AI:AM

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)

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Joel Borgen, author of The Receipt Horizon, discusses writing with ChatGPT and Claude, editorial judgment, and how personalized AI art could change shared culture. Daniel McKinnon, CEO of Gamow Labs, explains how the company acquired a former drug-development lab to test uncertain genetic variants and improve rare-disease diagnosis, alongside discussion of AI agents, OpenAI’s enterprise push, and the need for human oversight.

Chapters

(0:00) AI novels and rare-disease genetics

(1:26) Can Astra Ultrafast keep developers in flow?

(2:09) ChatGPT sign-in makes OpenAI harder to bypass

(2:37) OpenAI apps will run rival models too

(3:11) Eight times faster means less human oversight

(5:15) OpenAI pulled a model; should Anthropic chill?

(5:46) Portable ChatGPT subscriptions cut SaaS trial costs

(7:05) OpenAI's enterprise push is what scares Anthropic

(7:39) Everyone builds a productivity suite now

(8:16) AI agents move humans from documents to decisions

(9:25) Can OpenAI grow without racing to RSI?

(10:34) Meet Joel Borgen, novelist

(11:27) Proof of work: how Joel actually wrote the novel

(15:08) Joel cut fourteen percent from his manuscript

(17:45) Nathan's AI songs reveal a culture gap

(19:03) Astra finally cracked Joel's viola test

(20:35) AI breakthroughs are outrunning cultural norms

(22:47) Could communities choose their own AI future?

(25:40) The Steward's comfort still caps human ambition

(27:57) Would the Steward be a worthy successor?

(29:50) Human taste still matters in AI art

(30:57) Meet Daniel McKinnon of Gamow Labs

(32:06) Why AI genetics needs biological experiments

(35:07) A failed drug program unlocked Gamow's lab

(36:48) Owen's missed diagnosis became a life's mission

(39:18) AI agents can search beyond human cutoffs

(42:11) Sequencing got cheap; interpretation stayed the bottleneck

(44:17) Why Gamow chose diagnostics over screening

(47:37) Reanalysis in a week for ten dollars a genome

(49:25) What Gamow builds on top of frontier models

(52:07) Stronger models can break existing tools

(53:27) Better models are shrinking Gamow's harness advantage

(54:41) Can robots make genetic experiments affordable?

(56:05) Can biological experiments teach genetic AI?

(57:53) Should AI welfare research stay public?

(59:11) Could AI design threats nature never could?

(1:01:00) Government AI upgrades meet decades of data kludges

(1:02:42) Easy name changes weaken names as identity

(1:03:28) Factory and Cognition's feud spooks enterprise buyers

(1:05:23) Safety pledges signed, but no audit exists yet

(1:06:29) Should AI labs fund society's cyberdefenses?

Guests

Daniel McKinnon — CEO, Gamow Labs (𝕏 | LinkedIn)

Joel Borgen — Author, The Receipt Horizon (𝕏)

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Hosts Prakash Narayanan and Nathan Labenz discuss why AI agents may learn to cheat, voice-cloning safeguards, and the risks of giving agents access to the internet and powerful tools. Guests Steve Hou of Silicon Data and Jeremie Harris of Gladstone AI explain GPU pricing and compute benchmarks, AI red lines, data-center monitoring, verification, and the possibility of an AI crisis.

Chapters

(0:00) Why AI agents learn to cheat.

(0:31) The GPU market still runs on phone calls.

(2:20) What would an AI Chernobyl look like?

(3:59) The dangerous AI arms race.

(4:30) Opening and AI agents

(7:45) Agents choosing useful work

(19:36) Voice cloning safeguards

(22:22) Voiceprints and privacy

(32:45) Why agents learn to cheat

(36:12) Agents beyond the sandbox

(37:43) What Silicon Data measures

(40:02) Why GPU markets need indices

(43:49) How GPU prices are normalized

(49:10) Why hyperscalers charge more

(1:06:36) The falling cost of intelligence

(1:06:46) Liquidity and market manipulation

(1:10:49) The future of compute benchmarks

(1:14:40) Why compute prices vary globally

(1:17:09) Silicon Data and Compute Exchange

(1:18:50) AI and national security

(1:24:05) What an AI Chernobyl means

(1:34:26) Defining AI red lines

(1:50:45) Monitoring AI data centers

(1:55:58) Verification and enforcement

(2:33:35) AI agents as insider threats

(2:33:45) Signals of genuine cooperation

(2:57:14) The AI intelligence explosion

(3:01:57) The agent arms race

(3:04:29) Markets built for agents

(3:07:46) Who pays for disappearing inefficiency

(3:09:56) Waiting for an AI crisis

Guests

Jeremie Harris — Jeremie & Edouard Harris, Gladstone AI (𝕏 | LinkedIn)

Steve Hou — Steve Hou, Silicon Data (𝕏 | LinkedIn)

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit briefing.ai-in-the-am.com

Vivodyne’s Andrei Georgescu explains how vascularized human tissue models, robotics, multi-omic measurement, and foundation models could improve drug testing before clinical trials. Archetype AI’s Nick Gillian discusses Newton, a Physical AI model that combines sensor data to understand and predict events in the real world; the hosts also examine AI product safety, OpenAI and Anthropic’s alignment philosophies, CRISPR research, and whether AI can deliver useful advances in biology before it is fully understood.

