There is a question I keep returning to — one that sits underneath the policy debates, the appropriations fights, the executive orders, and the increasingly urgent memos circulating through the national security establishment:
Can America actually make things with biology?
Not design them. Not discover them. Not publish papers about them. Make them. At scale. Reliably. On domestic soil. With a workforce that exists and supply chains that don’t route through adversarial nations.
The answer, right now, is: barely.
And it looks like this. Last year I stood in a pilot biomanufacturing facility — one of the few we have — and watched a team troubleshoot a fermentation run that had gone sideways at 5,000 liters. The organism was producing at bench scale. It had produced at 500 liters. At 5,000 liters, oxygen transfer became the constraint — at least, that’s what the team suspected in real time. The metabolic profile shifted. Yield dropped by a third. The lead process engineer — one of a few hundred people in the country with this specific operational expertise — was working through it with a combination of sensor data, experience, and what I can only describe as biological intuition. There was no model to consult. No binder. No runbook. No second shift that had seen it before. She was debugging a living system at industrial scale, mostly alone, in a building that smelled like warm yeast and sounded like a submarine engine room.
That scene is the bioeconomy. The rest is narration.
I’ve spent two+ years as a commissioner on the National Security Commission on Emerging Biotechnology, producing the most comprehensive governmental blueprint for biomanufacturing reindustrialization the United States has published. The NSCEB report is thorough. It is specific. I believe in the architecture. But government reports — even good ones — describe what should happen. They do not describe what it feels like to build in the gap between should and is. And they do not tell the builders, the operators, and the capital allocators where the openings actually are.
That’s what I want to do here. This is the first in a series.
The podcast audio was AI-generated using Google’s NotebookLM.
The Gap Nobody in Washington Understands Viscerally Enough
Two pieces of writing crystallized the biomanufacturing problem for me recently, and neither was about biology.
The first is Aaron Slodov’s “American Shenzhen” framework — a detailed blueprint for rebuilding U.S. hardware manufacturing capacity: government as anchor tenant, 75/25 commercial-to-defense revenue models, special economic zones, streamlined permitting, venture-backed manufacturing startups. The insight is structural: Shenzhen didn’t emerge from a single policy. It emerged from a system — procurement signals, physical infrastructure, workforce pipelines, and regulatory architecture reinforcing each other simultaneously.
The second is Oliver Hsu’s recent primer on factory economics for a16z, which articulates what venture capital is only now internalizing: the IP is the process. In these companies — and biomanufacturing startups are definitionally among them — the moat is not intellectual property in a patent filing. It is the production process itself. The yield curves. The learning rates. The operational knowledge embedded in people and equipment and process.
I read both and thought: this is the framework I wish I could have injected directly into the NSCEB’s deliberations.
Because here is what the data looks like from inside the commission. The United States’ share of global API production has collapsed from roughly 23% to approximately 3% over three decades. More than 90% of generic pharmaceuticals consumed in the U.S. depend on imported ingredients. Industry surveys indicate that roughly 80% of biopharma organizations are actively engaged with Chinese CDMOs. China holds approximately 58% of global synthetic biology patent filings, 28% of biological manufacturing patents, and 30% of novel antibiotics patents. That’s not an edge. That’s installed capacity. That’s a country that has been running production volume while America has been running conferences about it.
The NSCEB report lays this out. The number one message is the urgency. The window for American biomanufacturing reindustrialization is open, but it is not open indefinitely. And the dynamics that will close it are not political. They are economic.
The Learning Curve Is a Race — And We’re Losing It
This is where Hsu’s factory economics framework becomes essential, and where the venture and builder community needs to pay close attention.
Wright’s Law tells us that costs decline predictably with each doubling of cumulative production. The learning curve is the race that defines factory companies. The competitor with more cumulative production has lower costs. Lower costs win more contracts. More contracts mean more production. More production steepens the curve. The advantage compounds.
China is further down the biomanufacturing learning curve than the United States in multiple product categories. Every year America does not build domestic production capacity is a year China accumulates more volume, drives costs lower, and makes the gap harder to close. This is not a static competition. It is a dynamic one, and the dynamics favor whoever starts manufacturing first and fastest.
Now, biology adds a complication that makes this race harder than any hardware equivalent. Biological systems are stochastic. A fermentation run that works at 10 liters may behave differently at 10,000 liters — not because of engineering error, but because living organisms respond to conditions in ways we don’t fully understand. That engineer I watched troubleshooting the 5,000-liter run? She was navigating exactly this problem. The yield curve in biomanufacturing is less predictable than in semiconductors, aerospace, or any other production domain. And yield is the single highest-leverage variable in factory economics. A 20-point yield advantage can create a cost differential that determines who survives.
