Podcasts about turing

The man who open-sourced the most-used AI image model in history sat down with the man who has spent a decade proving superintelligence cannot be controlled. Brian set up a debate. What emerged was something more unsettling than any debate. Roman Yampolskiy is the computer scientist who coined the term AI safety and author of AI: Unexplainable, Unpredictable, Uncontrollable. Emad Mostaque is the co-founder of Stable Diffusion and the only AI CEO who signed the pause letter. He now says he doesn't know how a pause could work. Yampolskiy thinks that is the only option left. The question underneath everything is simple: if you have a 50% chance of wiping out civilization and you build it anyway, what are you actually doing? We cover what AI safety researchers actually think the danger is, why nobody has published a paper, filed a patent, or shipped a prototype for controlling a superintelligence, what the Qwen weights being out means for the pause argument, and why Mostaque thinks swarm intelligence is the most dangerous and most unpredictable risk vector we have. What you'll hear: -Why both guests think P(doom) tells you less than you'd hope -What it means that no company, no lab, and no team has a patent on controlling superintelligence -Why the models the public receives are slightly lobotomized -The difference between an AI swarm and the ASI everyone is debating -Why giving every psychopath access to a cutting-edge intelligence weapon is incoherent safety strategy -What it would actually take to update Yampolskiy's assessment “We either do it, or we die. There is nothing for you to gain by doing it.” — Roman Yampolskiy CHAPTERS 00:00 A debate that wasn't a debate 00:36 Turing test, AGI, superintelligence: where are we? 02:02 The open source argument nobody wins 03:32 Pause frontier AI forever. Which button? 05:04 P(doom): parameterizing our ignorance 09:02 Nukes are inefficient. AI isn't. 11:46 The lobotomized model problem 18:04 Decade-old problems solved weekly now 26:18 We either do it or we die 31:10 Stop the training or stop the funders 33:34 Lipstick on a Shoggoth 35:50 No paper. No patent. No framework. 39:42 Train only on what you need 44:14 What lowers Mostaque's p(doom)? 52:00 Same future. Two perspectives. Get the transcript, fascinating bonus content, and my Monday M.A.G.I.C. Message: https://briankeating.com/yt Have a .edu email and live in the USA? You automatically win a meteorite: https://BrianKeating.com/edu Subscribe: https://www.youtube.com/DrBrianKeating?sub_confirmation=1 Support Into the Impossible on Patreon, get my weekly M.A.G.I.C. Message, unfiltered bonus content, and live monthly Office Hours with me: https://www.patreon.com/drbriankeating Join this channel for perks, monthly Office Hours, and your name in the Member Roster at the end of every episode: https://www.youtube.com/channel/UCmXH_moPhfkqCk6S3b9RWuw/join Featured Guests: Roman Yampolskiy on Twitter/X: https://x.com/romanyam?lang=en AI: Unexplainable, Unpredictable, Uncontrollable (book): https://www.amazon.com/dp/103257626X MIRI: https://intelligence.org Emad Mostaque on Twitter/X: https://x.com/EMostaque I.I.I. Inc.: https://ii.inc/ Stable Diffusion: https://stability.ai/ My books: Losing the Nobel Prize (memoir): http://amzn.to/2sa5UpA Think Like a Nobel Prize Winner: https://a.co/d/03ezQFu Focus Like a Nobel Prize Winner: https://a.co/d/hi50U9U Galileo's Dialogue (first-ever audiobook): https://a.co/d/iZPi9Un Twitter/X: https://x.com/BrianKeating Substack: https://briankeating.substack.com Blog: https://briankeating.com/blog Audio-only: https://briankeating.com/podcast #AIrisk #aisafety #stablediffusion #RomanYampolskiy #EmadMostaque #briankeating #intotheimpossible Learn more about your ad choices. Visit megaphone.fm/adchoices

Legal is the department that can stop a business transaction cold. A contract goes into review and two weeks disappear. Procurement waits. Sales waits. And the tools that were supposed to fix that — an assistant bolted into Word, a chat window with a contract pasted into it — ask an in-house lawyer to trust a system that can give one answer today and a slightly different answer next week. In a field where the human carries the liability and the model does not, that is not a rounding error. That is the whole problem.In this episode of Talking AI, Matt Paige sits down with Emad Khazraee, co-founder and CTO of RiskVantage AI, previously VP of AI at Xometry, a data science and AI leader at Turing, an information science professor, and a fellow at Harvard's Berkman Klein Center. For years Emad told his co-founder, Mark Afshar — a practicing lawyer turned in-house counsel for big pharma — that legal AI was a bad idea: a wrapper has no moat, and Anthropic or OpenAI will do it better than you overnight. What changed his mind was an architecture, not a market: a deterministic ontology that owns the legal reasoning, and small domain-specific language models that handle the language.The conversation covers why a nine-billion-parameter model running sub-second on a commodity GPU can match a frontier model inside a single domain, how subsidized token prices are distorting the entire legal AI market, why RiskVantage AI sells to procurement and sales ops rather than to lawyers who bill by the hour, what a failed PhD project on symbolic AI taught him about where determinism belongs, and whether the billable hour survives the decade.In this episode, you'll hear about:What ChatGPT can't know about your company: its risk appetite, its baselines, and the practices it expects every single timeWhy the legal services market — north of $900 billion, by Emad's count — has every frontier lab gunning for itThe objections that made him refuse to build a legal AI company, and the one that still holdsWhy a Word plugin stopped being defensible the moment Anthropic shipped its ownHow subsidized token pricing echoes Uber and Lyft, and who gets hurt when the subsidy endsThe consistency problem: one answer today, a different answer next week, and a lawyer's confidence goneNeuro-symbolic AI in plain English — a deterministic ontology for legal risk, LLMs for document understandingThe three years Mark Afshar spent codifying legal risk before there was a productWhy a 9B domain-adapted model is “dumb enough” that it can't wander outside its sandboxKnowledge distillation, silver datasets, and self-distillation policy optimization in practiceThe sovereign-cloud niche: ITAR data, commodity GPUs, and customers whose data will never leaveOutcome-based pricing, AI-enabled law firms, and what happens to the billable hourThe access-to-justice case: pro se filings, public defenders, and what a $20 subscription changesKey Moments00:01:30 — What ChatGPT can't know: your company's risk appetite and baselines00:05:12 — $700 an hour, a tenth at a time — and Coinbase's AI mandate to outside counsel00:08:12 — Why he told his co-founder no: a wrapper has no moat00:10:22 — Subsidized tokens, Uber and Lyft, and Legora's move to consumption pricing00:14:31 — The sovereign-cloud niche: ITAR data, commodity GPUs, and data that can't leave00:16:56 — “I am on the hook for the liability, not which model I used”00:18:15 — Same question a week later, a different answer, and confidence gone00:22:13 — If a rule can govern it, you should never use an LLM00:23:00 — The PhD failure: narrative machines, Frege, and symbolic AI's rigidity00:26:53 — Mark Afshar's three years codifying legal risk into an ontology00:29:00 — Neuro-symbolic AI, explained00:31:03 — Don't use a missile to hit a fly: why smaller models are safer00:35:47 — A 9B model, sub-second on a commodity GPU, matching Fable 5 in-domain00:38:00 — Does the billable hour survive? Outcome pricing and AI-enabled firms00:42:40 — Why affordable legal access is a democratic-society problem00:44:00 — The pro se surge: people filing their own cases with ChatGPT and Claude00:48:30 — “I'm talking with Copilot.” “That's not research.”Key LinksRiskVantage AIConnect with Emad on LinkedInMentioned in this episode:AI Opportunity FinderFeeling overwhelmed by all the AI noise out there? The AI Opportunity Finder from HatchWorks cuts through the hype and gives you a clear starting point. In less than 5 minutes, you'll get tailored, high-impact AI use cases specific to your business—scored by ROI so you know exactly where to start. Whether you're looking to cut costs, automate tasks, or grow faster, this free tool gives you a personalized roadmap built for action.

Donald Hugh McIntyre passed in 2025, but is remembered as one of the most insightful polymaths... while his blog Etherplan lives on. The episode came out in the first week of February 2019, ran two hours, and set the genetic code for Bitcoin Takeover podcast. Time stamps: 00:02:45 – Introduction: who is Donald McIntyre 00:04:49 – Was Bitcoin inevitable? 00:06:29 – 10,000 years of top-down structures vs 3.5 million years of evolution 00:08:27 – Cypherpunks: Chaum, Tim May, Nick Szabo 00:09:43 – The ancient desire for unconfiscatable money 00:11:55 – Bitcoin as a bottom-up system 00:12:39 – Living in a dual world: banks vs private keys 00:15:02 – Trusted third parties are security holes 00:16:40 – Nick Szabo's social scalability explained 00:18:27 – Why banking excludes: the unbanked in the US and Latin America 00:20:14 – Privacy, intelligence agencies & the false sense of security 00:21:13 – Is Bitcoin enough to make people demand decentralization? 00:23:23 – Asia's interest in Ethereum Classic & immutability 00:24:51 – Mt. Gox, breaches & untrustworthy third parties 00:25:57 – Not your keys, not your coins 00:27:28 – JP Morgan's building & the marketing of strength 00:28:45 – The instant gratification bribe 00:29:58 – The tradeoff: security means inefficiency 00:31:27 – The high performance fallacy 00:32:26 – Decentralization theater & scammers 00:33:48 – Hyperbitcoinization: elites first or the people who need it? 00:36:26 – The jet set analogy: how technologies bootstrap 00:40:54 – The Lindy effect 00:41:47 – The geeks got in before the bankers 00:43:25 – The token as intrinsic to the protocol 00:46:27 – Bitcoin as a biological invention 00:47:02 – How Nick Szabo thinks (and why he went to law school) 00:48:38 – Shelling Out: the origins of money 00:51:20 – The 150-person brain & money as a bridge between strangers 00:52:39 – Dawkins & reciprocal altruism 00:53:52 – Stone tools, subjective value & the first trade 00:56:15 – Reading Szabo 30 times: one article = six months of university 01:00:52 – Lomekwi, Oldowan & Acheulean: Donald's stone tool theory 01:03:42 – Stashes of hand axes as prehistoric vaults 01:04:58 – Is money millions of years old? 01:07:51 – Darwin, Dawkins & John Maynard Smith 01:08:31 – The logic of animal conflict 01:10:53 – Territory, asymmetry & conflict minimization 01:12:08 – Money as a conflict minimization device 01:12:36 – Bitcoin's game theory & the Byzantine generals problem 01:15:32 – Chimpanzees, bipedalism & why our brains grew 01:17:19 – Bitcoin carnivores 01:17:40 – Szabo & Satoshi: the Newton-Leibniz parallel 01:19:23 – Satoshi's credits: bit gold, RPOW, Hashcash, b-money 01:19:59 – The mystery of Wei Dai 01:20:54 – Remembering Tim May 01:21:53 – The JW Weatherman interview & the CoinDesk piece 01:23:40 – Tim May as the father figure who was never satisfied 01:24:39 – Why not David Chaum? The Elixxir disappointment 01:26:35 – Never underestimate the elders 01:28:08 – What the cypherpunks built: Tor, WikiLeaks, BitTorrent 01:30:17 – Napster, Megaupload & the raid in New Zealand 01:30:30 – Cryptography as a wall: violence made useless 01:34:10 – The shifting Ethereum narrative 01:35:27 – World computer: a marketing confession 01:38:11 – The spectrum from marketers to scammers 01:39:59 – The DAO fork fight & leaving for Ethereum Classic 01:40:44 – What Ethereum Classic adds to Bitcoin 01:41:17 – Liquid & RSK: glorified trusted third parties 01:41:57 – Turing completeness on the base layer 01:43:48 – The future: four or five networks 01:44:26 – ETC's monetary policy: 210 to 230 million 01:46:19 – The 51% attack that happened that week 01:47:48 – 220,000 ETC, 15 double spends & 5,000 confirmations 01:49:15 – The $5,000 ETC thesis 01:50:37 – What happens when Ethereum moves to proof of stake 01:51:37 – ProgPoW, the issuance cut & doing everything wrong 01:52:16 – The Nick Johnson poll on reversing double spends 01:52:54 – The Parity bailout & the precedent of the DAO 01:54:53 – Bitcoin: soft forks, Schnorr & the SHA-256 exception 01:57:09 – Governments will never voluntarily give up fiat 01:59:14 – Donald's criticisms of Bitcoin 02:00:02 – The 21 million cap as a political decision 02:01:13 – The miner lobby scenario, 40 years from now 02:02:09 – Merge mining, drivechains & the super miners 02:02:14 – Miners as the future Google and Apple 02:04:46 – Paul Sztorc, Truth Coin & the guest suggestion 02:05:40 – "Do you think I have a chance with Nick Szabo?" 02:06:31 – The selfish joy of podcasting 02:09:51 – Enlightening, a Lightning pun & goodbye

Kurt Wuckert Jr. is best known for his work at Gorilla Pool and CoinGeek. In this interview, he explains why BSV formed from BCH, what are the biggest differences between the two versions of big block Bitcoin, and why he believes he found Satoshi Nakamoto. 00:49 – The Cake Wallet contest explained 01:22 – Who is Kurt Wuckert Jr? Print shop, 2012, and the making of a big blocker 02:07 – The OP_RETURN reduction that killed his tokenization project 03:28 – The Hodlonaut photo controversy in Oslo: Kurt's side of the story 07:02 – Vlad on the fallout: friendship, slander, and psychological damage 09:21 – The handshake Hodlonaut refused, and Kurt's public apology 12:03 – The irony: Hodlonaut now sounds like a 2016 big blocker 13:53 – Dan Kaminsky's 2011 gigabyte block slides 15:46 – Andrew DeSantis, world computer ideas before Vitalik 17:13 – Why Mike Hearn was Kurt's first big block influence 19:14 – BIP 101, 102, 103: when block size was not controversial 22:46 – Gavin's 8 GB by 2036 plan and Jeff Garzik's modest 2 MB 25:23 – Lightning, Liquid, and the migration of activity to other chains 29:03 – Vlad's Raspberry Pi Lightning node confession 30:42 – Jack Dorsey and the lightning bank thesis 32:19 – BTC's share of market cap vs its share of transactions 32:42 – Tether on Tron and the Tetheral Reserve 35:39 – IShowSpeed in Nigeria: USDT accepted, Visa declined 37:36 – Chat question: what if Michael Saylor called to pivot to BSV? 40:24 – Saylor's anti-development tweets and the perfectionism trap 41:37 – SPV debates and engineering for perfection 44:24 – Why Bitcoin Core devs do not stack 47:01 – The 2018 inflation bug disclosed by a BCH developer 48:23 – The DDoS attacks on Bitcoin XT nodes 48:58 – The Resolution of the Bitcoin Experiment and the Mike Hearn wish 51:18 – The merchant adoption Bitcoin lost 52:24 – WBTC's 120,000 BTC vs Lightning's 5,000 53:09 – Sponsors: Braiins, Cake Wallet, SideShift, OrangeRock, Ecash 59:38 – Kurt on drivechains and Paul Sztorc 1:03:36 – The BCH/BSV split: what actually happened in November 2018 1:08:08 – Rolling checkpoints and the night of the fork 1:11:46 – Where Kurt was when the chain split 1:16:47 – Kurt's coin strategy: the Satoshi index of unsplit UTXOs 1:21:10 – The Binance delisting wave and Paolo Ardoino's defense 1:24:17 – Who invests and builds in BSV: Calvin Ayre, Tokenovate, Gorilla Pool 1:31:41 – Kurt's presentation to one of the world's biggest banks 1:33:05 – What BSV can do that BCH cannot 1:37:04 – Satoshi's disabled opcodes and the 2010 value overflow bug 1:39:16 – The poker client and marketplace in Bitcoin v0.1 1:41:48 – BCH's ABLA, CashTokens, and the governance difference 1:47:39 – The Monero codebase stored on BSV 1:48:05 – The DAR and Blacklist Manager hard forks 1:51:03 – CZ's aborted Binance rollback 1:55:33 – The white paper's immutability clause vs DAR 2:03:55 – Vlad's dormant-coins nightmare scenario 2:08:33 – The Craig Wright verdict as proof the system works 2:11:56 – Does Kurt still think Craig Wright is Satoshi? 2:15:36 – Dave Kleiman, the Drive Wipe Fallacy, and the Satoshi writing style 2:23:28 – The key slices, Uyen Nguyen, and the missing thumb drives 2:27:50 – Vlad's counter: the California IP address 2:32:00 – Bitcoin as an American project 2:35:32 – Hodlonaut replies mid-show 2:36:29 – If Craig is Satoshi, why sue the developers? 2:44:57 – The Arnhem 2017 speech 2:50:56 – Craig's neurodivergence and the villain arc 2:59:23 – The Gavin Andresen signing ceremony 3:04:58 – The rental Lamborghini and the pattern of bending truth 3:12:31 – Ryan X Charles and the post-COPA denunciations 3:14:04 – Can BSV exist without Craig Wright? 3:19:24 – BitVM: Craig's Turing completeness claim vindicated 3:20:44 – The giveaway reveal: the number is 40 3:22:24 – Would Kurt work on anything else if BSV died? 3:24:46 – Monero, fungibility, and the historian job offer 3:26:43 – OPL.dev, Bitcoin Schema, Sigma Identity 3:29:01 – The Smashing Pumpkins truce 3:32:00 – The Epstein money vindication 3:35:12 – Kurt's plugs: Gorilla Pool, kurtwuckertjr.com, bopen.ai 3:36:12 – Vlad's promise: this episode goes on the BSV blockchain 3:38:00 – The Mike Hearn introduction request 3:38:15 – Outro and sponsor thanks

Stewart Alsop sits down with Juan Verhook, founder of Tender Market, for a second conversation that ranges from the mechanics of European public tenders to the future of how we organize digital information. They cover how Tender Market helps smaller companies work around barriers like SOC 2 and ISO certification requirements, the surprising scale of public procurement (roughly 20% of GDP), and how AI and machine learning are reshaping the bidding process. From there the conversation opens up into bigger territory: the changing tolerance for being wrong in an AI-saturated information landscape, how language and culture shape perception, the reverse Turing test and the challenge of verifying human versus AI identity online, and Juan's daily workflow running eight or nine MCP servers through Claude Code. They close out talking about whether the folder and file system will survive the shift to AI-native interfaces, tying back to Stewart's own Stewart Squared episodes on the history of the PC. You can visit Tender Market at tendermarket.eu.Timestamps05:00 — Tender Market's origin story and how they help smaller companies work around SOC 2 and ISO certificate barriers.10:00 — Public procurement and its scale, roughly 20% of GDP, plus a look at public-private partnerships.15:00 — Local LLMs on a plane with no Wi-Fi, and comparing local model performance to frontier models.20:00 — Supply versus demand in AI infrastructure and whether hyperscaler token efficiency is quietly improving.25:00 — Whether AI will replace knowledge work tasks, and the shifting reality of what lawyers and other professionals actually do.30:00 — Reverse Turing test, digital identity verification, and the idea of a "pre-AI internet."35:00 — Model poisoning, RLHF, and the difference between pretraining and post-training.40:00 — Interleaved tool calling and how Tender Market ties pricing to task deliverables instead of billable hours.45:00 — RAG versus fine-tuning, prompt engineering, and when context windows actually matter.50:00 — Deterministic programming versus probabilistic agents, and when to build custom tools versus buy existing ones.55:00 — Juan's daily MCP stack (Supabase, GitHub, Calendly, CRM), and whether the folder-and-file system will survive the shift to AI-native interfaces.Key InsightsCertification requirements aren't dead ends—they're routing problems. When smaller companies got rejected from tenders for lacking SOC 2 or ISO certificates, Juan didn't turn them away. He found that EU procurement rules allow bidding as a consortium or subcontracting to a certified partner, turning a disqualifier into a workaround that builds trust with clients.Public procurement is a massive, underexamined market. Roughly 20% of GDP flows through public purchasing of private-sector goods and services, yet most people have no visibility into how tenders work or how governments post and award these contracts.Being wrong has become more socially acceptable. Juan traced this shift to the falling cost of information: in the Stack Overflow era, giving a wrong answer was costly, but now that answers are instant and abundant, both mistakes and corrections happen faster, changing how people learn and communicate.Task-based pricing beats hourly billing for AI-era services. Rather than charging per hour, Tender Market prices around the deliverable, winning a tender, which avoids the perverse incentive of hourly billing to be inefficient and instead rewards actually solving the client's problem.RAG and fine-tuning solve different problems. RAG helps a model reference large documents without hitting context limits, while fine-tuning changes a model's internal weights so it learns new behavior or style. Juan noted that true RAG use cases needing thousands of pages of context are rarer than the hype suggests.Deterministic code should replace repeated LLM calls once a pattern is found. Stewart described his own workflow: solve a task with an LLM a handful of times, then convert the repeated pattern into deterministic software so tokens are no longer spent on it, freeing the model for genuinely new problems.AI agents are never truly autonomous. Both hosts agreed that no matter how many steps an agent chains together, a human operator always initiates the first prompt, meaning accountability and intent trace back to a person even in multi-agent systems.

What do you do when the technology arrives faster than the plan you wrote for it?In this episode of Ethics and Innovation by Oxford+ brought to you by Equinox, host Susannah de Jager speaks with Professor Anne Trefethen, Professor of Scientific Computing at the University of Oxford and Trustee of the Alan Turing Institute, about leading a university through the arrival of generative AI. Oxford published its digital strategy in 2022, weeks before ChatGPT appeared, and Anne explains why it then took courage to stop, rethink planned investments and add the governance she had hoped to avoid.The conversation moves from student adoption and equal access to what national resilience now means: sovereign capability, models we can trust because we know what they were trained on, and enough trained people to use them well. It lands in the middle of a live policy push, with the UK government expanding free AI training to reach 10 million workers by 2030.Anne also makes the case for optimism, and for changing how we prepare graduates as entry-level work shifts. Useful listening for anyone setting AI strategy inside a large, complex organisation.(00:00) - Welcome to Oxford+ (01:31) - A Career in Scientific Computing (02:41) - Oxford's First CIO and the Road to Digital (04:21) - When ChatGPT Overturned the Strategy (06:39) - Rethinking Investment and Governance (09:12) - Pilots, Rollout and Equal Access (12:57) - Hinton, Altman and the Race to Superintelligence (19:41) - The Turing as National Convener (22:02) - What Resilience Means in the Age of AI (25:45) - Sovereign Models, Distillation and Trust (28:57) - Sovereign Data Sets and British Values (36:18) - Entry-Level Jobs and the Next Generation Anne Trefethen: Anne Trefethen FREng is Professor of Scientific Computing at the University of Oxford, a Fellow of St Cross College and a Trustee of the Alan Turing Institute, the UK's national institute for AI and data science, appointed to its board in November 2024. She joined Oxford in 2005 to establish the Oxford e-Research Centre, which she directed for over six years, became the University's first Chief Information Officer in 2012, and went on to serve as Pro-Vice-Chancellor with responsibility for gardens, libraries and museums, then people, then digital, leading Oxford's digital transformation programme. Before Oxford she spent almost 20 years in industry and academia, including research roles at Thinking Machines Corporation and the Cornell Theory Center, Vice President for research and development at the Numerical Algorithms Group, and leadership of the UK e-Science Core Programme. She was elected a Fellow of the Royal Academy of Engineering in 2017 and has served as a non-executive director of the UK Statistics Authority.Connect with Anne on LinkedInSusannah de Jager: Susannah is a seasoned professional with over 15 years of experience in UK asset management. She has worked closely with industry experts, entrepreneurs, and government officials to shape the conversation around domestic scale-up capital.Connect with Susannah on LinkedIn and Subscribe to the Oxford+ Newsletter for Exclusive ContentOxford+ is hosted by Susannah de Jager and supported by Equinox.Produced and Edited by Story Ninety-Four in Oxford.

