The Factory Belongs in the Design

In December 2025, researchers reported designing active enzymes for three distinct catalytic mechanisms with RFdiffusion2, identifying working candidates after experimentally testing fewer than 96 sequences in each case. The authors also noted that the designed enzymes remained less active than native enzymes. Design capability had advanced; performance still needed work. 1

Earlier that year, researchers studying enzymes for PET plastics recycling raised a different concern. Published performance claims were difficult to compare because testing materials and conditions were inconsistent—and often disconnected from industrial operation. 2

Together, these findings expose a gap between what biology can be designed to do and what it can reliably deliver. Industrial enzymes offer one route into programmable biology: connecting molecular design, experiments and production to turn a specified function into a repeatable commercial capability.

At Aldea, we are interested in companies that make biological performance increasingly repeatable—and retain the learning that makes the next batch, process or product better.

Design for the whole process

For an industrial enzyme, we would ask how it handles variable inputs, how long it remains active, how much preparation the material requires, and what it costs to recover a usable product.

The PET researchers make those dependencies explicit. They identify material preparation, temperature, acidity and the amount of plastic in the reactor as important to evaluating performance under commercially relevant conditions. 2

Sometimes the advance is not in the enzyme itself. A separate 2025 study in PNAS showed that reactively mixing certain difficult polyester wastes made them much more accessible to enzymatic breakdown. Changing the material expanded what the biology could accomplish in the tested system. 3

Our read is that the factory should help define the molecule, rather than merely receive it. If material preparation drives cost, the next design target might be an enzyme that tolerates less processing. If recovery is expensive, the priority might be a cleaner product stream rather than a faster reaction.

Epoch Biodesign illustrates this approach in a different polymer: nylon 6,6. The company describes a platform combining enzyme design, laboratory screening, process optimization and manufacturing, with recycled nylon intended for use in existing supply chains. 4 Nylon 6,6

Consider a hypothetical design choice in a process like this. Incoming waste must be prepared, the target polymer broken down, useful material recovered and the resulting product qualified by a customer. A faster enzyme could reduce reactor time while leaving an expensive recovery step untouched. A more selective enzyme might instead make recovery easier or the output more consistent. The best design depends on which change improves the cost of material the customer can actually use.

Qualification gives the feedback an economic endpoint. A cheaper process that produces material outside the buyer’s specification has not delivered a better product. A process that consistently meets that specification across variable inputs could support repeat orders and reduce the cost of fulfilling them.

That is why production experience belongs in the next design brief. It can reveal whether to prioritize activity, tolerance to variable inputs, easier recovery or consistent quality. These are illustrative commercial mechanisms, not claims about Epoch’s achieved unit costs or qualification results.

The experiment becomes part of the product

More flexible molecular design and better choices about what to test next make this connection increasingly useful.

RFdiffusion2 expands design flexibility by generating enzyme structures around the chemical groups required for a reaction, without prescribing where those groups must sit in the protein sequence. That broadens the possible starting points for experimentation. 1

In August 2026, researchers reported that REAP, a system combining machine learning with robotic experimentation, improved the targeted activity of a P450 enzyme 57-fold over five cycles relative to its starting variant. Experimental results informed subsequent predictions and the choice of variants to test. 5

A different approach targets production conditions. OpenAI and Ginkgo Bioworks reported a 40% reduction in reaction cost per gram for a test protein against the previous best laboratory baseline. The work used model-guided experiments in one cell-free production system; transfer to other proteins and larger-scale operations remained unproven. 6

These are laboratory results, not industrial cost curves. But they demonstrate progress in both selecting biological designs and improving the conditions in which biology performs.

For us, programmability means a biological capability can increasingly be specified, measured, iterated and reproduced under relevant conditions, with experimental and operating evidence shaping the next design.

Where a company earns its advantage

A product company could build its advantage through a proprietary enzyme and process that together deliver reliable output at a competitive cost. The enzyme itself can remain the central asset; process knowledge helps realize its value. Performance across real input conditions could create operating knowledge that a rival cannot reproduce simply by accessing the same design tools.

For an infrastructure company, it could mean becoming embedded in a valuable development workflow: connecting design choices, experimental results and production requirements, with the rights to reuse the resulting knowledge. The product must improve the customer’s development economics, not merely coordinate laboratory activity.

A more defensible dataset may not be a catalogue of molecules. It may be a record of which molecules work, under which conditions, and at what cost.

We would look for evidence that this record changes outcomes: fewer failed development cycles, more predictable performance, lower production costs or faster customer qualification.

This does not require owning every laboratory or factory. Partners can provide capacity. The company needs to control the distinctive biological capability, development system or customer relationship through which it captures value.

Two ways for learning to compound

Learning can compound within a product or across products.

A product company may deepen its advantage by making one valuable process more reliable and economical. Better consistency, lower processing costs and expanding customer adoption can support a substantial business without requiring a new application every year.

A platform makes an additional claim: the tools and knowledge from one program make the next program easier. That requires evidence of transfer, not just success repeated through another round of bespoke work.

A company should not need to call itself a platform to justify an ambitious outcome. For a product business, the evidence is improving performance and economics within that process. A platform must also show that development becomes faster or more predictable across programs.

A loop is not automatically a moat

The countercase for a development platform is a laboratory full of one-off projects.

If each new target requires an entirely different test, customer data cannot be reused, and successful programs leave little transferable knowledge, the company may be building a capable research service rather than a compounding platform.

The advantage could also accrue to an established manufacturer with the relevant customer relationships and operating knowledge. Or broadly available models and laboratory providers could make coordination easier to replicate. A startup needs a distinctive capability, not simply a connected workflow.

Owning physical infrastructure does not resolve this. A factory can add capital requirements without creating a learning advantage. The test is whether operating it improves the product and strengthens the economics enough to justify the capital committed.

Watch what carries forward

Three things would strengthen our conviction.

First, performance survives relevant conditions. The biology remains useful across the input variation, operating demands and quality requirements that customers actually bring.

Second, the economics survive scale. The measure is the cost of usable, customer-qualified output, including preparation, processing and recovery—not an isolated improvement in laboratory activity.

Third, learning becomes an economic advantage. Within a product, operating experience improves consistency, cost or customer value. Across a platform, one program makes the next faster or more predictable. In either case, the company must retain a meaningful share of that value.

The lab and the factory need not sit downstream of intelligence. They can be where the company’s intelligence compounds.

Disclosure: Aldea is an investor in Epoch Biodesign.