This is the core tension, and I want to name it precisely because I think it’s the single most important concept in biomanufacturing strategy:
Biomanufacturing fails in circles. It only scales in spirals.
The circle: the learning curve demands production volume, but production volume requires facilities that won’t get built without capital, and capital requires the predictable yields that only come from production experience. No yields, no capital. No capital, no facilities. No facilities, no learning. No learning, no yields. The system is closed. Nothing moves.
The spiral: break into the circle at any point — with government demand signals, with patient capital, with science that steepens the yield curve — and the circle becomes a spiral. Production generates learning. Learning improves yields. Yields unlock capital. Capital builds more facilities. Facilities train the workforce. The workforce improves operations. Operations improve yields. The system opens. Everything moves.
In a circle, you die waiting for certainty. In a spiral, you manufacture your way into it. The circle is what kills startups. The spiral is what makes nations competitive.
The NSCEB understood this. The report’s six pillars — political commitment, private sector mobilization, defense integration, innovation infrastructure, workforce, and allied coordination — are designed as a system specifically to break the circle and start the spiral. Push on every node simultaneously. That’s the architecture.
But here’s what I want to tell the builders and investors directly: the government is going to be slow. The NSCEB report took two years. Implementation will take longer. If you wait for the full system to be in place before you move, you will be too late.
The opportunity is in the gap between the signal and the infrastructure — and that gap is open right now.
How a Builder Wins in the New Landscape
Let me be concrete about this. Instead of listing the plays abstractly, I want to walk through what the next twelve to thirty-six months look like for someone building a domestic biomanufacturing company — and how the NSCEB architecture, as it comes online, changes the game at each stage.
You start with a facility strategy
This is the first decision and the one most biotech founders get wrong, because they’re trained to think about molecules first and production second. In the new landscape, production is the strategy. You need a facility — or access to one — that can run at pilot scale today and commercial scale within three years. The NSCEB recommends a network of precommercial biomanufacturing facilities operated through DOE and DOC, plus a $120 million biopharma manufacturing center under the Defense Bioindustrial Manufacturing Program. If you are a startup, the question is whether you build your own or position to be the anchor tenant in one of these government-catalyzed facilities. Either way, think about geography: proximity to a research university with relevant programs, an existing labor pool with manufacturing experience (not just biology PhDs — people who know how to run plants), and state-level incentive structures. The NSCEB’s regional hub model matters here. The companies that co-locate with the hub infrastructure will compound advantages that distant competitors cannot replicate.
The procurement signal de-risks your first offtake
The DBIMP at $762 million or more. Advanced market commitments and offtake agreements from DOD and HHS. The BIOSECURE Act — signed into law in late 2025 — already restricting federal contracts with foreign biotechnology companies of concern. That’s the stick. The DBIMP and AMCs are the carrot. If you are building domestic production capability for APIs, sustainable aviation biofuels, biomaterials, or engineered proteins, the federal procurement pipeline is about to open in ways it hasn’t before. This is Slodov’s “government as anchor tenant” — and it’s the single most important de-risking event for early-stage biomanufacturing companies. The companies that have production capability when the procurement dollars flow will capture the contracts. The ones that wait for certainty will find the contracts already allocated and the learning curve already claimed.
Government de-risking shifts your capital structure
This is where the NSCEB does something genuinely novel. The report recommends an Independence Investment Fund at the Department of Commerce — subordinated capital, loan guarantees, co-investment structures. Combined with targeted tax credits for biomanufacturing capex, this changes the financial physics.
Hsu’s framework explains why. Factory startups get stuck in the equity-only phase of the capital stack because they can’t demonstrate predictable yields to unlock lower-cost capital. Venture equity is expensive. It is also the wrong capital for manufacturing ramp. What you need for a factory is a progression: equity for R&D and process development, venture debt for equipment with clear payback, equipment financing for production expansion, and project finance for new facilities with contracted offtake. Each transition requires demonstrating more operational predictability than the last.
Government de-risking — subordinated capital from and Independence Investment Fund, guaranteed demand from AMCs, tax credits that improve the capex math — is what enables these transitions. It doesn’t replace private capital. It unlocks it. The VCs who understand this will structure their investments to ride the government co-investment, not compete with it. The ones who try to fund biomanufacturing the way they fund SaaS will keep writing checks that don’t come back.
The science infrastructure becomes your tooling tailwind
The NSCEB proposes $5 billion over five years to make biology predictably engineerable. $540 million over three years to make scale-up predictable and cost-competitive, targeting four bottlenecks — chassis organisms, feedstocks, process technology, and critical inputs like growth media and purification resins. Plus $640 million for AI-ready biological data standards at NIST. A Web of Biological Data at DOE. An NSF cloud labs network. Six Centers for Biotechnology in the National Laboratories.
This is not a research agenda. It’s an industrial one.
