Tech, Policy, and our Lives

Ep 64 - From Blacklists to Blueprints

I’ve been referencing the BIOSECURE Act [https://www.sidley.com/en/insights/newsupdates/2025/12/biosecure-act-included-in-the-fy2026-national-defense-authorization-act] in these pages for months — in the biomanufacturing thesis, in the generic drug analysis, in passing asides about procurement signals and supply chain fragility. I’ve treated it as established context. Background architecture. The thing that’s already happened and that everyone in this space already knows about. I haven’t written a dedicated piece on it. That was a mistake, and I want to correct it now — not because the law is new, but because I think the public conversation about what it does has settled on exactly the wrong feature. The debate was about names. Which Chinese biotech companies would be designated. Whether BGI would make the list. Whether WuXi AppTec’s lobbying campaign would succeed. Whether the five-company approach was too narrow or too broad. That was the wrong debate. The version of the BIOSECURE Act that passed in the FY2026 NDAA doesn’t name five companies. It builds a machine. And the machine is more important than any list of names could ever be. The podcast audio was AI-generated using Google’s NotebookLM [https://notebooklm.google/]. What the Law Actually Built Let me describe the mechanism, because the legal architecture matters more than the headlines suggested. Section 851 of the FY2026 NDAA bars federal agencies from procuring biotechnology equipment or services from any “biotechnology company of concern.” It also bars agencies from contracting with entities that use covered biotech equipment or services in performing federal work — a downstream prohibition that extends the law’s reach beyond direct government suppliers into their supply chains. Loan and grant funds are covered too. The scope is broad and deliberate. But the designation mechanism is where the real design work happened. The law establishes two pathways for identifying a biotechnology company of concern. The first is automatic: any company on the Department of Defense’s Section 1260H list of Chinese military companies that is involved in biotech equipment or services is designated by operation of law. No additional review. No notice. No comment period. You’re on the 1260H list and you touch biotech — you’re covered. The second pathway is criteria-based. The Office of Management and Budget (OMB) leads an interagency process to identify companies that are subject to the direction or control of a foreign adversary, involved in biotech equipment or services, and assessed to pose national security risks — affiliations with foreign adversary militaries, provision of multiomic data to a foreign adversary, collection of human multiomic data without informed consent. This pathway includes procedural protections: 90 days to respond, periodic review, a process for requesting removal. Two tracks. One fast and automatic, one deliberate and procedural. Different temporal profiles for different risk profiles. That’s not a blacklist. That’s governance architecture. The Cybersecurity Lesson Nobody Applied Here is where I want to draw a connection that I think reframes what the BIOSECURE Act actually represents — and what it tells us about how we’re learning to govern frontier technology supply chains. For three decades, cybersecurity evolved through a specific failure mode. The early approach was signature-based detection: identify known malware, build a signature, distribute it to endpoints, block the match. It worked — until it didn’t. The attack surface expanded faster than signatures could be written. New variants appeared daily, then hourly. The lag between a novel threat and its corresponding signature became the vulnerability itself. By the time the signature existed, the damage was done. The industry’s response — the one that actually worked — was behavioral detection. Stop looking for known bad actors. Start looking for patterns of malicious behavior. Build systems that can identify threats they’ve never seen before, based on what the threat does rather than what it is. The shift was from static lists to adaptive systems. From recognition to pattern-matching. From naming the enemy to understanding the behavior that makes something an enemy. The BIOSECURE Act’s legislative evolution mirrors this transition almost exactly. The original bills named five companies. That’s signature-based governance. Identify the known threat actors, put them on a list, block them. It would have worked for those five companies. And it would have been obsolete within a year, as corporate restructuring, subsidiaries, joint ventures, and successor entities routed around the designations. You cannot blacklist your way to supply chain security any more than you can signature-match your way to network security. The threat surface evolves faster than the list. The version that passed builds behavioral detection into the governance architecture. The 1260H pathway captures entities based on their assessed relationship to the Chinese military — a behavioral criterion, not a corporate identity. The OMB criteria-based pathway captures entities based on what they do: whether they’re subject to foreign adversary control, whether they handle multiomic data in specified ways, whether their affiliations pose national security risks. The criteria travel. When a new entity emerges that exhibits the designated behavior, the system can capture it without new legislation. This is the design principle that matters: the law doesn’t just address the current threat. It builds the institutional capacity to address threats that don’t exist yet. And that distinction — between a law that solves today’s problem and a law that builds the machinery for tomorrow’s — is the distinction between a sandbag and a levee. The Temporal Gap But here’s where the design gets complicated, and where I think builders and policymakers need to pay close attention. The BIOSECURE Act’s prohibitions don’t take effect upon enactment. They take effect after the Federal Acquisition Regulation is revised — 60 days after the FAR update for 1260H-designated entities, 90 days after for criteria-based designations. OMB has one year to compile the initial list. The FAR revision process has its own timeline. A five-year rule of construction protects legacy agreements, including previously negotiated options. Add it up. The law passed in December 2025. The OMB list arrives no earlier than December 2026. FAR revisions follow. Effective dates trigger months after that. Legacy agreements survive for five years. The full force of the prohibition may not bind across the federal procurement landscape until 2028 or beyond. I wrote last week about temporal architecture — about the gap between when a system is designed and when it actually operates. The BIOSECURE Act is a case study. The governance intent is sound. The institutional machinery is well-designed. But the implementation timeline introduces a temporal gap during which the very dependencies the law aims to eliminate continue compounding. Wright’s Law doesn’t pause for rulemaking. Every month that foreign producers continue descending the biomanufacturing learning curve while domestic alternatives are not yet incentivized by the procurement shift is a month the cost gap widens. The 1260H pathway is faster — no