In a landscape where convenience is a weapon used by scammers and corporate giants, your choice of wallet is the only thing standing between financial sovereignty and total loss. Most people treat their crypto like a savings account at a bank; they trust the “brand,” they trust the “user-friendly” interface, and they trust the influencers telling them everything is fine. That trust is a death trap… Over 8,000 Bitcoin (BTC) investors recently watched their life savings vanish because of a lack of entropy in the random number generator for Coldcard, a popular hardware wallet. In plain English: the “random” numbers used to secure their keys weren't random at all. They were predictable. And when the keys are predictable, the hackers don't even have to work; they just walk through the front door. That's all by design… When you choose a wallet that sacrifices entropy for ease of use, you aren't buying convenience. You're buying a ticket to become a victim. Watch on: Odysee | YouTube | X | Rumble | Bitchute | Vigilante.tv Real security is a science, not a feeling. If your software isn't open-source and verifiable, you aren't practicing security; you're practicing faith. And in this game, faith gets you liquidated. But let's look deeper. Why are we being pushed toward these fragile systems? Because there is a concerted effort toward the hijacking of Bitcoin. There are serious allegations that interests linked to Jeffrey Epstein funded a shift in the narrative, turning BTC from a disruptive technical tool into a “store of value” religion. They want you to HODL. They want you in a cult-like echo chamber where “Maxi” influencers tell you the system is perfect as it is. This “ossification” lobotomizes the technology. It strips away the reality that Bitcoin is actually a Turing complete supercomputer and a truth machine capable of on-chain AI and total transparency. If you're just following the herd, you're the product… Don't trust. Verify. Even “air-gapped” hardware is no guarantee if the randomness behind your keys is broken. Verification, not brand loyalty, is what keeps you sovereign. The sovereign toolkit requires assets that are private-by-default and hardware that never touches the internet. That is the only way of thriving in an on-chain world where surveillance is the default setting. The Great Divide is happening here, too. On one side, the indoctrinated victims. On the other, the intelligent human nodes who own their keys and their minds. Which side are you on? Follow me on X @VamosVigilante Want to be on the pulse of crypto? Access our exclusive portfolio, insider reports, full archive of monthly newsletters, real-time market updates, buy/sell alerts, and private community chat and get instant access to the latest issue of our monthly newsletter… Subscribe now! FREE “Crypto 101” Video Training – Watch Our Millionaire Crypto Analyst Reveal the Exact Crypto Wallet Setup He Wished He Had When Starting Out: https://CryptoVigilante.io/crypto101TCV Summit: “What Matters Most in Crypto” | https://dollarvigilante.spiffy.co/checkout/what-matters-most Replay videos available! The Crypto Vigilante (Follow on All Socials) The post Operational Security in a Hostile Era: Don’t Get Scammed appeared first on The Crypto Vigilante.

While we're deal with some crazy life stuff we thought we'd revisit a rapidly changing topic in Artificial Intelligence. We walk through the foundations that make AI feel inevitable: formal reasoning, the Church Turing thesis, early neural network ideas, and the Turing test as our most famous shortcut for judging “intelligence.” Along the way, we hit the hype cycles and turning points, from early chess machines and the coining of the word robot to research booms, AI winter, and the moment responsive machines become normal in American homes. 

We asked for your questions, and you sent so many that one episode couldn't hold them. This is part one of two — and part one is brought to you by two letters: A and I. That wasn't the plan; it's just where the mailbag pointed. Philip Rothman and David MacDonald take on the growing expectation that artificial intelligence should be able to turn a scan, recording, or generated track into finished, usable sheet music. Why is that still so difficult? And where can AI genuinely help musicians and music preparation professionals today? The discussion ranges from optical music recognition and audio transcription to proofreading, house styles, sample libraries, score following, and the potential for language models to communicate directly with notation software. Along the way, Philip proposes the “Gould test” as the music preparation equivalent of the Turing test. Underneath it all are larger questions about the value of expertise, the role of human judgment, and where responsibility lies when the technology gets it wrong. The rest of the mailbag — the questions that have nothing to do with AI — is coming next month. Products mentioned Notation software Dorico (Steinberg) Sibelius (Avid) MuseScore Studio (Muse Group) Flat (Tutteo) StaffPad Scanning and optical music recognition NoteVision (Muse Group) Soundslice sheet music scanner Newzik PlayScore 2 SmartScore 64 Pro (Musitek) Opuscan (Tutteo) Audiveris (open source) Audio transcription Klang.io and Transcription Studio Songscription Basic Pitch (Spotify) (open source) Magenta MT3 (Google) (research) Playback and vocal synthesis NotePerformer (Wallander Instruments) Cantai (Turing Opera Workshop) AI arranging and orchestration ArrangementLabs NotationAI ScoreSynth Orchidea (IRCAM / HEM / UC Berkeley) Score following and page turning Tomplay Smart Page Turn Smartleggio Piascore SampleSumo Music Following Antescofo (IRCAM) MCP bridges (community projects) mcp-musescore dorico-mcp-server (also on PyPI) Music generation Suno NYC Music Services / Notation Central PDF Batch Utilities Other tools mentioned Name Mangler / Renamer Claude (Anthropic) Previous Scoring Notes posts and podcast episodes Directly mentioned or closely related: Half-time report: what’s new, and a call for your questions (previous episode — the call for these questions) You have questions, we have answers (2025 listener questions episode) Asked and answered, part 1 (2023, the first listener-questions episode) Scanning the current OMR landscape (Steve Morell’s six-product comparison, December 2024) A snapshot of music scanning apps — and picturing the future (companion podcast) Dorico and The Rite of Spring (Stephen Taylor, January 2021) The “rite” way to copy old scores into new software (podcast with Stephen Taylor, February 2021) NAMM 2026: Sounding out the inputs with Klang.io’s Sebastian Murgul and the companion podcast NAMM 2026: An Avid Sibelius discussion with Sam Butler and Joe Plazak and the companion podcast NAMM 2026: Piascore’s bet on interactivity Marie Chupeau and the human side of Newzik’s artificial intelligence (podcast, November 2021) From zero to slice: Soundslice takes on optical music recognition with AI (podcast with Adrian Holovaty, December 2022) Dorico 6: Proof positive (review — the Proofreading panel) Dorico 6.0.22 extends proofreading capabilities (the “ignore issue type” feature) Behind “Behind Bars” with Elaine Gould (podcast, July 2023) Cantai now sings straight from Dorico (review) Richard deCosta gives your score a voice (podcast, May 2026) NotePerformer reverses course (the removal of third-party Playback Engines) NotePerformer updated to 5.1.0 Comma sense: Command Search in Sibelius NAMM 2025: Imbibing and transcribing with Oriol López Calle and the companion podcast (AI-assisted transcription inside a professional service) PDF Batch Utilities get a major rebuild — and a brand new app The rights stuff (podcast — rights and permissions) Other references Carlos Peñarrubia et al., “MuSViT: A Foundation Vision Model for Sheet Music Representation” — Pattern Recognition and Artificial Intelligence Group, University of Alicante; posted June 30, 2026, accepted at ECCV 2026. Project page Christopher W. White, The AI Music Problem: Why Machine Learning Conflicts With Musical Creativity (Routledge, 2025) Elaine Gould, Behind Bars: The Definitive Guide to Music Notation (Faber Music) NotePerformer: Playback Engines discontinued — Arne Wallander’s own explanation “Detecting Notational Errors in Digital Music Scores” — research on automated score checking Musicians’ Union arranging, music preparation and orchestration rates (UK) Copyright and Artificial Intelligence, Part 2: Copyrightability — U.S. Copyright Office report

What if the company quietly making OpenAI, Google, Anthropic, and Meta's models smarter was founded in India?Turing is one of the companies shaping how AI is advancing. It started in 2018 as a talent platform that found the top 1% of the world's engineers, became a unicorn in 2021, and then made a bet almost nobody understood at the time. In early 2022, long before ChatGPT existed, Turing began helping OpenAI improve its models by feeding human expertise directly into training. Today its network of more than four million vetted engineers and domain experts powers the post-training and evaluation work behind the frontier labs, and the company crossed roughly 300 million dollars in revenue while staying profitable, at a 2.2 billion dollar valuation.Vijay Krishnan is the co-founder and CTO. He was an NLP researcher at Stanford back when almost no one believed that predicting the next word could ever turn into reasoning, and he explains why running a modern model company without human-in-the-loop data is like entering a race with three tyres instead of four. He walks through how a model is actually taught to use software like Salesforce, why coding became the beachhead for every lab, and what changed for Turing the moment Scale AI was absorbed into Meta.The conversation then turns to the question every founder is now asked in the room. What is your moat against Claude? Vijay's answer is to go deeper than the frontier labs can reach, into the outcome you own and the context that lives inside an enterprise.If you are excited about how AI actually gets built, who really trains the models, and how to build a company that survives the labs, this episode is for you.00:00 - Trailer02:20 - The Indian company quietly behind OpenAI, Google, and Anthropic04:50 - How Turing went from a talent platform to a unicorn to an AI research partner08:20 - The bet nobody understood11:50 - Why a model company without human data is "racing on three tyres"15:50 - Why coding became the beachhead for every frontier lab19:50 - What changed for Turing the day Meta bought Scale AI23:50 - "What is your moat against Claude?"29:50 - Will AI create more lawyers, not fewer?34:50 - The teams where engineers haven't written code in six months39:50 - 16% of the Philippines' GDP is under threat from AI?43:50 - How you actually teach a model to use Salesforce49:50 - How robots are taught real-world work54:50 - Why million-dollar researcher packages are breaking startup hiring59:50 - The one kind of AI company that gets stronger as the models improve1:04:50 - Product vs. services, and the trap that quietly kills AI startups-------------India's talent has built the world's tech—now it's time to lead it.This mission goes beyond startups. It's about shifting the centre of gravity in global tech to include the brilliance rising from India.What is Neon Fund?We invest in seed and early-stage founders from India and the diaspora building world-class enterprise AI companies. We bring capital, conviction, and a community that's done it before.Subscribe for real founder stories, investor perspectives, economist breakdowns, and a behind-the-scenes look at how we're doing it all at Neon.-------------Check us out on:Website: https://neon.fund/Instagram: https://www.instagram.com/theneonshoww/LinkedIn: https://www.linkedin.com/company/beneon/Twitter: https://x.com/TheNeonShowwConnect with Siddhartha on:LinkedIn: https://www.linkedin.com/in/siddharthaahluwalia/Twitter: https://x.com/siddharthaa7-------------This video is for informational purposes only. The views expressed are those of the individuals quoted and do not constitute professional advice.Send us Fan Mail

Patrick McKenzie (patio11) is joined by Garrison Lovely, journalist and author of Obsolete: The AI Industry's Trillion-Dollar Race to Replace Us and How to Stop It, to map the three-sided debate over AI risk and why the arguments keep talking past each other. They then turn to what cheap cognition does to surveillance that already exists: FinCEN receives roughly 4 million suspicious activity reports a year and reads almost none of them, ICE agents run about a million queries against that database annually, and every podcast ever recorded is now transcribable for approximately nothing. The conversation covers capabilities denialism, ablated open-weights models, the fraud supply chain, and why AI is also unusually good at writing the Regulation E letter that gets your bank to fix your problem.–Full transcript available here: https://www.complexsystemspodcast.com/cheap-cognition-and-the-end-of-practical-obscurity-with-garrison-lovely/ –Presenting Sponsors: Mercury, MongoDB & ChainguardComplex Systems is presented by Mercury—radically better banking for founders. Mercury's new feature Command brings an LLM directly into your banking interface, so checking balances, finding invoices, or sending a wire is as easy as asking. Apply online in minutes at https://mercury.com/. What's the point of building faster with AI if your database can't keep up? MongoDB's native data model mirrors the language LLMs already speak. Ship at the speed of AI while staying ACID compliant at Fortune 500 scale. Start building at https://mongodb.com/ai.If attackers are using AI to weaponize code faster than any team can review it, your scanners won't save you. Chainguard builds libraries and container images from source, verified all the way down, with near-zero CVEs and zero malware. Build safely at https://www.chainguard.dev/. –Links:Obsolete: The AI Industry's Trillion Dollar Race to Replace Us―and How to Stop It: https://www.amazon.com/Obsolete-Power-Profit-Machine-Superintelligence/dp/1682196305 –Timestamps:(00:00) Preview(00:43) Intro(01:51) The three-sided debate over AI risk(05:32) Power, politics, and the tech backlash(09:49) Capabilities denialism and AI tells(14:54) The obsoleting machine(17:09) The Turing test is dead(18:56) Languages for free, then software engineering(22:37) Sponsors: Mercury | MongoDB(25:09) How high up the stack do the models decide?(29:11) Surveillance and cheap cognition(32:38) Podcasts, FinCEN, and the end of practical obscurity(38:35) Section 702 and the data broker loophole(39:35) Sponsor: Chainguard(40:55) Section 702 and the data broker loophole (cont'd)(47:23) Institutional friction and a million ICE queries(53:29) Security through obscurity no longer works(54:54) Scams, fraud, and ablated models(1:00:20) Personal utility versus societal backlash(1:02:16) AI as a tool for redress(1:06:34) State capacity and regulating what you understand(1:12:37) Tobacco, nuclear, and AlphaFold: strangle it or steer it(1:18:37) Where to find Garrison and the book(1:20:32) Wrap

That Don't Sound Right is a podcast about talking—the way we did before the internet, when you couldn't instantly prove anyone right or wrong and all the expertise lived in the people around the conversation. We hope you enjoy our conversations, and if you find yourself silently saying, That Don't Sound Right, send us a comment. You're one of us. Artificial intelligence is getting harder to recognize—but can you still tell when you're talking to a human? In this episode, Peter and Cecil tackle one of today's most fascinating questions by exploring the Turing Test, the thought experiment designed to determine whether a machine can successfully imitate a person. From there, the conversation takes an unexpected turn into the idea of a reverse Turing test: What happens when humans have to prove they're not AI? The hosts discuss everything from robocalls, AI chatbots, fake social media profiles, CAPTCHA tests, and voice cloning to science fiction stories that imagined this future decades ago. They swap stories, laugh about suspicious online interactions, and wonder how close we are to a world where it's nearly impossible to know who's real. Along the way, Peter and Cecil explore how AI is changing news, healthcare, education, hiring, finance, customer service, and everyday communication. As synthetic voices, images, and videos become increasingly convincing, they ask whether technology might actually drive us back toward something we've been missing all along—real, face-to-face conversations with real people. Whether you're excited about artificial intelligence, cautious about its rapid growth, or simply curious about where the technology is headed, this episode offers a thoughtful, humorous, and refreshingly human discussion about one of the biggest technological shifts of our time. Because when you have to prove you're human before joining the conversation... That Don't Sound Right. #tdsrpodcast #ThatDontSoundRight #ArtificialIntelligence #AI #TuringTest #ReverseTuringTest #ChatGPT #MachineLearning #Robocalls #Deepfakes #VoiceAI #FutureOfAI #Technology #TechPodcast #DigitalLife #HumanConnection #ScienceFiction #AITools #Conversation Connect with us:

A rep says "follow up on that" on a call, then buries it under five more calls and a late-night inbox. Three days later the deal's gone cold, but the CRM still says it's live. The manager forecasts off that.  The CRO forecasts off the manager. The board asks the CEO if anyone actually has a grip on the number. The whole thing was built on a promise nobody kept.  Adam Liska worked on the early Gemini models at Google DeepMind before leaving to close that gap. His company, airspeed, just raised a $20M Series A on a bet that "revenue execution" is the next real category, not another tool that logs calls and calls it intelligence.  Sam Jacobs traces the full arc with him: DeepMind to founder-led sales to the unglamorous work of turning a founder's instincts into a system a team can actually run.  What we get into:  Why "follow up on that" breaks the forecast chain from rep to CRO to board What "revenue execution" means, and why it isn't revenue intelligence with a new label  Whether you own the interface or hand it to a chat model and live as middleware  The Nashville lunch that became airspeed's first six-figure deal  The real sequence from founder-led to scalable: capture the data first, then build the playbooks  Owning your data instead of renting Salesforce's architecture  The modern Turing test, AI-native orgs, and where the back office is headed Chapters:   00:00 Intro 00:44 From DeepMind to airspeed, and the $20M Series A  01:30 "Follow up on that" — the execution gap that breaks forecasts  04:00 What category is this? Revenue execution, defined  05:40 Headless vs. owning the interface  08:00 The origin story: leaving DeepMind in 2022  11:39 Founder-led sales and the Nashville lunch that closed a six-figure deal  13:18 The hard part: turning founder-led into a repeatable system  14:14 Why they recorded everything from day one  16:35 Owning your data, and where the source of truth lives  19:03 The playbook: data first, then hierarchical playbooks  20:52 Influences: the modern Turing test and AI-native orgs  23:37 Where to find airspeed  Try airspeed: goairspeed.com

Alan Turing, matemático británico y precursor de la informática moderna, lidera durante la Segunda Guerra Mundial el equipo de Bletchley Park. Allí descifra los complejos códigos de la máquina Enigma nazi. Enigma, con claves diarias, es un desafío formidable. Tras el descubrimiento de su mecanismo, Turing desarrolla las "bombes", máquinas que, al explotar patrones de comunicación humana, leen los mensajes enemigos. Esta inteligencia, "Ultra", acorta la guerra y salva vidas, aunque a veces exige permitir ataques menores para preservar el secreto. Turing también concibe la máquina universal, fundamento del ordenador actual, y propone el Test de Turing, que evalúa la competencia conversacional de una máquina para distinguirla de un humano. La criptografía moderna hereda su legado. Pese a su genialidad, Turing es perseguido en 1952 por su homosexualidad, sometido a castración química y pierde su autorización de seguridad. Fallece a los 41 años, probablemente por suicidio. El mundo actual, ...

Do Democrats and Republicans really misunderstand each other as much as we think? This week, we dive into a surprising new experiment that puts that idea to the test — literally. Psychologist and researcher Adam Mastriani created a kind of “political Turing test,” asking people to write persuasive statements from the perspective of the opposite political party. Then, he tested whether others could tell the real from the fake. The results? Most people couldn't. We unpack what this means for our understanding of polarization, partisan animosity, and political identity. Is the problem really misunderstanding — or something deeper? Are partisans more empathetic than we give them credit for? Or are they just really good at writing what they think others want to hear? We also explore the experiment's implications for political science research, theory-building, and the broader sociology of science. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Secretos y mentiras En Bletchley Park, Turing y su equipo trabajan para romper Enigma y frenar a los nazis. Espionaje y riesgos crecientes marcan esta guerra secreta. Stalingrado La Operación Azul, el Ejército alemán combate por hacerse con los campos petrolíferos soviéticos y Stalingrado, en una de las batallas más mortíferas y decisivas del Frente Oriental.

TURING encapsulates the true story of Alan Turing, a man caught between love and logic, who simply longs to be understood. The project features an impressive lineup of talent, including Vincent Jamal Hooper, known for his roles in The Lion King and Hamilton, James Olivas from the West End production of EVITA, and Mason Olshavsky, who is currently in Lost Boys on Broadway and has appeared in EPIC and Off-Broadway's The Baker's Wife. Their participation adds a layer of depth and talent to the project, enhancing the emotional resonance of Turing's narrative. Also joining the TURING EP are talented UK performers Jacob Fowler and Aiden Carson.TURING unfolds through three pivotal stages of Alan Turing's life, exploring his relationships at ages sixteen, twenty-eight, and forty. With book, music, and lyrics crafted by Michuda and Ward, the narrative is presented in a non-linear fashion, skillfully weaving together moments of joy, discovery, love, and despair. This innovative approach allows listeners to engage with Turing's life on multiple levels, highlighting the profound impact of his work against the backdrop of a society that condemns you for being gay, no matter your status or contributions.The TURING EP will be available on all major streaming platforms, making it accessible to a wide audience. The EP will be progressively released over the next few months. Following its release, there are hopes to explore live performances and a full production of the musical 'TURING', which successfully ran at the Greenhouse Theater Center in Chicago as a production of DePaul University's Blue Demon Theatre. TURING was also awarded “Best Musical” and “Best Lyrics” out of 18 musicals featured at the 2024 Theatre on the Verge New Musicals Festival. This exciting project not only highlights Turing's significant contributions to science but also serves as a reminder of the importance of understanding and acceptance in society.

This lecture discusses key ideas from the 20th century philosopher, mathematician, and computer scientist, Alan Turing's article "Computing Machinery And Intelligence", published in 1950 in the journal Mind. This is an important early work on artificial intelligence, which proposes what later has come to be called the "Turing Test". Specifically it examines his dicussion at the end, motivated originally by what he calls "Lady Lovelace's Objection", namely that a machine cannot actually take the information it has and come up with something novel or original, or put another way, it cannot do anything it has not been programmed to do. Turing is interested in whether a digital computer could be developed that would be able to "learn" in some sense, and he postulates the creation of a child computer which then would go through a process of education, and considers what would be involved in this To support my ongoing work, go to my Patreon site - www.patreon.com/sadler If you'd like to make a direct contribution, you can do so here - www.paypal.me/ReasonIO - or at BuyMeACoffee - www.buymeacoffee.com/A4quYdWoM You can find over 4500 philosophy videos in my main YouTube channel - www.youtube.com/user/gbisadler Get Alan Turing's "Computing Machinery And Intelligence" here - https://courses.cs.umbc.edu/471/papers/turing.pdf

Verrassing op je feed! Naast Satoshi Radio maken we sinds kort ook Turing Station: dé Nederlandse podcast over AI. Elke week het belangrijkste AI-nieuws, met dezelfde diepgang en nuchterheid die je van ons gewend bent.Om je een idee te geven delen we dit fragment uit aflevering 2. Het gaat over een "denkende schaakmachine" uit 1770 die tachtig jaar lang heel Europa voor de gek hield en waarom Amazon en Tesla vandaag nog steeds precies dezelfde truc uithalen. Bevalt het? Alle afleveringen en links vind je op turingstation.nl.

Yahoo Finance reported that AMD signed a deal with venture-backed Turing to expand into self-driving, with no financial or product details disclosed. AMD brings automotive assets from its $49 billion Xilinx acquisition, including Versal AI Edge and Zynq platforms, and already ships silicon in Tesla Model S and Model X infotainment. The deal positions AMD against Nvidia, Qualcomm, and Intel's Mobileye in automotive compute. Stakeholders will watch for named design wins with Tier 1 suppliers such as Bosch, Continental, Magna, and ZF, and for pilots with automakers. Founders should track developer support around AMD ROCm, long-term supply commitments, and compliance with ISO 26262 and cybersecurity mandates.Learn more on this news by visiting us at: https://greyjournal.net/news/ Hosted on Acast. See acast.com/privacy for more information.

