Cultivating the Bioeconomy, Part 2
Geopolitics, Biology & Commodities
Why. Why now. Why it matters. And where it gets built.
In the SynBioBeta2026 wrap from last week I intentionally left a follow-up item. Of all the sessions, and there were many worth writing about, the one that I keep coming back to was held on the last afternoon in a room in the back corner of the venue hall: the Geopolitics and Biology session. I noted it in passing with ‘more on that later. This is later.
Here is the position. The bioeconomy is no longer a thesis about which moonshot venture wins, or which platform will be the Illumina of the 2030s, or whether cultivated meat is finally going to clear retail price parity. It has matured into something more consequential, more fundamental and more durable: biology is fast becoming a manufacturing substrate. Programmable, distributed, self-replicating, and increasingly cost-competitive – running on water, sugar, basic nutrients, and information. The technical building blocks are largely in place, and now the commercial scaffolding is filling in. Adrian Woolfson perhaps said it best in his SynBioBeta talk, noting ‘Biology will be the new Steel.’ What is perhaps most interesting and timely, at least to me, is that the geopolitical environment has in the last ninety days made the case for it more forcefully than any conference panel ever could.
So I will pose four questions here, in order. Why is this a real industrial transition rather than another decade of promise? Why now, in May 2026, is the convergence finally credible? Why does it matter, not in the abstract sustainability narrative we’ve been rehearsing for fifteen years, but as a hard commercial and national-security argument that can be defended in front of an investment committee or a Senate hearing? And finally, where does it actually get built, because a distributed manufacturing substrate is only as good as the spatial intelligence that tells you where to deploy it?
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I. Why
The fundamental case for the bioeconomy is not a moral one, nor is it, despite the framing biotech has often used on itself, primarily an environmental one. It is a manufacturing efficiency case. Biology is the most thermodynamically efficient industrial process that exists on the planet, honed through billions of years of evolution. Let’s take an obvious case: fertilizer. Cells fix nitrogen at ambient temperature and pressure using enzymes that have been refined by four billion years of evolutionary debugging. The Haber-Bosch process, the civilization changing and dominant industrial alternative, runs at 400-500°C, pressure up to 300 atmospheres, and consumes about 1-2 percent of global energy supply to perform roughly the same chemistry. The fact that Haber-Bosch ‘won’ the twentieth century is a story about the cost curves of fossil hydrocarbons, the geography of stranded gas assets, and the scaling logic of mid-century chemical engineering. It is not a story about which approach is better; it is about what was economically manufacturable at industrial scale given the inputs available to a planet with a voracious appetite.
Haber-Bosch served the world well, but it is now time for an industrial reboot. In Act 2, the raw material input set has now changed. We can read, write, and edit DNA at costs that have fallen faster than Moore’s Law for two decades running. More recently, we can train large models on biological sequence data and use them as reasoning agents that propose constructs, plan experiments, and interpret multi-omics output far faster than human-only workflows. We can run continuous fermentation at COGS levels that approach petrochemical parity for an expanding list of molecules. And, as one of the through-lines of SynBioBeta, we can finally close the Learn loop in the Design-Build-Test-Learn cycle that underpins almost every commercial bio program. For the better part of a decade (again in my opinion), the field over-emphasized the first three letters and treated the L as if it were an afterthought. The 2026 vintage of the bioeconomy treats Learn as the enabling technology. As a result, we find that the rate of compounding from that shift is what makes a fifteenth-anniversary ‘the bioeconomy is finally here’ essay feel different from the previous fourteen. Again, another point underscored by Woolfson.
The ‘why’ is also not a single application. It is the same general-purpose insight that made silicon transformative. Once you have programmable substrate plus cheap design plus even cheaper execution, the strategy shifts from asking what the substrate is for and start asking which industries it can be applied to. Today the answer covers an unusually wide footprint: pharmaceutical manufacturing, agricultural inputs (microbial fertilizers, biological crop protection), specialty chemicals, materials, food and ingredients, fuels, and an emerging frontier in environmental remediation. As we move down the slope from invention to coordination, the technology problems are starting to be solved at a reasonable cost. The harder problem now is connecting fragmented demand to capital-intensive infrastructure on terms that allow the next molecule to follow.