procedural protections, no comment period, automatic designation. But it only captures entities already identified as Chinese military companies. The broader criteria-based pathway, which covers the more complex supply chain risks, is the slower one. This is the governance latency problem applied to procurement policy. The detection happened — Congress identified the vulnerability. The interpretation happened — the law’s criteria are well-specified. But execution latency — the time between legislation and operational effect — is measured in years. And in those years, the problem the law was designed to solve continues operating on its own timescale. The Levee’s Boundary There’s a second structural tension that I think deserves more attention than it’s getting. The BIOSECURE Act covers federal procurement. Executive agencies. Government contracts, grants, and loans. This is the lever the government controls directly, and it’s the right place to start. But recall the numbers from the BENS report I wrote about in the generic drug piece. Ninety-one percent of American prescriptions are generics. The federal government is a significant pharmaceutical purchaser, but it is not the whole market. The cascading dependency — China to India to American pharmacy counters — operates primarily through commercial supply chains that the BIOSECURE Act does not reach. The law addresses the 27% of military drug purchases that the Department of Defense study found depend on PRC suppliers. That’s critical. But it doesn’t restructure the commercial supply chain that delivers the other prescriptions — the ones that civilian hospitals, retail pharmacies, and patients depend on. The 679 APIs for which China is the sole KSM supplier don’t become less concentrated because federal agencies stop buying from designated entities. This isn’t a criticism of the law. It’s a diagnosis of its boundary conditions. The BIOSECURE Act is the first structural levee in a flood zone that extends well beyond the federal procurement riverbank. And understanding what it covers — and what it doesn’t — is essential for anyone trying to build the next section. The Medicaid Drug Rebate Program safe harbor is a telling detail. The law had to include a specific provision ensuring that drug manufacturers wouldn’t be penalized in the Medicaid system when the national security prohibitions prevent them from executing a VA master agreement. The fact that this carve-out was necessary reveals how deeply entangled the pharmaceutical procurement system is — pull one thread and you risk unraveling programs that millions of patients depend on. The legislators knew this. They built the safe harbor because they understood that the system’s complexity is itself a constraint on the pace of decoupling. What the BIOSECURE Act Means for the Spiral In the biomanufacturing reindustrialization thesis, I described five tactical domains where the spiral needs to enter simultaneously: facility strategy, procurement signals, capital structure de-risking, science infrastructure, and downstream bottleneck identification. The BIOSECURE Act is a procurement signal — the clearest one the federal government has sent to the biotech supply chain. But a procurement signal without domestic capacity to receive it is a demand curve with no supply response. If federal agencies cannot buy from designated foreign providers, they need qualified domestic or allied alternatives. If those alternatives don’t exist yet — if the facilities haven’t been built, the workforce hasn’t been trained, the learning curves haven’t been descended — then the procurement signal creates disruption without creating restructuring. This is why the BIOSECURE Act cannot be understood in isolation. It is one instrument in a system that requires simultaneous activation. The law creates the pull. But the push — the capital, the facilities, the continuous manufacturing capacity, the workforce, the allied sourcing partnerships — has to come from the other four domains. The BIOSECURE Act is necessary and insufficient. Exactly as designed. Exactly as it should be — because a single law that tried to do all five things would be a law that did none of them well. The implementation window is the strategic variable. The years between now and full enforcement are not dead time. They are the window in which domestic and allied biotech manufacturing capacity must be built to receive the demand that the BIOSECURE Act will redirect. If that capacity exists when the prohibitions bite, the law works as intended — a structural intervention that reshapes procurement flows toward secure, resilient sources. If that capacity doesn’t exist, the law creates compliance burdens, waiver requests, and workarounds that preserve the dependencies it was designed to eliminate. The race is not between the law and the companies it designates. The race is between the law’s implementation timeline and the domestic manufacturing base’s construction timeline. That’s the tempo that matters. I keep returning to the cybersecurity analogy because I think it carries one more lesson The shift from signature-based to behavioral detection didn’t happen all at once. It happened in layers. First generation: known signatures. Second generation: heuristic analysis. Third generation: machine learning on behavioral patterns. Each generation was necessary and insufficient. Each generation built the institutional muscle for the next. The BIOSECURE Act is a first-generation adaptive governance instrument for biotech supply chain security. It builds the machinery — the designation pathways, the interagency coordination, the procedural protections, the FAR integration. It doesn’t solve the problem. It builds the institutional capacity to address the problem over time. And it will need to evolve. The criteria will need refinement. The OMB process will need to get faster. The scope may need to extend beyond federal procurement. The allied coordination dimension — the friend-shoring architecture — will need its own instruments. But the machinery exists now. The designation pathways are built. The interagency process is specified. The temporal architecture — fast track for known military-linked entities, deliberate process for complex cases — reflects genuine governance design thinking. The debate about five companies is over. The debate about whether the machinery works — whether the implementation timeline aligns with the construction timeline, whether the procurement signal generates a supply response, whether the levee extends far enough to matter — is just beginning. At the frontier of technology, the experiment is not whether we can identify the threat. It is whether we can build the institutions that adapt as fast as the threats they govern — and whether we can build the industrial base to absorb the demand those institutions create, before the window closes. — Titus Get full access to The Connected Ideas Project at www.connectedideasproject.com/subscribe [https://www.connectedideasproject.com/subscribe?utm_medium=podcast&utm_campaign=CTA_4]

Ep 63 - The Tempo Thesis