The companies that will benefit most are not the ones waiting for this infrastructure to be complete. They’re the ones building tools and platforms that accelerate it — and that become indispensable as the infrastructure scales. AI-driven biodesign. Automated process optimization. Digital twins for fermentation and downstream processing. Scale-up prediction models trained on the standardized data that NIST and DOE are about to generate. These are venture-scale opportunities that ride the government investment wave while creating independent commercial moats.
I want to be specific about why this matters for the circle-to-spiral conversion. The reason biomanufacturing yield curves are shallower than semiconductor yield curves is not that biological systems are inherently unoptimizable. It’s that we lack the data infrastructure, the computational tools, and the standardized measurement frameworks to learn from production experience systematically.
We don’t have a design-for-manufacturing paradigm in biology yet.
Every biomanufacturing facility is, to a significant degree, reinventing the wheel — because the data from the last facility isn’t in a format the next facility can use. The companies that build the connective tissue — the platforms that turn production data into transferable operational knowledge — will be the ones that steepen the learning curve for the entire industry.
Then workforce becomes the edge that compounds. The NSCEB data is stark: unmet demand across biotech roles runs 38-68%. Bioindustrial manufacturing demand is up 23% with pipelines lagging far behind. But the workforce follows the facilities. Shenzhen didn’t train workers and then build factories. It built factories and the workforce grew around them — through proximity, repetition, and operational experience accumulating over time.
The companies that build production facilities in regions with existing educational infrastructure and labor pools — and that invest in training as a core strategic function, not an HR afterthought — will have an advantage that compounds quarterly. The skills that matter — debugging a bioreactor at scale, optimizing downstream processing, managing GMP compliance while maintaining throughput — are developed on production floors, not in classrooms. Every month of production experience your team accumulates is a month your competitors don’t have.
This is the spiral in action. The facility generates production experience. Experience improves yield. Better yield attracts capital. Capital expands production. Expanded production trains more workers. More workers improve operations. Better operations improve yield again. Each rotation of the spiral, the moat deepens.
The Ugly Middle: Where Factories Actually Bleed
If you’ve read this far and you’re thinking mostly about upstream — organisms, fermentation, titers — you’re making the mistake most people make. Upstream is where the science lives. Downstream is where the margins go to die.
Purification, analytical characterization, QA/QC, validation, fill-finish — this is where biomanufacturing timelines stretch and costs compound. It’s less glamorous than strain engineering. It is also where the majority of production cost and schedule risk accumulates. The U.S. constraint is often not the bioreactor — it’s the chromatography columns, the single-use filtration systems, the analytical instrumentation, and the trained QC analysts who can run them. These are physical, unglamorous bottlenecks, and they are frequently the binding constraint that determines whether a facility can actually deliver product at the quality and timeline a contract requires.
Any serious builder knows this. The NSCEB’s scale-up grand challenge targets process technology and critical inputs for exactly this reason. But the policy language is clinical. The operational reality is that downstream processing is where you discover whether your factory actually works — and in biomanufacturing, unlike semiconductors, you often discover it at the worst possible moment: when you’re trying to meet a delivery commitment.
The companies that solve downstream — that build the equipment, develop the resins, automate the analytics, train the QC workforce — will own a piece of every biomanufacturing company’s cost structure. That is not a niche. That is a platform.
What Could Kill This
I’ll be direct about the risks, because the builders need to price them.
Fragmentation
The NSCEB recommends a National Biotechnology Coordination Office in the Executive Office of the President — the institutional equivalent of the National Space Council, but for biotech. Without it, or with a version that lacks statutory authority and dedicated financial oversight, the recommendations scatter across HHS, DOD, DOC, DOE, USDA, NSF, EPA, and a dozen sub-agencies. I’ve watched this happen. An EOP office without teeth is a convening body that produces memos. The NBCO needs to be the one with the pen on the national biotechnology strategy and the authority to align agency budgets behind it. Whether it gets that authority is a political question, not a technical one.
Partial funding
Congress may fund pieces of the system but not the system itself. And partial funding of a system is worse than no funding at all — it produces individual components that can’t function without the others. The circle doesn’t break if you only push on one node. You get a biopharma manufacturing center with no trained operators. A workforce program with no facilities to train in. A data standards initiative with no production data to standardize. The system logic is the NSCEB’s greatest strength and its greatest vulnerability — because systems require comprehensive investment, and comprehensive investment requires sustained political will, and sustained political will requires visible threats, and biomanufacturing’s threats are invisible until they’re catastrophic.
Speed
This is the risk that keeps me up. Government will be slower than the competition. That’s structural. Authorization takes years. Appropriation takes more. Implementation takes more. Each year of delay is a year China accumulates production volume, drives down costs, and locks in the learning curve advantage that compounds and compounds and compounds. The window doesn’t close in a day. It closes in a decade of days where nothing happened fast enough.