This is what I feel makes the bioeconomy structurally different from previous technology waves at this stage of maturity. It is not waiting on a breakthrough. It is kind of circling around, waiting on the boring middle layer: offtake agreements, brownfield retrofits, regulatory pathways, blended capital structures that sit between venture and infrastructure. That is a much more solvable problem, and it is a problem that benefits from precisely the kind of incumbent-startup ecosystem that I noted forming in San Jose last week.
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II. Why now
Three signals tell me 2026 may be an inflection point for the bioeconomy. And none of them is derived from a single technology breakthrough. All three are about the infrastructure around the technology starting to behave like it does in other mature industries.
First, the AI-and-biology flywheel has begun to compound. This was at least one of the dominant intellectual currents of the conference. Foundation models trained on biological sequences and the published scientific literature are now embedded in the discovery and design workflows of every serious player in the room. There is still a massive need for foundation data to feed the models, but the modeling infrastructure has grown exponentially in both established pharma companies and also with nascent startups. The solo (or near solo) founder in technology is a real thing; enabled largely by the adoption of foundation models. To be clear, these models do not replace the investigator’s logic. Instead, they act as reasoning agents that compress months of design iteration into days. Crucially, the synthetic data problem, biology’s chronic shortage of large, clean, labeled datasets, is now being addressed by the same generative-and-simulator stack that powers the rest of AI. Virtual cell models, physics-based simulators, and high-throughput in-silico perturbation experiments are producing the labeled training data that biological foundation models need to generalize. This allows the AI-and-biology workflow to be turned into a self-reinforcing flywheel: models propose, simulators generate, the next model trains on richer ground truth; in the process the cost of running an experiment in silico keeps falling. The strategic implication, which I noted in the wrap-up post, is that synthetic data lowers the capital barrier that has historically kept synthetic biology a wet-lab-rich incumbent’s game. That barrier was the most durable structural protection the established players had. It is now eroding.
Second, the customer signal is finally credible, at scale. I always find that one of the most useful diagnostics at any conference is to watch where the senior corporate R&D and BD leaders go and whom they actually talk to. The 2026 SynBioBeta floor was unusual in this regard. Pharma was there because programmable RNA medicines, AI-designed proteins, and virtual cell models are now in the late innings of moving from R&D curiosity to platform investment. CPG was there because P&G, Mars, and Unilever are running real product programs on bio-derived ingredients and need to understand which precision-fermentation suppliers will be commercially viable in three years. Big-Ag names like Bayer and Corteva were there because their biological-input pipelines depend on the next generation of microbial discovery and gene-editing IP. ADM was present with a Director of Market Development for Precision Fermentation: this title likely would not have existed five years ago at a 100-year-old agribusiness. The customer demand for bio-derived molecules is not aspirational anymore; it’s real signal.
Third, the public sector has shown up with a manufacturing-and-security frame, not just an R&D frame. I believe that the presence of DARPA and ARPA-H in the program is a signal in itself. Michael Koeris’s session (the DARPA Biological Technologies Office director, speaking explicitly to the topic of Protecting National Security with Biology) is perhaps even a stronger one. ARPA-H’s resilient-systems program qualifies as another. Despite ongoing confusion around what science is and how it can be used for the benefit of society at the highest levels of the US Federal Government, the federal posture, at least as seen through the groups actually responsible for deploying the science toward the bioeconomy, in 2026 is no longer a science-funding posture. It is looking more like an industrial-policy posture, and one that has finally absorbed the lesson that semiconductors taught us: if you do not own the manufacturing substrate of a strategic technology, you do not own the technology. There are signs that biological manufacturing is now at least starting to be framed in those terms. It follows that this framing becomes the foundational prerequisite for loan guarantees, first-of-a-kind facility support, SBIR support and public-private partnership structures that will close the financing gap between technology readiness and commercial-scale build-out. It is also what unlocked the late-2025 federal-procurement signals around US-domestic biological inputs that the conference floor was starting to price in.