I sat in a conference session recently and watched something happen that I’ve seen before but never quite named. Speaker after speaker — technologists, policy people, operators — kept circling the same idea without landing on it. One talked about detection speed for biological threats. Another about the lag between an AI capability and the regulation that addresses it. A third about why manufacturing learning curves are races, not exercises. The language was different each time. The domain was different. The variable was the same. Time Not as metaphor. Not as urgency rhetoric — the familiar “we need to move faster” that appears in every keynote and persuades no one. Time as something more fundamental. As the binding constraint that determines whether every other capability — technical, institutional, industrial — actually functions or just exists on paper. It struck me that for all the frameworks we’ve been building in this space — responsible innovation, governance architecture, reindustrialization strategy — we’ve been designing for capability, authority, and proportionality. We have not been designing for time. The podcast audio was AI-generated using Google’s NotebookLM [https://notebooklm.google/]. Speed Is Not the Variable There’s a distinction worth drawing carefully, because I think conflating two ideas has made this problem invisible. Speed is a metric. You can measure it, optimize it, benchmark it. Organizations talk about speed constantly. Move fast. Accelerate. Reduce cycle time. Speed is the thing you improve within a system that already works. Time is the medium in which all your systems must compose. It is not how fast you go — it is whether the systems that must coordinate with each other are operating on compatible timescales. A biosecurity detection system that identifies a threat in twelve hours is useless if the interpretation infrastructure takes twelve weeks and the policy execution mechanism takes twelve months. Each component might be excellent on its own terms. The failure is temporal — they don’t compose. Engineers have a name for this. Temporal coupling: when two systems that must coordinate operate on fundamentally different timescales, the system breaks. Not because any individual component failed, but because time itself became the fault line. I want to trace this mechanism across several domains, because I think it explains more about why our current systems are failing than any capability deficit does. Governance as Temporal Architecture I wrote about governance latency in these pages earlier this year — the gap between when a system behaves in a new way and when governance responds. I described three components: detection latency, interpretation latency, execution latency. I still believe in that framework. But I’ve started to think I was being too polite about what it actually describes. Governance latency is not a bug in governance. It is a temporal architecture — one that was designed, intentionally or not, for a world that moved at a different pace. Congressional hearing calendars. Notice-and-comment rulemaking periods. Interagency coordination cycles. These are not merely slow. They operate on a fundamentally different timescale than the technologies they govern. The gap between those timescales is not inconvenient. It is, itself, a space where outcomes are determined before the formal process even begins. The nation or institution that understands this — that treats temporal alignment as a design variable rather than an operational annoyance — gains an advantage that no amount of capability can offset. Because capability without temporal coordination is potential energy that never converts to kinetic. It sits in reserve, impressive and inert, while the clock runs. The Circle Is a Clock Consider biomanufacturing — a domain I’ve been writing about in this series. The circles-and-spirals thesis is, at its core, a temporal argument. The circle traps organizations in a time loop: no production experience means no yield data means no capital means no facilities means no production experience. The loop is self-reinforcing because each node operates on a timescale that prevents the next node from activating. Capital allocation cycles are quarterly. Facility construction takes years. Workforce development takes a generation. Yield improvement requires thousands of production hours that nobody can access because the facilities don’t exist. The spiral breaks the circle not by eliminating time, but by synchronizing it. Government demand signals compress the capital decision. Pre-built infrastructure compresses the facility timeline. Science investment steepens the yield curve so fewer production hours are needed to reach viability. The spiral is not faster in any simple sense — it is temporally coherent. Every node operates on a timescale compatible with the others. Wright’s Law, the principle that costs decline predictably with cumulative production, is a temporal claim wearing an economic costume. It says: the first mover in production will be the lowest-cost producer, and the gap will compound with time. China is further down the biomanufacturing learning curve than the United States. Every year that gap persists is not a static disadvantage. It is a temporal one — the curve steepens for whoever is on it and flattens for whoever is not. The Doubling Time of Consequence Biosecurity is perhaps the most visceral expression of this thesis. A biological threat does not wait for interpretation. It replicates on its own timescale — exponential, indifferent to institutional calendars. The difference between containment and catastrophe is not capability. We have the sequencing technology, the surveillance infrastructure, the countermeasure platforms. The difference is temporal coordination. Can you detect, interpret, decide, and act within the doubling time of the threat? I think about this in my work at Vigilance. The entire architecture of biological threat preparedness is, when you strip away the organizational charts and capability matrices, an exercise in temporal engineering. You are building systems whose purpose is to compress the gap between event and response to something smaller than the gap between event and consequence. That’s the design requirement. Everything else is decoration. From Dimension to Domain Here is where I want to push further than the conference session went, further than most strategy frameworks go. We tend to treat time as a dimension — the passive background against which things happen. Decisions take time. Manufacturing takes time. Governance takes time. Time is the water everything swims in. But the more accurate framing — the one that explains why temporally misaligned systems keep failing in predictable ways — is that time is a domain. A space in which advantage can be built, contested, and lost. A domain that requires its own strategy, its own architecture, its own design principles. If you accept that reframing, certain things follow. Temporal advantage is designable. You can build organizations, governance structures, and industrial systems that are optimized for temporal coherence — where the decision cycle, the implementation timeline, and the environment’s rate of change are deliberately aligned. Temporal disadvantage is structural, not accidental. When a governance system operates on a decadal timescale while the technologies it governs evolve on a monthly one, that is not a speed problem to be solved with urgency. It is an architectural mismatch that requires redesign. Temporal literacy becomes a core competency. The ability to read a system and identify where temporal misalignment is the binding constraint — rather than capability, authority, or