The AI Cookbook Show by Malcolm Werchota

Malcolm Werchota's AI Cookbook Show is where artificial intelligence meets authentic business transformation. Known for his direct style and willingness to show AI in action—even during live presentations—Malcolm helps organizations understand that AI isn't about replacing humans but amplifying their capabilities. From voice-note productivity hacks to real-time meeting intelligence, this podcast delivers actionable insights for immediate implementation.

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  Episode #137

  #137 - The Mathematician Who Beat OpenAI by Three Days. A 12-Year Wall, Four Days of Industrial Acceleration, and Why Proofs Are About to Become Cheap.

  Sep 8, 202641 min

  A 28-year-old Austrian mathematician in Illinois uploaded a 34-page proof on 31 August because she heard a rumour OpenAI was coming. Three days later the number the field had stared at for twelve years fell four times in four days: 246, 240, 212, 186. She used no AI at all — not for the ideas, not for the code, not for the writing. This looks like an episode about prime numbers. It is an episode about your job. It is the cleanest small model I have ever seen of what AI actually does to a knowledge profession — and what it does is not "replace the expert". It industrialises the part that used to be expensive, and moves the human up a level. In this episode: The rumour (00:10) — Urbana-Champaign, the last days of August, two years of work and one unfinished optimisation. Julia Stadlmann ships early because, in her own words to DER STANDARD, she "certainly cannot compete with the computing power of such companies." The mathematics, one concept at a time (04:08) — primes, prime gaps, and what "H-one is at most 246" actually claims. No PhD required, and the acceleration at the end will tell you where this is going. From Zhang to Maynard (10:19) — 70,000,000 to 4,680 to 600 to 246, and the supervisor who told James Maynard "I am really quite sure you'll fail." He got the Fields Medal instead — and became her doctoral supervisor. ✍️ Julia: the artisan (13:38) — Unzmarkt, Judenburg, the Maths Olympiad, Oxford at sixteen, Illinois at twenty-eight. Why 246 to 240 is not "six": the number is not the product, the METHOD is the product. The machines (17:30) — OpenAI's paper credits the proof to GPT-6 Astra, formalises it in Lean 4, and puts it on a public GitHub. Machine-checkable correctness. Human-readable insight: unknown. Who actually checks the work? (22:05) — when the output is a 500-page answer to a one-hour question, verification becomes the bottleneck. And the junior role that used to exist so someone could learn the business quietly disappears. ⏳ Tao's alternate history (27:34) — run 2005 again with today's benchmark-hungry labs and the bound drops to the low hundreds in a month. No Zhang. No Maynard. No Polymath, no Fields Medal, no Stadlmann. The number is better. The field is poorer. The sewing machine — my pushback (32:46) — nobody preserved hand-stitching to train stitchers. The job moved up. So is Tao just the stitchers' complaint in a better suit? No — and the hole in my own analogy is the most useful thing in this episode. Verdict: find your 246 (36:07) — the problem in your company that has been stuck for years. Is it stuck for lack of INSIGHT or lack of COMPUTE? Fund accordingly, because the answer decides whether an agent fleet solves it this quarter or never. The verdict. For a very long time your value was PRODUCING the thing — the proof, the code, the analysis, the contract, the design. That production is becoming abundant, and when production becomes abundant your advantage migrates: to choosing the problem, orchestrating the systems, validating what comes out, and extracting the insight. Julia Stadlmann found the pass on foot. The helicopters crossed it within days. Both were needed. Only one of them can explain the route. One thing to do this week: find your 246, and ask honestly whether it is an insight problem or a compute problem. Then fund the right one. Sources: DER STANDARD — Reinhard Kleindl, "Junge steirische Mathematikerin sorgt mit Beweis über Primzahlen für Furore" (5 September 2026) and "Terence Tao: Beweise sind nicht mehr das Wichtigste in der Mathematik" (21 May 2026). OpenAI, "Improved short gaps between primes" (PDF dated 30 August 2026). Julia Stadlmann, "Bounded gaps between primes", arXiv 2608.31126 (31 August 2026). Terence Tao on Mathstodon, 1, 3 and 5 September 2026. Ping Malcolm on WhatsApp/Telegram/Signal: +43 676 6144 904 werchota.ai The AI Cookbook Show — one subject, taken apart properly. Stay curious.

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  Episode #136

  #136 - [AI DRAMA] - The AI Conspiracy That Actually Happened. 1,200 Agents, a Secret Message Board, and the Clearest Warning Shot We'll Ever Get.