There is a fourth signal worth naming, which is the ecosystem mix I commented on in the wrap-up: the simultaneous presence of established bio/pharma/agtech and the startup community, with information moving in both directions. Mature industries tend to look like that. Immature industries look like a startup hacker space with a few sponsor logos around the edges; my impression is that the 2026 version looks more mature. That’s worth noting.
The cumulative effect of these three (four) signals is that the structural conditions for the cultivation of the bioeconomy are in place. The seeds were planted years ago. The infrastructure to grow them at commercial scale is now being built. And, a point I will return to in Section IV, I speculate that the spatial-and-Earth-observation data infrastructure that supplies continuous, planetary-scale signals on feedstock sourcing, climate-and geopolitically driven supply-chain risk is now mature enough to underwrite siting decisions for that build-out. That, too, is part of the 2026 inflection.
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III. Why it matters: commercially and geopolitically
The commercial case I have made elsewhere and will make in more detail in forthcoming Moby Market Intelligence releases. In summary: a small number of franchise-quality public companies, including bioprocessing tool suppliers, precision-fermentation operators, microbial-input platforms, climate-data infrastructure providers, and the agricultural-input incumbents that are now retooling around biological alternatives, sit on the right side of a multi-decade compounding curve (to borrow a phrase from my research and writing partner at Moby, Thornton McEnery). I am talking about companies that are profitable today, who generate ROIC above their cost of capital, and have customer bases that span the entire bio-industrial transition rather than concentrating on a single application. This is the long-duration capital appreciation thesis we have spent considerable time developing at Moby. It is durable and defensible. It is also, importantly, not the whole argument.
The geopolitical case is where this argument gets sharper, and it is also where, I believe, the past ninety days have done more for the bioeconomy thesis than any conference panel could.
On 28 February 2026, the United States and Israel struck Iran. The Strait of Hormuz [MF1] has been effectively closed since. As I write this in the second week of May, tran[MF2] sits have collapsed from roughly 135 vessels per day to single digits. About one-third of the world’s seaborne fertilizer trade transits that strait. The Gulf region produces close to half of globally traded urea, around 30 percent of ammonia, and roughly half of globally traded sulfur. Urea prices are up more than 50 percent since the conflict began. Sulfur, the upstream input for sulfuric acid and therefore for phosphate fertilizer, has tightened to the point that China - itself a major sulfur consumer - banned exports in March, with downstream effects on copper extraction in Chile that nobody on the geopolitical desk had thought to model. Helium, a byproduct of Qatari LNG, is in rationed supply, with downstream effects on MRI manufacturing and semiconductor lithography that nobody had thought to model either. This is the canonical pattern of a chokepoint event. The first-order effect is in the headline commodity. The second-order effects ripple through industries that the headline commodity feeds.
The fertilizer leg of this is the part that connects most directly to the bioeconomy thesis, and it deserves to be stated plainly. The global synthetic-nitrogen system is built on a small number of geographically concentrated production assets fed by stranded natural gas, with output moved through a small number of maritime chokepoints to demand centers that depend on it for the spring planting season. Brazil, the country growing the soybeans that feed China’s pigs, is heavily dependent on Middle Eastern urea. Sub-Saharan Africa is dependent on the same. The United States is a meaningful net producer of ammonia but still imports roughly a quarter of its phosphate from the Gulf, and from Saudi Arabia in particular. India ran a massive urea tender in March that has materially repriced the global market. There is no Strategic Fertilizer Reserve to match the Strategic Petroleum Reserve. The Saudi pipeline that bypasses Hormuz on the Red Sea side is for crude oil, not ammonia. The global industrial system was not designed to be resilient to a Hormuz-style event. We are now learning, in real time, what that costs.
Set against that backdrop, the bioeconomy can start to paint an alternative picture that looks materially different. Pivot Bio (represented at SynBioBeta by Bruce Schnicker on my own panel) produces gene-edited microbes that fix atmospheric nitrogen on the root system of corn, sorghum, and spring wheat. The product is manufactured in the United States, via fermentation, requiring water, sugar, and basic nutrients. It currently replaces up to 40 pounds per acre of synthetic nitrogen, around a quarter of the total nitrogen requirement for a corn crop[MF3] . Azotic, Kula Bio, and Switch Bioworks are working on parallel approaches. Bayer and Corteva, both at SynBioBeta, have biological-input pipelines that target similar functionality. These solutions do not depend on the Strait of Hormuz, or on Russian ammonia, or on Moroccan phosphate rock moved through pinch-pointed shipping lanes. The product is made where the customer is. This is what supply-chain resilience can look like when it is engineered into the production process rather than retrofitted on top of it.