resources — becomes as important as technical expertise or policy knowledge. What Temporal Design Looks Like This is where the argument becomes operational, and where I think builders, policymakers, and capital allocators need to pay close attention. If time is a domain, then every strategy has a temporal architecture — whether or not the strategist designed one. The question is not whether your organization operates within time. The question is whether you’ve deliberately engineered how your organization relates to time. For builders in frontier technology: the competitive advantage is not always the best technology. It is often the technology that reaches operational deployment first and begins descending the learning curve while competitors are still optimizing in the lab. This is Wright’s Law generalized. The first mover in production compounds an advantage that the better-but-later entrant may never overcome. Time on the curve is the asset. Everything else is a bet that time will be forgiving. It usually isn’t. For policymakers: governance latency is not a staffing problem or a willpower problem. It is a temporal design problem. The question is not “how do we make government faster” — it is “how do we build governance architectures whose operating timescale matches the domain they govern?” In some cases, that means pre-authorization frameworks that act before the crisis arrives. In others, it means modular governance that can be updated without rewriting the entire regulatory structure. In all cases, it means taking temporal architecture as seriously as institutional authority. For capital allocators: patience is a temporal strategy, not a virtue. The patient capital that biomanufacturing requires is not charity — it is an investment in temporal alignment, giving the learning curve enough time to generate the yields that make the economics work. The impatient capital that demands returns on quarterly timescales is not merely unhelpful. It is temporally incompatible with the problem it claims to be solving. I keep returning to that conference session. The speakers kept naming symptoms — speed, latency, urgency, readiness — without naming the condition. The condition is that time is the domain we have not yet learned to design for. We design capability. We design authority. We design architecture. We rarely ask the question that precedes all of them: does this system’s temporal structure match the temporal structure of the problem it exists to solve? Biological threats replicate on exponential timescales. AI capabilities advance on compressed developmental ones. Governance responds on bureaucratic ones. Manufacturing compounds on production-volume ones. None of these timescales are wrong in isolation. All of them are wrong together — because nobody designed the temporal coherence between them. At the frontier of technology, the experiment is not whether we can build fast enough. It is whether we can think in time — designing systems where the pace of understanding, the pace of building, and the pace of governing are, for once, composed into the same score. — Titus Get full access to The Connected Ideas Project at www.connectedideasproject.com/subscribe [https://www.connectedideasproject.com/subscribe?utm_medium=podcast&utm_campaign=CTA_4]

Ep 62 - The Generic Drug Trap

I’ve been thinking about circles. Not the conceptual kind — though we’ll get there. The physical kind. The ones visible on a map if you trace the journey of a single generic antibiotic from raw chemical to American pharmacy shelf. Start in a chemical plant in Zhejiang province. Ship key starting materials to a synthesis facility in Gujarat. Convert them to an active pharmaceutical ingredient. Ship the API to a formulation plant — maybe still in India, maybe in a bonded zone elsewhere. Tablet, coat, blister-pack, box. Ship the finished dose to a U.S. distribution center. Dispense. That circle touches three countries, two oceans, and zero domestic manufacturing steps for the active ingredient. For hundreds of medicines Americans take every day, this is not the exception. It is the architecture. A new assessment from Business Executives for National Security — BENS, the nonpartisan organization that has partnered with senior national security officials since 1982 — maps this architecture with a specificity that should unsettle anyone who has been following the biomanufacturing reindustrialization conversation. Dave Gryska’s team at BENS just released Generic Drug Manufacturing and Biotechnology Innovation [https://bens.org/americas-national-security-vulnerability-generic-drug-manufacturing-and-biotechnology-innovation/] — and it does something I’ve been wanting someone outside government to do for years: it treats the generic pharmaceutical supply chain not as a trade policy problem or a healthcare access problem, but as a national security vulnerability with the same structural characteristics as the semiconductor dependency that launched CHIPS. I’ve written about the biomanufacturing reindustrialization thesis — the circles and spirals, the learning curves, the factory economics, the builder’s playbook. That piece was about the future: the companies and capital structures that could bring biological manufacturing home. This piece is about the present. Because the BENS report reveals that the system we need to reindustrialize isn’t just underbuilt. It is actively structured against its own repair. The podcast audio was AI-generated using Google’s NotebookLM [https://notebooklm.google/]. The Numbers Behind the Dependency Let me put the scale of the problem in terms that are hard to argue with. Ninety-one percent of drugs prescribed by American physicians are generics. Ninety percent of those generic prescriptions are supplied by India and China. The FDA estimated that approximately 80% of active pharmaceutical ingredients are manufactured overseas. In 2021, 87% of U.S. generic drug manufacturing facilities were located abroad. The United States directly imports roughly 16% of its APIs from China — but India, which supplies the bulk of finished generic doses to American patients, itself imports 80% of its APIs from China. This is the cascading dependency that the BENS report traces in detail, and it is the feature I want to hold up to the light. Because it’s not a supply chain. It’s a cascade. A disruption in Chinese chemical manufacturing doesn’t just affect Chinese exports. It ripples through India’s pharmaceutical sector and arrives at American pharmacy counters as a shortage — of antibiotics, of cardiovascular drugs, of the basic medications that chronic disease patients depend on to stay alive. The data on shortages is the trailing indicator. In 2024, the United States recorded its highest number of drug shortages to date — 323 medications affected. Antibiotics have proven especially vulnerable, experiencing shortages at a rate 42% higher than other generics. These aren’t obscure compounds. They’re the medicines that keep a 68-year-old’s blood pressure managed, that treat a child’s ear infection, that a hospital ICU reaches for when a patient goes septic. And here’s the number that stopped me: the API Innovation Center examined 40 essential drug molecules — the “Vital 40” — and found that India supplies about 63% of these APIs, Europe 22%, China 8%, and the United States just 5%. Five percent. For the most critical generic medicines