Commercial viability
Even with government de-risking, domestic biomanufacturing must eventually be cost-competitive without subsidies. If production costs remain significantly higher than foreign alternatives — and they currently are for many product categories — the demand that procurement creates won’t expand into commercial markets. This is where the grand research challenges are existential, not optional. Making biology predictably engineerable is not an academic aspiration. It is the prerequisite for the learning curve to steepen. It is the prerequisite for the spiral to accelerate past the point where government support is needed.
The Frontier Firm Meets the Factory Floor
Everything I’ve described so far — the learning curves, the capital stack, the workforce — is hardware. And reindustrialization at speed is always a software problem, too. The companies that win won’t just be factory companies. They’ll be software companies that happen to make molecules.
I’ve been thinking about this through the lens of what Microsoft calls the Frontier Firm — organizations restructured around AI agents, where human-agent teams replace traditional hierarchies and the “Work Chart” replaces the org chart. I’ve argued that governance architecture has to be designed before capability scales, not after. That autonomous systems which outpace human oversight create accountability vacuums. That velocity without accountability doesn’t scale — it detonates.
Biomanufacturing is about to test that thesis in steel and concrete.
The companies that will win the biomanufacturing reindustrialization are not going to be traditional biotech companies that happen to build factories. They are going to be Frontier Firms that happen to work in biology. AI-driven biodesign. Autonomous process optimization. Digital twins that predict fermentation behavior before you run the batch. Agent systems managing QA/QC workflows at a pace and consistency that human teams alone cannot sustain. The economics demand it — yield curves, learning rates, cycle time optimization are all domains where AI-agent integration is not optional but existential.
And this is where the two theses connect. The Frontier Firm needs governance architecture: accountability ledgers, rules of engagement, clear chains of human responsibility for autonomous decisions. The biomanufacturing Frontier Firm needs all of this plus regulatory compliance in one of the most heavily regulated production environments on Earth. FDA, EPA, USDA. GMP. Process validation. Batch records. Chain of custody.
The companies that build both — the AI-agent operational architecture and the governance frameworks to make it regulatable — will be the ones that define the next era of biological manufacturing. This is not a future problem. The tools exist now. The regulatory conversations are happening now. The builders who move first will set the standards everyone else has to follow.
I’ll go deeper on this in future editions — what the biomanufacturing Frontier Firm actually looks like, how AI-agent systems integrate with GMP production, where the governance models from defense autonomy apply to factory floors. This is a series, not a single argument.
The Wager
Here’s where I land.
The NSCEB designed a system. Six pillars, interdependent, mutually reinforcing. Political architecture. Capital formation. Defense procurement. Innovation infrastructure. Workforce. Allied coordination — extending the production base through AUKUS, Quad, NATO, and the Wassenaar Arrangement, because reindustrialization is not autarky. It is a strategic rebalancing of where critical capabilities live and whose supply chains they route through.
But what occupies my thinking now — what I think about when I’m not thinking about the policy machinery — is the builder’s version of this thesis. The version where a venture-backed company builds domestic fermentation capacity and captures the DOD offtake before the procurement system fully stands up. Where a startup creates the AI-driven biodesign platform that rides the NIST data standards buildout. Where a regional hub — maybe in Oklahoma City, maybe outside St. Louis, maybe in a place nobody’s thought of yet — becomes the Shenzhen of biomanufacturing. Not because a government report said it should, but because someone built the factory, trained the workers, ran the bioreactors, solved the oxygen transfer problems at 5,000 liters, and drove down the learning curve faster than anyone thought possible.
The government can signal demand. It can de-risk capital. It can fund science. It can build infrastructure. It can coordinate allies.
It cannot manufacture. It cannot operate. It cannot descend the learning curve.
That requires builders.
The factory is the product. America has been designing the product for decades while outsourcing the factory. The policy signal to bring the factory home is louder than it has ever been. The NSCEB’s architecture is designed to break the circle and start the spiral — but the spiral only turns if someone is inside it, building.
The window is open, but the curve is compounding.
Who builds it?
At the frontier of biology, the experiment is not whether we can engineer life. We can. The experiment is whether we can manufacture it — here, reliably, at the scale the century demands. The competition isn’t waiting. They’re doubling cumulative volume.
— Titus
This is the first in a series on the biomanufacturing reindustrialization thesis. Upcoming editions will cover: the biomanufacturing Frontier Firm and AI-agent integration in GMP production; the capital stack in detail — how LPs, VCs, and government co-investment vehicles should be structured; the downstream bottleneck and the companies solving it; and the workforce problem as a compounding strategic advantage. If you’re building in this space, I want to hear from you.
