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  • Post last modified:November 29, 2025
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Inside AI’s Bet on Programmable Proteins: Models, Money, Moats

 

 

What Changed and Why It Matters

The story isn’t AlphaFold anymore. It’s what comes after.

Capital, compute, and wet labs are converging to make proteins programmable. Profluent raised fresh funding backed by Jeff Bezos to scale AI models for protein design. New foundation models like ESM3 and ProGen3 are moving from predicting structure to generating function. Automated labs are compressing the design–build–test loop.

Here’s the shift: AI is turning biology into an engineering discipline. That unlocks speed, reproducibility, and—eventually—distribution.

“AlphaFold pulled off the biggest artificial intelligence breakthrough in science to date…”

“…but didn’t end it.”

 

 

The Actual Move

  • Profluent secured $106 million to scale its AI for programmable proteins, with backing from Jeff Bezos. The company introduced ProGen3, positioning it as a next-gen protein generation model family.

“ProGen3 is a family of AI models for protein generation, involving billion-parameter language models trained on over 3.4 billion protein sequences.”

  • EvolutionaryScale launched ESM3, a gigantic protein language model.

“EvolutionaryScale’s AI tool, called ESM3, is what’s known as a protein language model. It was trained on more than 2.7 billion protein sequences …”

  • Latent Labs emerged from stealth with $50 million to build foundation models that make biology programmable.
  • Automated lab systems are maturing. New integrated platforms promise faster evolutionary experiments, bringing down cycle time and access barriers.

“This integrated system not only accelerates the pace of protein engineering but also democratizes access to evolutionary experimentation…”

  • Academic results continue to validate design. University of Washington highlighted Science papers showing machine learning excels at creating new proteins.

“Machine learning can accelerate solutions for protein design challenges.”

  • The context: AlphaFold accelerated structural biology, but the frontier is function, dynamics, and design—areas now dominated by generative approaches.
 

 

The Why Behind the Move

Zoom out and the pattern becomes obvious: programmable proteins are becoming a platform.

• Model

 

    • Foundation models trained on billions of sequences enable conditional, controllable protein generation.

 

    • ProGen3 and ESM3 reflect a scale-plus-control strategy: bigger corpora, richer conditioning, tighter feedback from assays.

 

 

• Traction

 

    • Academic benchmarks are crossing into applied design. Labs report AI-designed proteins that perform tasks not seen in nature. Community interest is compounding.

 

 

• Valuation / Funding

 

    • $106M into Profluent and $50M into Latent Labs show investor conviction that models plus wet-lab validation can convert into pipelines and partnerships.

 

 

• Distribution

 

    • The likely GTM: partner with pharma, biotech, and CROs. Offer design-as-a-service, APIs, and validated libraries targeting enzymes, binders, and editors.

 

 

• Partnerships & Ecosystem Fit

 

    • Automated labs will be key. Tight model–experiment loops create data moats and faster iteration. Expect alliances across cloud labs, assay platforms, and compute providers.

 

 

• Timing

 

    • Post-AlphaFold momentum meets cheaper sequencing, better synthesis, and scalable robotics. The stack is finally aligned for product cycles.

 

 

• Competitive Dynamics

 

    • Multiple model camps are emerging: ProGen3, ESM3, and new entrants like Latent Labs. Differentiation will hinge on functional control, assay throughput, and real-world hits.

 

 

• Strategic Risks

 

    • Wet-lab bottlenecks and noisy assays can throttle progress.

 

    • IP around designed sequences is complex.

 

    • Biosecurity and governance matter.

 

    • Model hallucinations translate to costly bench time if not checked by robust feedback loops.

 

 

 

What Builders Should Notice

  • Close the loop: pair generative models with automated experiments. Feedback is the moat.
  • Sell outcomes, not models: validated functions beat raw benchmarks.
  • Own the data flywheel: proprietary assays and negative results compound advantage.
  • Start narrow: one high-value function can unlock partnerships and revenue.
  • Compliance is product: biosecurity and governance are now features.
 

 

Buildloop reflection

“The moat isn’t the model. It’s the loop that proves function.”

 

 

Sources

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