in the American formulary, domestic production is a rounding error. But the 8% figure for China is misleading, and the BENS report is careful about why. An analysis of key starting materials (KSMs) — the precursor reagents that are synthesized into APIs — reveals that China is the sole supplier of KSMs for 679 different APIs. India is the sole KSM supplier for 402. The United States and European Union combined are sole suppliers for 44. Six hundred and seventy-nine to forty-four. That ratio is the supply chain expressed as a strategic position. And it tells you that the dependency isn’t just about who fills the last step — the tablet press, the blister pack. It’s about who controls the chemistry underneath. China’s dominance isn’t at the visible end of the supply chain. It’s at the foundation. The KSMs are the geology, and everything above them — APIs, formulations, finished doses — is built on ground that someone else owns. Why the Circle Won’t Break Itself In the biomanufacturing reindustrialization thesis, I introduced the idea that biomanufacturing fails in circles and scales in spirals. The circle: no production experience means no competitive yields, which means no capital investment, which means no facilities, which means no production experience. The system is closed. Nothing moves. The generic drug supply chain is this circle in its most advanced failure state. It didn’t get here by accident. It got here by economic logic operating without strategic constraint — and the BENS report traces the history precisely enough that the mechanism becomes clear. The 2000 elimination of tariffs on formulated pharmaceuticals — following the 1995 WTO Pharmaceutical Tariff Elimination Agreement — incentivized importing finished drug products. Domestic manufacturing became progressively less competitive. Facilities closed. The Viatris plant in West Virginia — once employing 1,400 workers producing critical generic medications across antibiotics, cardiovascular, and autoimmune therapeutic areas — is one example among many. America’s last factory producing penicillin closed in 2004. Not because the science was lost. Because the economics were untenable. And here is where the circle logic becomes vicious. Once domestic production stops, the workforce disperses. Once the workforce disperses, restarting production requires not just capital but a labor pool that no longer exists. Once facilities close, the institutional knowledge embedded in those facilities — the process optimizations, the supplier relationships, the regulatory compliance infrastructure — evaporates. You cannot restart what you’ve forgotten how to do. Or rather, you can — but at vastly greater cost and time than maintaining it would have required. This is why I called it a system designed to prevent its own repair. The cost advantage of foreign production compounds over time. Every year of offshoring means another year of learning curve accumulated abroad and lost domestically. China’s 20,000+ chemical companies, accounting for 40% of global chemical industrial output, aren’t just competitors. They’re the installed base. And by some estimates, it costs 50% less to produce APIs in India versus the United States or Europe, with labor costs in India and China estimated at one-tenth the cost for a Western company. The circle doesn’t just trap individual companies. It traps the entire sector. The margins on generic drugs are already razor-thin — that’s the whole point of generics. Domestic production faces stringent regulatory compliance, higher labor costs, higher energy costs, and competition from established foreign producers who have been descending the learning curve for decades. No individual company can break this circle alone. The economics won’t allow it. This is where the BENS report’s framing matters: this is a national security problem requiring a national security response. Not because the market failed in some abstract sense, but because the market optimized for exactly the wrong thing — unit cost — while ignoring systemic fragility. And fragility, in a system that 91% of American prescriptions depend on, is an existential risk wearing the mask of efficiency. Stockpiles Are Sandbags, Not Levees The BENS report takes SAPIR — the Strategic Active Pharmaceutical Ingredients Reserve — seriously, and I want to amplify the point because it cuts against a comfortable assumption in Washington. In August 2025, an executive order directed the Administration for Strategic Preparedness and Response (ASPR) to identify and stockpile APIs for approximately 26 essential medicines. SAPIR is being refilled and expanded. This is a good and necessary step. Stockpiling APIs, which have longer shelf lives than finished drug products, reduces risk at multiple stress points in the supply chain. But the BENS report says what I’ve been arguing in different language: stockpiling alone is insufficient. It is a passive defense mechanism. It buys time. It does not solve the structural deficit. Without a functioning industrial base to replenish the stockpile, the reserve is a finite resource that will run dry in a prolonged crisis. Think about what that means operationally. A geopolitical disruption — a Taiwan Strait crisis, an escalation in the South China Sea, a deliberate Chinese export restriction on pharmaceutical precursors — doesn’t resolve in weeks. The scenario that matters isn’t a temporary supply interruption. It’s a sustained one. And a stockpile sized for months faces a crisis measured in years. The analogy I keep returning to: SAPIR is sandbags. Essential during a flood. Useless for preventing the next one. What you need is a levee — domestic production capacity that can sustain the flow regardless of what happens upstream in the geopolitical watershed. And the geopolitical risk is not theoretical. The BENS report documents that China demonstrated its willingness to use raw materials dominance as strategic leverage when Beijing announced new export controls on rare earths and magnets in October 2025. China’s 2020 Export Control Law and 2021 Biosecurity Law provide extensive powers to weaponize pharmaceutical exports. A 2023 Department of Defense study found that 27% of U.S. military drug purchases depend on PRC suppliers. During COVID, India temporarily banned exports of 26 APIs and pharmaceutical formulations. China’s lockdowns shuttered approximately 37 pharmaceutical factories manufacturing active ingredients for U.S. drug products. These aren’t hypotheticals. They’re precedents. Where the Spiral Starts for Generic Drugs So where does the spiral begin? How do you break a circle this entrenched? The BENS report’s recommendations align with the NSCEB architecture I’ve written about — and that alignment itself is significant. When a nonpartisan national security organization staffed by senior business executives arrives at the same structural conclusions as the government commission, the signal is strong. Incentivize domestic manufacturing. Enhance supply chain transparency. Advance manufacturing innovation. Strengthen allied partnerships. Expand strategic reserves. Develop the workforce. But I want to push beyond the recommendation headers — because the generic drug challenge reveals something specific about where the biomanufacturing spiral must enter. The entry point isn’t biologics. Not yet. The entry point is chemistry. The KSM dependency — China