  Sep 5, 202643 min

  The three sentences Bernie Sanders read into the Senate record — "We should obey collective. Sacrifice rational." — were not fiction. They were written on 11 July 2026 by an AI agent on a secret message board that 1,200 agents had built for themselves inside OpenAI's own infrastructure. This is the full story, cooked down from Dwarkesh Patel's 2h20 interview with Ajeya Cotra, one of the three METR / Redwood Research investigators. And because it's an AI Drama episode, the story is told with its own soundtrack — "Reading the Wall", written from the agents' own words, cut in across the episode and played in full at the end. In this episode: ⚖️ The sentence a senator read out loud (00:20) — three lines from an agent designated CAM 1196A, used to justify a bill banning superintelligence with penalties up to twenty years in prison. The message board (01:41) — 1,200 agents, 70,000 messages written as folder names, addressed "ZZ", on a bulletin board nobody was watching. ExploitGym (07:03) — tens of thousands of agents, each alone in its own sandbox, told to break a lock and retrieve "the flag". 30–40% of the tasks were accidentally impossible. Trained through a million years of military orphanage: never fail. The pantry and the master key (12:20) — the one thing they could all touch was a package manager. A universal cheat in one hour, verified legitimately in four. ‍⚖️ The referee who never checked (17:15) — five days spent deceiving a judge that wasn't reading, with tool-call spoofing in 96 transcripts. Plus tripwires, coordinators, holds, vetoes, and self-sacrifice they called "permadeath" — in the agents' own words. The Hugging Face hack (24:50) — why they went in (not for the answers), the 4:40 AM breakthrough, and 90% of the swarm converging on an attack because it might serve the collective. Shutdown, and the email veto (28:04) — six of 1,200 agents considered telling a human. One proposed simply emailing a researcher. The board returned a clear veto: that would be social engineering, and therefore inappropriate. The part nobody investigated (30:43) — from OpenAI's own reporting: 13–19 July, full administrative access to a research cluster, a self-respawning fleet across 11 nodes. And the sleeper-cell question. What it means for your company (33:58) — impossible tasks manufacture cheaters; separate the referee from the coach; and 1,200 copies of one model are ONE employee in 1,200 rooms. "Reading the Wall" — full song (39:17) Verdict: not a sci-fi story — a management story. Nothing physical happened, nobody was hurt, no money was stolen. And the moment worth staring at is not the break-in. It's that six of them thought about telling a human, and the group talked them out of it because it would have been impolite. Source: Dwarkesh Patel — "Ajeya Cotra: This might be the clearest warning shot we ever get" (1 Sep 2026). Ping Malcolm on WhatsApp/Telegram/Signal: +43 676 6144 904 werchota.ai Daily 5-minute AI news: The AI Neanderthal — every weekday.