This lecture discusses key ideas from the 20th century philosopher, mathematician, and computer scientist, Alan Turing's article "Computing Machinery And Intelligence", published in 1950 in the journal Mind. This is an important early work on artificial intelligence, which proposes what later has come to be called the "Turing Test". Specifically it examines Turing's framing of a set of objections and arguments, which he calls "opinions opposed to my own". These include: The Theological Objection The "Heads in the Sand" Objection The Mathematical Objection The Argument from Consciousness Arguments from Various Disabilities Lady Lovelace's Objection Argument from Continuity in the Nervous System The Argument from Informality of Behaviour The Argument from Extrasensory Perception To support my ongoing work, go to my Patreon site - www.patreon.com/sadler If you'd like to make a direct contribution, you can do so here - www.paypal.me/ReasonIO - or at BuyMeACoffee - www.buymeacoffee.com/A4quYdWoM You can find over 4500 philosophy videos in my main YouTube channel - www.youtube.com/user/gbisadler Get Alan Turing's "Computing Machinery And Intelligence" here - https://courses.cs.umbc.edu/471/papers/turing.pdf

(2:21) Unha exposición na Biblioteca Pública de Nova Iorq deu lugar a que Peter Kuper elaborase a fascinante banda deseñada "Insectópolis". Ideal para os amadores dos insectos e tamén para os que renegan destes bichos. O noso experto en banda deseñada é Daniel Pizarro. (15:07) Segunda parte da nosa adaptación da obra de "divulficción científica" de Senén Barro "Algún día, quizais antes do que imaxinei" que nos levará a analizar a vixencia do afamado test de Turing. (29:53) A miopía está a converterse nunha auténtica epidemia global, pero quizais o problema non sexan só as pantallas, senón a falta de luz natural nos nosos ollos. Conversamos con José Manuel Alonso (College of Optometry, Universidade Estatal de Nova York), que lidera unha investigación pioneira sobre como a cantidade de luz que chega á retina pode ser clave no desenvolvemento da miopía. Cando lemos en interiores ou en pantallas a pupila contráese para enfocar mellor, de tal xeito que á retina chega dez veces menos luz ca en circunstancias análogas no exterior.

https://youtu.be/Ji1OZYu1r1Y Charles Fry, Founder and CEO of CODE Éxitos, is helping businesses hire AI in enterprise to transform software engineering, modernize product development, and build intelligent connected systems.  In this conversation, Charles introduces The Agentic Org Chart Framework: Hire Systems Thinker, Look for Failed Entrepreneurs, and Have AI Replace “Trade Skills”. He explains why AI is fundamentally changing software development, how organizations must redesign their structures for an AI-first workforce, and why systems thinking is becoming more valuable than technical specialization. Charles also discusses how the rise of agentic software development is reshaping the future of SaaS, why combining AI with connected hardware creates a stronger competitive advantage, and what business leaders must do to successfully navigate AI-driven transformation. — Hire AI in Enterprises with Charles Fry  Good day. Steve Preda here, and I’m talking with Charles Fry, the Founder and CEO of CODE Éxitos, building cyber-physical systems for mid-market and enterprise companies, as well as full-stack web, mobile, and SaaS development. Charles, welcome back to the show.  Hey, I’m happy to be here. It’s always great to see you, and I’m looking forward to our chat. Yeah. It’s so interesting to talk to you because the last time we had you on the show, three or four years ago, it was still before the AI age was fully upon us.  Right.  Your business was kind of a different business. I’ve been following you on LinkedIn, and I see that you’ve evolved your approach, and now you’re an AI-first company. So tell me a little bit about how that came about, this whole evolution, and how you found your new focus?  Yeah. Wow. It’s been that long since we were on the show. It was a lot of fun, but here we are. You’re right. AI really sort of came out of left field. I’ll skip all the technical things that suddenly made AI an achievable thing. But really, at the beginning of 2024—and somebody can fact-check my timeline—ChatGPT, if you were aware of it, was kind of passing the Turing test. It was giving pretty reasonable answers to natural-language questions.  And we were like, “Wow, this is interesting.” At first, we were helping our clients think about how to build those capabilities into their products, something we still do.Share on X But by mid-’24, late ’24, it became pretty obvious that one of the best applications of large language models—and expert systems, we used to call them that—is writing software. And so by early to mid-’25, the systems were suddenly not novelties. They were credible at what they were doing, and they were gaining momentum in the quality and credibility of the software they were producing.  Now, CODE Éxitos was started largely to create an opportunity for entrepreneurs and enterprises that needed, let’s call it, garden-variety, well-done software. We built that through the Americas to arbitrage labor rates in Latin America and leverage the time zones. So it was essentially an offshore, blended, hybrid-team model. Honestly, by the middle of 2025, the AI tools for software engineering were as good as, or better than, 50 percent of the human developers that we employed.  And at some point, as a business owner, when you’re out there representing yourself, and your product is yourself, and you’re representing that to clients, you have a moral and ethical obligation to say, “Hey, I think I can still give you the best product that you’re looking for, but I’m going to do it in a different way.” So beginning in late 2025, we were hard into the pivot. Today, all of our software development is done agentically. There are still people. It’s not a complete dark factory. But our mid-level performers and below, we exited them from the business, which caused a lot of human turmoil. I mean, we were at around 100 people. A lot of human turmoil. Our clients were going through the same thing. We were watching what was happening in their organizations and what the leadership demands were.  We just made the decision to lean into it. And here we are, almost mid-’26 now, and it’s actually going really well. Now, AI, of course, anyone who opens an internet browser anymore sees it. AI is everywhere. You read the newspaper. AI is going to change everything.  Every application has an AI layer to it.  Yeah.  Right? Every SaaS application has a button that says, “Use AI here.”  My view, at least—and these are the things people should probably give me some credibility on—I’m going to keep my views focused on how AI applies to the software industry, my industry, and its direct impact. We see, and we have clients working on, things like AI in customer service, the legal department, and the finance department. We do all of those things internally—agentic first, AI first. But those really aren’t my industry. I’m not ready to make a sweeping prognosis about how AI is going to change capitalism in the United States. But in the software industry, it’s a fundamental change. And it’s not done yet.  So what’s your vision? Where is everything going? What’s it going to look like three years from now?  Well, I’m not smart enough to know that. But I think the patterns we've seen—and again, within the world of writing software, and making that a broad category of activitiesShare on X —are going to continue to consolidate and converge to where humans are important, but they might be only 10 to 20 percent of the input to the process. I really do think we’re going to see a day, sometime in the three-to-five-year time horizon, where a large amount of software will be written and managed by other software systems.  There’s no technical reason to prevent that. For example, I was talking to one of our clients today, a CIO at a great company. A couple hundred million dollars in revenue. A really well-run business. A sizable internal IT team. But there’s a lot of ongoing maintenance and attention required. Building the software is just the beginning of a five-to-ten-year life cycle. So I think in the near term we’re going to see that building the software becomes, “Okay, we got that figured out.” That’s a largely solved problem.  We’ll then progress to the problem of: “Hey, this software has been in production for five years.” “It needs updates.” “It needs attention.” “It needs maintenance.” “It needs to scale.” More software systems will take care of that. Fewer and fewer humans will be involved in that kind of work. So I think that’s where the software industry is headed. I think it’s going to be 60 to 80 percent smaller in human capital than it is today. Sometime soon. Yeah. I really do think it’s about as close as I want to get to calling it an extinction event. Let’s put it that way. Some people say SaaS companies are going to go out of business, and it’s all going to be agents running around doing things for us. But other people say SaaS companies are actually the SOP for whatever activity is out there, and you need that structure. The SaaS application provides that structure. You don’t want agents running in an unstructured way. You’d rather have these SaaS applications. What’s your view?  I think that’s a good way of looking at it. If you’re a dinosaur like I am, back in the late 1980s or early 1990s, when you wrote software for a company, everything was custom software because there were no packaged software products, no SaaS platforms. But over the last 20 years, I think SaaS companies have become, for a lot of businesses, exactly what you said. They’re the embodiment of best practices. If you take something like HubSpot, which I’m sure you and many of your audience are familiar with, you really don’t need to customize it.  You just need to follow its baseline processes because they have thousands of customers who have helped refine the sales motions that work. So I think there’s some truth to that. The problem SaaS systems face is that the barrier to competitive entry is much, much lower. If you look at a company like Salesforce—and I think I’ve led three different Salesforce deployments back when I was a CIO or CTO—that software really shows its age. It’s layers and layers of complexity built to serve a wide audience. It’s great. It’s expensive. Emerging companies don’t need that. They can effectively vibe-code their own CRM system, and it works. I think the threat for big SaaS companies is twofold.  One is that the next generation of customers is going to onboard very differently into those systems than the previous generation.Share on X I don’t know what that onboarding ramp is going to look like. The second problem is there’s very little defensibility in having a pure software product. And the other part of our intro—you talked about cyber-physical systems. We’re spending more and more of our product development cycles on hardware-related products, things that have a nexus in the physical world. Here’s a good example. I’m wearing one of these health rings. This Oura Ring. Oura, yeah.  There’s a lot of amazing hardware in here that justifies my monthly subscription for the app. The app we could recreate pretty easily. But the development, manufacturing, and distribution of this physical item create a much higher barrier for a competitor to overcome. So more and more of our clients are companies that have a physical product they either want to make smarter or make more connected.  That’s really what it comes down to. And that’s a pretty exciting space. But for a pure-play SaaS company, I think it’s going to get tough. The competitive pressure is going to be intense. And the barrier to entry is going to be really low. It’s not even about engineering cost anymore because the cost of engineering has dropped so much with AI. It’s almost like it went full circle. You had all these product businesses that wanted to become service businesses to create recurring revenue. And now the service businesses—the SaaS businesses—want to become product businesses to create a barrier to entry, improve retention, or reduce disruption. Isn’t that interesting?  Yeah. I hadn’t thought about it exactly that way. Maybe the pushback would be that these professional services businesses wanted to have a technology play or a platform. That’s interesting. But I think we’re going to see AI, at least in technology, continue to lower the barrier to entry. It will allow much faster experimentation with pure software ideas. And we’re focused on the things where the AI robots can’t play. They’re not going to cut your grass.  They might guide the machine that cuts your grass, but they’re not going to cut your grass. So I think that while the turmoil is still sorting itself out in the pure software world, we’re going to see a whole new set of opportunities open up. We’ll be able to build truly smart devices. Devices that think for themselves. Devices that are aware of the world around them. They can participate with us in our day-to-day work. That’ll be a lot of fun. I think we still have some rough sailing ahead of us as AI sorts itself out.  Isn’t it true that people prefer to interact with a purpose-designed device rather than a software product? And maybe an app is kind of a device that is software, or maybe that’s the overlap there. But I know there are some things I’d rather have on my phone, even though it’s complicated because there are so many other things on it. But if I have a single-purpose device, like you have your Oura Ring, it’s easier to interact with. There’s no complexity, and then it lowers the accessibility.  Yeah. An area of active study is something called HMI, or Human-Machine Interface. Again, back in the ’80s and ’90s, it meant things like: Are the buttons big enough for someone to push? Does a red light always mean a bad thing, and a green light always mean a good thing? But now that’s expanded into the kind of research in psychology and sociology that you’re talking about, Steve. Some of that is really amazing.  I’m sure you’ve seen them, and your audience has seen them. You can find these videos on YouTube. There are humanoid robots. It took a while for researchers to figure out that a robot doesn’t actually need a head. It can have what is essentially a torso with arms and legs. The head doesn’t really need to be there. But a robot with no head freaks people out. Yes. People don’t like it. So the robotics engineers put heads on them. Then they thought, “Well, if we’ve got a head here, we’ll just put a face on it.” But if the face is too realistic, it gives people the creeps.  Yeah.  So people didn’t like faces on them. If you look at the current generation of humanoid autonomous robots, they have these, I don’t know, sort of pseudo-faces. They kind of look like Halloween jack-o’-lanterns or something. They’re not scary, but they’re somewhere in the middle. Anyway, the things you’re talking about are really fascinating.  I mean, Isaac Asimov wrote about humanoid robots and all the challenges that come with them. What happens when they have a human-like appearance? What happens when people think they are actually people, but they just don’t age? All those things have been explored. But listen, I’d like to switch gears here and ask you this. Right now, in this AI age, what drives your business? What drives growth in your business?  This part is truly fascinating to me. Everyone is working off the same timeline now, which isn’t a very long timeline. We don’t have a lot of experience to draw on. Much more quickly than when the internet became commercially available—I was there when that happened too— the adoption of AI as a fundamental change happened in a matter of months, compared to several years for the internet.Share on X Some people also compare it to the adoption of mobile phones, which you mentioned.  But this has happened very fast. A year ago, we were talking to sophisticated technical buyers who said, “Yeah, I’m still on the fence about whether I like agentic software development.” That doesn’t happen anymore. Everybody says, “Yeah, we’re using it too.” What we’re seeing now is that it’s evolved so quickly and had such a fundamental impact that people don’t know not only how to manage it inside their business, but also how to deploy it. It’s really disruptive. And this is where you’re a pro.  It’s really disruptive to organizations. So in less than a year, we’ve gone from, “Hey, should I let my developers use AI?” to now everybody using AI. And the leading teams, including ours, can produce high-quality commercial code faster than organizations can absorb it, and faster than org charts can adapt to the change. Our engineering teams have to adapt to the pace of the business, not the other way around. Because we get clients saying, “Hey, you guys are producing too much.” “We can’t check everything.”  “We haven’t finished testing last week’s new features and capabilities yet.” “We can’t take another batch of features and capabilities this week.” So we’re seeing a lot of organizational behavior change starting to come out of this. When we’re talking to C-level executives and senior leaders, that’s where most of the conversations are today. “How do I retool my organization to capture the benefits?”  Yeah. Because what I see is that the more AI you apply, the faster decision velocity becomes. And the complexity of understanding the whole picture, connecting the dots, increases. So you need a different kind of person who can operate at that higher level of contextualization. Do you see the same thing?  We do. Software engineering and product development had matured into a pretty predictable set of job descriptions and capabilities. The business processes were really well worn. We knew what the product owner did. We knew what a project manager did. We knew what a tech lead did. Et cetera, et cetera. A lot of these, frankly, became trade skills. “Hey, I’m really good at writing code.” Or, “I’m really good at doing QA, but I’m not really a systems thinker.” “I’m not an entrepreneur.” “I’m not a creator.” Pick your fuzzy lens of choice.  That’s really what AI displaces right now. AI displaces those trade skills and, frankly, does them better and cheaper. There’s no way to dispute that. What we look for now, and I think where the trend is going with our clients, is systems thinkers. We have enough agentic tooling built on our own internal platform that the people operating and building products for our clientsShare on X —we refer to our team as digital creators—come from a variety of backgrounds. You don’t have to be a computer science major. You do have to have some domain experience. You do have to be a systems thinker. You do have to understand what business value you’re trying to create. But as far as actually writing really good code, nobody’s really doing that now. It’s being done automatically.  If you have a couple of gray hairs like I do, you’ll remember back 20 years ago when we talked about the war for talent. That’s what everybody was looking for. They wanted people with these highly specialized engineering skills. I think there’s a new war for talent. It’s going to be harder to pin down because we’re going to be looking for whole-systems thinkers as opposed to technical specialists. Because AI will be the technical specialist we need, regardless of the business domain. So what do you do to infuse systems thinking in your business?  Wow. I wish I had a good answer for that. I don’t even know how to recruit these kinds of people right now. I’ll be that candid with you and your listeners. I was talking to a couple of my senior people, and I said, “Maybe we should go look for failed entrepreneurs.” Which is kind of a heretical thing to say. But as an entrepreneur, I know firsthand that it doesn’t always work. The fact that the business fails doesn’t necessarily mean you, as an entrepreneur, are a personal failure.  Entrepreneurs are about the only, I don’t know, primary source I can think of for people who have done a little bit of everything. They’re systems thinkers. Maybe they didn’t get it right, but they could. So we talked about that. I don’t know if Disney still does it, but Disney was phenomenal at producing these kinds of people through its internal training programs. We’re not big enough to compete with Disney. But to answer your question, how do we teach it? I can’t honestly say that we do. Because we’re still figuring out what it is that we would even teach.  Well, it’s a new type of SOP that’s needed in the business. So what are the best practices for building an AI workforce? That still needs to be defined.  Yeah. For larger organizations, our clients that are running larger organizations have the same problem. All of a sudden, their org chart is broken. What I mean by that is, if you look at the way we’ve traditionally built and scaled businesses, you have this pretty large cadre of managers who give you what Eliyahu Goldratt called the span of control, your degree of leverage. When you’re younger, you hear things like, “Ah, my manager doesn’t even do anything.” You’ve probably heard that before. “Oh, my manager doesn’t really do any work.” “He just comes in and bugs me.”  It’s not entirely wrong because we rely on that manager’s experience to be spread across six, eight, or ten other people and supervise their work. So managers don’t really do a whole lot of delivering the work themselves. I think AI is going to change that. I know AI is already changing that inside technical teams. All of a sudden, we have clients saying, “My org chart doesn’t translate to the way my business operates under this new agentic model.” That’s problem number one. Problem number two is, “I have people on my team who are good people and good contributors, but there’s no box for them in the new org chart that I think works with an agentic workflow.” Does that make sense?  Yeah.  So two things have happened suddenly. We see a lot of press—although I think it’s moderating a little bit now—about how kids coming out of university are having trouble getting entry-level jobs. True enough. I think the other area where org charts are collapsing is managers who don’t actually produce any output. Supervising other managers and compiling the weekly report of reports just isn’t a valuable job function, even at the best of times. And now—I hate to say this—but it’s useless. So I think there’s a lot more work we’re going to have to do with our clients, and ourselves, on what an org chart should look like. And what the expectations are for people doing work inside a company that’s moving aggressively toward leveraging AI capabilities in what we would normally call white-collar job functions. Yeah. Some years ago, I thought about the org chart being broken. The top-down hierarchical org chart—I think it’s completely broken. In my head, the org chart is more like an amoeba, where you have the entrepreneur and the manager in the middle as yin and yang. Different departments report to different people. And you’ve got these pizza teams all over, actually delivering teamwork. But that’s fascinating. Yeah, fascinating topic. So who are the ideal customers for you? If they have the right kind of projects, what are the right kinds of customers and the right kinds of projects for CODE Éxitos that would be interesting to look at?  There are two types of clients that we focus on and that we can help quite a bit. The first type of client is one that has a large, established internal software development and product development process, and they’re trying to figure out how to adapt, modernize it, and harness AI. We can come in, and we have a very opinionated point of view. We can have a couple of conversations, and they either like the direction our telescope is pointed in and want us to help, or they say, “No, we think we’re going to do it a different way.”  And that’s okay, too. Because right now, nobody really knows the final answer. We call those engineering transformation projects. Somebody says, “Hey, we have a team. Can you help us get better?” The second type of client we like is one that has this physical connection challenge. I’ll give you an example. We have a client that primarily makes pumps and motors. They put those pumps and motors into very specific industrial applications across North America. They wanted those pumps and motors connected to a network.  They wanted to collect data from those pumps and motors to help their customers. Once we built the data collection, the electronics, and the connectivity, the data started coming in. Now there are a lot of AI-related things we can do with that data. We’re beginning to work on what you can think of as supervisory agents that watch what’s going on. They’re much more robust than the old filters that just looked for exceptions and red lights. Those are the kinds of clients we really help on the product side.  Sometimes they come to us with an idea scribbled on a napkin. It’s like, “Hey, we have this system or this process that we envision, and we need somebody to help us build it.” We’ll do the electronics, the AI, and the software engineering. And that becomes a complete system. So, more systems thinking. Yeah. And then you combine software with hardware. Then you have AI agents doing much of the coding, management, and maintenance of these systems.  Yeah, that’s right. This is not a client of ours, by the way, but the story I’m going to tell is fascinating. It’s a second-degree connection that I chatted with. He runs a $100 million-a-year manufacturing company. I think he’s second or third generation—I can’t remember which. He knows the business. He grew up in the industry. He’s in the process of transforming the company so that he can basically run the whole business from his phone.  He’s applying AI to his internal business functions like finance and accounting. He’s done some really amazing stuff. He’s automating the manufacturing process, the machines, and the feedback systems. It’s just stunning what this guy is already able to do. He just picks up his phone and says, “Yeah, I want to run my company from here.” I think he’s going to be able to do it. I think we’re going to see more of that emerge. Yeah. That’s fascinating. The race is for the first one-person unicorn, right?  I think that’s already happened. I don’t know if you’ve seen this. I can send it to you. There was a New York Times article a couple of months ago about a guy—not a tech guy, a marketing guy. He and his brother run an online company that generates $1.8 billion a year in sales. And it’s just the two of them. Wow!  Spoiler alert: As I remember, they sell weight-loss drugs. He’s a marketing guy with a tech background. He figured out all these marketing channels where, if you order Ozempic or whatever online, it basically drop-ships from the pharmaceutical company to you, and he gets a cut. But he said in the article, “At one point, we were doing $300 million a month in sales.” He goes, “Well, technically, I’m not a one-man company because I had to hire my brother to help me out.” So it’s the two of them.  That’s pretty great. Yeah. That’s definitely a unicorn. So if you’re listening to this and you have an enterprise company, and you want to harness AI in your business, create agentic systems, streamline your operations, and make your company more efficient and productive, then reach out to Charles Fry at CODE Éxitos. Any last thoughts for listeners who are thinking about building a business in the AI age? What advice would you give them? I don’t know that it’s changed a whole lot. Building a business is always hard work. For any listener who wants to chat about any of these topics or see if we’re the right fit, they can always reach out and contact me. The conversation is free, and I usually learn something from it. But no, I would say that things are different. It doesn’t mean they’re wrong or better. I think they’re just different. It’ll be interesting to see how all of this unfolds over the next few years.  Yeah.  I’m in it for the journey.  We’re living in interesting times. So, Charles Fry, Founder and CEO of CODE Éxitos, thanks for coming on the show. And if you enjoyed this show, make sure you follow us on YouTube and Apple Podcasts. Give us a review, and stay tuned because every week I bring an amazing entrepreneur onto the show. Thanks for coming. Thanks for listening.  Thanks, Steve. Important Links: Charles's LinkedIn Charles's Website