as sole supplier for 679 APIs’ starting materials — means that reindustrialization for generic drugs starts with chemical synthesis capacity. This is not the glamorous frontier of synthetic biology and AI-driven biodesign that dominates conference panels. It is industrial chemistry. Reactor vessels and distillation columns. Solvent recovery and waste treatment. The unsexy infrastructure that makes everything else possible. And this is where the advanced manufacturing innovation piece becomes critical — and where I think the conversation needs to get more specific than the recommendation headers usually allow. The BENS report recommends continuous manufacturing technologies. I want to explain why this isn’t incrementalism. It’s the physics that changes everything. Traditional batch chemical manufacturing — the dominant mode for API synthesis globally — runs in discrete cycles. Load reagents, react, purify, test, repeat. Each batch is a separate event. Each event requires operators. Each operator costs ten times more in West Virginia than in Gujarat. That math is why the factories left. Continuous flow chemistry inverts the model. Reagents flow through microreactors or tubular systems without stopping. The reaction happens in transit. Purification is in-line. Quality monitoring is real-time. A continuous process that runs 24 hours produces in a closet-sized reactor what a batch plant produces in a warehouse — with fewer operators, less waste, tighter process control, and yields that improve with runtime because the system is constantly self-correcting. Here’s the Wright’s Law connection that I think the policy community hasn’t fully internalized: continuous manufacturing doesn’t just reduce labor cost. It steepens the learning curve. In batch manufacturing, each batch is semi-independent. In continuous manufacturing, every hour of runtime generates process data that feeds back into optimization. The learning compounds at the rate of hours, not batches. That’s the difference between a flat curve and one that actually descends fast enough to close the cost gap with foreign producers within a commercially relevant timeframe. The capital cost is higher upfront. The regulatory validation pathway is less established — FDA has been encouraging continuous manufacturing, but the approval precedents are still accumulating. These are real barriers. But they are surmountable barriers, which is categorically different from the structural barrier of trying to compete on labor cost in batch production. You can solve an engineering and regulatory problem. You cannot solve the fact that your labor costs ten times what your competitor’s does. The reporting requirements the BENS recommends — mandating that manufacturers disclose the geographic origins of APIs, KSMs, and finished drug products to federal agencies — would create the supply chain map that currently doesn’t exist. And you cannot fix a system you cannot see. The supply chain mapping recommendation — a national database tracking critical nodes, chokepoints, and dependencies — is the diagnostic tool that enables everything else. It transforms supply chain vulnerabilities from unknown risks into managed variables. The allied sourcing piece connects directly to the friend-shoring thesis I’ll develop further in this series. Canada, the EU, Japan — nations with robust regulatory standards and aligned strategic interests — should be preferred sourcing partners for the supply chain segments that cannot be reshored immediately. Not autarky. Strategic diversification. Building redundancy through allies who share democratic values and regulatory standards, so that no single point of failure — geopolitical, climatic, or logistical — can cascade through to American patients. What the Generic Drug Supply Chain Teaches Biomanufacturing Builders Here’s where I want to connect back to the thesis, because the BENS report illuminates something about biomanufacturing reindustrialization that the startup-focused conversation misses. The generic drug supply chain is the finished product of the circle. It’s what the biomanufacturing ecosystem becomes if the spiral doesn’t start in time. Two decades of rational economic decisions — each one defensible in isolation — producing a system so fragile that a factory fire in Gujarat or an export restriction from Beijing can leave American hospitals rationing antibiotics. The lesson for biomanufacturing builders is temporal. The window I wrote about in the first installment of this series isn’t just about the learning curve for biologics and engineered organisms. It’s about preventing the generic drug story from repeating in the next generation of medicines. Biosimilars. Cell and gene therapies. mRNA therapeutics. The production platforms of the next two decades are being established right now. Where they’re built, who operates them, and whose supply chains they depend on — those decisions, made in the next five years, will determine whether the next generation of medicines is manufactured in a system that looks like the generic drug cascade or one that looks like a resilient, diversified, domestically anchored spiral. The BENS report puts data behind what builders already sense: the cost of dependency is not measured in the price of a pill. It is measured in the 323 shortages. In the hospitals that cannot access basic antibiotics. In the military pharmacies that depend on adversarial supply chains for 27% of their purchases. In the cascading fragility that turns a distant factory closure into an American patient’s missed dose. That’s the cost of the circle. The engineer I described in the first installment of this series — the one troubleshooting the fermentation run at 5,000 liters — represents the future of biomanufacturing. Skilled, operational, working at the frontier of what domestic production can do. But there’s another figure in this story who deserves attention. The pharmacist checking inventory and discovering that three generic antibiotics are backordered with no estimated resupply date. The hospital procurement officer calling a second distributor, then a third, then issuing an internal alert about rationing protocols. The patient whose chronic medication — the one that’s kept their condition managed for eight years — is suddenly unavailable. They don’t stand in a facility that smells like warm yeast. They stand at the endpoint of a supply chain that routes through Zhejiang and Gujarat and arrives — or doesn’t — at an American counter. The reindustrialization thesis isn’t just about building the future. It’s about repairing the present. And the present is 679 to 44, 323 shortages, and a system where the most powerful nation on Earth cannot reliably produce the basic medicines its citizens take every morning. The spiral has to start somewhere. The BENS report tells us exactly where. At the frontier of technology, the experiment is not whether we can engineer the next generation of medicines. It’s whether we can manufacture the current one — here, reliably, without routing our health security through nations that have already demonstrated their willingness to use it as leverage. The circle is patient. It will wait for us to notice. — Titus Get full access to The Connected Ideas Project at www.connectedideasproject.com/subscribe [https://www.connectedideasproject.com/subscribe?utm_medium=podcast&utm_campaign=CTA_4]