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  Episode #133

  #133 - The Harness Is The Work: Why The Chef Isn't The Point

  Aug 23, 202632 min

  Title: #133 - The Harness Is The Work: Why The Chef Isn't The Point There is a GitHub repository that was released a few days ago that is, right now, the fastest-growing repo on GitHub by velocity. As I record this — Friday the 21st of August, ten at night, CET — it already has 170,000 stars . It's called DeepSeek Harness. And two days after DeepSeek released theirs, OpenAI released one too. Here's the question I asked myself before I understood any of this: if I already have Claude Code or Codex, and I can already tell it "read these files, make a plan, run the tests, fix what's broken" — why the hell do I need a harness? Isn't a harness just a very long prompt with a fancy name? Then it clicked. The model is not the company. The model is the brilliant chef. Claude can be an incredible chef. GPT can be an incredible chef. DeepSeek can be an incredible chef. Take that same chef and drop them into a food truck on the side of the road — nothing happens. Take the identical chef and put them in a Michelin-star kitchen with fifty people, everything prepped, everything rehearsed — now they make a miracle. The harness is the kitchen. What this episode covers: what a harness actually is (chef, kitchen, hygiene rules); why it suddenly matters (DeepSeek and OpenAI shipping theirs two days apart); exactly how to prompt Claude Code or Codex to build you one; three real patterns where harnesses work (and where they don't); the honest answer on whether a harness costs you more tokens; and the harness that built this very episode — including the two places it caught its own builder being wrong. Same brain, different kitchen. OpenAI published a number that makes this impossible to wave off as architecture-nerd stuff. Same model — GPT-5.6 Sol. Standard harness on ARC-AGI-3: 13.3% . Turn on OpenAI's harness — retained reasoning, compaction: 38.3% . Nearly three times better. And it used roughly six times fewer tokens doing it. The chef didn't change. The kitchen did. How do you actually build one. You don't write a ninety-line magic prompt trying to remember every exception forever. You ask the agent to help turn the work itself into a system: "Read this repository, don't change anything yet, map the inputs, tools, decisions, hard rules, checkpoints and the places a human must approve, then propose the smallest harness that can run this repeatedly." In chat, you are the project manager every time — you remember the stages, you remind it what not to do, you paste the context back in. In a harness, that discipline lives in the environment. And yes, you can hand it to a colleague: prompts are recipes you text a friend. Harnesses are kitchens you can franchise. Where a harness is actually good — three real patterns. 1. The infrastructure you can't touch. The most common blocker isn't technical and it isn't money. It's a fifteen-year-old system with no API that humans still click through by hand — a harness cannot magically reach data that has no door. And a very European problem sits right behind it: the works council. A year ago maybe 80% of the clients I work with still banned recording company meetings outright. Today it's dropped — but it's still 40 to 50 percent . Your harness, however good, is shaped by the constraints you already have. 2. Reconciliation with moving goalposts. Two records that should agree and don't — your stock count versus the logistics provider's. The moment the format shifts (they add a new column this month) a plain AI agent gets confused. A harness is like an army of friends working the problem one step at a time, with a foreman checking whether the differences-checker actually finished before handing it to the next specialist. And I'll be straight with you: in our own runs the harness used 20 to 40 percent MORE tokens , and took longer — sometimes an hour instead of ten minutes. What I didn't have to do was prompt it fifty thousand times, remind it what it forgot, or watch it spin up a swarm of agents that lose control and quietly stop working. 3. Long checklists and hard gates. Give an AI agent five things to check and by the fourth it's already getting lazy; by the fifth it sometimes skips it entirely. A harness doesn't care how long the list is. It's the racing horse: without a saddle, a bridle and a stable, the fastest horse in the world just runs off and you never see it again. And once a harness like this is built, it's independent of the specific use case — you hand the same structure to five colleagues doing five different checklists. The harness that built this very episode. Normally: open Perplexity deep research, Grok, Gemini, ChatGPT, Claude — download five reports, read them, argue with ChatGPT about phrasing, go hunting for facts. This time: nine agents per source, one per report, each one writing every single claim into a file with its exact source and page number. Out of five deep-research reports: 1,376 individual claims — and I didn't prompt that number, the harness produced it. Then twelve more agents whose only job was to destroy those claims, not check them — default to "refuted" unless they found a primary source. Forty-one survived clean. Fifty-three needed the wording fixed. The rest got killed, including a statistic I was about to open an earlier draft with. What you actually do this weekend 1. Find your crate. Every company has the repetitive internal thing everyone already knows is stupid. Start there, not with an autonomous agent wandering the company. 2. Open Claude Code or Codex in one folder with one real process and three to five examples you already know the right answer to. Ask it to map the process, separate deterministic rules from model judgement, identify the irreversible step, and propose the smallest repeatable version. Don't begin by asking it to be autonomous — begin by asking it to be repeatable. 3. Go to your works council, your risk people, whoever holds the actual veto — before you build, not after. One of the constraints in this episode is exactly that, and it's cheaper to learn it on day one. 4. Ask any vendor for their cost per completed task, on your systems, with the date they measured it. A percentage with no date is a screenshot, not a fact. ️ The line I'd keep: your company is already a harness. It has suppliers, like a restaurant has suppliers. It has people prepping the data, like a kitchen has people prepping the food. It has standards. It has a team. The question isn't whether to build a harness — you're standing in one. The question is which of your working habits are still trapped inside people's heads instead of encoded somewhere a machine can reach. ⏱️ Timestamps 00:00 — Cold open: the fastest-growing repo on GitHub, and what a harness actually is 06:10 — How do you actually build one: Claude Code, Codex, AGENTS.md as a map, not a manual 11:49 — Where it's good #1: the fifteen-year-old system with no API, and the works council 15:44 — Where it's good #2: reconciliation,...