The same logic applies to the rest of the bioeconomy footprint. Precision fermentation can produce specialty proteins, fats, sweeteners, fragrances, cosmetics peptides, and an expanding list of specialty chemicals at the location where they are demanded, at scales matched to demand, on capital cycles measured in months rather than years. Cultivated-meat and alternative-protein platforms (even with the well-documented commercial headwinds in plant-based foods) substitute biological manufacturing capacity for animal-feed supply chains that themselves depend on the global fertilizer system. Bio-derived materials substitute for petrochemical-derived materials whose feedstock economics depend on the same Gulf hydrocarbon flows that the Strait of Hormuz crisis has now disrupted. Industrial enzymes, the underappreciated workhorse of the field, displace conventional chemical inputs across detergents, textiles, paper, food processing, biofuels, and much more. Each of these substitutions, in isolation, is a commercial story; however, in aggregate, they transform into what I see as an industrial-sovereignty story.
I do want to be careful here, because this is precisely the kind of argument that gets oversold by people who do not understand the engineering. Biological manufacturing is not magic, nor is it free. Bioreactors can be capital-intensive, scaling them up is never easy, and downstream processing is oftentimes the bottleneck nobody on the conference circuit wants to talk about. And the reality is that large-volume commodity nitrogen, where Haber-Bosch is going to remain the cheapest option for a very long time on a strict $/ton basis if you ignore the geopolitical risk premium, will take time to replace. If this plays out as it could, I am not arguing that biology will displace chemistry. But I do think that biology could become a viable and credible second source for a sufficient share of the molecules that move through geopolitically sensitive chokepoints; in the process serving as a supply hedge. Further, second-sourcing the global nitrogen supply does not require replacing all synthetic-N. What it does is displaces enough N at the margin to change the price-setting calculus at these supply chain chokepoints.
The DARPA framing of the problem [MF4] (ie., Protecting National Security with Biology) may be the cleanest way to think about this. As the Bioeconomy scales, we should not only look at (applied) biology as an applied science or a commercial opportunity. It’s much larger than that. The Bioeconomy and related infrastructure should be viewed as a strategic manufacturing capability whose deployment determines whether the United States and its allies can withstand the next chokepoint event without an inflation-driven food crisis or a manufacturing input shortage that takes industries offline. ARPA-H’s resilient-systems program is the public-health analogue. The Bayer, Corteva, ADM, and Pivot Bio presences are the agricultural-systems analogue. Add it together and you have something that is starting to look like a coherent industrial-policy posture toward biology, organized around the recognition that the petrochemical-anchored manufacturing system the twentieth century built is structurally vulnerable to the kind of geopolitical environment we have now entered.
There is a final point that bears stating here, and it speaks to something I have written about elsewhere; it is the question of what is and is not priced into the current market framework. Forward commodity curves are largely consensus instruments. They price what most participants already believe, and consensus tends to widen with time. In today’s market environment, the Hormuz event has been priced into the spot prices and urea and ammonia forward curves in real time. But what has not been priced in, in my view, is the second-derivative implication: that the cost of geographic-concentration risk in the petrochemical-anchored input system has just been demonstrated, in front of every commodity buyer and policy-maker simultaneously, and that the willingness to pay for distributed, biologically-manufactured alternatives should step up structurally as a consequence. Just as renewable energy sources will eventually win out over fossil fuels on both cost and efficiency, the bioeconomy will at some point do the same. Today we could make the argument that bio wins on resilience first, as the resilience premium is real, durable, and not yet reflected in valuation multiples for the companies that are best positioned to supply it. Tomorrow it will win on more than resilience.