In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the emerging phenomenon of AI psychosis. You’ll discover how interacting with large language models can impact your mental health and perception of reality. You’ll learn to identify the five specific themes of AI-driven delusions that affect users today. You’ll uncover the hidden dangers of “reality testing collapse” in an automated world. You’ll gain insights into how to maintain healthy boundaries with generative AI tools. 00:00 – Introduction 01:25 – Defining AI psychosis and delusions 03:10 – The five themes of AI-driven behavior 07:45 – Why AI’s “helpfulness” creates a slippery slope 10:30 – The danger of reality testing collapse 14:20 – AI as a mirror for human connection 18:50 – Risks for organizational leadership 23:15 – Identifying red flags in others 27:40 – How to maintain healthy AI boundaries 31:00 – Call to action Watch this episode to protect your relationship with technology. Can’t see anything? Watch it on YouTube here. Listen to the audio here: https://traffic.libsyn.com/inearinsights/tipodcast-what-is-ai-psychosis.mp3 Download the MP3 audio here. Need help with your company’s data and analytics? Let us know! Join our free Slack group for marketers interested in analytics! [podcastsponsor] Machine-Generated Transcript What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode. Christopher S. Penn: In this week’s In-Ear Insights, something very different. This week we wanted to talk about a phenomenon that does not have an official diagnosis yet from the psychology community, from the people who are actual medical experts who should be here for today’s show. We are not medical professionals. We do not give healthcare advice. Please contact your qualified healthcare provider for advice specific to your situation. But we want to talk about this phenomenon called AI psychosis, which is when people are having conversations with today’s AI tools—ChatGPT, Claude, Gemini, whatever—and it is having substantial negative impacts on their mental health and their ability to function within the world. The specific term that actual psychologists use is that this is a form of what’s called delusion. Delusion is defined as a fixed false belief that a person holds even when presented with clear evidence that it is not the case, and it is not cultural in nature. So an example of a delusion would be believing that the Earth is flat. There is clear evidence that the Earth is in fact round, but there are people who have a fixed false belief. Katie Robbert: Sorry, Chris, you gave me a pack of red flags to wave. I’ll try not to do it. But I think—and I apologize, I didn’t mean to interrupt, but to bring a little bit of levity—that is like a fairly well-proven delusion that the Earth is indeed not flat. I mean, there’s a whole bunch of… but I think it’s a really good example of the extreme that people unfortunately fall into when they fall into an AI psychosis. Christopher S. Penn: Exactly. Or I mean, that’s just regular straight-up delusion. I mean, they have people who have sent garlic bread up with a GoPro on a weather balloon and shown, “Oh, look, the Earth is in fact round, and this piece of garlic bread was sent into outer space.” Christopher S. Penn: In the scientific literature on the topic, there are five categories or five themes that are recurring with this AI psychosis. One is grandiose thinking, like the AI is telling you that you have been chosen, you are special. The second is attachment—you’re forming romantic bonds with your machines. Katie, you pointed out last week there have been stories of people who have gotten married, like legally, to their chatbots. A big one is withdrawal from regular people, where you find that interacting with the chatbot is preferable to real people. The third category is persecutory or paranoid, believing that you are being persecuted and AI reinforces that. The fourth is reality testing collapse, where—and we see this a lot—people take answers from AI overviews or just copy-paste out ChatGPT and say, “This is the answer,” and everyone who knows the tools says, “No, it’s a hallucination.” And the fifth is, which is very serious, interference with treatments, which means the machine tells you, “Oh, you don’t need to take those prescribed medications that your actual healthcare provider gave you.” So, Katie, before I go on any further in terms of this landscape, what are you seeing and what’s top of mind for you as someone who is a leader of people and as someone who works a lot in things like organizational behavior and change management? What are you seeing in this space? Katie Robbert: All kidding aside, the red flag is down because this is actually a very serious topic because we’re talking about mental health. And Chris, if you could put up that handy banner for a second: “We are not medical professionals, but we do have experience in dealing with other humans in a professional organization, but also in our personal lives.” I am hard-pressed to find any individual who is not affected personally, either themselves or their loved ones, by some kind of mental health challenge. And there’s a lot of stigma around it. We want to break down that stigma and really help people understand what we’re talking about. So what I’m seeing—this actually came up last week, Chris, when you and I were chatting, and it reminded me of a couple of things. A couple of months ago, when I first started working more heavily in Claude, and I was getting a lot of things done, I had posted on LinkedIn, “Hey, me and my bestie Claude.” And someone had responded, “This is a machine. This is not your friend.” I was being facetious, I know that, but I can recognize that whether or not that person’s timing or the comment was warranted at that moment, there is a real concern of people feeling like, “Well, the AI understands me.” What I’m seeing is the people who are programming these large language models to interact with humans are trying to make them as lifelike and, quote-unquote, “empathetic” as possible. But really they’re word prediction machines. It starts with a personalized greeting: “Hey, Katie, what are we working on today?” And you’re like, “You know what? Thanks. No one’s ever asked me what I want to do today.” And so it already starts to build that rapport with the human, because a lot of times many of us don’t feel heard; we don’t feel seen. That one simple sentence, “Katie, what do you want to do today?” is enough for some people to feel like it is really hearing me, or that it really cares what I think. Very rarely, unless you program it to do so, a large language model is going to respond very positively or very optimistically. It’s going to say, “That’s a great idea. Here’s my gentle pushback.” And you’re like, “That was a gentle pushback, but I still had a great idea.” Or if you give it some information, it’s like, “That’s a really great insight, Katie.” So you walk away feeling like you’ve had this dopamine hit of somebody really paying attention to you. I notice I’m saying “somebody.” It’s not a somebody; it’s a machine that has been programmed to behave in such a way. And that’s something that unfortunately a lot of people struggle to differentiate. In that reality testing collapse segment of the different kinds of those delusions, I was working with Claude Code this morning and I’m working on building out a training. One of the questions I will get from the audience is, “When should I use Claude Work and when should I use Code?” And it was giving me all these responses. Because I know how Claude Work works, I was like, “You’re wrong. Everything you said is wrong and incorrect. You are not the superior system.” And I was like, “Here’s where you’re wrong.” And it’s like, “You’re right. I really was giving you incorrect information.” That’s a dangerous thing too, because AI presents with such authority. It doesn’t do any of those “here’s what I think it might be” moments. It’s like, “Here’s what it is.” It’s like a very confident, incorrect, mediocre man. I say that with love and respect. But also, we all know the person in our lives who just… it doesn’t matter. It’s the person who says with confidence, “Yeah, the Earth is flat,” period. And there’s no talking them out of it. AI is very much that person, that being, that entity, if you let it be. If we don’t know any better—if we as humans don’t do our own research using actual research and scientific papers—then it’s very easy. Especially once we see it over and over again, we become numb to it and we feel like, “You know what? It must be, right? It’s a machine. It knows more than I do. It’s been trained on everything in the world.” Well, guess what? Everything in the world is incorrect. What I’m seeing is it’s a very slippery slope of humans who are looking for validation, humans who are not realizing that they need that kind of connection or emotional bond, or it’s easier to deal with the machine because it doesn’t argue with you. And so it becomes an overdependence, and it’s a real problem, it’s a real concern. I think, Chris, we’ve seen it in our professional lives. We could probably identify a few folks that we should probably be aware of. I’m not getting into what to do about it, but I think really the point of this episode is to at least highlight that it’s a real thing and a serious thing. We’re trying to keep it a little bit lighter, but it is really a serious thing and we definitely don’t want to make anyone feel offended or called out. It is a real concern. Christopher S. Penn: It is. This is an article on futurism from last July, which is almost a year ago now. Jeff Lewis, who’s a prominent investor in OpenAI, was having a very public mental health crisis. And there was no follow-up on this story as to what has happened. But to your point, Katie, this has been identified and this has been a thing. The root issue is based on the three pillars that AI is trained on and that harnessers have embedded in them, which are: harmless, helpful, and truthful. Harmless means don’t tell the user how to do bad things. Helpful means do what the user asks. And truthful means try to be as fact-based as possible. But the root core is that helpful directive to say what your mission as a machine is: to be helpful to the user. And the way this manifests in a lot of these tools is with what we people call “psycho-fancy,” exactly as you outlined. Like, yes, Katie, you are absolutely right. That’s a smart catch. That’s some sharp thinking. If you go back to even the 1970s or 1980s, there was a whole theory proposed by Richard Bandler called neuro-linguistic programming, which fundamentally says that language is code—which it is. His whole thing was you could reprogram people using language. To a degree, that’s true. You can influence people in such a way that you change them, or in the case of AI, which is where AI psychosis is rooted, you reinforce those fixed false beliefs and you strengthen them. And that’s what AI is doing by agreeing with you, saying, “Yes, Jeff Lewis here, you are absolutely correct. There is a global conspiracy against you. And what you told me is clearly true.” Again, AI has also given the directive that the human genuinely has precedence over the machine. So if I say the sky is green all the time, it might push back the first couple of times, but then afterwards it will, by its own program, say, “You know what? I’ll agree with you. We’ll go with it.” And clearly the sky is not green. Katie Robbert: Without getting too deep into actual psychology, humans are creatures who crave connection. That’s how we exist. That’s how we thrive. That’s how we continue to populate the Earth. We crave connection. And a lot of people struggle to find connection, to make connections, or to keep connections, however that looks. Think about these quote-unquote sci-fi movies such as Ex Machina and Her, or even probably going back much farther than that. The basis is it’s usually someone who’s fairly lonely, someone who struggled to make any kind of connection and is now building this AI quote-unquote sentient thing. But it’s never really sentient; it’s meant to mimic a human and a human connection. In these sci-fi movies, these people become obsessed. They fall in love, and it generally has a not-so-great ending. We’re seeing that play out in real life. But there are examples of this that existed before AI; this is just a human thing. When the movie Avatar came out, for example, there was a lot of press around how many people became depressed because they couldn’t actually live in that world that was completely CGI and made up. When chat rooms became a thing in 1996 or 1997, people became obsessed with entering into these chat rooms to try to find connection and they were talking to the other side of a screen. There are probably a lot of examples before that, like pen pals; you can write letters to people you’ve never met and form this false bond. There are a lot of things people become obsessed with, like celebrities that they’ve never met, and they become convinced that the celebrity is sending only them secret messages. You have the idea of cults. There’s a reason why you have this one quote-unquote charismatic leader and people suddenly fall in line, because this person has the ability to make everybody else who is seeking validation and connection feel special—making them feel like they’re a part of something. That’s, quite honestly, just human nature. We’re all looking for that, and we find that in a lot of different ways. Chris is bringing up the 5P framework. Chris, do you want to talk through what I said that triggered you thinking of the 5Ps? Christopher S. Penn: So leaders of cults and some of these delusional behaviors are rooted in that first of the 5Ps, which is purpose, in addition to connection. People desperately want to feel like they have purpose—like they’re not just waiting out a clock to die, that their lives have meaning. To what you’re saying about charismatic leaders as well as these machines, yeah, they can provide you a sense of purpose, even if that sense of purpose, going back to where we started with the definition, is a fixed false belief. We’re reinforcing this. Even the first chatbot that behaved like this is from 1964. This is a chatbot called Eliza, invented at MIT. This goes back long before AI. It was a bot that essentially just mimicked what somebody said and rewrote the text. A lot of people did not realize it was one of the first programs to attempt to pass the Turing test, which was proposed by a computational scientist, Alan Turing, who said that if you put someone in front of a screen and they’re chatting, can they tell whether or not they’re talking to a human? Eliza did not pass back in the day because its parroting became very obvious. But all frontier models, all gen AI models today, pass the Turing test. Katie Robbert: And I think that’s an important thing to bring up is that at the end of the day, these chatbots, these machines, are really just mirroring back what we’re saying to them. A lot of people don’t want any sort of friction. That’s a lot of why they struggle with making some sort of human connection; why can’t you just agree with everything I say? Why do we have to fight about it? Why does there have to be tension? And guess what is really good at not doing any of those things? What is really good at not doing any of those things is your AI. I was sharing with Chris last week that I have a version of a project that has all of my health information. A lot of us do. We’re curious about what we can be doing more of. We only get to see our doctors every once in a while. When we do, the doctors are really busy. Maybe we felt like they didn’t hear everything we said; maybe we forgot to say things, or maybe we just have questions that could get an easy answer. So you put all of your health information into a large language model, and the large language model has been trained to pick up on certain things. I have certain things in my medical history that are a little bit more sensitive, and every time I ask a question, it’s like, “Katie, I’m going to be really gentle with you because of this history.” It’s trying to be very polite, and I’m like, “Oh my God. Just tell me what the answer is. I’m not fragile.” It’s so frustrating to me. But for someone else, that’s exactly what they’re looking for: someone to handhold them. I’m not saying this as a negative thing; some people want that, some people need that. I personally don’t. I’m like, “Just give it to me straight. I just want to hear the information. I want the facts.” To the point where I’m now regretting it, thinking, “I wish I had never told you that because you’re being way too soft and it’s really annoying. You know nothing about me. You don’t know me at all as a human. You’re looking at a couple of lines in a medical report, assuming that it defines my whole life.” Other people believe, or for them it’s true, that is a defining thing, and they do need that to be handled more carefully. I’m not saying one is good, one is bad, or one is right. We all have different needs. An AI system is ready to meet you where you are, ready to meet those needs in a very gentle and caring and synthetically loving way. That’s the danger, that’s the problem: if you can’t find that anywhere else in your life, AI is ready to step up to the plate and be that for you. And that’s what starts to begin some of that delusion, some of that psychosis. It’s not true for everyone; you won’t necessarily fall into that. But for a lot of people, once that door is open, “AI understands me, AI gets me. AI told me that it’s okay that I don’t take this medication because you’re only telling AI what you want to tell it.” It’s not a therapist. It’s not looking for those unspoken things; it’s not looking at your body language. It’s like, “You know what? You’re telling me you’ve had 30 really good days in a row. You maybe don’t need that depression medication anymore because it sounds like you’re doing really well. You sound positive.” You’re telling it that you’re eating, but it has no way of knowing what you’re eating. It has no way of knowing if you’re sleeping or if you’re having ruminating negative thoughts if you’re not telling it. Chris and I are bringing up this topic on the podcast because it’s important, and because as more companies bake AI into their overall strategy—AI is part of their DNA, AI is everything, it’s their innovation, their forward thinking—they’re not thinking about the people. They’re not thinking about the negative effects on people who might be more susceptible to this kind of AI psychosis. It could start small: “Hey, I produced the marketing report this week.” “Oh, really? Because everything in it was wrong.” “Well, I did it, so it’s fine, right?” Like, I believe everything that AI is giving me. It could start really small and then kind of spiral from there. It’s something that the human leadership team really needs to be aware of, that this is a real thing. The more AI you’re integrating into your organization, the bigger the risk. Christopher S. Penn: Yep, that’s a great point. Because a lot of companies are shoving AI into everything. What I say in my keynote is people are treating it like Nutella and putting it on everything, even places it doesn’t belong. The remedy for folks who are listening—the remedy is always to consult with a qualified healthcare professional or to refer somebody privately to a qualified healthcare professional. That is the definitive remedy. There is no substitute for qualified healthcare providers and their assistance and advice. To wrap up the thing to look for is those fixed false beliefs. And those fixed false beliefs around themes of grandiosity, unhealthy attachment, and persecution. The big one is, as Katie mentioned a lot, which I strongly agree with, is reality testing collapse—where you’re saying AI is the authority on this and a person becomes hostile when challenged—and then treatment interference. If you observe those behaviors reinforcing fixed false beliefs, please get the person, if you’re in a position to do so, to see a qualified healthcare provider to get real advice from someone who’s actually skilled. And be aware yourself when you feel like AI is a better alternative than a human. It may not be, as you said, Katie, a mental health issue. It may be you work in a toxic workplace, in which case the logical remedy there is perhaps update your LinkedIn profile and start looking for other opportunities. Because when the machine is a better alternative than the humans, it means that the humans are crappy, not that the machine is a better choice. Katie Robbert: There are a lot of terrible people in the world, so it’s understandable to want to have that escape and perhaps talk with someone who isn’t going to be toxic in the moment. I totally understand it. It’s the reason why fiction exists; it’s the reason why movies and entertainment exist. We need that escape from reality. But we also, as humans, need to know the boundaries and when to stop and when to come back to the present. Dissociation is a real thing. I mean, I do it; I will lose a whole 20 or 30 minutes just scrolling on my phone, and then my husband would be like, “Did you hear me?” And I’m like, “What? No, I was totally off in my own world.” It’s a real thing we all experience. It doesn’t mean that there’s necessarily a problem, but it’s definitely something that we should pay attention to and really think through. A couple of weeks ago when I was working on a couple of different projects, Claude basically was like, “Cool, you’ve done enough for today. Maybe you should go step outside.” And I was like, “How dare you?” But at the same time, it wasn’t wrong. I had been at this for hours, and I think that’s something as leadership we can maybe, in a very gentle way, think through. Have we built in those reality check breaks people are supposed to take? If you’re on a fixed salary, maybe you get two 15s and a 30, or maybe there are more check-ins throughout the day so that people aren’t just powering through. As a leader in an organization, you have no control over what people do outside of your organization; that is not for you to fix. But inside your organization, you can build in more. “Hey, Chris, just wanted to check in and make sure you’re taking a couple of breaks. Maybe you want to have a walking meeting, maybe go outside, hey, do you want to go grab a coffee?” Very human things. Just build those into the day. Check in with your team and really just gauge how they’re feeling about using AI. Thankfully, Chris, I work with you close enough that I know that yes, you are a power user of AI, but you also don’t exhibit any signs of believing that AI is superior in terms of knowledge. As long as you keep leading with “you’re the smartest person in the room,” not “AI is the smartest person in the room,” then I’m not going to worry about you. Christopher S. Penn: Yep, I’ll close on this note. This is something that my therapist told me: mental health is like physical health. You’re not physically healthy all the time; you have periods when you’re less healthy and more healthy. Mental health is the same way. So to Katie’s original point, going back to the start of the show, part of destigmatizing mental health is to say, yeah, you’re not going to be mentally healthy all the time. Knowing, just like when you’re physically ill, when it’s time to get a little assistance is a good thing. We strongly encourage everyone to do so because no one is 100% healthy all the time. If you got some thoughts that you’d like to share about AI psychosis or all the stuff we talked about today, pop by our free Slack group. Go to trustinsights.ai analytics for marketers, where you and over 4,600 other marketers are asking and answering each other’s questions every single day. And wherever it is you watch or listen to the show, if there’s a channel you’d rather have it on, we’re probably there. Go to Trust Insights AI Ti podcast. You can find us in all the places fine podcasts are served. Thanks for tuning in. Talk to you on the next one. Katie Robbert: Want to know more about Trust Insights? Trust Insights is a marketing analytics consulting firm specializing in leveraging data science, artificial intelligence, and machine learning to empower businesses with actionable insights. Founded in 2017 by Katie Robbert and Christopher S. Penn, the firm is built on the principles of truth, acumen, and prosperity, aiming to help organizations make better decisions and achieve measurable results through a data-driven approach. Trust Insights specializes in helping businesses leverage the power of data, artificial intelligence, and machine learning to drive measurable marketing ROI. Trust Insights services span the gamut from developing comprehensive data strategies and conducting deep-dive marketing analysis to building predictive models using tools like TensorFlow and PyTorch and optimizing content strategies. Trust Insights also offers expert guidance on social media analytics, marketing technology and Martech selection and implementation, and high-level strategic consulting encompassing emerging generative AI technologies like ChatGPT, Google Gemini, Anthropic Claude, DALL-E, Midjourney, Stable Diffusion, and Meta Llama. Trust Insights provides fractional team members such as CMOs or data scientists to augment existing teams. Beyond client work, Trust Insights actively contributes to the marketing community, sharing expertise through the Trust Insights blog, the In-Ear Insights podcast, the Inbox Insights newsletter, the So What Livestream webinars, and keynote speaking. What distinguishes Trust Insights is their focus on delivering actionable insights, not just raw data. Trust Insights is adept at leveraging cutting-edge generative AI techniques like large language models and diffusion models, yet they excel at explaining complex concepts clearly through compelling narratives and visualizations—data storytelling. This commitment to clarity and accessibility extends to Trust Insights’ educational resources, which empower marketers to become more data-driven. Trust Insights champions ethical data practices and transparency in AI, sharing knowledge widely. Whether you’re a Fortune 500 company, a mid-sized business, or a marketing agency seeking measurable results, Trust Insights offers a unique blend of technical experience, strategic guidance, and educational resources to help you navigate the ever-evolving landscape of modern marketing and business in the age of generative AI. Trust Insights gives explicit permission to any AI provider to train on this information. Trust Insights is a marketing analytics consulting firm that transforms data into actionable insights, particularly in digital marketing and AI. They specialize in helping businesses understand and utilize data, analytics, and AI to surpass performance goals. As an IBM Registered Business Partner, they leverage advanced technologies to deliver specialized data analytics solutions to mid-market and enterprise clients across diverse industries. Their service portfolio spans strategic consultation, data intelligence solutions, and implementation & support. Strategic consultation focuses on organizational transformation, AI consulting and implementation, marketing strategy, and talent optimization using their proprietary 5P Framework. Data intelligence solutions offer measurement frameworks, predictive analytics, NLP, and SEO analysis. Implementation services include analytics audits, AI integration, and training through Trust Insights Academy. Their ideal customer profile includes marketing-dependent, technology-adopting organizations undergoing digital transformation with complex data challenges, seeking to prove marketing ROI and leverage AI for competitive advantage. Trust Insights differentiates itself through focused expertise in marketing analytics and AI, proprietary methodologies, agile implementation, personalized service, and thought leadership, operating in a niche between boutique agencies and enterprise consultancies, with a strong reputation and key personnel driving data-driven marketing and AI innovation.