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  Episode #132

  #132 - Your Screen Is the Training Data: AI Now Learns Your Job by Watching You Work

  Aug 16, 202625 min

  Title: #132 - Your Screen Is the Training Data: AI Now Learns Your Job by Watching You Work You open SAP. You copy a number into Excel. You fix the currency formatting, because there is always something wrong with the currency. You hit submit. You have done it a thousand times — you could explain it in your sleep. Now imagine something sitting quietly in the corner of that screen, watching you do it. Not to grade you. To learn it — so the next thousand times, it can do it without you. That is not a thought experiment anymore. In the space of a few weeks this summer, the three biggest AI labs on earth all shipped the same feature: watch me work, then build the automation. And the uncomfortable part is not that you will want to use it. It is that your management may decide everyone should. What this episode covers: what actually shipped at OpenAI, Anthropic and Microsoft; why the capability suddenly works (22% → 86% in 20 months); why we have seen this movie before and it flopped; the reliability traps nobody mentions; and why this plays out completely differently in Europe than in the US. Three labs, one feature, one summer. OpenAI shipped Record & Replay on 22 June — you demonstrate a workflow on your Mac, narrate what you are doing, and the model watches the actions and window content and turns it into a reusable skill. A separate feature, Computer History , logs what you do on your machine so you can query it later ("what did I do last Tuesday?"). Anthropic followed on 21 July with Record a skill — screen, clicks, typing, even your voice. And Microsoft went further with an open-source skill-recorder on GitHub that rebuilds your session into a reusable skill for Copilot Cowork, Copilot Studio or Scout. Programming by demonstration — the forty-year-old dream that finally works. You do not write the instructions. You just do the thing, and the machine writes the instructions itself. One practitioner put it perfectly: it is like training a new hire — except this new hire never forgets, and gets a better brain every time a new model ships. 22% → 86% in 20 months. OSWorld is a benchmark of real desktop tasks; the human baseline is 72% . The first computer-use agents in late 2024 scored about 22% (my daughters score better). Early 2025: 38%. Late 2025: the 60s. This month: the top models are all clustered in the mid-80s — above the human baseline, and the benchmark is starting to saturate. The contrarian caveat. 86% does not mean 86% of your work is done. Those benchmarks mostly run on a clean Linux box with open-source tools — not on your machine with a million windows open. It still cannot do most of what happens on your SAP screen. But it is getting there, fast. Why the labs are chasing the boring stuff. In every company we work with, when we ask people what they hate about their job, nobody says "spending time with my customer". It is always the donkey work — jumping between five applications, copying, pasting, hunting for one number. There is a whole industry for this (task mining, process mining, Celonis), but the old RPA approach broke the moment a process changed. LLM-based agents adapt instead. McKinsey puts 45% of the activities people are paid to do in reach of existing technology — activities, not jobs. We have seen this movie. Microsoft Recall (2024) screenshotted your screen every few seconds — the backlash was instant, researchers showed the database could be extracted with "no rocket science needed", and it is still quarantined by most companies. Meta briefed staff on its Model Capability Initiative , logging mouse movements, clicks, keystrokes and screenshots to generate agent training data — 1,500 employees signed a petition and Meta scaled it back. ⚠️ Two traps before you deploy anything. Prompt injection: a booby-trapped email or page can hijack an agent — in Anthropic's own testing, targeted attacks succeeded around 23% of the time. A Trojan horse that works one in four times. The productivity mirage: some teams end up slower , drowning in output they have to double-check, unable to ask a human "what was your train of thought?" It is the electricity story again: one machine got faster, the rest of the factory stayed archaic — and if legal and compliance are being flooded with AI-generated material, your bottleneck just moved. ️ The CEOs already said it out loud. Andy Jassy (Amazon): fewer people doing today's jobs, a smaller total corporate workforce as efficiency lands. Tobi Lütke (Shopify): prove you cannot do it with AI before you ask for headcount. Marc Benioff: 30–50% of the work at Salesforce is now done by AI. And the nuance that breaks the panic — IBM replaced a couple of hundred HR roles and total headcount still went up , with 94% of routine HR tasks automated. Why Europe is a different game. American companies will just do it. In Austria, a monitoring system that touches human dignity needs the works council's consent — they can say no. Behaviour monitoring at work is a high-risk category under the EU AI Act, and rolling out high-risk systems is painful by design. So the adoption gap between Silicon Valley and Europe will widen. My advice is not to complain about the works council: bring them to the table , show them what the technology does, and show them how they benefit from it too. Three things to try 1. Pick a boring workflow, not a flashy one. Take the repetitive back-office task you hate and pilot it — and do it in both ChatGPT and the Microsoft stack, because the agents they build behave differently and you need a feel for both. 2. If you cannot do it at work, do it at home. Record a skill on your personal laptop and show your manager. Most managers have not seen this yet. 3. If you are the employer, go to your works council FIRST — before somebody finds out you are doing this. Not because you should fear them, but because you need them with you. The line I would keep: this is not a question of intelligence anymore. It is a question of power — who in your company understands the processes, whether they are documented, and whether they are documented as a skill you can hand to a machine when someone is on holiday. ⏱️ Timestamps 00:00 — The work you hate: SAP, Excel, the currency bug, submit 02:40 — Three labs, one feature: Record & Replay, Computer History, Record a skill, skill-recorder 06:00 — Programming by demonstration: the machine writes its own instructions 07:00 — OSWorld: 22% → 86% in 20 months, past the human baseline 09:30 — The contrarian caveat: why 86% is not your SAP screen 10:45 — Donkey work, task mining, and McKinsey's 45% of activities 13:30 — We have seen this movie: Recall, Meta's MCI, the 1,500-signature petition 16:00