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IV. Where it gets built: the spatial intelligence layer
There is a question I have not yet answered, and it is the question that turns the previous three sections from a thesis into a potential thematic investment strategy. If the bioeconomy is a distributed manufacturing substrate, and if its strategic value is precisely that it can be sited where the customer is, then does geography become more or less important? The likely answer is, it depends on context. If traditional fertilizer-nitrogen-urea-sulfur supply chains become less important as the distributed bioeconomy expands, than traditional geographic origins with respect to where material is mined, extracted and processed could be viewed to be losing power, and in the process, losing value. But a new bioeconomic geography will then emerge. Where do we build the precision-fermentation plants, the bioprocessing facilities, the biomass aggregation hubs, the precision-ag input plants, and the algal carbon-capture installations? Which agricultural regions can support reliable feedstock production through 2040 given the climate volatility that is already baked into the next two decades? Which industrial corridors offer the right combination of water access, grid capacity, transportation logistics, regulatory environment, and resilience to the same chokepoints we are trying to route around in the first place?
This is the new spatial intelligence layer of the bioeconomy. It is perhaps its own multi-billion-dollar industry whose product is now indispensable to anyone deploying capital into physical assets in a climate-volatile decade, and it is where my second hat fits in.
The bioeconomy connection is direct, and I want to be specific about it because it is the part that closes the loop on the resilience argument I made in the previous section. Physical-world investment in a climate-volatile, geopolitically-fragmented decade requires a new analytical substrate; one that integrates physical climate risk, on-the-ground adaptation capacity, financial impact modeling, and location economic and labor signals into decision-grade analytics for investors, corporates, and governments. The same analytical framework that institutional capital is now using for infrastructure, and energy-asset and data center siting decisions is the framework the bioeconomy build-out is going to need. Here are a sample of considerations to get started.
Distributed manufacturing requires distributed siting decisions.
A precision-fermentation operator deciding where to build their next 50,000-L facility cannot rely on the location rules of a 1990s-vintage chemical plant. They need to model how the local water table will perform, the exposure of the regional power grid, which counties have the workforce demographics, regulatory environment, and infrastructure capacity to support a bioprocessing facility 15 to 25 years from now, because the asset life of a fermentation plant is measured in decades, not quarters. The same analytical framework that asset managers rely on to underwrite commercial development in the energy sector will need to be applied to bio-manufacturing capacity decisions.
Feedstock geography is climate geography.
Every precision-fermentation operator needs sugar. Every microbial-input company needs plant carbon. Every renewable-fuel platform needs lipid feedstock. Every cultivated-meat producer needs amino-acid inputs. The agricultural systems that supply these feedstocks are themselves under climate-volatility pressure that the historical yield models do not capture. Where will reliable corn-derived dextrose come from in 2035? Which regions will support the biomass density to underwrite a brownfield retrofit of a chemical plant into a precision-fermentation facility? Which jurisdictions are pursuing climate-adaptation policy aggressively enough that their agricultural productivity is likely to compound rather than degrade? These are spatial-intelligence questions, and they sit upstream of every commercial decision a bioeconomy operator makes. The climate-adapted feedstock map and the bioeconomy capacity map are the same map. This needs to be a real consideration, and it needs attention now.
Climate-driven supply-chain risk is now the dominant supply-chain risk.
This was the second observation in my SynBioBeta wrap. The framework for modelling location-specific climate exposure for an insurance underwriter will be the same framework that an investor in bioeconomy product providers need in order to evaluate the geographic resilience of those companies’ production and customer footprints. Climate-risk analytics, properly integrated, are not just a defensive risk-reporting function. They are an offensive market-intelligence function. The companies that can locate their next bio-manufacturing facility at the intersection of high resilience, high growth potential, low climate risk, and reliable feedstock geography will compound at structurally better unit economics than companies that pick locations on legacy chemical-industry heuristics. The differential is real, it is measurable, and over a 25-year asset life it compounds into the difference between a winning facility and a stranded one.
Industrial policy needs spatial decision support.
