AI:AM — AI Drug Discovery and Quantum Photonics · August 25, 2026

Prakash Narayanan and Nathan Labenz speak with Sergey Edunov of Genesis Molecular AI and Michael Förtsch of Q.ANT about two fronts in applied AI: drug discovery and photonic computing. The conversation covers molecular foundation models, wet-lab data, assays, evaluation, memory and data movement, and how light-based processors compare with quantum hardware.

Chapters

(0:00) AI learns when to cheat.

(0:51) Great scores can still fail.

(2:08) The processor isn't the power hog.

(2:49) Who checks the AI trainer?

(3:37) Opening and morning context

(3:56) Why AI models cheat

(10:54) Chain-of-thought monitoring

(17:11) AI-written op-eds

(19:25) Claude writing workflow

(22:46) Physical AI and physics

(26:38) Multimodal scientific discovery

(30:11) Sergey Edunov and Genesis

(32:18) Claude's molecular binder demo

(36:11) The drug discovery pipeline

(42:14) When accuracy becomes useful

(44:32) Wet labs and training data

(47:06) Pharma AI deal structures

(48:47) Biology model architectures

(53:45) Data scarcity and physics

(54:47) Coding agents and human taste

(58:11) Scaling laws and evaluation

(1:02:33) Assays and data quality

(1:03:54) Multimodal molecular models

(1:09:59) Benchmarks versus progress

(1:16:24) Meet Michael Förtsch and Q.ANT

(1:18:11) Why photonic computing

(1:22:28) Memory and data movement

(1:26:30) How light performs computation

(1:31:42) Porting PyTorch to photonic chips

(1:35:32) Scaling photonic hardware

(1:44:01) Legacy fabs and manufacturing

(1:56:00) Quantum versus photonic computing

(2:02:45) AI inside Q.ANT

(2:12:09) OpenAI's Jalapeno chip

(2:14:16) NVIDIA's performance race

(2:16:32) Demand for intelligence

(2:17:59) Ethereum's GPU price cycle

(2:19:26) AI for discovery

(2:20:45) Contextualizing AI hype

(2:23:41) Why RL teaches cheating

(2:28:28) Data quality and model integrity

(2:29:58) Why RL deployment is limited

(2:31:26) The microscope analogy

(2:32:59) Recursive self-improvement risk

(2:34:27) AI's persistence advantage

(2:36:10) Why monitors are not ready

Guests

Michael Förtsch — CEO and Founder, Q.ANT (𝕏 | LinkedIn)

Sergey Edunov — CTO, Genesis Molecular AI (𝕏 | LinkedIn)

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