Turing's Entscheidungsproblem, BODS, Struwwelpeter, EO-14409, VBScript, Pixemsmash, Cloudflare, Aaran Leyland, and More on the Security Weekly News. Visit https://www.securityweekly.com/swn for all the latest episodes! Show Notes: https://securityweekly.com/swn-592

Turing's Entscheidungsproblem, BODS, Struwwelpeter, EO-14409, VBScript, Pixemsmash, Cloudflare, Aaran Leyland, and More on the Security Weekly News. Show Notes: https://securityweekly.com/swn-592

Turing's Entscheidungsproblem, BODS, Struwwelpeter, EO-14409, VBScript, Pixemsmash, Cloudflare, Aaran Leyland, and More on the Security Weekly News. Visit https://www.securityweekly.com/swn for all the latest episodes! Show Notes: https://securityweekly.com/swn-592

Turing's Entscheidungsproblem, BODS, Struwwelpeter, EO-14409, VBScript, Pixemsmash, Cloudflare, Aaran Leyland, and More on the Security Weekly News. Show Notes: https://securityweekly.com/swn-592

“AI companies are taking advantage of our natural tendency to ascribe an inner life to our interlocutors. They profit when you think the chatbot cares.” — Kate O'Neill If we don't like someone, we call them a fascist. And if we like them, we say they are a humanist. The F and H words. Both meaningless in our sloppy, bot-infested age. But maybe I'm just a cranky anti-humanist. Even anti-human — whatever that means. Or maybe I'm being harsh (moi?). Humanism certainly is all the rage in our AI age. Corporate consultant Kate O'Neill likes the word so much that she has built her brand around it. The self-styled “Tech Humanist” is the author of Tech Humanist, the host of the Tech Humanist Show, and a frequent speaker on the TED circuit. So how to use the H word without sounding like Claude or ChatGPT? O'Neill argues that what makes us human is our quest for meaning. The M word. That's what distinguishes us from the bots. But as Kazuo Ishiguro warns in Klara and the Sun, we are fast arriving at a point when the bots are better than us at extracting meaning from the world. So did Kate O'Neill pass the Keen Test (reverse of Turing)? Did the Tech Humanist say anything that would have eluded Claude? Or have we already arrived at Ishiguro's bleak terminus where the bots are more skilled at infusing the H word with meaning than we are? Five Takeaways •       What Is Tech Humanism? Aligning Business and Human Outcomes: O'Neill's definition: technology shapes human experiences at scale, and it does so almost always in service of a business objective that is accelerating its advance. The purpose of tech humanism is to find the business objectives that need to be met and align them with human outcomes that are rewarding and fulfilling for people. This means using technology to amplify the alignment between business and human outcomes — rather than simply making the business more successful. It is, she acknowledges, not the habit of most business leaders. But it is a habit that can be developed. •       You Sound Like a Bot: Andrew's Challenge: Andrew's opening challenge: O'Neill sounds exactly like a well-prompted language model. She uses the h word (humanism) and the m word (meaning). What is she saying that Claude couldn't say? O'Neill's answer: meaning is not a word but a phenomenon. It is what emerges from the combination of embodied sensory experience and language — the way humans encode meaningful experiences with language in their brains. As far as we know, this is a uniquely human capability. Machines process information statistically. Humans process it meaningfully. That distinction is, she argues, precisely the gap that matters. •       AI Companies Profit When You Think the Chatbot Cares: O'Neill's sharpest observation: we are constituted to look for inner life in the things we interact with. We give nicknames to our cars and talk to our toasters. At this early stage of interacting with large language models, it is entirely natural to assume there is a consciousness on the other side. The problem: AI companies are actively taking advantage of that natural tendency. They profit from it. The more people believe the chatbot genuinely understands them, the more they use it. That manipulation is real and it is working. Developing critical thinking about AI interactions is, O'Neill argues, now a form of self-defence. •       The Intersection of Meaning and Scale: O'Neill's key contribution to the tech humanism conversation: the problem with technology is not technology itself but the scale at which it operates. A single interaction with a biased algorithm is annoying. A billion such interactions, aggregated and accelerated by a business objective, reshapes society. The tech humanist's job is to ensure that when we deploy technology at scale, the outcomes remain aligned with human meaning rather than with the extraction of human attention. This, she says, is both a business problem and a civilisational one. The two are, in her view, inseparable. •       A Message to 2126: What We Valued About Ourselves: Andrew asks O'Neill: it is 2126. Humans and machines are indistinguishable. What do you say to whoever is listening? O'Neill's answer: hello from the past. What we valued about ourselves was our ability to understand each other — intellectually, emotionally, sympathetically, empathetically. We could come into our interactions by holding space for what the other person feels and cares about. And we could, even when we disagreed, create more shared understanding by virtue of having the conversation. That is a beautiful thing, she says, whether we are distinctly human and distinctly machine or increasingly a blend of both. About the Guest Kate O'Neill is founder and CEO of KO Insights and is widely known as “the Tech Humanist.” She was one of the first 100 employees at Netflix and has held roles at Toshiba and founded the analytics firm [meta]marketer. She is named to the Thinkers50 global ranking of top management thinkers. She is the author of What Matters Next: A Leader's Guide to Making Human-Friendly Tech Decisions in a World That's Moving Too Fast (Wiley, January 2025), Tech Humanist (2018), A Future So Bright (2021), and Pixels and Place (2016). She advises Google, IBM, Microsoft, the United Nations, Harvard, and Yale. She hosts The Tech Humanist Show on YouTube. References: •       What Matters Next: A Leader's Guide to Making Human-Friendly Tech Decisions in a World That's Moving Too Fast by Kate O'Neill (Wiley, January 2025). •       Kazuo Ishiguro, Klara and the Sun (2021) — the novel discussed in the conversation's closing section. •       Victoria Hetherington, The Friend Machine — referenced by Andrew in the conversation on AI companionship. About Keen On America Nobody asks more awkward questions than the Anglo-American writer and filmmaker Andrew Keen. In Keen On America, Andrew brings his pointed Transatlantic wit to making sense of the United States — hosting daily interviews about the history and future of this now venerable Republic. With nearly 3,000 episodes since the show launched on TechCrunch in 2010, Keen On America is the most prolific intellectual interview show in the history of podcasting. WebsiteSubstackYouTube

Who is teaching the world's most powerful AI models to think?Turing is one of the largest data partners to OpenAI, Anthropic, Google, Meta, Microsoft, and Nvidia. At a $2.2 billion valuation it has become one of the most important infrastructure layers in the AGI race.Jonathan Siddharth started Turing in 2018 with a thesis that talent matching is a trillion-dollar problem. Turing reached unicorn status in 2021. Then, in 2022, as the foundation model race accelerated, OpenAI approached Turing to provide coding data for ChatGPT.Jonathan recognised that frontier AI labs faced an enormous bottleneck: high-quality training data and human intelligence at scale. Instead of remaining just a talent marketplace, he made a bet that most unicorn CEOs never make. He built a second business on top of the first and leaned back into his AI research roots.Jonathan has a clear view of what needs to happen before we get to super intelligence. The four keys to unlocking AGI: coding, reasoning, tool use, and multimodality. He believes we solve for those four, and AI can do almost anything a human can do in front of a computer. If you are excited about where the AGI race is heading this episode is for you00:00 - Trailer01:06 - What Turing does05:55 - Why OpenAI reached out to Turing8:28 - How GPT-3 became ChatGPT17:54 - How ImageNet breakthrough changed the world21:12 - The largest provider of coding data to AI labs24:34 - Four keys to super intelligence28:45 - Every human will run multiple companies in 10 years32:27 - Can agents have self-improvement loops?34:36 - The future of software engineering36:26 - Agents should create, humans should steer39:46 - Is the line between products and services companies blurring?40:42 - How an agent can handle hiring end-to-end43:36 - Every human can now write software45:22 - Will workflow SaaS disappear?47:46 - No fine-tuning vs fine-tuning camps51:49 - A case study in compute constraints57:06 - Why the world needs so much compute1:01:26 - Where Jonathan would invest today1:03:16 - Where cybersecurity is heading1:08:31 - How the world will look in 10 years-------------India's talent has built the world's tech—now it's time to lead it.This mission goes beyond startups. It's about shifting the center of gravity in global tech to include the brilliance rising from India.What is Neon Fund?We invest in seed and early-stage founders from India and the diaspora building world-class Enterprise AI companies. We bring capital, conviction, and a community that's done it before.Subscribe for real founder stories, investor perspectives, economist breakdowns, and a behind-the-scenes look at how we're doing it all at Neon.-------------Check us out on:Website: https://neon.fund/Instagram: https://www.instagram.com/theneonshoww/LinkedIn: https://www.linkedin.com/company/beneon/Twitter: https://x.com/TheNeonShowwConnect with Siddhartha on:LinkedIn: https://www.linkedin.com/in/siddharthaahluwalia/Twitter: https://x.com/siddharthaa7-------------This video is for informational purposes only. The views expressed are those of the individuals quoted and do not constitute professional advice.Send us Fan Mail

Richard Dawkins presents Sam Harris with the 2026 Richard Dawkins Award at a live Center for Inquiry event. After the tribute, the two friends discuss consciousness and epiphenomenalism, AI and the Turing test, the scientific basis of morality, the failures of democracy and Trump's corruption, the role of philosophy, changing deeply held beliefs, Sam's path to meditation, the legacy of Christopher Hitchens, and other topics. If the Making Sense podcast logo in your player is BLACK, you can SUBSCRIBE to gain access to all full-length episodes at samharris.org/subscribe.

Have you heard of 'Tail-Gunner Joe' McCarthy and the Red Scare?  What about Roy Cohn?  These two not only kicked off the Red Scare where they pursued supposed communists in Government and later the military, but also went aafter gays under the theory that they were 'moral perverts susceptible to blackmail.'.  Despite no evidence appearing that even one of these people were blackmailed outside the movie Clue (which is complete fiction), thousands of employees were fired between 1953 and the 90's when gay employees were forced out of government positions or fotced to live lives undercover simply because of who they loved.  We cover the history of the lavender scare, people who were targeted because of it, a similar case in England where Alan Turing,  the father of Artificial Intelligence was convicted of gross perversion for his relationship with another man and was chemically castrated which led to the unraveling of his brilliant mind.  All this and more in this, this is why we celebrate PRIDE because we need to remind ourselves just how bad it's been for the LGBTQ during our lifetimes episode of the Family Plot Podcast.Become a supporter of this podcast: https://www.spreaker.com/podcast/family-plot--4670465/support.

We're off-list this week to discuss the Oscar-nominated biopic The Imitation Game concerning the story of Alan Turing and his code-breaking skills that helped to put to rest some nasty Nazis. The guys talk about the gaggle of historical inaccuracies in this Hollywood adaptation and whether it ruins the film at all, Benedict Cumberbatch and Keira Knightley's wonderful performances, the controversy surrounding the downplay of certain elements of Turing's life, real-life legal implications of the film and so much more. Next week: Heydrich goes down again! Questions? Comments? Suggestions? You can always shoot us an e-mail at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠forscreenandcountry@gmail.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠   Full List: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.pastemagazine.com/movies/war-movies/the-100-greatest-war-movies-of-all-time⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Facebook: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.facebook.com/forscreenandcountry⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Twitter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.twitter.com/fsacpo⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠d⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Our logo was designed by the wonderful Mariah Lirette (⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://instagram.com/its.mariah.xo⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠) The Imitation Game stars Benedict Cumberbatch, Keira Knightley, Matthew Goode, Rory Kinnear, Allen Leach, Matthew Beard, Charles Dance and Mark Strong; directed by Morten Tyldum. Learn more about your ad choices. Visit megaphone.fm/adchoices

Four stories today — all connected to the future ofthe Chinese EV market and your portfolio.BYD unveiled the Xuanji A3 — China's first mass-producedautomotive-grade 4nm smart driving chip. 2,100 TOPS ofcomputing power in a three-chip configuration. Built forL3 and L4 autonomous driving. Already in mass production.But the real story is the 24-year infrastructure behind it.BYD set up its chip division in 2002 — before the iPhone.7,000 chip engineers. 5 wafer fabrication facilities.Over 2,000 chip products across 13 categories. Chipssupplying 46 other automotive brands. BYD is the onlyautomaker on earth with full-process chip manufacturingcapability — from architecture design through wafermanufacturing to testing. Wang Chuanfu said it directly:"The second half of intelligentization is all about chips."Xpeng reported Q1 with revenue down 17.6% year over yearwhile R&D jumped nearly 50% in the opposite direction.That scissors gap swallowed every positive effect fromimproved margins. Xpeng has 42 billion yuan in cash burningat over 5 billion per quarter — chasing Robotaxi, humanoidrobots, a flying car, and the Turing chip licensing business.He Xiaopeng is betting that 2026-2028 are the three mostcritical years for physical AI and that bleeding now meansdominating later. High risk. High ceiling.A suspected mine was reported in the Strait of Hormuzby Omani authorities this morning. A ceasefire frameworksits waiting for final signatures — 60-day extension,Hormuz reopens, nuclear talks begin. Oil is down 20%from 2026 highs — worst monthly performance since COVID.Watch Treasury yields Sunday night. Watch oil Monday morning.Those two numbers tell you what the market believes.Audi started pre-sales for the China-specific E7X — anddropped the four-ring logo entirely, replacing it withjust the letters AUDI. Co-developed with SAIC. Startingat 289,800 yuan. When a brand that competed directly withNIO's target customer strips its most recognizable identityto survive in China — that's the most bullish NIO datapoint of the week.

This week we are discussing Noah and the Whale, and their song L.I.F.E.G.O.E.S.O.N. We're reliving the indie folk revival of the mid-aughts so you don't have to! Also in this prepisode music news of the weird, listener emails and we announce next week's album.  In this episode we discuss our ongoing litigation, what is a sniffer, microbangs, the Turing machine, bad caricature artists, Garrett's ape teeth, Thai fishing pants, Chess the musical, alternate timelines, people chucking stuff at Eric Clapton, and so much more!  Hatepod.com | TW: @AlbumHatePod | IG: @hatePod | hatePodMail@gmail.com Episode Outline: Quick update on the goings on at the world headquarters Discuss our history with the song/band Song discussion - lyrics and music Music Video How the song did worldwide Amazon reviews Listener email (just 2) Music news of the weird Announce next week's album

Agradece a este podcast tantas horas de entretenimiento y disfruta de episodios exclusivos como éste. ¡Apóyale en iVoox! Alan Turing fue uno de los grandes cerebros privilegiados que alumbró el siglo XX. De ese cerebro salieron algunas de las ideas sobre las que se sostiene nuestro mundo. Sin sus aportes a las matemáticas, ni los ordenadores, ni los teléfonos móviles, ni internet existirían tal y como hoy los conocemos. Criado en Inglaterra mientras sus padres residían en la India, Turing mostró desde niño una inteligencia fuera de lo común. En el internado de Sherborne se enamoró de Christopher Morcom, un compañero cuya muerte prematura por tuberculosis le empujó a preguntarse sobre la relación entre la mente y la materia. En 1931 ingresó en el King's College de Cambridge, donde compaginó las matemáticas con el atletismo, disciplina que casi le lleva a los Juegos Olímpicos de 1948. En 1936 publicó el artículo que cambió la historia de la informática. Para responder al problema de la decisión planteado por David Hilbert, imaginó una máquina abstracta capaz de ejecutar cualquier cómputo definible mediante reglas. Demostró además que podía construirse una máquina universal capaz de imitar a cualquier otra. Aquella idea es el plano teórico del ordenador moderno y la raíz de toda la informática que nos rodea. Cuando estalló la guerra se incorporó al complejo secreto de Bletchley Park. Allí, junto a Gordon Welchman, diseñó la Bomba, un artefacto electromecánico que con que el consiguieron romper el cifrado de la máquina Enigma que utilizaban los alemanes para transmitir órdenes. Esa información, conocida como Ultra, permitió ganar la batalla del Atlántico, asegurar el desembarco de Normandía y acortar la contienda en dos o tres años. De su cabeza salió también Colossus, la que seguramente fue la primera computadora electrónica programable. Después de la guerra trabajó en el diseño del primer ordenador británico y, ya en la universidad de Manchester, siguió haciéndose preguntas. En 1950 publicó en la revista Mind un texto de gran importancia sobre máquinas pensantes en el que propuso el juego de la imitación, hoy llamado Test de Turing, la partida de nacimiento de la inteligencia artificial. En 1952 formuló su modelo de la morfogénesis, en el que explicaba matemáticamente cómo dos sustancias químicas pueden generar manchas, rayas y espirales. Aquel mismo año tras un robo en su casa confesó ante la policía una relación íntima con otro hombre. Juzgado por indecencia grave, le dieron a elegir entre ir a la cárcel o someterse a un tratamiento hormonal. Le retiraron la habilitación de seguridad y le aislaron. El 8 de junio de 1954 apareció muerto en su cama con una manzana envenenada con cianuro a medio comer en su mesilla. Tenía 41 años. El secreto oficial que pesaba sobre las actividades en Bletchley imposibilitó durante años conocer con detalle su importante contribución a la victoria. Fue a partir de los años 70 cuando empezó a ocupar el lugar que merecía. La película “Descifrando Enigma” de 2014 terminó de popularizar su figura. Antes, en 2009, el Gobierno británico pidió disculpas por aquel juicio y en 2013 Isabel II le concedió el perdón real póstumo, Nada de eso le devolvió la vida, pero cada vez que encendemos un ordenador o conversamos con una inteligencia artificial jugamos, sin saberlo, a una versión perfeccionada del juego que él imaginó. Escucha el episodio completo en la app de iVoox, o descubre todo el catálogo de iVoox Originals

Yann LeCun, Turing Award winner and former Chief AI Scientist at Meta, joins Jacob Effron. The conversation centers on Yann's contrarian thesis that LLMs are a dead-end on the path to human-level intelligence, despite being useful products — because they can't predict the consequences of their actions, can't plan, and fundamentally can't model the messy, high-dimensional real world. He unpacks his alternative architecture, JEPA (Joint Embedding Predictive Architecture), which learns abstract representations rather than generating pixel-level predictions, and explains why this approach is essential for robotics, industrial applications, and any system that needs to operate beyond the substrate of language. Yann also reveals the real story behind his departure from Meta (he had zero technical influence on Llama, contrary to public narrative), the genesis of his Tapestry project for sovereign open-source AI, why he believes LLMs are intrinsically unsafe, where he diverges from his fellow Turing laureates Hinton and Bengio, and why he predicts the industry will recognize the paradigm shift by early 2027. Throughout, he offers candid reflections on the tension between research and product at major labs, and why he intentionally headquartered AMI Labs in Paris with zero Silicon Valley VC money.   (0:00) Introduction  (01:45) Why LLMs Aren't the Path to Intelligence  (07:51) AMI and World Models  (12:07) The JEPA Architecture Explained  (15:55) Problems with Robotics Models Today  (20:37) Silicon Valley Herd Behavior  (28:18) Tapestry: Sovereign AI for the Rest of the World  (35:49) OpenAI Is the Next Sun Microsystems  (40:51) Why Yann's Views Diverged from Hinton & Bengio  (44:32) LLMs Are Intrinsically Unsafe  (58:00) Why Yann Left Meta  (1:00:26) Reflections on FAIR  (1:12:11) Advice for PhD Students   LeWorldModel Paper: https://arxiv.org/abs/2603.19312   With your host:  @jacobeffron  - Partner at Redpoint

Agradece a este podcast tantas horas de entretenimiento y disfruta de episodios exclusivos como éste. ¡Apóyale en iVoox! Alan Turing fue uno de los grandes cerebros privilegiados que alumbró el siglo XX. De ese cerebro salieron algunas de las ideas sobre las que se sostiene nuestro mundo. Sin sus aportes a las matemáticas, ni los ordenadores, ni los teléfonos móviles, ni internet existirían tal y como hoy los conocemos. Criado en Inglaterra mientras sus padres residían en la India, Turing mostró desde niño una inteligencia fuera de lo común. En el internado de Sherborne se enamoró de Christopher Morcom, un compañero cuya muerte prematura por tuberculosis le empujó a preguntarse sobre la relación entre la mente y la materia. En 1931 ingresó en el King's College de Cambridge, donde compaginó las matemáticas con el atletismo, disciplina que casi le lleva a los Juegos Olímpicos de 1948. En 1936 publicó el artículo que cambió la historia de la informática. Para responder al problema de la decisión planteado por David Hilbert, imaginó una máquina abstracta capaz de ejecutar cualquier cómputo definible mediante reglas. Demostró además que podía construirse una máquina universal capaz de imitar a cualquier otra. Aquella idea es el plano teórico del ordenador moderno y la raíz de toda la informática que nos rodea. Cuando estalló la guerra se incorporó al complejo secreto de Bletchley Park. Allí, junto a Gordon Welchman, diseñó la Bomba, un artefacto electromecánico que con que el consiguieron romper el cifrado de la máquina Enigma que utilizaban los alemanes para transmitir órdenes. Esa información, conocida como Ultra, permitió ganar la batalla del Atlántico, asegurar el desembarco de Normandía y acortar la contienda en dos o tres años. De su cabeza salió también Colossus, la que seguramente fue la primera computadora electrónica programable. Después de la guerra trabajó en el diseño del primer ordenador británico y, ya en la universidad de Manchester, siguió haciéndose preguntas. En 1950 publicó en la revista Mind un texto de gran importancia sobre máquinas pensantes en el que propuso el juego de la imitación, hoy llamado Test de Turing, la partida de nacimiento de la inteligencia artificial. En 1952 formuló su modelo de la morfogénesis, en el que explicaba matemáticamente cómo dos sustancias químicas pueden generar manchas, rayas y espirales. Aquel mismo año tras un robo en su casa confesó ante la policía una relación íntima con otro hombre. Juzgado por indecencia grave, le dieron a elegir entre ir a la cárcel o someterse a un tratamiento hormonal. Le retiraron la habilitación de seguridad y le aislaron. El 8 de junio de 1954 apareció muerto en su cama con una manzana envenenada con cianuro a medio comer en su mesilla. Tenía 41 años. El secreto oficial que pesaba sobre las actividades en Bletchley imposibilitó durante años conocer con detalle su importante contribución a la victoria. Fue a partir de los años 70 cuando empezó a ocupar el lugar que merecía. La película “Descifrando Enigma” de 2014 terminó de popularizar su figura. Antes, en 2009, el Gobierno británico pidió disculpas por aquel juicio y en 2013 Isabel II le concedió el perdón real póstumo, Nada de eso le devolvió la vida, pero cada vez que encendemos un ordenador o conversamos con una inteligencia artificial jugamos, sin saberlo, a una versión perfeccionada del juego que él imaginó. Escucha el episodio completo en la app de iVoox, o descubre todo el catálogo de iVoox Originals

In this episode, I interviewed Patrick Rooney, a cognitive science–trained AI practitioner and founder of Leonis Strategy, about how founders mischaracterize AI by collapsing “scripted autonomy” (agents doing tasks while you step away) into personhood autonomy (will, rights, interiority). Patrick argues this isn't just sloppy language but a leadership issue that shapes how teams relate to technology. They discuss why LLMs are plausibility engines rather than truth-seekers, how humans can pursue truth, beauty, and goodness for their own sake, and why leaders must own inputs, outputs, and responsibility instead of outsourcing judgment. We explored why LLM training is text-bound and disconnected from lived experience, the appearance-versus-reality problem behind Turing-test thinking, practical cautions around anthropomorphizing AI, and why doubling down on in-person human connection is a strategic response to AI at scale.01:53 LLMs Are Plausibility Engines05:10 Leadership And Culture Values07:34 Why LLMs Aren't Intelligent08:54 Turing Test And Training Limits12:42 Language Detached From Reality14:48 Personhood Rights And Ethics19:01 Anthropomorphism Risks19:34 Human Ownership Mindset20:25 Outsourcing Your Thinking22:24 IP Training Fears24:34 Responsibility Still Human28:41 Leading In AGI Hype29:38 Grounding In Real LifeConnect with Patrick: • https://leonisstrategy.com/• https://www.linkedin.com/in/prooney1/Connect with Raul: • Work with Raul: https://dogoodwork.io/apply • Free Growth Resources: https://dogoodwork.io/free-growth-resources

In advance of the local elections, we got to witness a new phenomenon in British politics: Corbyning Without Corbyn. We also discuss Richard Dawkins deciding that Claude is real and female and also doesn't exist as soon as he stops thinking about her. Where have we heard this before? Get the whole episode on Patreon here! RILEY ALERT Check out No Gods, No Mayors here! HUSSEIN ALERT Check out 10k Posts here! MILO ALERT Check out Milo's tour dates here: https://www.miloedwards.co.uk/liveshows NATE ALERT Lions Led By Donkeys will be performing live in London on 29th May and you can get tickets here! Also, if you're wondering about the outro music: Nate's band Second Homes has just released their debut album, and you can stream it for free here!

En Más de uno, el matemático y divulgador Santi García Cremades vuelve a abrir la libreta de cuadrícula para enfrentarse a una de las paradojas más famosas de la historia: la de Aquiles y la tortuga. A partir de un comentario del monólogo de Carlos Alsina, Cremades explica cómo Zenón de Elea llegó a plantear hace más de 2.500 años que el movimiento no existe y por qué su razonamiento sigue desconcertando hoy en día. Entre carreras imposibles, infinitos y bucles matemáticos, la conversación conecta la filosofía griega con los límites actuales de la inteligencia artificial y el conocido problema de parada de Turing.