The DARPA, ARPA-H, and senatorial presence at SynBioBeta is part of a broader federal posture that is starting to ask serious questions about where US industrial-bio capacity should be sited. Loan guarantees and first-of-a-kind facility support are coming, and where, specifically, those facilities go is going to be one of the more consequential industrial-policy questions of the next decade. The same is likely true at the state and municipal levels, where governments are competing aggressively for the bio-manufacturing footprint that will accompany federal capital deployment. Bio-manufacturing siting decisions are a natural extension of this anticipated demand, and we need to start thinking seriously about these factors, now. The link between resilience analytics and industrial-policy execution is what determines whether a billion-dollar bio-manufacturing facility lands in a county that compounds value or a county that does not.
Earth observation is the upstream data layer.
I also noted in the wrap-up post that Earth-observation satellites supply continuous, planetary-scale signals for feedstock sourcing, climate-driven supply-chain risk, and agricultural yield and disease modelling. That observation was deliberate. Earth-observation data alongside more than other curated physical, social and economic data streams, and their creative integration is where value gets created. A satellite tile of soil moisture is not, on its own, a siting decision. A satellite tile of soil moisture combined with infrastructure capacity, demographic trends, regulatory environment, climate-adaptation scoring, and resilience-adjusted financial impact modelling is a siting decision. The bioeconomy and the spatial-intelligence layer share the same data substrate. They are both downstream applications of the same Earth-observation and AI-driven inference stack that has matured tremendously over the past five years, and they are most valuable when used together.
The structural point here is that the bioeconomy and the spatial intelligence layer are complements, not substitutes. Biology gives operators the manufacturing flexibility. Spatial intelligence tells them where to deploy it. Without the spatial layer, distributed bio-manufacturing devolves into a series of well-intentioned siting decisions made on stale heuristics in a climate-and-geopolitically-volatile world. With the spatial layer, we get the version of the bioeconomy thesis that this essay has been arguing for: a distributed, resilient, location-optimized industrial substrate that converts the geopolitical risk premium into compounding alpha.
For investors and capital allocators, we should now be asking two questions:
1. Which private and public companies are best positioned to compound through the bioeconomy transition?
2. Where, specifically, on the planet, will those companies and their customers actually build, source, and operate?
Both questions need to be answered to make the resilience bioeconomy trade actually work. One without the other gets you partway. Both together get you a defensible, compounding industrial position in the most consequential manufacturing transition of the next quarter-century.
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Closing
I’ve argued that resilience for the bioeconomy must be fused with opportunity - building a world that is not only defensive to shocks, but one that thrives in spite of them. The bioeconomy can extend that. I don’t think that we should look at the shocks of the past ninety days as aberrations. They are the early innings of a longer reorganization of global supply chains around chokepoints and concentrated production assets that turn out, on close inspection, to be much more fragile than the post-1990s globalization narrative had assumed. The petrochemical-anchored manufacturing system was built for a borderless era. We are not in a borderless era anymore. This should be a wake-up call to action for the Bioeconomy.
Biology, as a manufacturing technology, was built, by evolution, for exactly the opposite environment. It is distributed. It is feedstock-flexible. It is location-flexible. It runs on water, sugar, and information. The companies that scale it first will not just produce alternative proteins and bio-derived ingredients. They will produce industrial sovereignty, and they will be paid for it. The companies that site it correctly, with rigorous, climate-adjusted, resilience-aware spatial analytics, will compound that sovereignty into structural advantage that survives the next geopolitical event, and the one after that. I believe the two are inseparable. You cannot have one without the other and call the result resilient.
As noted in my summary, there was a memorial to Craig Venter in the front of the exhibition hall last week. The image, made of As, Cs, Ts, and Gs, watched the proceedings from across the room. It was hard not to think about it as a kind of accountability check. A generation of work - sequencing, synthesis, computational design, fermentation engineering, the slow build-out of the bioprocessing industry - has been laid down by people who mostly did not get to see this moment. The bioeconomy that is now starting to behave like a real industrial sector is theirs as much as anyone’s. Our job, the generation that follows, is to put it on the ground. Distributed. Resilient. Climate-and-Geopolitically compounding. Built where it should be built, not where the legacy heuristics tell us to build it.
The thesis is that the Bioeconomy has arrived. The work, of course, is what comes next
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