Building Rhea's Factory: How AI-Designed Enzymes Could Finally Solve Plastic Recycling

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Only 10% of the plastic we manufacture gets recycled. We've been trying to solve this for a hundred years using the same mechanical and chemical tools that created the problem. What if biology—specifically, engineered enzymes—is the missing piece?

In this episode of Just Now Possible, Teresa Torres talks with Arzu Sandıkçı (co-founder and CEO) and Mert Topcu (co-founder) of Rhea's Factory, a startup using engineered enzymes and AI to achieve what mechanical recycling can't: breaking plastic all the way back to its original molecular building blocks.

Arzu brings a background in molecular biology and enzyme engineering. Mert brings 20 years in tech, including a decade at Google as a product manager. Together, they've built an AI platform that uses protein language models, multi-step agentic pipelines, and proprietary wet lab data to design novel enzymes that deconstruct plastic polymers into their original monomers—selectively, at low temperatures, and at industrial scale.

You'll hear how they evolved from a human-orchestrated pipeline to an agentic AI scientist, why they sometimes want the model to hallucinate, and what it means to explore an enzyme design space that makes everything nature has ever evolved look like a tiny dot.

Show Notes

Guests

Arzu Sandıkçı, Co-founder & CEO, Rhea's Factory

Mert Topcu, Co-founder, Rhea's Factory

In this episode:

Why only 10% of plastic gets recycled—and why mechanical and chemical methods hit a ceiling

How enzymatic recycling breaks plastic all the way back to its original monomers, unlike traditional methods that just shorten polymer chains

Why enzymes are selective: they can target specific plastic types even in mixed waste streams

The discovery of a plastic-eating bacteria in Japan that opened the door to enzymatic recycling

How AlphaFold and the Nobel Prize in Chemistry transformed what's possible in enzyme engineering

How Rhea's Factory uses protein language models (PLMs) and multi-step AI pipelines to design novel enzymes computationally

The evolution from a human-orchestrated pipeline to an agentic AI scientist

How guardrails at each pipeline step keep the AI pointed in the right direction without limiting exploration

Why wet lab data—even just hundreds of proprietary data points—can be enough to train a powerful domain-specific prediction model

Why Mert sometimes wants the model to hallucinate (and how high temperature settings help explore the full enzyme design space)

The business constraint: enzymatic recycling must compete economically with cheap, oil-based plastic production

What's next: a process agent, a 5,000-ton demo plant in California, and enzymes for new plastic types

Resources & Links

Rhea's Factory — Enzymatic plastic recycling technology

AlphaFold — DeepMind's AI system for protein structure prediction (inspiration for the Nobel Prize in Chemistry)

Maven AI Evals Course — The course Teresa took to learn about evals (35% off with Teresa's affiliate link)

Chapters

00:00 Meet the Founders
01:50 Why Plastic Circularity
03:19 Mechanical vs True Recycling
04:52 Biology as the New Tool
07:20 Necklace and Pearls Analogy
13:22 Low Energy Reactor Process
17:33 Origin Story and PET Enzyme
22:52 Protein Folding and AlphaFold
28:32 AI Designed Enzymes
34:28 Protein Language Models Stack
37:14 Multi Step Protein Generation
39:00 Building on Foundation Models
40:50 Lab First Success Metrics
43:10 From Human to Agentic Orchestration
43:59 Problem Statements as Inputs
46:18 Guardrails at Every Stage
47:48 Prediction Models and Data Limits
50:03 Industrial Reality and Cost
52:30 Agentic Parallels and Orchestrators
57:45 Impact on Timelines and Diversity
01:03:23 When Hallucination Helps
01:04:09 Scaling Up and Process Agents
01:06:56 Enzyme Blends for Mixed Plastics
01:07:49 Why Clamshells Aren't Recyclable
01:09:34 Closing Thoughts and Thanks

Full Transcript

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