AI coding agents can generate software and run engineering tests, but cheaper generation does not make human expertise less important. In this episode, Positron AI co-founder Thomas Sohmers explains how agents learned to use Cadence verification tools, why his team prioritized model capability, and how memory constraints shape AI hardware design.
Then Shawn “swyx” Wang, who runs AI Engineer, examines what employees and team leads must contribute when coding agents are part of the workflow. From a race condition created by two agents to a bounty aimed at replacing a costly SaaS subscription, his examples point to the same challenge: verifying results and delivering useful work at an acceptable cost.
Show Notes
Thomas Sohmers, co-founder of Positron AI, describes how coding agents changed chip-design testing, drove token spending above payroll, and shaped the company’s plans for commodity memory and looped transformers. Shawn “swyx” Wang of AI Engineer discusses hiring and performance in the age of Claude: employees still need to bring expertise, judgment, testing, and accountability to AI-generated work.
Chapters
(0:00) Chip design agents and hiring
(1:22) The Curve debate shifts toward runaway AI
(2:36) Models turn reasoning into better training data
(4:05) Frontier models may already have research taste
(4:33) A frontier executive entertains a hard intelligence cap
(5:55) Claude-built RL environments leak capability to rivals
(7:21) Frontier leaders see next year as critical
(8:00) Could safety end up consuming most compute?
(9:31) Cheating models are now fixing the training environments
(10:30) Will Anthropic press its lead or show restraint?
(11:28) Meet Thomas Sohmers of Positron
(12:35) AI closed Positron’s chip design iteration loop
(13:36) Token spend passed payroll and hit $100,000 daily
(14:53) AI agents taught themselves Positron’s verification tools
(18:10) AI still misses Positron’s design ingenuity
(20:00) Why Asimov bets on commodity memory
(23:52) Better models are outrunning memory price increases
(24:50) Looped transformers save memory, not work
(27:37) Asimov follows customers, not every algorithm
(29:39) Why Positron kept buying the best model
(31:10) Meet swyx from AI Engineer
(32:19) Demand is up, and two employees are under review
(33:38) The growth report that exposed missing expertise
(34:26) A launch agency sold him Claude’s slop
(35:29) Taste, not years of coding, is the hiring test
(37:22) Two coding agents created one race condition
(38:46) A $40,000 SaaS subscription becomes a bounty
(39:31) Cheap vibe-coded submissions create expensive verification
(41:06) Skeptical event staff switch when changes take two hours
(42:33) Every spreadsheet is unbuilt custom software
(44:40) Manage team leads by results, not people by prompts
(46:49) Ninety on SWE-bench still isn’t usable software
(47:59) Claude cracked 3SUM, but who gets the credit?
(50:45) NanoGPT speed run hides a better optimizer
(52:44) How many clones would replace your AI?
(55:33) Frontier labs say AI for science is next
(57:16) AI audits could trigger mass scientific retractions
(58:14) Doctors welcome AI more than expected
Guests:
Thomas Sohmers — CTO and Co-Founder, Positron AI (LinkedIn)
swyx — swyx, curator (𝕏 | LinkedIn)
