh Social Security outlays.

This should be intuitive: previous research by economists Murphy and Topel shows eradicating cancer (one disease of aging) would have a net present value of over $80T to the US.

In the long run — if we can reverse aging by decades — we may need to flip SS on its head. Today, we subsidize the decline of older adults. In a world with healthier older adults in positions of power, we may need to better subsidize the development of youth.

Why assume GDP is a useful societal vector?

Because it correlates with most metrics of welfare, from lower infant mortality to (as the last decade has shown) a path toward lower emissions.

Rich countries can afford to be greener; people in wealthy neighborhoods live longer; and new data suggests the wealthiest even have the most babies, reflecting a U-shaped fertility curve with births rising again at the top.

We believe automation won’t obviate the need for more cognitively sharp, biologically young humans.

GDP can go up as a result of high but ineffective spending—say, on healthcare.

But in the very long run, high ineffective spending will lower GDP.

This is partly why we model yearly returns to GDP as well as net present values over decades.

(See preprint for per capita results.)

Timelines for scientific breakthroughs are fundamentally malleable.

Still, we interviewed 102 scientists to collect views on timelines, funding amounts, and R&D approaches underserved by commercial incentives.

Out of 102 scientists, zero replied that age reversal is impossible.

92% believe proving a 5-year age reversal in humans would require less than $50B in funding.

45% think this could be done with less than $1B, since funding direction matters more than funding amount. Research on the aging brain is a case in point, having so far been devoted mostly to neurodegenerative, late-stage diseases.

Even assuming unrealistically aggressive investments ($30B), the returns from these tiny R&D breakthroughs would dwarf any plausible investment amount.

Our surveys also led to a list of 50 R&D approaches underserved by markets.

Frequent answers included “the extracellular matrix” and “replacing aging”—the idea that even if we can’t fully map the biology of aging, we can still replace its parts, as shown by the replacement of tissues and organs since the 20th c.

The list of brilliant people who offered feedback on this project is long.

I’m grateful to the 102 scientists I interviewed for this project, dozens of whom shared extensive comments, and to the many policy entrepreneurs, economists, writers, and researchers who offered support and advice.

@jacobkimmel @geochurch @SGRodriques @RuxandraTeslo @kpfortney @wysscoray @NirBarzilaiMD @byersblake @MartinBJensen @Danbelsky @BrackLab @AlexJColville @AaronCravens5 @jpsenescence @ydeigin @MarcoQuarta12 @tGaoTeng @DaneGobel @jenngarrison @VadimGladyshev @D_R_Goodwin @Replacing_Aging @xinjin @j_n_justice @mkaeberlein @BradStanfieldMD @emilkendziorra @RohanKrajeski @MichaelFloreaX @alexkesin @_Carol_Mag @moreisdifferent @mahdi_moqri @NikoMcCarty @innovationwonk @KarlPfleger @JamesPeyer @YianniPsaltis @guidoputignano @Thomas_Rando @M_S_Ringel @JarodRutledge1 ...

@MichaelShadpour @Atelfo @JianShuLab @MarkHamalainen @alantomusiak @juliervaughn @statto @DToddWhite @MiguelCCoelho @ShriyaPBhat @MaxUnfried @marton_mes @madiueland @Sierra_Lore_ @kulesatony @shoseph @prufrocktheory @ArtirKel @jgooten @brad_w_e @amplesa @stuartbuck1 @SamoBurja @johanknorberg @MTabarrok @tkalil2050 @PeterDiamandis @adamgries @YvankadeSoysa @rv_sehgal @sebastiangiwa @agingdoc1 @sean_simonini @bsgallagher @rSanti97...

@MichaelShadpour @Atelfo @JianShuLab @MarkHamalainen @alantomusiak @juliervaughn @statto @DToddWhite @MiguelCCoelho @ShriyaPBhat @MaxUnfried @marton_mes @madiueland @Sierra_Lore_ @kulesatony @shoseph @prufrocktheory @ArtirKel @jgooten @brad_w_e @amplesa @stuartbuck1 @SamoBurja @didaoh @jeffreychen24 @AndreaBMaier @AAparicioDeN @VGorbunova79085 @aubreydegrey @JoanneZPeng @patrickmineault @BrennanOverhoff @Ben_Reinhardt @omarabudayyeh @EricDai_BioE @stebbins @bettslacroix @CJHandmer @AdamMarblestone
(+ many more not on X!)

Biggest thanks of all goes to @jamesfickel, who gave me the chance to obsess over these questions while based at The Amaranth Foundation, without the traditional bottlenecks of outdated grant deliverables that would have produced a less ambitious project.

I’m also grateful for the support by the American Federation for Aging Research and the The Methuselah Foundation.