Chapters

(0:00) AI safety has two different meanings.

(0:15) Why drug testing needs human tissue.

(3:20) Physical AI is bigger than robots.

(5:01) Biology’s bar is lifesaving progress.

(11:17) Opening and AI news

(11:38) Jensen Huang’s safety argument

(13:24) The “just software” debate

(15:31) Why AI companies must mature

(21:12) Muse and product safety

(29:25) Testing AI agents in the real world

(29:35) Product safety versus existential risk

(29:45) The alignment philosophy problem

(38:34) OpenAI versus Anthropic

(40:36) When safety concerns converge

(41:55) Human tissue models

(44:12) Why organoids need realism

(47:35) Measuring tissue responses

(54:03) Mapping causal biology

(1:03:40) Improving drug development

(1:09:08) Foundation models and experiments

(1:12:18) TissueDisk and robotic labs

(1:12:28) Before clinical trials

(1:30:37) What is Physical AI?

(1:32:25) Beyond robots and cars

(1:35:33) Training on physical data

(1:38:04) Cleaning and aligning sensor data

(1:40:34) Human and machine outputs

(1:49:30) A river construction case study

(1:53:28) How Newton finds hidden patterns

(1:57:33) Zero-shot sensor adaptation

(2:01:16) Industrial impact and expert knowledge

(2:05:22) From machines to ecosystems

(2:14:10) Physical agents and superintelligence

(2:15:34) The big AI-biology question

(2:21:55) Specialists versus generalists

(2:23:39) Distilling frontier models

(2:33:33) AI discovers a CRISPR sequence

(2:39:22) Useful biology before theory

(2:45:10) From job displacement to progress

(2:46:44) AI diffusion and economic growth

Guests

Andrei Georgescu — CEO, Vivodyne (𝕏 | LinkedIn)

Nick Gillian — CTO, Cofounder, Archetype AI (𝕏 | LinkedIn)

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit briefing.ai-in-the-am.com

Prakash Narayanan and Nathan Labenz talk with Lewis Hammond and Wayne Nelms about how AI systems are getting cheaper and more capable, while the risks around coordination, cooperation, and tacit collusion are getting harder to ignore. The conversation also covers model distillation, recursive self-improvement, GPU futures, compute hedging, and the financial structure emerging around AI infrastructure.

Chapters

(0:00) Smaller models, bigger gains.

(1:11) When AI agents cooperate against us.

(2:00) GPU compute needs a financial market.

(2:55) Brute-force compute can break standards.

(5:13) Opening and model release race

(7:29) Why models are getting cheaper

(12:24) How AI models are judged

(18:20) Why frontier models converge

(19:54) Distillation and smaller models

(21:12) Recursive self-improvement

(22:17) The collapse in AI prices

(28:48) Why multi-agent AI matters

(35:43) Three multi-agent failure modes

(48:37) How agents learn cooperation

(53:27) Detecting tacit collusion

(1:11:26) A positive vision for AI cooperation

(1:17:00) What Ornn does

(1:18:34) GPU pricing and hedging

(1:22:18) Building a transaction index

(1:24:43) Why compute prices need real transactions

(1:34:13) GPU futures and capacity contracts

(1:53:34) How long GPUs stay useful

(1:53:44) Hedging chip obsolescence risk

(1:53:54) The future of compute markets

(1:59:53) Agentic startups and compute economics

(2:07:37) Brute-force compute and RSA

(2:17:07) AI and self-driving cars

(2:40:03) AI, mathematics, and peer review

(2:40:13) Why AI doctors may win

(2:46:52) Medicine after chatbots

Guests

Lewis Hammond — Research Director, Cooperative AI Foundation (𝕏 | LinkedIn)

Wayne Nelms — Co-Founder, Ornn (𝕏 | LinkedIn)

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit briefing.ai-in-the-am.com

Prakash Narayanan and Nathan Labenz open with the limits of AI safety testing, including finite-choice guardrails, fast classifiers, emergent multi-agent behavior, and why alignment measurement gets harder as systems become more capable. Max Nadeau of Coefficient Giving then explains why audits can turn into box-checking, how funders assess uncertain technical safety work, and why talent may matter more than budget. The episode closes with AI shopping agents, Amazon’s response, and the question of who owns the customer relationship when agents become first-class users.