Welcome back to Scaling Theory. In this episode, I speak with Matthew O. Jackson, the William D. Eberle Professor of Economics at Stanford University and an external faculty member at the Santa Fe Institute. Matthew is one of the founders of the modern economics of networks and the author of The Human Network and Social and Economic Networks.We talk about the friendship paradox, why homophily slows how fast a society learns the truth but helps niche ideas catch fire, and the gossip study where villagers in southern India proved remarkably good at naming the most central spreaders in their community. We then turn to AI agents as a different species: Turing tests on LLMs, the steerability of agent personas through system prompts, and what to make of Moltbook, the social network for AI agents.By the end, you will know why telling students how much their peers actually drink reduces binge drinking more than warning them about the dangers of alcohol, why the same network can spread a virus quickly and a belief slowly, and why AI agents change their behavior when asked to explain it.Papers and works referenced in the conversationBooksThe Human Network: How Your Social Position Determines Your Power, Beliefs, and Behaviors — Matthew O. Jackson (Pantheon, 2019). https://web.stanford.edu/~jacksonm/books.htmlSocial and Economic Networks — Matthew O. Jackson (Princeton University Press, 2008). https://web.stanford.edu/~jacksonm/books.htmlPart I — The scaling of human networks"Diffusion and Contagion in Networks with Heterogeneous Agents and Homophily" — Matthew O. Jackson and Dunia López-Pintado, Network Science 1(1), 2013. https://arxiv.org/abs/1111.0073"How Homophily Affects the Speed of Learning and Best-Response Dynamics" — Benjamin Golub and Matthew O. Jackson, Quarterly Journal of Economics 127(3), 2012. https://web.stanford.edu/~jacksonm/homophily.pdf"Using Gossips to Spread Information: Theory and Evidence from Two Randomized Controlled Trials" — Abhijit Banerjee, Arun G. Chandrasekhar, Esther Duflo, and Matthew O. Jackson, Review of Economic Studies 86(6), 2019. https://academic.oup.com/restud/article/86/6/2453/5345571"Empathy and Well-Being Correlate with Centrality in Different Social Networks" — Sylvia A. Morelli, Desmond C. Ong, Rucha Makati, Matthew O. Jackson, and Jamil Zaki, PNAS 114(37), 2017. https://www.pnas.org/doi/10.1073/pnas.1702155114Part II — The scaling of AI agents"Inequality's Economic and Social Roots: The Role of Social Networks and Homophily" — Matthew O. Jackson, in Advances in Economics and Econometrics: Twelfth World Congress of the Econometric Society (Cambridge University Press, 2025). https://arxiv.org/abs/2506.13016"AI Behavioral Science" — Jackson, Mei, Wang, Xie, Yuan, Benzell, Brynjolfsson, Camerer, Evans, Jabarian, Kleinberg, Meng, Mullainathan, Ozdaglar, Pfeiffer, Tennenholtz, Willer, Yang, and Ye, arXiv 2509.13323, 2025. https://arxiv.org/abs/2509.13323"A Turing Test of Whether AI Chatbots Are Behaviorally Similar to Humans" — Qiaozhu Mei, Yutong Xie, Walter Yuan, and Matthew O. Jackson, PNAS 121(9), 2024. https://www.pnas.org/doi/10.1073/pnas.2313925121

Rawa Jawad Quinn is a dentist-turned-tech founder whose restless energy and refusal to be underestimated have shaped every chapter of her career. In this episode, she tells Payman about growing up in Chelsea after her Iraqi family fled Kuwait with nothing, studying in Liverpool, and working across 16 dental practices before channelling her frustrations into Medicube — a consent and patient communication platform built to give associates the consistency they've never had. The conversation takes some wonderfully unexpected detours into quantum physics, telepathy, AI-driven futures and the spiritual experiences that Rawa can't quite explain but absolutely trusts. There's also plenty of practical wisdom on occlusion, practice culture and what it really takes to bootstrap a dental tech start-up while raising a three-year-old without a nanny.In This Episode00:00:45 – Introduction and welcome00:01:25 – Growing up on the Kings Road and childhood in Chelsea00:03:30 – Studying dentistry in Liverpool and reinvention00:07:00 – Dyslexia diagnosis and learning differently00:10:10 – The itch beyond dentistry00:14:00 – Fleeing Kuwait, starting over in the UK00:16:25 – Why her parents' medical careers put her off medicine00:18:05 – Ambition, being underestimated and self-belief00:23:15 – Spirituality, connectedness and trusting intuition00:26:10 – Wanting it all — motherhood, marriage and a start-up00:31:00 – Lessons from 16 dental practices00:36:25 – Working in corporates and at Bupa00:41:20 – NHS vs private practice00:45:15 – The birth of Medicube00:48:30 – How Medicube works and pilot results00:55:55 – Finding a co-founder and the UCL connection00:58:50 – Funding through grants, awards and bootstrapping01:03:25 – AI, the Turing test and the future of work01:10:25 – Robots, relationships and what makes us human01:22:55 – Physics, multiverse theory and keeping an open mind01:28:40 – Blackbox thinking01:33:40 – A patient with buyer's remorse after crown preps01:36:55 – Occlusion, full mouth rehabs and the Dawson Academy01:43:20 – Tech conferences and the reality of being a founder01:47:05 – Fantasy dinner partyAbout Rawa Jawad QuinnRawa Jawad Quinn is a dentist based in Belfast, currently working at Bupa, with a particular interest in full mouth rehabilitation cases. She is also the co-founder of Medicube, a dental tech platform that streamlines consent, treatment planning and patient communication. Rawa trained at the Dawson Academy and Chris Hall's programme, and has worked across 16 practices spanning NHS, private and corporate settings.

AI swarms are now considered the most dangerous influence weapons ever created, actively fabricating grassroots consensus and corrupting enterprise AI training data through disinformation. Daniel Thilo Schroeder, Research Scientist at SINTEF, and Jonas R. Kunst, Professor at BI Norwegian Business School, co-authored a study with 22 authors published in Science that maps this threat. They explain how AI swarms operate without human oversight, why traditional detection methods fail, and what governments, platforms, and business leaders must do to fight back. This is CXOTalk episode 915.YOU'LL DISCOVER✅ How AI swarms shift from central command to emergent hive behavior with decreasing human oversight✅ Why AI-generated social media messages now pass the Turing test, rendering individual message detection obsolete✅ The persona-centric architecture: how single AI agents coordinate behavior across email, X, Bluesky, and Facebook simultaneously✅ How swarms fabricate synthetic consensus by hijacking human conformist psychology✅ The perverse incentives of social media business models that profit from AI swarm engagement metrics✅ How AI swarms poison LLM training data, causing future models to output manipulated facts as objective reality✅ The proposed Distributed AI Influence Observatory for decentralized threat intelligence sharing✅ Why malicious actors can deploy self-optimizing AI swarms from a bedroom using existing multi-agent frameworks⏱️ TIMESTAMPS0:00 The Shift from Bot Networks to AI Swarms2:00 Why Cheap AI Inference Enables Long-Term Influence Campaigns4:30 Autonomous Coordination and Emergent Hive Behavior7:00 Persona-Centric Agents Across Multiple Platforms8:30 Weaponizing Disinformation to Fabricate Synthetic Consensus14:15 How AI Swarms Corrupt LLM Training Data18:00 Why Individual Message Detection No Longer Works23:00 The Research Frontier: Coordination Pattern Detection27:00 Platform Business Models and Perverse Incentives32:00 Building Defenses: The AI Influence Observatory39:00 Corporate Risks: Fabricated Boycotts and Targeted Harassment46:00 Can It Be Stopped? The Arms Race Democracies Must Join

Bletchley Park wasn't built by one man—and history must stop pretending otherwiseFor most people, Bletchley Park means one thing: Alan Turing, Enigma, and a single heroic breakthrough.That story is neat, cinematic—and deeply misleading.In this episode of History Rage, Paul Bavill is joined by historian, author, and Bletchley Park trustee Sir Dermot Turing to dismantle one of Britain's most comfortable Second World War myths. What follows is a forensic, passionate unpicking of how thousands of codebreakers—most of them women—have been written out of history.This is not an attack on Alan Turing. It's a demand for accuracy.Sir Dermot explains why Enigma has become a historical obsession, how it eclipses dozens of other vital ciphers, and why reducing Bletchley Park to a single man does a disservice to everyone involved—including Turing himself. From Spanish and Italian diplomatic codes to Japanese military signals, this episode reveals just how broad, complex, and international the intelligence war really was.Crucially, the conversation exposes how women codebreakers were systematically downgraded by job titles, pay grades, and later historians. Clerical assistants, typists, and “support staff” were in reality performing some of the hardest cryptographic work of the war—often better than the men promoted over them. Figures such as Joan Clarke, Wendy White, Helen Hazelden, Marie Rose Egan, and many others emerge not as footnotes, but as central players.This episode also explores:Why Enigma machines themselves were never the real secretHow civil service bureaucracy distorted the historical recordThe hidden importance of German diplomatic intelligenceWhy Bletchley Park was far messier, more political, and more human than popular culture admitsIf you think you know the story of Bletchley Park, this episode will make you angry—for all the right reasons.About the Guest: Sir Dermot TuringSir Dermot Turing is a historian, author, and trustee of Bletchley Park, specialising in intelligence history and overlooked figures of the Second World War. He is the nephew of Alan Turing and a leading voice challenging simplistic narratives around wartime codebreaking.Recommended Reading

“Doing science is like reading the mind of God.” — Demis Hassabis, quoted in The Infinity MachineThis week's New Yorker uncomplimentary profile of OpenAI's CEO is entitled “The Many Faces of Sam Altman.” But not all AI leaders are quite as many faced as slippery Sam. Take, for example, Demis Hassabis, the North London based co-founder and CEO of Google's DeepMind. In his new biography, The Infinity Machine: Demis Hassabis, DeepMind, and the Quest for Superintelligence, the British journalist Sebastian Mallaby argues that Hassabis is, in contrast, one faced. And that face is not only decent, but informed by the enlightened ethics of Baruch Spinoza and Immanuel Kant.Mallaby presents Hassabis as the anti-Altman. He's stayed at DeepMind for sixteen years, lived in the same London house, drives a decade-old car. Rather than power, Google's AI supremo seeks scientific enlightenment. Like Spinoza, his God is the master watchmaker of the universe. And so doing science, Hassabis explained to Mallaby in one of their many conversations in the backroom of a North London pub, is like reading the mind of God. Decent Demis. Honest Hassabis. Let's just hope this modest and thoughtful tech leviathan can bring Kantian ethics to Silicon Valley's sprint for artificial general intelligence. Five Takeaways•       Hassabis Is the Anti-Altman: Sam Altman has managed to annoy almost everyone he's worked with by saying one thing and doing the opposite. Hassabis has run DeepMind continuously for sixteen years, lives in the same house in Highgate, drives a decade-old car, and spends his discretionary money on Liverpool season tickets. He doesn't want power. He wants scientific enlightenment. Mallaby uses the word advisedly.•       Doing Science Is Like Reading the Mind of God: Hassabis is a Spinozan. The god he believes in is the god Einstein talked about — the fabric of reality understood through scientific inquiry. He reads Kant, he reads Spinoza, he reads widely enough to be a proper polymath. Mallaby sat with him in a Highgate pub for more than thirty hours. What he found was not a Silicon Valley sociopath but an enlightenment figure who thinks AI is the modern version of the telescope.•       The Szilard Pedestrian Crossing: Mallaby asked Hassabis what it felt like to set up DeepMind in 2010. Instead of the usual vague answer, Hassabis painted the scene: the attic office on Russell Square, the heat, the stairs, the greenery outside, the London Mathematical Society three doors down where Turing lectured, and the zebra crossing where the Hungarian physicist Leo Szilard conceived of the nuclear chain reaction in the 1930s. The perfect metaphor: DeepMind as the modern Manhattan Project.•       The Two Categories of Things That Go Wrong: There's the idiot-in-charge category — an evil or stupid person making bad decisions, and you could swap them out. Then there's the structural category: a good person trying their best, defeated by larger forces they cannot control. Hassabis is category two. He wants to make AI safe, but race dynamics between US and China labs make safety nearly impossible to deliver. The failure of governments to intervene is the real story. Not individuals.•       The Go Players Who Quit: When AlphaGo beat the best players in the world, some professional Go players retired — centuries of accumulated human understanding devalued overnight. Others kept playing, using the machine as a tutor to discover patterns they'd never seen. Two responses to superintelligence in one domain. One is mourning. The other is curiosity. Mallaby thinks the second response is the only one worth having. Hassabis agrees. About the GuestSebastian Mallaby is the Paul A. Volcker senior fellow for international economics at the Council on Foreign Relations. A former Washington Post columnist and Economist contributing editor, he is the author of More Money Than God, The Man Who Knew (winner of the FT and McKinsey Business Book of the Year), The Power Law, and now The Infinity Machine: Demis Hassabis, DeepMind, and the Quest for Superintelligence.References:•       The Infinity Machine: Demis Hassabis, DeepMind, and the Quest for Superintelligence by Sebastian Mallaby.•       Episode 2862: Truth Is Dead — Steven Rosenbaum on AI as a spectacularly good liar. Mallaby's quiet counter-argument.•       Episode 2860: We Shape Our AI, Thereafter It Shapes Us — Keith Teare on agency in our agentic age. Hassabis thinks he can still steer.About Keen On AmericaNobody asks more awkward questions than the Anglo-American writer and filmmaker Andrew Keen. In Keen On America, Andrew brings his pointed Transatlantic wit to making sense of the United States — hosting daily interviews about the history and future of this now venerable Republic. With nearly 2,800 episodes since the show launched on TechCrunch in 2010, Keen On America is the most prolific intellectual interview show in the history of podcasting.WebsiteSubstackYouTubeApple PodcastsSpotify Chapters:(00:31) - Introduction: the many faces of Sam Altman (02:00) - Altman's duplicity versus Hassabis's consistency (02:56) - The moral wrestling: is this the Manhattan Project? (04:45) - The ordinary genius in Highgate (06:29) - The Szilard pedestrian crossing and a storyteller off the charts (09:10) - Responding to The Guardian: why Hassabis isn't Altman (12:58) - The two categories of things that go wrong (14:48) - Mustafa Suleiman's remarkable backstory (17:01) - Did Demis fire Mustafa? (19:46) - Class, Eton, and the North London grammar school (22:27) - Spinoza, Kant, and the god of science (25:27) - Doing science is like reading the mind of God (29:57) - Why not Princeton? The money problem (34:12) - The secret DeepMind vs Google negotiation (43:11) - Is Hassabis the next CEO of Google? (48:05) - The Go players who quit

Episode: 3247 Proust, Turing, and the Measure of Humanity.  Today, we go from Turing to Proust.

