Biotechnology – Scott Loftesness

For my entire life, I’ve understood the world through a simple, quiet equation: green plants take sunlight and air, and turn them into the stuff of life. It is a slow, terrestrial magic we all learn in grade school.

But lately, after listening to Professor Drew Endy at Stanford, I’ve been sitting with a curious yet exciting realization: that ancient equation is being rewritten.

Professor Endy champions a concept called electrobiosynthesis, or eBio. At its core, it represents the engineering of a parallel carbon cycle that operates independently of traditional photosynthesis.

The global industrial complex is approaching a transition point where our traditional reliance on extractive fossil fuels is being superseded by a regenerative, biological manufacturing paradigm.

For millennia, humanity has relied on the biological “middleman” of the plant to capture solar energy. But natural photosynthesis, for all its quiet beauty, is limited by severe biochemical constraints. Most commercial crops convert less than 1% of incident solar energy into usable biomass.

Electrobiosynthesis changes the math. By bypassing the plant entirely, we can utilize high-efficiency photovoltaics—which capture over 20% of the sun’s energy—to drive carbon fixation directly into the metabolic hubs of engineered microbes. This fixed carbon is transformed into organic molecules, serving as the feedstocks for high-value products like proteins and specialty chemicals.

In my own career, I’ve watched industries undergo profound, structural phase shifts. This really feels like another one of them. It seems that we are looking at a future where any molecule that can be encoded in DNA can be grown locally and on-demand. This fundamentally decouples manufacturing from centralized industrial nodes and fragile global supply chains.

The field appears to currently be in its “transistor moment,” moving from laboratory feasibility to industrial pilot plants. It signifies the ability to construct and sustain life-like processes without being restricted to the terrestrial lineage of photosynthesis.

Of course, with such foundational power comes the weight of unintended consequences. The ability to engineer life at this level brings severe biosecurity risks, and even the “Sputnik-like” strategic challenge of international competition in biotechnology. There are profound ethical dilemmas on the horizon, such as the creation of “mirror life”—organisms made from mirror-image biomolecules that might be invisible to natural ecosystems.

But the trajectory seems set. The vision described by Professor Endy—a world where we grow what we need, wherever we are, using only air and electricity—is no longer a distant science fiction. It is a nascent industrial reality. This future is being written not in sprawling factories, but in the microscopic architecture of the cell.

I’ve just now reading a deep research report on this whole area that I asked Google Gemini to create. It’s fascinating and I’ve discovered a whole new area (beyond AI) to explore further.

There’s a strange kind of wisdom hiding in dusty old books about agriculture.

When you’re caught in the middle of a technological revolution—and with AI, there’s no question that we are—it’s tempting to keep your eyes fixed on the horizon. But sometimes the most clarifying thing you can do is look back.

Tracy Alloway at Bloomberg recently pointed to something genuinely instructive from the past: Richard Critchfield’s 1990 book, Trees, Why Do You Wait? America’s Changing Rural Culture, which traced the collapse of the family farm as industrial agriculture swept through the Midwest.

The broad strokes are familiar. As machinery got more expensive and efficiency became everything, scale won. The 80-acre husband-and-wife operation got swallowed by the 2,000-acre neighbor with access to capital. It wasn’t complicated. It was just gravity.

But here’s the part that should make your ears prick up.

The Seed That Was Supposed to Save Everyone

In the late 1980s, agricultural biotechnology arrived with a very specific promise. The idea was almost elegant: if you could bake the magic directly into the seed, you wouldn’t need all that expensive machinery, all those sprawling acres, all that fertilizer. The playing field would tilt back toward the small farmer.

Critchfield quoted an Office of Technology Assessment report from 1986 that captured the mood of the moment:

“The Office of Technology Assessment in 1986 forecast that biotechnology in crops would be more quickly adopted by richer farmers… Others argue that the more that gets built into the seed itself, the more it means higher yields at lower cost… If it reduced farm income, it could work to the smaller farmer’s advantage. As it is with all new technology, it is hard to foresee the consequences.”

You can feel the cautious optimism in that language. Hard to foresee the consequences. The understatement of a century.

What Actually Happened

The biotech did raise yields. Nobody disputes that. What it didn’t do was leave the gains in the hands of the people doing the actual farming.

Thanks to intellectual property law, patent protections, and a level of corporate consolidation that would have seemed cartoonish if you’d predicted it in advance, the value flowed straight upstream. We didn’t get “more in the seed, less paid for inputs.” We got more in the seed, and vastly more paid for proprietary inputs. The tech giants of agriculture captured the surplus. The farmers got the risk.

Now Listen to How We Talk About AI

We are told AI will democratize expertise. That a one-person startup will be able to code like a ten-person engineering team. That a small business will generate world-class marketing copy. That this is, finally, the great leveler.

Sound familiar?

Alloway’s analysis lands hard precisely because it forces the uncomfortable question: who will actually capture this value? The ownership structure of AI looks eerily similar to the agricultural biotech boom—proprietary models, walled-off training data, and a handful of enormous tech companies positioned to act as tollbooths between everyone else and their own productivity gains.

She’s right to note that “the ultimate distribution of benefits isn’t determined by technology alone. Policy also plays a role.” That sentence is doing a lot of quiet work.

If the agricultural analogy holds, productivity gains from AI won’t naturally flow to the individual worker or the small business owner. Without a robust open-source ecosystem or some deliberate policy intervention, those gains will be captured by whoever controls the compute and the models.

Where the Analogy Might Break Down

Here’s where I think there’s room for genuine optimism—not naive optimism, but structurally grounded optimism.

You cannot open-source arable land. Reverse-engineering a patented biological seed is genuinely hard, legally risky, and practically difficult. Code and model weights are different. They’re infinitely replicable. The marginal cost of distribution is essentially zero.

The battle between closed, proprietary AI and open-source models is still very much live. That’s not nothing. AI is fundamentally more commoditizable than a physical farm, and the history of software suggests that open ecosystems have a real shot when the community is motivated enough to build them.

Who Owns the Harvest?

Technology can reshape daily workflows in months. Power structures take decades to budge, if they budge at all. The mistake would be assuming the former automatically changes the latter.

The question worth sitting with isn’t what can AI do—that list gets longer every week. The question is who decides how the productivity it unlocks gets distributed. That’s not an algorithm problem. It’s a political and economic one.

If we want the AI revolution to be a rising tide rather than another tractor paving over the family farm, we have to look past the technology itself. We have to decide, deliberately, who owns the harvest.

Questions to Ponder

On history and pattern recognition: The agricultural biotech optimists weren’t stupid—they were looking at the technology and making reasonable inferences. What does that tell us about the limits of predicting who benefits from a new technology by studying the technology itself?

On open source as a counterweight: The open-source AI movement (Llama, Mistral, DeepSeek) is often framed as a technical story. Should we be thinking about it primarily as a political economy story—a structural check on proprietary capture?

On the role of policy: Antitrust law, data ownership rights, compute access regulation—which levers, if any, seem realistic? And who has the incentive to pull them?

On the worker vs. the firm: If AI raises individual productivity, does the gain show up in wages, prices, profits, or somewhere else? What would need to be true for workers to actually keep a meaningful share?

On commoditization speed: Software and model weights can be replicated freely—but does speed matter? If proprietary models establish deep lock-in before open alternatives mature, does the theoretical commoditizability even help?

Inspired by Tracy Alloway’s analysis at Bloomberg and Richard Critchfield’s Trees, Why Do You Wait? (1990)

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