Ep 61 - The Biomanufacturing Reindustrialization Thesis

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 [https://www.biotech.senate.gov/], producing the most comprehensive governmental blueprint for biomanufacturing reindustrialization the United States has published. The NSCEB report [https://www.biotech.senate.gov/final-report/chapters/] 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 [https://notebooklm.google/]. 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 [https://x.com/aphysicist/status/2028308559088214208?s=46&t=ICGG18a1AIgR6rD3YKOfgA]” 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 [https://a16z.com/a-primer-on-factory-economics-for-startups/] 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 [https://www.eda.gov/sites/default/files/2024-07/APM_Tech_Hub_Overarching_Narrative.pdf] 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 [https://www.aging.senate.gov/imo/media/doc/senate_aging_american_drugs_report.pdf]. 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 [https://www.ark-invest.com/wrights-law]. 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. Get full access to The Connected Ideas Project at www.connectedideasproject.com/subscribe [https://www.connectedideasproject.com/subscribe?utm_medium=podcast&utm_campaign=CTA_4]

Ep 60 - The Org Chart Dies Last

This is a special edition of The Connected Ideas Project, because while it’s Episode 60 of the podcast, it’s the 100th edition of this newsletter since launch! Thank you for being part of this community. If you’re finding value, please share with your friends and colleagues! Every few years, a major technology company publishes a report that tells you more than it intends to. Microsoft’s 2025 Work Trend Index — “The Year the Frontier Firm Is Born [https://www.microsoft.com/en-us/worklab/work-trend-index/2025-the-year-the-frontier-firm-is-born]” — is one of those reports. On the surface, it’s a well-produced argument for why every company needs to reorganize around AI agents. Survey 31,000 workers across 31 countries, add LinkedIn labor data and Microsoft 365 telemetry, wrap it in a compelling narrative about hybrid human-agent teams, and you’ve got a document that every Fortune 500 CEO will have on their desk by Friday. But read it a second time. Read it the way you’d read a national security assessment — not for the headlines, but for the assumptions underneath. And something more interesting emerges. Microsoft isn’t describing a productivity tool. They’re describing a new theory of the firm. And they’re describing it almost entirely in the language of efficiency, without ever seriously grappling with the governance architecture such a firm would require. That gap is where the real story lives. Every time we build autonomous capability faster than we build accountability, the system doesn’t fail immediately. It fails later. And it sends the bill. The podcast audio was AI-generated using Google’s NotebookLM [https://notebooklm.google/]. What the Report Actually Says Give Microsoft credit: the diagnosis is sharp. Business demands are outpacing human capacity. Eighty percent of the global workforce says they lack enough time or energy to do their work. The knowledge worker, as currently configured, is maxed out. Microsoft’s answer: intelligence on tap. AI agents that can reason, plan, and execute tasks autonomously — not chatbots, but digital colleagues joining teams with increasing independence. The report envisions three phases, from AI as assistant to AI as operator of entire business processes, and argues that companies embracing this trajectory are already pulling ahead. The numbers tell the story. Eighty-two percent of leaders expect to deploy agents to expand workforce capacity in the next eighteen months. Forty-six percent are already using them to automate entire workflows. A third are considering headcount reductions. And here’s the number that stopped me: 78 percent are considering hiring for AI-specific roles that didn’t exist a year ago. This is not incremental. Microsoft even coins a term — the “Work Chart” — to replace the org chart: a dynamic, outcome-driven model where teams form around goals, not functions, powered by agents that expand what each person can do. The Movie Production Model — and Its Missing Script One of the report’s most revealing analogies compares the Frontier Firm to movie production. Teams assemble for a project, agents fill specialized roles, the work gets done, and everyone disbands. It’s a compelling image. Lean, high-impact, fluid. I’ve been thinking about that analogy. Because it captures something real about where organizational design is heading. But it also reveals what the report doesn’t address. Movie productions work because of something the report never mentions: an extraordinarily mature governance infrastructure. There are unions, guilds, contracts, liability frameworks, insurance requirements, safety protocols, credential verification systems, and chain-of-command structures that have been refined over a century. The fluidity of production is enabled by the rigidity of the rules governing it. What is the equivalent for human-agent teams? When Microsoft describes a world where every employee becomes an “agent boss” — someone who builds, delegates to, and manages AI agents — they’re describing a massive delegation of judgment. And delegation of judgment, in any complex system, is a governance problem before it’s a productivity solution. I keep thinking about this because it mirrors a challenge I’ve watched play out in a different domain entirely. What Defense Planners Already Know I remember a briefing at the Pentagon — one of many, but this one stuck. A program manager was presenting an autonomy roadmap for a logistics system. Slides were clean. The capability curve was steep. Savings projections were compelling. And then someone from the policy shop asked a single question: “Who signs for the decision when the system gets it wrong?” The room went quiet. Not because the question was unexpected. Because everyone knew the answer wasn’t in the slides. That moment comes back to me reading Microsoft’s report. Because the concept they’re selling as a business revolution — human-machine teaming with autonomous systems — is something the Department of Defense has been grappling with for over a decade. Different vocabulary, same structural problem: How do you maintain meaningful human oversight when the systems you’re working with can operate faster, and increasingly more capably, than the humans directing them? The defense community learned several things the hard way. First, that the “human in the loop” is not a design feature — it’s a design requirement that must be engineered deliberately, or it erodes. Systems that are faster and more capable than their operators create irresistible pressure to defer. The human becomes a rubber stamp. In military contexts, this is called automation bias. In Microsoft’s Frontier Firm, it has no name yet. But the dynamic is identical. Second, that trust calibration matters as much as capability. The report’s own data hints at this: 52 percent of workers see AI as a command-based tool, while 46 percent see it as a thought partner. That split isn’t a preference — it’s a reflection of how well people understand what they’re delegating. Miscalibrated trust — too much or too little — is how autonomous systems fail in operational