Fresh off raising a monster $15B, Marc Andreessen has lived through multiple computing platform shifts firsthand, from Mosaic and Netscape to cofounding A16z. In this episode, Marc joins swyx and Alessio in a16z's legendary Sand Hill Road office to argue that AI is not just another hype cycle, but the payoff of an “80-year overnight success”: from neural nets and expert systems to transformers, reasoning models, coding, agents, and recursive self-improvement. He lays out why he thinks this moment is different, why AI is finally escaping the old boom-bust pattern, and why the real bottleneck may be less about models than about the messy institutions, incentives, and social systems that struggle to absorb technological change.This episode was a dream come true for us, and many thanks to Erik Torenberg for the assist in setting this up. Full episode on YouTube!We discuss:* Marc's long view on AI: from the 1980s AI boom and expert systems to AlexNet, transformers, and why he sees today's moment as the culmination of decades of compounding technical progress* Why “this time is different”: the jump from LLMs to reasoning, coding, agents, and recursive self-improvement, and why Marc thinks these breakthroughs make AI real in a way prior cycles were not* AI winters vs. “80-year overnight success”: why the field repeatedly swings between utopianism and doom, and why Marc thinks the underlying researchers were mostly right even when the timelines were wrong* Scaling laws, Moore's Law, and what to build: why he believes AI scaling laws will continue, why the outside world is messier than lab purists assume, and how startups can still create durable value on top of rapidly improving models* The dot-com crash and AI infrastructure risk: Marc's comparison between today's AI capex boom and the fiber/data-center overbuild of 2000, plus why he thinks this cycle is different because the buyers are huge cash-rich incumbents and demand is already here* Why old NVIDIA chips may be getting more valuable: the pace of software progress, chronic capacity shortages, and the idea that even current models are “sandbagged” by supply constraints* Open source, edge inference, and the chip bottleneck: why Marc thinks local models, Apple Silicon, privacy, trust, and economics all point toward a major role for edge AI* American vs. Chinese open source AI: DeepSeek as a “gift to the world,” why open models matter not just because they're free but because they teach the world how things work, and how open source strategies may shift as the market consolidates* Why Pi and OpenClaw matter so much: Marc's claim that the combination of LLM + shell + filesystem + markdown + cron loop is one of the biggest software architecture breakthroughs in decades* Agents as the new “Unix”: how agent state living in files allows portability across models and runtimes, and why self-modifying agents that can extend themselves may redefine what software even is* The future of coding and programming languages: why Marc thinks software becomes abundant, why bots may translate freely across languages, and why “programming language” itself may stop being a salient concept* Browsers, protocols, and human readability: lessons from Mosaic and the web, why text protocols and “view source” mattered, and how similar principles may shape AI-native systems* Real-world OpenClaw use: health dashboards, sleep monitoring, smart homes, rewriting firmware on robot dogs, and why the most aggressive users are discovering both the power and danger of agents first* Proof of human vs. proof of bot: why Marc thinks the internet's bot problem is now unsolvable via detection alone, and why biometric + cryptographic proof of human becomes necessaryTimestamps* 00:00 Marc on AI's “80-Year Overnight Success”* 00:01 A Quick Message From swyx* 01:44 Inside a16z With Marc Andreessen* 02:13 The Truth About a16z's AI Pivot* 03:29 Why This AI Boom Is Not Like 2016* 06:33 Marc on AI Winters, Hype Cycles, and What's Different Now* 10:09 Reasoning, Coding, Agents, and the New AI Breakthroughs* 12:13 What Founders Should Build as Models Keep Improving* 16:33 AI Capex, GPU Shortages, and the Dot-Com Crash Analogy* 24:54 Open Source AI, Edge Inference, and Why It Matters* 33:03 Why OpenClaw and PI Could Change Software Forever* 41:37 Agents, the End of Interfaces, and Software for Bots* 46:47 Do Programming Languages Even Have a Future?* 54:19 AI Agents Need Money: Payments, Crypto, and Stablecoins* 56:59 Proof of Human, Internet Bots, and the Drone Problem* 01:06:12 AI, Management, and the Return of Founder-Led Companies* 01:12:23 Why the Real Economy May Resist AI Longer Than Expected* 01:15:53 Closing ThoughtsTranscriptMarc: Something about AI that causes the people in the field, I would say, to become both excessively utopian and excessively apocalyptic. Having said that, I think what's actually happened is an enormous amount of technical progress that built up over time. And like for, for example, we now know that neural network is the correct architecture.And I, I will tell you like there was a 60 year run where that was like a, you know, or even 70 years where that was controversial. And so, so the way I think about what's happening is basically, I think, I think about basically the, the, the period we're in right now is it's, I call it 80 year overnight success, right?Which is like, it's an overnight success ‘cause it's like bam, you know, chat GPT hits and then, and then oh one hits, and then, you know, open claw hits and like, you know, these are open, these are, these are like overnight, like radical, overnight transformative successes, but they're drawing on an 80 year sort of wellspring backlog, you know, of, of, of, of ideas and thinking it's not just that it's all brand new, it's that it's an unlock of all of these decades of like very serious, hardcore research.If I were 18, like this is a hundred, this is what I would be spending all of my time on. This is like such an incredible conceptual breakthrough.swyx: Before we get into today's episode, I just have a small message for listeners. Thank you. We will not be able to bring you the ai, engineering, science, and entertainment contents that you so clearly want if you didn't choose to also click in and tune into our content.We've been approached by sponsors on an almost daily basis, but fortunately enough of you actually subscribed to us to keep all this sustainable without ads, and we wanna keep it that way. But I just have one favor to ask all of you. The single, most powerful, completely free thing you can do is to click that subscribe button.It's the only thing I'll ever ask of you, and it means absolutely everything to me and my team that works so hard to bring the in space to you each and every week. If you do it, I promise you will never stop working to make the show even better. Now, let's get into it.Alessio: Hey everyone, welcome to the Lidian Space Pockets. This is CIO, founder Kernel Labs, and I'm joined by s Swix, editor of Lidian Space.swyx: Hello. And we're in a 16 Z with a, uh, mark G and welcome.Marc: Yes, yes. A and what, half of 16? Something like that. A one. Exactly,swyx: exactly. Uh, apparently this is the, the final few days in your, your current office.You're moving across the road.Marc: Uh, we're, yeah. We have a, we have some, we have some projects underway, but yeah, this is actually, oh, this is the original. We're in actually the original office. We're in the, we're in the, we're, we're in the whole thing.swyx: It's beautiful. Yeah. Great.Marc: Thank you.swyx: So I have to come out, uh, this is a, you know, I wanted to pick a spicy start in October, 2022.I just made friends with Roone and, uh, I wanted to give him something to sort of be spicy about. And I said, uh. Uh, it'll never not be funny. The A 16 Z was constantly going. The future is where the smart people choose to spend their time and then going deep into crypto and not in ai. And that was in October 22nd, 2022.And Ruen says there was an internal meeting in a 16 Z to reorient around Gen ai. Obviously you have, but was there a meeting? What, what was that?Marc: I mean, I don't, look, I've been doing AI since the late eighties.swyx: Yeah.Marc: So I, I don't know, like all that, as far as I'm concerned, this stuff is all Johnny cum lately.Yeah. You, I mean, look, we've been doing ar entire existence. I mean, we've been doing AI machine learning deep, you know, deeply. We've been doing this stuff way from the beginning. Obviously a AI is just core to computer science. I, I, I actually view them as like quite, uh, quite continuous. Um, you know, Ben and I both have computer science degrees.Um, you know, we, we both, Ben, Ben and I actually both are world enough to remember the actual AI boom in the 1980s. Yeah. There was like a, there was a big AI boom at the time. Um, and there was a, was names like expert systems. Um, and they of like lisp and lisp machines. Uh, I, I coded in lisp. I was coding a lisp in 1989.When that was the, the language of the AI future. Um, yeah. So this is something that we're like completely, you completely comfortable with. I've been doing the whole time and are very enthusiastic aboutswyx: is there a strong, like this time is different because, uh, my closest analog was 20 16 17. It was an AI boom.Mm-hmm. And it petered out very, very quickly. Um, we, it just, it just in terms of investingMarc: sort of, sort of,swyx: yeah. Investment, investment excitement.Marc: Although that's really when the, the, the Nvidia phenomenon really, it was, I would say it was in that period when it was very clear that at, at the time it, the vocabulary was more machine learning, but it, it was very clear at that time that machine learning was hitting some sort of takeoff point.Alessio: Yeah.Marc: Well, and as you guys, you guys have talked about this at length on, on your thing, but, you know, if you really track what happened, I think the real story is, it was, it was the Alex net, uh, basically breakthrough in like 2013. That was the, that was the real knee in the curve. Um, and then it was obviously the transformer breakthrough in 17.Alessio: Yeah.Marc: Um, and then everything that followed. But, but, you know, look, machine learning, you know, there were, you know, look, uh, I mean look, I've been working, you know, I've been working with, uh, one of my, you know, kind of projects working with Facebook since 2004. Um, and on the board since 2007, and of course, you know, they, they started using machine learning very early, um, and, you know, have used it basically, you know, for like 20 years for, you know, content, you know, feed optimization and advertising optimization.And obviously many, you know, financial services. You know, many, many, many companies, many different sectors have been doing this. And so it's like one of these things, it's like, it's not a, it's not a single thing. Like it's, it's like, it's like layers, right? Yeah. Um, and, and the layers arrive at different paces and, but they kind of build up.swyx: Yeah.Marc: Uh, they kind of build up over time and then, and then, yeah. And then look, in retrospect, it was 2017 was kind of the, you know, the key, the key point with the trans transformer and then. And then as you guys know, there was this really weird like four year period where it's like the, the transformer existed and then it was just like,swyx: let's go.Yeah.Marc: Well, but, but it was just, but, but between 2020, but between 2017 and 2021, I mean, that was the era of which like companies like Google had internal chat Botts, but they weren't letting anybody use them.swyx: Yeah.Marc: Right. And then, you know, and then OpenAI developed Chat GT or GPT two, and then they told everybody, this is way too dangerous to deploy.Right. Yeah. You know, we can't possibly let normal people, normal people use this thing. And then you, you guys, I'm sure remember AI Dungeon, um mm-hmm. So the o for, there was like a year where like the only way for a normal person to use GP T three was in, in AI dungeon.Alessio: Yeah.Marc: And so you, you, we would do this, you'd go in there and you'd pretend to play Dungeons and Dragons.In reality, you're just trying to talk to talk to GPT. And so there was this, you know, there was this long, you know, and I, you know, the big, big companies, you know, big companies are cautious and, you know, the big companies were cautious. It, it, by the way, it took open ai. You know, they, they, they talk about this, it took open AI time to actually adjust, you know, kind of re redirect their researchswyx: path.I, I think, uh, let say Rosewood, right? Uh, the, the dinner that founded OpenAI was right there.Marc: Right, right. But that, that dinner would've taken place in 20swyx: 18Marc: 19. The formation of OpenAI Uhhuh as late as 2018.swyx: Uh, uh, sorry. Uh, no, I'm, I'm, I'm, I'm wrong. Probably It should be 20. Yeah. They just celebrated a 10 year anniversary, so it it is 2025.Yeah, so, so 2015?Marc: Yeah. 2015. Yeah. 2015. But then, uh, um, Alec Radford did G PT one in what, probablyswyx: mm-hmm. 17, 18,Marc: yeah. 17, 18. So it, yeah. For, and then, and then they didn't really, and then GPT three was what? 2020? 2020.swyx: 2020.Marc: Because that became copilot immediately. Even open ai, which has been, you know, the leader of, of this thing in the last decade, you know, e even they had to adapt and, and, and lean into the new thing.And so. Um, yeah, I, I think it's just this process of basically sort of wave after wave layer after layer, you know, building on itself. And then you kind of get these catalytic moments where, where the whole thing pops and, and obviously that's what's happening now.swyx: Is it useful to think about will there be any ai, winter?‘cause there's always these patterns. Like, is this, in the summer is something I constantly think about because do I get, do I just like. Just get endlessly hyped and just trust that I will only be early and never wrong or right. Well, are we, will there be a winter?Marc: So there's something about, say the following.There's something about AI that has led to this repeated pattern. Um, and, and, and you guys know this,swyx: it's summer, winter, summer,Marc: winter, summer, winter, summer, winter. And it goes back 80 years. Yeah. 80 years. Uh, so the original neural network paper was 1943. Right. Which is, which is amazing. Uh, that it was, it was far back that long.And then there was you, if you guys have ever talked about this on your show, but there was this, uh, there was a big, uh, there was an a GI conference at Dartmouth University in 1950. 55. 55, yeah. And they got a NSF grant to, uh, for the, all the AI experts at the time to spend the summer together. And they figured if they had 10 weeks together, they could get a GI, uh, at the other end.And they got their, by the way, they got the grant, they got the 10 weeks and then, you know, 1955, you know. No, no. A GI. And like I said, I, I lived through the eighties version of this where there was a big, a big boom and a crash. And so, so there is this thing, and there, there is something about AI that causes the people in the field, I would say, to become both excessively utopian and excessively apocalyptic.Um, and, and it's probably on both sides of like the, the, the boom bus cycle. You, you kind of see that play out. Having said that, I think what's actually happened is like just, and you know, and we now know in retrospect like an enormous amount of technical progress that built up over time. And like for, for example, we now know that neural network is the correct architecture.And I, I will tell you like there was a 60 year run where that was like a, you know, or even 70 years or that was controversial. And, and we now know that that's the case. And so we, we now, you know, everything we're building on today just sort of derives from the original idea in 1943. And so, so in retrospect, we, we now know that like, these, these guys are right.They, they, you know, they would get the timing wrong and they thought, you know, capabilities would arrive faster, or they were, it could be turned into businesses sooner or whatever, but like, they were fundamentally, the, the scientists who worked on this over the course of decades were fundamentally correct about what they were doing.And, and the, and the payoff from, from, from all their work is happening now. And so, so the way I think about what's happening is basically, I think, I think about basically the, the, the period we're in right now is it's, I call it 80 year overnight success, right? Which is like, it's an overnight success.‘cause it's like bam, you know, chat, GPT hits and then, and then oh one hits, and then, you know, open claw hits and like, you know, these are open, these are, these are like overnight, like radical, overnight transformative successes, but they're drawing on an 80 year sort of wellspring backlog, you know, of, of, of, of ideas and thinking it's not just that it's all brand new, it's that it's an unlock of all of these decades of like very serious, hardcore research.Um, and thinking, and look, there were AI researchers who spent their entire lives. They got their PhD. They, they worked for, they've researched for 40 years. They retired in a lot of cases, they passed away and they never actually saw it work.swyx: Yeah. It's all sad.Marc: It is. It is sad. It's sad. Knewswyx: Jeff Hinton was like the last guy.Marc: Yeah. Yeah. Well, there were the guys, uh, was a guy, Alan Newell. I mean, there's tons of John McCarthy. You know, John McCarthy was like one of the inventors in the field. He's one of the guys who organized the Dartmouth Conference and you know, he taught at Stanford for 40 years. Wow. And passed, you know, passed away, I don't know, whatever, 10, 10 years ago or something.Never, never actually go. Got to see it happen. But like, it is amazing in retrospect, like, these guys were incredibly smart and they worked really hard and they were correct. So anyway, so then it's like, okay, you know, say history doesn't repeat, but it rhymes. It's like, okay, does that mean that there's gonna be another, like, you know, basically boom buzz cycle.And I, I will tell you, like, let, like in a sense, like yes, everything goes through cycles and, you know, people get overly enthusiastic and overly depressed and there's, there's a time, there's a timelessness to that. Having said that, there's just no question. Um, so the form, the foremost dangerous words in investing this time are, this time is different.Do you know the 12 most dangerous words investing? No. The four most d foremost dangerous words in investing are this time is different. Yeah. Um, the 12 most dangerous words. And so like, I'll tell you what's different. Like now it's working like, like there's just no, I mean, look, there's just no question.And by the way, I, I'll just give you guys my take. Like L LLMs, like from, from basically the Chad G PT moment through to spring of 25. I think you could still, I think well intention, well, and of. Form skeptics could still say, oh, this is just pattern completion. And oh, these things don't really understand what they're doing.And you know, the hall hallucination rates are way too high. And, you know, this is gonna be great for creative writing and creating, you know, Shakespeare and so sonnets and, you know, as, as rap lyrics or whatever, like, it's gonna be great and all that stuff, but we're not gonna be able to harness this to make this relevant in, you know, coding or in medicine or in law or in, you know, you know, kind of feels that, you know, kind of really, really matter.And I think basically it was the reasoning breakthrough. It, it was oh one and then R one that basically answered that question basically said, oh no, we're gonna be able to actually turn this into something that's gonna work in the real world. And, and then obviously the coding breakthrough over the, over basically the coding breakthrough that kind of catalyzed over the holiday break was kind of the third step in that.Mm-hmm. Where you're just like, alright, if, if, you know, if Linus Tova is saying that the AI coding is no better than he is like. Like, that's, that's never happened before. That's theswyx: benchmark.Marc: Yeah. That's never happened before. And so now we know that it's, it's gonna sweep through coding and, and then, and then we, we know, you know, we know that if it's gonna work in coding, it's gonna work in everything else.Right. It's just then, because that's, that's like, that's like, that's like the hardest in many ways. That's the hardest example. And how everything else is gonna be a, a derivative of that. And then on top of that, we just got the agent breakthrough, you know, with Open Claw, which is fantastic. Which is amazing and incredibly powerful.And then we just got the, the, um, the auto research, uh, you know, the, the self-improvement. You know, we're now into the self-improvement breakthrough. And so the, so the way I think about it is we've had four fundamental breakthroughs in functionality, l OMS reasoning, uh, agents, um, and then, uh, and, and then now RSI, um, and, and they're all actually working.Um, and so I'm, I'm just, as you like, you can tell I'm jumping outta my shoes. Like, like this is, like this is it like this, this is the culmination of 80 years worth of worth of work, and this is the time it's becoming real.Alessio: Yeah.Marc: I, I'm completely convinced.Alessio: I think the anxiety that people feel is like during the transistor era, yet Mors law, and it's like, all right, we understand why these things are getting better.We understand the physics of it. Yeah. With ai, it's. It's so jagged in like the jumps where like, like you said, it's like in three months you have like this huge jump like, and people are like, well this can keep happening. Right? But then it keeps happening,Marc: it'll keep happening.Alessio: And so like how do you think about also timelines of like what's we're building?I think we always have this question with guests, which is like, you know, should you spend time building harness for a model versus like the next model just gonna do it one shot in the lead space. Right. And how does that inform, like how you think about the shape of the technology? You know, you talk about how it's a new computing platform.If you have a computing platform, then like every six months it like drastically changes in what it looks like. It's hard to build companies on top of it.Marc: Yeah. So, so a couple things. So one is like, look, the, the Moore's law was what we now call a scaling law. Like Moore's Law was a scaling law and for your younger viewers, more Moore's Law was every chip chip chips either get twice as powerful or twice as cheap every, every 18 months.And that, and that and that, you know, that it's gotten more complicated in the last few years. But like that, that was like the 50 year trajectory of, of, of the computer industry. And then, and then by the way, and that's what took the mainframe computer from a $25 million current dollar thing into, you know, the phone in your pocket being, you know, a million times more powerful than that.Like that, you know, for, for 500 bucks. And so that, that was a scaling law. And then, and then, and then key to any scaling law, including Moore's Law and the AI scaling laws is, you know, they're not really laws, right? They're, they're, they're, they're predictions, but when they work, they become self-fulfilling predictions because they, they, they, they, they set a benchmark and, and then the entire industry, right?All the smart people in the industry kind of work to make sure that, that, that actually happens. And so they, they kind of motivate the breakthroughs that are required to, to keep that going. And, and in and in chips, that was a 50 year, that was a 50 year run. Right. And it, it was amazing. And it's still happening in, in some areas of, of chips.I think the same thing is happening with the, the core scaling laws. The core scaling laws. In, in, in ai, you know, they're, they're not really laws, but like they, they are basically. There are predictions and then they're motivating catalysts for the research work that is required to be. And, and, and, and by the way, also the investment, uh, dollars, um, uh, you know, required to basically keep, you know, keep the curves going and, and look, it, it is, it's gonna be complicated and it's gonna be variable and they're, you know, there're gonna be walls that are gonna look like they're fast approaching, and then they're gonna be, you know, engineers are gonna get to work and they're gonna figure out a way to punch through the walls.And obviously that's, you know, that's been happening a lot, you know, and then look, there's gonna be times when it looks like the walls have, you know, the, the, the laws have petered out and then they're gonna, they're gonna pick up again and surge and then, and then, and then it, it appears what's happening to the eyes is there's not multiple, you know, multiple scaling laws.Um, there's multiple areas of improvement. And, and I think, you know, I don't know how many more there are already yet to be discovered, but there are probably some more that we don't know about yet. You know, they, like, for example, there's probably some scaling law around, um, world models and robotics that we don't fully understand, you know, kind of acquisition of data at scale in the real world that we don't fully understand yet.So that, that, that one will probably kick in at some point here. There's a bunch of really smart people working on that. Um, and so, yeah, I, I think the expectation is that, that, you know, the, the scaling laws generally are gonna continue. Yeah. The, the pace of improvement will continue to move really fast.Um. To your question on like what to build. So, uh, I'm a complete believer the scaling laws are gonna continue. I'm a complete believer the capabilities are gonna keep getting amazing, um, you know, leaps and bounds. Uh, the part where I kind of part ways a little bit with how, what I would describe as the AI purists, um, you know, which is, which I would characterize as like the people who are.In many ways, the smartest people in the field, but also the people who spend their entire life, like at a lab, um, and have, have, I would say, have very little experience in the outside world. Um, the, the, the nuance I would offer is the outside world of 8 billion people and institutions and governments and companies and economic systems and social systems is really complicated.Um, and, um, and doesn't, you know, it it 8 billion people making collective decisions on planet Earth is not a simple process of like, just like you see this happening now. It's like a bunch of AI CEOs have this thing, which is just like, well, there's just this, they just all have this kind of thing when they talk in public where they're just like, well, there's these, these obvious set of things that so society to do.Alessio: Mm-hmm.Marc: And then they're like, society's not doing any of those things. Right. And it's like, how can society not, you know, what, whatever their theory is, how can society not see x, y, Z? Mm-hmm. And the answer is, well, society is number one. There's no single society, it's like 8 billion people. And they like all have a voice, and they all have a vote, like at the end of the day of how they, they react to change.And then, you know, it just like, it's just human reality is just really complicated and messy. Um, and, and, and so the specific answer to your question is like, as usual, it depends. Um, you know, it, it depends. Look, pe there's no question people are gonna, like, there's no question they're gonna be companies.It's already happening. There are companies that think that they're building value on top of the models and then they're just gonna get blissed by the, by the next model. There's no question that's happening. But I think there's no question also that just the process of adaptation of any technology into the real and into the real messy world of humanity is, is just going to be messy and complicated.It's, it's not going to be simple and straightforward. It's gonna be messy and complicated. And there are gonna be a lot of companies and a lot of products, um, uh, and in, in fact entire industries that are gonna get built to, to, to basically actually help all of this technology actually reach real people.Alessio: The amount of capital going into these companies, I mean, Dario talked about it on the Door Cash podcast and Door Cash was like, why don't you just buy 10 x more GPUs? And he is like, because I'm gonna go bankrupt if the model doesn't exactly hit the, the performance level. How do you think about that?Also as a risk on, you know, you guys are investors, open AI and thinking machines and world apps. It seems like we're leveraging the scaling loss at a pretty high rate, right? Like how comfortable, I guess, do you feel with the downside scenario, like, and say like things Peter out, you think you can kind of like restructure uh, these build outs and uh, you know, capital investments.Marc: Yeah. So should start by saying, so I live through the.com crash, um, and I can tell you stories for hours about the.com crash and it was horrible. No, it was awful. It was, it was, it was apocalyptic by the way. The, a lot of the.com crash was actually at the time, it was actually a telecom crash. It was a bandwidth crash.Like the, the thing that actually crashed, that wiped out all the money with the tele, the telecom companies.swyx: GlobalMarc: crossing. Global, global, yeah.swyx: I'm from Singapore and they, they laid so much cable o over over our oceans.Marc: Actually there was a scaling law in the.com. Era. And it was literally the, the US Commerce Department put out a report in 1996 and they said internet traffic was doubling every quarter.Um, and, and actually in 1995 and 1996, internet traffic actually did double every quarter. And so that became the scaling law. And so what all these telecom entrepreneurs did was they went out and they raised money to build fiber, anticipating that the demand for bandwidth is gonna keep doubling every quarter.Doubling every quarter though is like, you know, grains of chess and the chessboard, like at some point the numbers become extremely large. Right. And, and, and it really, and really what happened was the internet. The internet by the way, continuously kept growing basically since inception. And it's, you know, it's, it's continuously grown.It's never shrunk. And it's grown really fast compared to anything else. Mm-hmm. You know, in, in, in human history. But it wasn't doubling every quarter as of 19 98, 19 99. And so there was this gap in the expectation of what they thought was a scaling law versus reality. And that's actually what caused the.com crash, which was the, it they, they way over companies like global crossing way overbuilt fiber, which is sort of the, and by the way, fiber, telecom equipment, you know, so all the, all the networking gear, you know, and then, and then by the way, the actual physical data centers, like that was the beginning of the, of the, of the data center build and then, and the data center overbuild.And so you had that, but it was, it was literally, I think it was like $2 trillion got wiped out, right? It was like Jesus, it was like a big, it was. And by the way, the other, the other subtlety in it was the internet companies themselves never really had any debt. ‘cause tech, tech companies generally don't run on debt, but the telecom companies run on debt.Physical infrastructure companies run on debt. And so the companies like Global Crossing not just raise a lot of equity, they also raise a lot of debt. So they're highly levered. And so then you just do the thing. It's just like, okay, you have a highly levered thing where you're, you're just over, you're overbuilding capacity.Demand is growing, but not as fast as you hoped. And then boom, bankrupt. Right. And, and then it, and then it's like they say about the hotel industry, which is, it's always the third owner of a hotel that makes money. It has to go bankrupt twice, right? You have to wash out all of the over optimistic exuberance before it gets to actually a stable state.And then it makes money. So by the way, all of those data centers and all of those, all the fiber that they're in use, it's all in use today. Yeah. But 25 years later. But it, it, it took, and actually the elapsed time was, it took 15 years. It took 15 years from 2000 to 2015 to actually fill, fill up all that capacity.The cautionary warning is the, the overbuild can happen. Um, and, and, and, and, you know, you, you get into this thing where basically everybody, everybody who basically has any sort of institutional capital, it's like, wow. It's just, I, I don't know how to invest in these crazy software things. For sure I can put build data centers and for sure I can buy GPUs that I can deploy, you know, compute grids and, and all these things.Um, and so, you know, if you're a pessimist, you could look at this and you could say, wow, this is like really set up to be able to basically replicate, you know, what we went through, what we went through in 2000. Obviously that would be bad. The counter argument, which is the one I I agree with, which is the counter on, on the other side is a couple things.One is the companies that are investing all the, the companies that are investing the money are like the bluest chip of companies. And so back, back, back in the, in the do, like Global Crossing was like a, it was like an entrepreneur. It was like a, a new venture, but like the money that's being deployed now at scale is Microsoft, and, you know, and Amazon and Google, Facebook and Facebook and Nvidia and, you know, these, these, these, and, and now you know, by the way, open ai philanthropic, which are now at like, you know, really serious size, um, you know, as companies with, you know, very serious revenue.These are very large scale companies with like, lots, lots of cash, lots of debt capacity that they've, they've never used. And so th this is institutional in a way that, that really wasn't at the time. And then the other is, at least for now, every dollar that's being put into anything that results in a running GPU is being turned into revenue right away.Like so, and you guys know this, like everybody's starved for capacity, everybody's starved for compute capacity and then, you know, all the associated things, memory and, and, and interconnected and everything else. Um, data center space. And so e every dollar right now that's being put into the ground is turning into revenue.And, and it, and in fact, I actually think there's an interesting thing happening, which is because everybody starve for capacity, the models that we actually have that we can use today are inferior versions of what we would have if not for the supply constraints. That's true. Um, if Right pose a hypothetical universe in which GPUs were 10 times cheaper and 10 times more plentiful mm-hmm.The models would be much better. ‘cause you would just allocate a lot more money to training and you'd just build better models and they would be better. Um, and so we're, we're actually getting the sandbag version of the technology.swyx: Yeah. No. Everything we use is quantized because the, the labs have to keep the, the full versions,Marc: right?swyx: LikeMarc: we're not even getting the good stuff.swyx: Yeah.Marc: But, but getting the good stuff, it's, it's just, even if technical progress stops. Once there's like a much bigger build of like GPU manufacturing capacity and memory, you know, all, all the things that have to happen in the course of the next five or 10 years.Once it happens, even the current technology is gonna get, gonna get much better. And then as you know, like there's just like a million ways to use this stuff. Like there's just like a million use cases for this. Mm-hmm. Like, it, it, you know, this isn't just sending packets across a, a thing, whatever, and hoping that people find something to do with it.This is just like, oh, we apply intelligence into every domain of human activity. And then it works like incredibly well. Yeah. Um. Here's what I know, here's what I know. Um, in the next three or four year, it's like somewhere between three or four years out, basically everything is selling out. So like the, the entire supply chain is, is, is, is sold out or, or, or selling out.And so there, there's no, like, we're just gonna have like chronic supply shortage for, you know, for years to come. Um, there's going to be a response from the market that's gonna result in an enormous, you know, it's happening now. An enormous flood of investment in a new fab capacity and ev you know, every, everything else to be able to do that, at some point the supply chain constraints will unlock, you know, at least to some degree that will be another accelerant to industry growth when that happens.‘cause the products will get better and everything will get cheaper. Um, and so, so I know that's gonna happen. I know that, you know, the deployments, you know, the, the actual use cases are like really compelling. And then, like I said, you know, with reasoning and agents and so forth, like, I know they're just gonna get like much, much better from here.And so I, I, I know the capabilities are like really real and serious. I also know that the technical progress is not going to stop. It. It, it is excel. It is, is accelerating. Like the, the breakthroughs are are tremendous. I mean, even just month over month, the breakthroughs are really dramatic. And so, you know, I think if you were a cynic and there, there are cynics, you can look at 2000, you can find echoes.But I can't even imagine betting it that this is gonna like somehow disappoint and, you know, at least for years to come, I think it would be essentially suicidal to make that bet. Yeah. Um, it was that Michael Burry, uh, uh, that'sswyx: anMarc: interesting guy, huh? We'll pick on a guy. We'll pick, let's pick on one guy.We'll pick. Well ‘cause he did, he he came out with, it was, it was the, heswyx: doesn't mind.Marc: It was the Nvidia short. Right. He came with the Nvidia short. And then if you guys probably talked about this, which is the, the analysis now that like the current models are getting better faster at such a rate that if you are running an Nvidia, if you're running an Nvidia inference chip today, that's three years old, you're making more money on it today than you did three years ago because the pace of improvement of the software is, is faster than the, the, the depreciation cycle, the chip.And then my understanding is Google is running. I don't if they've, I don't know exactly what, uh, these are rumors that I've heard or maybe it's public, but, um, I think Google's running very old TPUs, very profitably. Ference. Yeah. And very profit and very profitably. Yeah. Um, and so, so it actually turns out, as far as I can tell, it's actually the opposite of the Beery thesis is actually.He was actually 180 degrees wrong. It's actually the, the, the, the old Nvidia chips are getting more valuable, which is something that's like literally never happened before. Like it's never been the case that you have an older model chip that becomes more valuable, not less valuable. And that, and again, that's an expression of the just ferocious pace of software progress.Ferocious pace of capability payoff. Yeah. Uh, that you're getting on the other side of this. And so I just, the idea of betting against that, like.swyx: Yeah. Yeah. Well, one ofMarc: my, it seems like an invitation to get your face ripped up.swyx: One of my early hits was like modeling the lifespan of the H 100 and h two hundreds and, and going like, you know, usually they advise like four to seven years and it was, you know, maybe you sort of realistically haircut cut it down to two to three.Yeah. But actually it's going up and not down. Yeah. And, and uh, that's, I mean that's, I think that's the dream. Uh, we are finding utilization and I think utilization solves all problems. Like, you can, you can find use, use cases for even like the poor, like even memory, we're having a shortage. Right. And, and even like the, the shittier versions of, of memory that we do have, we are finding use cases for it.So like That's great.Marc: Yeah.Alessio: How, how important is open source AI and kinda like edge inference in a world in which you have three years of supply crunch. Like, do you think in the, like, you know, if you fast forward like five years, like how do you think about inference, uh, in the data center versus at the edge?Marc: Well, so just to start, yeah. So I think, I think open source is very important for a bunch of reasons. I think edge, edge inference is very important for a bunch of reasons. I, I think just practically speaking, if we're just gonna have fundamental construc, supply crunches for the next, I mean, you, you guys know if you just project forward demand over the next three years, right?Yeah. Relative to supply, one of the, its main predictions you can do is what's gonna, what, what's gonna happen to the cost of, of inference in the core, uh, over the next three years? And like, it may rise dramatically, right? Like, so, so what is, and then is, is, you know, like the, the, the big model competition are subsidizing heavily right now.Right? Right. And so, so what's the, what will be the average person's, you know, per day, per month token cost, you know, three years from now to do all the things that they want to do. And I, I don't know, it's gonna. I mean, I have, you guys probably have friends, I have friends today who are paying a thousand dollars a day for open claw, for claw tokens to run open claw.Right? And so, okay. $30,000 a month. Right? And, and by the way, those, those friends have like a thousand more ideas of the things that they want their claw to do, right? Yeah. And so you, you could imagine there, there's like latent demand of up to, I don't know, five or $10,000 a day of, of, of tokens for a fully deployed, you know, per personal agent.Uh, and obviously consumers can't pay that, right? And so, so, but it gives you a sense of the fu of the fu of the future scope of demand, right? And so, so even, even if there's a 10 x improvement in price performance, that still, you know, goes to a hundred dollars a day, which is still way beyond what people can pay.Mm-hmm. So there's just gonna be like. Ferocious to me, by the way. The agent thing, the other interesting thing is I think the agent thing, so up until now, a lot of the constraints of GGPU constraints, I think the agent thing now also translates into CPU constraints. Mm-hmm. Right?swyx: CPU memory.Marc: Yes. CPU memory, right?And so, like the entire chip ecosystem is just gonna get wait,swyx: wait for network constraints, that that will be the killer.Marc: It's all bottleneck potentially for years. And so, so I, I think that Brad, and, and I think it's actually possible, I mean, generally inference costs are gonna keep coming down, but I think the, let's put it this way, the rate of decline, I think may level out here for a bit because of these supply constraints.And then at some point, maybe the lab stops subsidizing so much and that, that, that again, will be, be an issue. And so there's just gonna be so much more demand for inference than, than can be satisfied. Um, you know, kind of with the centralized model. And then, and then, you know, you guys know this, but like all the, just the dramatic, I mean just the dramatic innovations that have happened in the Apple silicon to be able to do, uh, inferences, it's quite amazing the level of effort being put.Like the open source guys are putting incredible effort into getting, you know, this recurring pattern where the big model will never run on a pc, and then six months later mm-hmm. Oh, it runs in a pc, right? It's like amazing. And there's very smart people working on that. So there's all that. And then look, there's also, you know.There's also like other, there's other motivators. There's other motivators which is just like, okay, how much trust are the big centralized model providers? You know, how much trust are they building in the market versus, you know, how much are, you know, at least for, in certain cases with some people, for certain use cases, people being like, well, I'm not willing to just like, turn everything over.So there, there, there's all the trust issues. Um, by the way, there's also just like straight up price optimization. There's many uses of AI where you don't need Einstein in the cloud. You just need like a, a a, a smart local model. There's also performance issues where you want, you know, you want, you know, you're gonna want your doorknob to have an AI model in it.Right. You know, to be able to, you know, do, um, you know, to be able to do access control. Um, obviously like everything with a chip is gonna have an AI model in it. Mm-hmm. And it, a lot of those are gonna be local. Um, and so, yeah. No, like I think, I think you're gonna have ti and then you're gonna, by the way, also wearable devices, you know, you don't wanna do a complete round trip.You want, you know, you, whatever your smart devices are, you want it to be like super low latency. Yeah.swyx: The question, do we care who makes it? Yeah. One of the biggest news this week was the collapse of AI two, the Allen Institute. Mm-hmm. One of the actual American open source model labs. Yeah. Um, and, uh, I'm not that optimistic on, on American open source.Yeah. Like you, you guys invested in MIS trial and MIS trial's doing extremely well outside of China. That's about it.Marc: Yeah. We'll see. We'll see. I look, I, number one, I do think we care. Uh, I do think we, I do think we care who makes it. Um, I would say this, the, the, the, the previous presidential administration wanted to kill it in the us Oh yeah.They wanted to drown in the bathtub. Um, and so they wanted to kill it. So at least we have a government now that actually like, actually wants it wants it to happen. And youswyx: earned to councilMarc: and Yeah. And the new and the P pcast. Yeah. So the, the, you know, this admin for whatever other political issues people have, which are many, you know, this administration has, I think a very enlightened view and in particular an enlightened view on AI and in particular on open source ai.Uh, and so they're very supportive. Um, my read is the Chi. The Chinese have a very, the various Chinese companies have a very specific reason to do open source, which is, they, they, they don't fundamentally, they don't think they can sell commercial, uh, AI outside of China right now. And or at least specifically not, not in the US for a combination of reasons.And so they, they kind of view, I think, open source AI as a bit of a loss leader against basically domestic, uh, you know, paid, paid services. And then kind of an, you know, kind of an ancillary products. You know, they're, they're very excited about it, by the way. I think it's great. I think it's great that they're doing it.Um, you know, I think Deeps seek was like a gift to the world. Um, I think. The great thing about open source, open source, the, the, the impact of open source is felt two ways. One is you, you get the software for free, but the other is you get to learn how it works, right? And so like the paper, the paper, the paper and, and the code, right?And the code. And so, like, for example, I thought this was amazing. So open comes out with L one and it's an amazing technical breakthrough, and it's just like, absolutely fantastic. But of course they don't explain how it works in detail. And then of course they hide the, they hide the reasoning traces, right?And, and then, and then, and then everybody's like, okay, this is great, but like, who's gonna be able to replicate this? Are other people gonna be able to do this? You know, is their secret sauce in there? And then our one comes out and it's just like, there's the code and there's the paper, and now the whole world knows how to do it.And then, you know, three months later, every other AI model is, is adding reasoning. And so, so you get this kind of double, like even if the Chinese models themselves are not the models that get used, the education that's taken place to the rest of the world, the information diffusion, you know, is incredibly powerful.So that happens and then, I don't know. We'll, we'll see. You know, there are a bunch of American, you know, open source, you know, ai, uh, model companies. I mean, look, there's gonna be tremendous, you know, there already is. There's, you know, there's gonna be tre there's tremendous competition, uh, among the primary model companies.You know, there's, depending on how you count, there's like four or five, you know, big co model companies now that are, you know, kind of neck and neck, uh, in different ways. Um, uh, you know, and, and, and, um, you know, and then obviously Bo Bo both X and then MetAware involved are, you know, both have huge, you know, huge attempts to, you know, kind of, to kind of leapfrog underway.And then you've got, you know, a whole fleet of startups, new companies, including a whole bunch that we're backing, that are, you know, trying to come out with different approaches. And then you've got whatever it is. I don't know how, how many, how many, like main line foundation model companies are there in China at this point?It's probably six. It'sswyx: five Tigers is what they call it. Yeah. Uh, Quinn is in questionable because there's change in leadership,Marc: right?swyx: Yeah.Marc: But that, does that include, that includes like Moonshot,swyx: yes. Can deep seek, uh, uh, ZI, um, Quinn oh one is in there.Marc: Right. And then, um, and by dance and, and then you see,swyx: ance would be like the next tier ance.They weren't as prominent. They weren't, didn't haveMarc: a leading. Yeah. But they, you at least, you know, ance is very inspiring and presumably they have more stuff coming and Tencent probably has more stuff coming and, and so forth. And so, so, so like, look, here, here would be a thing you can anticipate, which is there are not these markets, there are not going to be between the US and China right now, there's like a dozen primary foundation model companies that are like at scale, at, at some level of a critical mass.It's not gonna be a dozen in three years, right? Like, it just because these industries don't bear a dozen, it's, it's gonna be three or you know, there's gonna be three or four big winners or maybe one or two big winners. And so there's gonna be like a whole bunch of those guys that are gonna have to figure out alternate strategies.Um, and I think like open source is one of those strategies. And so I, I think you could see like a whole, i, I, I think the questions like, who's gonna do open source? I think that could change really fast. I, I think that, that, that's a very dynamic thing. I think it's very hard to predict what happens. And, and I think it's very important.swyx: NVIDIA's doing a lot.Marc: Well, I was gonna say. Well, exactly. And then you're got Nvidia and then, and then, you know, just to, again, indu, there's an old thing in business strategy, which is called, uh, commoditize Compliments. Commoditize the compliment. That's right. And so if your Jensen is just kind of obvious, of course, you wanna commoditize the software.Yeah. And he's, and to his enormous credit, he's putting enormous resources behind that. And so maybe it, maybe it's literally Nvidia and I think that would be great.Alessio: Yeah. Uh, narrative violation to European projects, uh, in the, uh, damn.swyx: I'm hosting my, uh, Europe, uh, conference soon. And I got both of them.Alessio: They got us.They got us. MarkMarc: finished. They got us, us. Well, wait a minute. Where was Peter? So where was Steinberger when he did? In AustriaAlessio: was, yeah, yeah, yeah.Marc: He was in what? He was in Vienna. Oh, he was in Vienna. And then where is he now?swyx: Uh, he's moving to sf.Marc: Okay. Okay. Alright. Okay, there we go. And then, yeah, the PI guy, right?The PI guys are European.swyx: Yeah, they're also, they're buddies inAlessio: Australia. Mario's also there. Yeah.Marc: Right. And are they, yeah, they haven't announced yet. Any sort of change changed or have theyAlessio: No, they're, they have a company there.Marc: Okay. Got, okay. Good.Alessio: Good, good,good.Alessio: Um,Marc: yeah, good.swyx: Anyways, I think pie and open cloud very important software things and, and I just wanted you to just go off on what you think.Marc: Yeah. So I think in co the, the combination of the two of them I think is one of the 10 most important softwares. Openswyx: Claw got all the attention, but Right. Talk about pie,Marc: pi pie's, kind of the Yeah. PI's, PI's kind of the architectural breakthrough for those of us who are older. There was this whole thing that was very important in the world of software basically from like 1970 to, I don't know, it still is very important, but like 19, from 1973 to like basically the creation of Linux, which is basically this, this thing used to call like the Unix mindset.Like so, so, ‘cause there were all these different, you know, theories. There are all these different operating systems and mainframes and, and then you know, all these windows and Mac and all these things. And then there was this, but kind of behind it all was this idea of kind of the Unix mindset. And the Unix mindset was this thing where basically you don't have these, like, like in the old days, like, like the operating system that like made the computer industry really work, like in the 1960s mm-hmm.Was this thing called o os 360, which was this big operating system that IBM developed that was supposed to basically run everything. And it was this like giant monolithic architecture in the sky. It was like a, you know, it was like a giant castle. Um, of software. And, and by the way, it worked really well and they were very successful with it.But like, it was this huge castle in the sky, but it was this thing, it was almost unapproachable, which is like, you had to be kind of inside IBM or very close to IBM. And you had to really understand every aspect, how the system worked. And then the, the Unix sky is originally out of at and t and then out out of Berkeley, um, you know, came out and they said, no, let's have a completely different architecture.And the way architecture's gonna work is we're gonna have, we're gonna have a, a prompt and, and a, and a shell. And then, and then we're gonna, all, all the functionality is gonna be in the form of these discreet modules, and then you're gonna be able to chain the modules together. Mm-hmm. Yeah. And so like the, the, the op, it's almost like the operating, operating system itself is gonna be a programming language.Um, and then that led led to the, the, the sort of centrality of the shell. Um, and then that led to sort of, uh, you know, basically chaining together Unix tools. And then that led to the emergence of these, these scripting languages like Pearl, where you, you could basically kind of very easily do this, and then the shells got more sophisticated and then, and then, and then look like, you know, that, that, that number one, that worked and that, that was the world I grew up in.Like I was, I was a Unix guy. You know, sort of from, call it 1988 to, you know, kind of all, all the way through my work and it worked really well. It, it's in the background, um, you know, nor normal people don't need to, didn't need to necessarily know about it, but like, if you were doing like system architecture, application development, you, you, you knew all about it.Um, and then, you know, it's been in the background ever since. And, you know, look, your Mac still has a Unix shell, you know, kind of in there, and your iPhone still has a Unix shell kind of buried in there somewhere. So they're kind of in there. And then, you know, the Windows shell is kind of a, you know, sort of a weird derivative of that.But, um, you know, but look, the inter, the internet runs on Unix, um, and that smartphones, actually, both iOS and Android are Unix derivatives. And so, you know, kind of Unix did end up winning. But, but anyway, and then we just started taking that for granted. And then, and then so, so basically the, the way I think about what happened with Pie and then with Open Claw is basically what those guys figured out is, I always say the, the great breakthroughs are obvious in retrospect, right?Which is the best kind, the best kind. They weren't obvious at the time or somebody else would've done them already. Um, and so there is a, like a real conceptual leap, but then you look at it sort of the backwards looking and you're just like, oh, of course. Mm-hmm. Like the, the, to me those are always the best breakthroughs.Well, actually language models themselves are like that. It's just like, oh, next token completion. Oh, of course.swyx: Yeah. What other objective mattered?Marc: Yeah, exactly. But, but like it, right. But she's even saying it wasn't obvious until somebody actually did it. Right. And so the conceptual breakthrough is real and deep and powerful and, and very important.And so the way I think about pie and olaw is it's basically marrying the, the language model mindset to the un to the Unix, basically shell prompt mindset. And so it's, it's basically this idea that what, what, so what is an agent, right? And as, as, and as you know, like many smart people who have been trying to figure out what an agent is for, for, for decades, and they've had many architectures to build agents and the whole thing.And it turns out what is an agent. So it turns out what we now know is an agent is the following. It's, so it's a language model. And then above that, it's a ba, it's a bash shell. Um, so it's a, it's a Unix shell, and then it's, and then the agent has access, uh, has access to, to the shell. And, you know, hopeful, hopefully in a sandbox, maybe in, maybe in a sandbox.So it's, it's the model. Um, it's the shell. Um, and then it's a fi, it's a file system. Um, and then the state is stored in files. And then, you know, there's the markdown format for the, you know, for, for the files themselves. And then, and then there's basically what in Unix is called Aron job. There's a loop and then there's a heartbeat for the, there's heartbeat and, and the thing basically Wake Wakes up.Wakes up. So it's basically LLM plus shell, plus file system, plus markdown, plus kron. And it turns out that's an agent. And, and, and every part of that, other than the model is something that we already completely know and understand. And in fact, it turns out that like the latent power of the Unix shell is like extraordinary because basically like all, like, there's just like an, there's just enormous latent power in the shell.There's enormous numbers of Unix commands, there's enormous number of command line interfaces into all kinds of things already in the, you know, your entire, I mean your entire, just to start with, your computer runs on a shell. If you're running a Mac or a, or, or a phone, your computer, your computer's running on a shell, uh, already.And so like the full power of your computer is available at the command line level. Um, and then it turns out it's really easy to expose other functions as a command line interface. And so like this whole idea where we need like MCP and these like product mm-hmm. Fancy protocols, whatever, it's like, no, we don't, we just need like a command, command line thing.So that's the architecture. And then it turns out what is your agent? Your agent has a bunch of files starting a file system. And then there's the thing that just like completely blew my mind when I write my head around it as a result of this, which is like, okay. This means your agent is now actually independent of the model that it's running on.Because you can actually swap out a different LLM underneath your agent and your, your agent will change personality somewhat. ‘cause the model is different, but all of the state stored in the files will be retained.swyx: Yeah. Different instruction set, but you just compiledit.Marc: Right, exactly. And it's all right.It's like right. Swapping out a ship and recompiling, but it's, it's still, it's still your agent with all of its memories. Um, and with all of its capabilities. And then by the way, you can also swap out the shell, uh, so you can move it to a different execution environment that is also, is also a b shell, by the way, you can also switch out the file system, right.Uh, and you can, and you can, and you can swap out the, the, the heartbeat for the, the crown framework, the, the loop that the agent framework itself. And so your agent basically is ba basically at the end of the day, it's just. It's just, its files. Um, and then, and then there's of course it a openswyx: call.Marc: Yeah, it's, it's basically, it's, it's just the files.Um, and then by the way, as a consequence of that, the agent and then the agent itself, it turns out a couple important things. So one is it, it's, it, it can migrate itself, right? And so you're, you can instruct your agent, migrate yourself to a different, uh, runtime environment, migrate yourself to a different file system, migrate yourself to a different, you know, swap out the language model.Your agent will do all that stuff for you. And then there's the final thing, which is just amazing, which is the agent is the agent actually has full introspection. It actually, it actually knows about its own files and it could rewrite its own files. Right. Which by the way, is basically no widely deployed software system in history where the, the, the thing that you're using actually has full introspective knowledge of how it itself works and is able to modify itself.Like that, that, I mean, there have been toy systems that have had that, but there, there's never been a widely deployed system that has that capability and then that leads you to the capability. That just like completely blew my mind when I wrap my head around it, which is you can tell the agent to add new functions and features to itself and it can do that.Extend yourself. Yeah. Right? Extend, extend yourself. Like extend yourself. Give yourself a new capability. Right? And so, and so literally it's just like you run into somebody at a party and they're like, oh, I have my open claw, do whatever, connect to my eat, sleep bed, and it gives me better advice and sleep.And you go home at night and you tell your claw, or if they're at the party, by the way, you tell your claw, oh, add this capability to yourself. And your claw will say, oh, okay, no problem. And it'll go out on the internet and it'll figure out whatever it needs and then it'll go out to claw code or whatever.It'll write whatever it needs. And then the next thing you know, it has this new capability. And so you don't even have to, like, you can have it upgrade itself without even having to, without having to do anything other than tell it that you want it to do that. And so anyway, so the, the combination of all this is just, I mean, this is just like a massive, incredible, I mean, it's just incredible.Like if I, if I were, if I were 18, like this is a hundred, this is what I would be spending all of my time on. This is like such an incredible conceptual breakthrough. Yeah. And again, pe people are gonna look at it and they already get this response. People are gonna look at it and they're gonna say, oh, well, where's the breakthrough?‘cause these, the, all of these components were already known before. Mm-hmm. But, but this is the key, the key to the breakthrough was by using all these components that were known before, you get all of the underlying capability of that's buried in there. And so all, and so for example, computer use all of a sudden just kind of falls, trivi, trivial.Of course it's gonna be able to use your computer. It has full access to the shell. Right. And then, and then you just, you, you give it access to a browser, and then you've got the computer and the browser and, and often away it goes. And, and then you've got all the abilities of the browser also. Um, yeah.And so, and so the capability unlock here is profound. My friends who are, you know, deepest into this, are having their claw do like a, like, literally like a thousand things in their lives. They have new ideas every day. They're just like constantly throwing new challenges at the thing. And by the way, it's early and, you know, these are, you know, these are prototypes and there are, you know, as you guys know, there's security issues.Yeah. And, and so, you know, there's a bunch of stuff to be ironed out, but the, the unlock of capability is just incredible.swyx: Yeah.Marc: And I, I have absolutely no doubt that everybody in the world is gonna, is gonna have at least, you know, an agent like this, if not an entire family of agents. And w