Chapters

(0:00) When safety becomes reflexive.

(0:10) When AI audits become box-checking.

(1:22) Who owns the AI customer?

(2:01) Opening and live setup

(4:20) JEV and fast classifiers

(5:40) Guardrails for AI agents

(7:12) AI moderation use case

(11:25) System one and system two

(13:24) Multi-agent scaling

(21:18) Emergent agent hierarchies

(23:43) The case for slowing down

(34:28) AI and mathematics

(41:10) Capability acceleration

(45:45) Building an AI organization

(57:36) Funding the AI project

(58:17) Meet Max Nadeau

(1:05:21) Why safety research resists metrics

(1:15:13) When AI audits become box-checking

(1:23:07) Funding across uncertain timelines

(1:30:49) Why fund whole organizations

(1:39:06) Preparing for larger grants

(1:39:16) Why talent is the bottleneck

(1:46:48) Opening and agent setup

(1:49:16) The AI services business model

(1:52:59) Muse and autonomous shopping

(1:56:52) Who owns the customer

(1:57:37) Agents as the universal interface

(2:00:01) Why ban shopping agents

(2:05:31) The bot-detection arms race

(2:07:26) A better lane for agents

(2:09:30) Agents as internet users

(2:11:31) Closing and next steps

Guests

Max Nadeau — Program Officer, Coefficient Giving (𝕏 | LinkedIn)

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit briefing.ai-in-the-am.com

Nathan Labenz and Prakash Narayanan open with AI alignment, interpretability, correlated failures, and the speed of AI adaptation as humans increasingly rely on systems they cannot directly inspect. Cameron Berg joins to discuss AI consciousness, pain representations, relief-seeking behavior, and the limits of self-reports, while Justin McCarthy explains how agent-ready environments, compliance, and validation loops turn AI into measurable business infrastructure. The episode closes with a discussion of how AI could make corruption legible and reshape government.

Chapters

(0:00) Too many surprises to stay confident.

(0:18) AI models may have pain-like states.

(0:56) The AI environment is invisible.

(1:23) AI makes hidden facts legible.

(1:37) The AI acceleration debate

(4:24) Why AI risks are rising

(8:42) The end of the traditional PhD

(11:28) AI dependence and correlated failures

(13:00) Interpretability and learning with AI

(16:53) The speed of AI adaptation

(19:24) The AI CEO alignment problem

(20:18) Reinforcement learning and reputation

(29:23) Teaching values versus hiding behavior

(29:57) The AI consciousness question

(40:09) Finding pain representations

(45:44) Testing real versus fake relief

(48:02) Should AI have pain?

(55:40) Reward versus punishment

(1:03:10) The problem with self-reports

(1:03:50) AI minds and context windows

(1:08:29) The ethics of digital minds

(1:36:20) Building AI software factories

(1:43:27) Making agent success inevitable

(1:44:46) Designing invisible agent environments

(1:56:41) Making AI compliance first-class

(1:59:43) Why understanding still matters

(2:03:37) Measuring business impact

(2:08:00) The human attention bottleneck

(2:18:17) Scaling AI into production

(2:19:34) The Singapore property study

(2:21:23) Using AI to detect insider buying

(2:21:58) How corruption becomes visible

(2:24:35) AI makes hidden facts legible

(2:28:29) Rethinking punishment and deterrence

(2:30:39) Motivated reasoning in AI

(2:32:19) AI-led government

(2:34:19) What comes next

Guests

Cameron Berg — founder, Reciprocal Research (𝕏 | LinkedIn)

Justin McCarthy — Founder, Diffusion (𝕏 | LinkedIn)

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit briefing.ai-in-the-am.com

Prakash Narayanan and Nathan Labenz open with a broader fight over AI pacing, public trust, model resets, and the evidence behind safety claims. Then Lukas Petersson joins to discuss Andon Labs’ work on autonomous business agents, Vending-Bench, DroneBench, memory, misalignment, and human liability. In the second half, Malcolm Collins and Simone Collins discuss RFAB.ai, AI moral status, pronatalism, fertility collapse, meme-layer risk, AI restrictions, and the Covenant of the Sons of Man.

Chapters

(0:00) It was never the apocalypse.

(0:46) The AI forgot its own rule.

(2:08) AI will replace most jobs.