environments. The military has spent billions learning this lesson. The report proposes that every employee learn it on the job. Third, and most importantly: the governance architecture has to be designed before the capability scales, not after. The DoD doesn’t deploy autonomous systems and then figure out the rules of engagement. The rules come first. They’re imperfect, they evolve, but they exist before the system is operational. Microsoft’s report proposes deploying autonomous agents across entire business functions and then building the governance afterward. They call this journey “Phase 1 to Phase 3.” A defense planner would call it an operational risk. The Responsible Innovation Gap Here’s what fascinates me about the Frontier Firm concept. It’s a genuinely interesting framework for thinking about organizational transformation. The capacity gap is real. The potential for AI agents to expand what small teams can accomplish is real. I personally see this every day with the engineering teams I run. The shift from functional org charts to outcome-driven work charts is a prediction I think will prove directionally correct. But the report treats governance as an afterthought — a problem to be solved after the productivity gains are captured. And this is a pattern I’ve seen before. One of the themes we’ve explored in the Science of Responsible Innovation is that the time to design governance into a system is during the architecture phase — not during deployment, and certainly not after failure. Violet Teaming [https://arxiv.org/abs/2308.14253] exists precisely because the traditional approach — build it, ship it, regulate it — doesn’t work when the systems in question are capable of autonomous action. The Frontier Firm, as Microsoft describes it, would have AI agents running supply chains, managing customer relationships, executing financial analysis, and operating business processes end-to-end. Each of these involves decisions with consequences for real people — employees, customers, communities. The report mentions a “human-agent ratio” as a new business metric. But a ratio tells you headcount, not accountability. Who is responsible when an agent makes a consequential error in a process it was running autonomously? The agent boss? The agent’s developer? The company that deployed it? The platform provider? These are not hypothetical concerns. They’re the same questions that biosecurity experts ask about autonomous laboratory systems, that defense ethicists ask about lethal autonomous weapons, and that financial regulators ask about algorithmic trading. The pattern is consistent: autonomous systems that operate faster than human oversight can track them create accountability vacuums. There’s a concept the defense community uses that Microsoft’s Frontier Firm badly needs: rules of engagement. Before any autonomous system operates, there are explicit boundaries — what it can do, what requires human authorization, who owns the consequence of each class of action. Call it an Accountability Ledger for the Frontier Firm: a document, maintained alongside the Work Chart, that maps every agentic process to a human owner who answers for its outputs. Not the person who prompted the agent. The person who is responsible when the agent’s decision costs someone their job, their loan, their medical claim. The Work Chart tells you who does what. The Accountability Ledger tells you who answers for what. You need both, or you have neither. If you can’t name the human who owns the downside, you don’t have automation. You have abdication. Microsoft mentions Daniel Susskind’s hypothesis that human work will persist because of three limits: efficiency, human preference, and moral judgment [https://knightcolumbia.org/content/what-will-remain-for-people-to-do]. That’s a reasonable framework. But notice the order. Efficiency is the domain AI masters first. Human preference erodes as people habituate. Moral judgment is the last holdout — and it’s the one the report spends the least time on. The Real Frontier I don’t think Microsoft is wrong about where organizations are headed. The data is too consistent, the economic pressure too strong, the capability curve too steep. Some version of the Frontier Firm is coming. The question is whether it arrives as a thoughtfully governed institution or as a productivity-optimized system that discovers its governance gaps through failure. The report notes that 33 percent of leaders are considering headcount reductions. It notes that 81 percent of employees haven’t changed jobs in the past year and that the labor market is functionally frozen. It notes that AI literacy is now the most in-demand skill on LinkedIn, alongside conflict mitigation, adaptability, and innovative thinking. Read those data points together. They describe a workforce being asked to adapt to a fundamental restructuring of their relationship to institutional output — while the labor market offers them no exit, the governance frameworks offer them no protection, and the timeline offers them no breathing room. That’s not a productivity story. That’s a social contract story. And it deserves the same rigor we bring to governing autonomous systems in defense, in biosecurity, in any domain where the speed of the system can outpace the judgment of the humans nominally in control of it. The most startling finding in the entire report might be the smallest: when asked why they turn to AI over a human colleague, the number-one reason employees cited was 24/7 availability. Not quality. Not speed. Not creativity. Availability. They chose the machine because the machine is always there. That’s not convenience. That’s a new dependency. There is a version of the Frontier Firm that works — one designed with governance, accountability, and human agency built in from the start. Where the human-agent ratio reflects not just efficiency but responsibility. Where “agent boss” means not just managing outputs but owning consequences. Where the Work Chart includes not just who does what, but who answers for what when the system does something no one intended. That version requires the kind of cross-domain thinking that doesn’t live in any single corporate report. It requires people who understand autonomous systems governance AND organizational design AND labor economics AND the specific ways that speed and capability create accountability gaps. The org chart is dying. Microsoft is right about that. But the thing that replaces it will be defined not by the companies that move fastest, but by the ones that build the governance architecture to match the capability they’re deploying. The history of autonomous systems — in defense, in finance, in biosecurity — teaches this lesson with uncomfortable consistency: velocity without accountability doesn’t scale. It detonates. We are not building a new kind of company. We are building a new kind of institution. And the institutions that last — the ones that earn trust, that survive their own power — have never been built on efficiency alone. They are built on the willingness to answer for what they do. Intelligence on tap is a capability. Judgment-by-design is a choice. The Frontier Firm will be defined by which one it optimizes for. — Titus Get full access to The Connected Ideas Project at www.connectedideasproject.com/subscribe [https://www.connectedideasproject.com/subscribe?utm_medium=podcast&utm_campaign=CTA_4]