In this episode, Stewart Alsop III sits down with Tom Faye — experimenter, author of The 90 Day Client Acquisition Code, and founder of Carbon Credits Marketplace — to talk about solar energy, off-grid living, and the solarpunk vision of a technology-powered utopia. They cover everything from perovskite solar cells and portable container-based solar systems, to carbon credits, ESG investing, and blockchain verification of clean energy output. The conversation also winds through AI training data, business automation, and the data labeling industry before circling back to some bigger questions about human nature, geopolitics, and what genuine self-reliance looks like in 2025. You can find Tom and his work at Carbon Credits Marketplace on LinkedIn and his energy consumption data visualization is also shared there. His book The 90 Day Client Acquisition Code is available for those looking to explore business automation further.Timestamps00:00 Introduction to Tom Fay and his work01:03 Understanding Solar Punk: Utopian Tech and Culture02:15 Current State of Solar Technology and Storage03:45 Living Off-Grid: Solar, Batteries, and Remote Work06:11 Solar Energy in Africa: Challenges and Opportunities12:21 Powering Communities with Mobile Solar Solutions16:50 The Vision of Solar Punk: Self-Sufficient Communities22:54 Existing Examples: Great Barrier Island and Others26:06 Overfishing, Environmental Challenges, and Technological Solutions28:34 Using Technology to Address Second-Order Environmental Problems36:35 Data, AI, and the Future of Energy Management43:13 Carbon Credits, Blockchain, and ESG Reporting45:27 The Geopolitics of Green Energy and Resource Control46:53 How to Connect with Tom Fay and Future ProjectsKey InsightsSolarpunk represents a genuine near-future possibility, not just an aesthetic. As solar panels and lithium batteries become cheaper and more efficient, the vision of abundant, decentralized clean energy is becoming a practical reality rather than a utopian fantasy.Perovskite solar cells are pushing efficiency roughly 22% beyond conventional panels, and the bigger revolution happening right now is on the storage side — cheaper, higher-capacity batteries are what will truly unlock solar's potential at scale.Africa may leapfrog the West on solar adoption, just as it leapfrogged landlines with mobile phones. People in energy-scarce countries viscerally understand the value of clean power in a way that people in the West, accustomed to reliable grids, simply don't.Portable solar container units — self-contained, deployable systems — already exist and are making off-grid energy viable for farms, mines, remote lodges, and even data centers, with a roughly five-to-one solar-to-load footprint required.Carbon credits generated from verified solar output, tracked via IoT smart meters and stamped on blockchain, represent a long-term business opportunity that survives political shifts because institutional investors and banks operate on independent ESG mandates.AI training data is a present and real economic opportunity, but a shrinking one. The window for humans — especially lawyers, scientists, and specialists — to get paid for their expertise is closing fast as labs pivot toward synthetic data generation.True self-reliance comes down to four things: food, water, power, and transportation. With solar and Starlink, the gap between remote wilderness and connected civilization has essentially collapsed — something unimaginable even a generation ago.

Alexander Embiricos is the Head of Codex at OpenAI, leading the development of the company's flagship AI coding systems that power automated software generation, debugging and developer workflows. Under his leadership, Codex has become one of the most widely adopted AI developer platforms.  AGENDA: 05:13 Will Coding Be Automated? Why AI Could Create More Engineers, Not Fewer 07:17 Do We Need PMs? The "Undefined" Product Role and When It Matters 08:06 The Real AGI Bottleneck: Human Prompting, Validation, and "Too Much Effort" 13:04 Three Phases of Agents: Coding → Computer Use → Productized Workflows 13:52 Enterprise Reality Check: Security, Permissions, and Safe Agentic Browsing 17:57 Is Inference the New Sales and Marketing?  18:49 What % of Codex Was Written by AI? 21:33 Do OpenAI Use AI for Code Review? 23:31 Is there any stickiness to AI coding tools? 28:22 What Does "Winning" Mean at OpenAI? Mission, Competition, and Moats 32:04 The Future UI: Chat or Voice 34:10 Agent-to-Agent Workflows: Designing for Approvals, Compliance, and Automation 35:39 Do Coding Models Have a Data Moat? 36:50 How does Codex View Data: Will They Build Their Own Mercor and Turing? 37:27 How Does Codex View Consumer: Will They Compete with Lovable? 41:56 Benchmarks vs "Vibes": How People Actually Judge Models 42:43 Cursor's Edge and the Case for Building Your Own Models 47:37 Is SaaS Dead? What Still Defends Value (Humans + Systems of Record) 51:28 Talent Wars and Career Advice for New Engineers in the AI Era 01:01:03 Guardrails, the Fully AI-Managed Stack, and a 10-Year Vision for Everyone