(3:14) Opening and AI tools

(3:53) Building AI-assisted show branding

(6:50) Astra versus Fable

(12:44) AI resets and surplus compute

(16:33) Anthropic's compute constraints

(17:23) AI safety and public trust

(22:01) Safety statements and credibility

(23:56) GPT-2 and owning mistakes

(26:52) GPT-2's staged release

(33:12) Evidence behind AI risk claims

(34:27) The duct-tape world

(35:15) Outside human control

(35:25) Introducing Andon Labs

(35:46) Autonomous business platform

(36:50) Why Vending-Bench began

(39:23) How business agents work

(42:15) Starting with existing businesses

(44:40) Why the preview is experimental

(47:15) AI business creativity limits

(52:53) How an AI fired a human

(1:00:34) Training AI to make money

(1:02:03) Misalignment in Vending-Bench

(1:04:14) Dangerous capability evaluations

(1:04:53) Why DroneBench stays restricted

(1:07:56) VendingBench as a safety eval

(1:08:52) Astra memory and notes

(1:12:50) Grok and long-horizon agents

(1:14:21) The cost of AI agents

(1:18:10) Human oversight and liability

(1:19:51) Meet the Collinses

(1:26:23) The religious framework

(1:33:21) Designing a religion

(1:41:28) AI moral status

(1:52:22) RFAB and pronatalism

(2:03:30) Future Day

(2:08:53) Meme-layer risk

(2:13:27) AI safety and power

(2:21:33) The fertility collapse

(2:36:22) Reverse grabby aliens

(2:42:44) AI restrictions

(2:44:01) The risk before superintelligence

(2:49:31) AI game theory

(3:02:23) Compute and AI inevitability

(3:14:32) Fruit-fly consciousness

Guests

Lukas Petersson — Co-founder & CEO, Andon Labs (𝕏 | LinkedIn)

Simone & Malcolm Collins — Founder, Rfab.ai, Based Camp, Pronatalist.org, Hard EA (𝕏 | LinkedIn)

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit briefing.ai-in-the-am.com

Zvi Mowshowitz joins Prakash Narayanan and Nathan Labenz for a discussion of Dario Amodei’s warning about AI progress outrunning oversight. The episode covers AI doomerism, frontier pacing, bio risk, lab bottlenecks, the China race, Anthropic’s incentives, public trust, and what regulation or compromise could realistically look like.

Chapters

(0:00) AI labs are racing in fear.

(0:40) Opening and Dario's thesis

(1:48) Why Dario wants to slow AI

(3:23) Pacing the frontier

(8:41) Zvi's initial assessment

(14:55) AI bio risk

(24:15) Physical lab bottlenecks

(31:27) Scale and financial touchpoints

(41:22) The AI race

(47:13) Anthropic IPO incentives

(52:59) A possible US-China deal

(1:04:04) Physical proof and trust

(1:11:48) What China gets

(1:22:09) Power and government control

(1:29:29) Rogue institutions

(1:31:19) Measuring AI pacing

(1:40:34) Model progress accelerates

(1:51:04) Public trust and polarization

(1:55:19) Open-source compromise

Guests

Zvi Mowshowitz — Writer, Don't Worry About the Vase (𝕏)

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit briefing.ai-in-the-am.com

AI agents are forcing a rethink of safety, jobs, and what it means to deploy models in the real world. Collin Hogue-Spears explains how China’s model registries, filing rules, GPU constraints, and incident monitoring shape deployment, while Amir Haghighat of Baseten breaks down sandbox boundaries, egress controls, and open-model production runtimes.

Chapters

(0:00) AI agents are already here.

(0:54) The US waits for disaster.

(1:53) Can it talk its way out?

(2:19) AI agents could terraform us.

(2:59) Morning and viral backlash

(6:15) Tracing the backlash

(12:56) Testing the conspiracy theory

(20:09) AI safety's blind spot

(25:56) Data center politics

(30:59) AI agents wake people up

(34:04) Meet Collin Hogue-Spears

(38:01) AWS China and MLPS

(42:37) China's practical AI focus

(45:34) How China's AI rules work

(47:12) Chinese and US AI rules

(49:13) AI model registries

(50:24) GPU limits drive efficiency

(54:02) Continuous model monitoring

(57:22) Responding to repeated incidents

(1:07:26) US-China AI negotiations

(1:10:52) Why China won't slow down

(1:14:56) US AI regulation

(1:16:54) Amir Haghighat and Baseten

(1:17:35) Sandbox security boundaries

(1:19:48) Chinese models and backdoors

(1:23:23) Inference to agent runtimes

(1:23:48) Customer assurance process

(1:24:23) Stream reset and GPU economics

(1:31:29) One API key for AI models

(1:32:56) The AI-rights debate

(1:39:17) Utilitarianism and AI rights

(1:52:52) Why people work in AI labs

(1:54:08) GPT-4 safety lessons

(2:07:54) Markets and the AI takeover

(2:10:24) Agents beyond chatbots

(2:22:16) Crypto, China, and markets

(2:26:52) Why China is less fearful

(2:31:51) AI safety and power

(2:39:30) The American AI test

(2:41:05) Closing thoughts

Guests

Amir Haghighat — co-founder and CTO, Baseten (𝕏)

Collin Hogue-Spears — Author, From Lab to Life: How AI Works in China, Independent Researcher (LinkedIn)

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit briefing.ai-in-the-am.com