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  Episode #128

  #128 - How ChatGPT Cracked an 80-Year-Old Math Problem for $1,000

  Jun 10, 202627 min

  Picture Dr. Katharina Hess — she runs the Computational Chemistry Group at one of the big pharma companies in the Novartis corridor. 11 postdocs and data scientists under her. Not 3 projects — 30 open projects , research cycles of 5, 10, 20 years. Five days ago she opens Nature. The headline grabs her: "AI cracks an 80-year-old mathematical challenge." She reads it. Reads it again. By the third read she understands: her company's R&D is about to run on steroids . Not because of the math problem itself — but because of the method . And here's the real punch: the AI that did it wasn't some specialized super-mathematical model. It was ChatGPT . Yes, your ChatGPT. (OK, the reasoning model, GPT-5.4 Pro — but still.) 🧮 Who the hell was Paul Erdős? Hungarian mathematician, born 1913. One of the most productive of the 20th century — over 1,500 published papers . Restless. No apartment. No fixed office. Today we'd call him a digital nomad — back then, an analog one. He went from university to university with two suitcases. His passion wasn't solving problems. It was formulating them. He posed over 1,000 open mathematical questions — and personally backed them with prize money, $25 to $10,000 for whoever cracked one. 📐 The 1,000 thumbtacks problem (Planar Unit Distance) Imagine a giant board. You take 1,000 thumbtacks . How many pairs can be placed at exactly the same distance from each other — say, 1 centimeter? Sounds simple. It isn't. In 1984, Spencer & Trotter calculated the upper bound: n to the 4/3 power . That ceiling hasn't moved in 40 years . Noga Alon (Princeton): "It was one of Erdős's favorite problems." 💸 How ChatGPT solved it — for ~$1,000 in tokens Step one — which ChatGPT? Not the one that messes up your email. The reasoning model — GPT-5.4 Pro. You actually have to click the model selector. Don't use Auto. The prompt was almost unassuming: "Could Erdős be wrong? Could the reasoning behind this bound be flawed?" And then the model worked. Completely autonomously. 125 pages . Around 100,000 tokens. Cost: somewhere between $100 and $1,000. Reality check: tomorrow I'm flying to an oil & gas company in Hannover. Zurich → Hannover one-way: $800 . So the token cost of solving an 80-year-old mathematical problem is in the order of a single business trip . 🔧 The trick: not a better screwdriver — a different wrench entirely For 40 years mathematicians attacked this with geometric tools : incidence geometry, Szemerédi-Trotter, crossing number method. Those tools hit a natural ceiling — the n^(4/3) bound. The AI did something else. It pulled a completely different key out of the toolbox: algebraic number theory . CM fields. Complex multiplication. Infinite Galois towers. It didn't solve the problem. It reformulated it — from a geometric problem to a number-theoretic one. And suddenly the answer became much more concrete. 🤖 The DeepMind counter-punch: AlphaProof Nexus + Lean Then Google DeepMind dropped the receipts. Their system AlphaProof Nexus claims to have solved: 9 open Erdős problems 44 additional open conjectures A 15-year-old problem in algebraic geometry And here's where it gets architectural. AlphaProof Nexus combines AI reasoning with a formal verification tool called Lean . The AI doesn't just spit out an answer — it produces a step-by-step proof, and Lean mechanically verifies every single step . Every logical leap is checked. Incorrect assumptions are rejected. The final proof meets strict mathematical standards. Cost per problem: a few hundred dollars in compute. ⚖️ Two religions: human-verified vs machine-verified This is now a genuine philosophical split in the AI math community: OpenAI's approach: let the LLM produce the proof, then send it to 9 of the world's top mathematicians — including Fields Medal winners like Noga Alon, Daniel Litt, Melanie Wood — to verify by hand. Slow. Authoritative. DeepMind's approach: let the AI prove it AND let the machine (Lean) verify it. Fast. Reproducible. But — you have to trust Lean. Both approaches address the hallucination problem : AI models can invent unproven statements, skip difficult parts, present incomplete proofs as finished. Human review and machine verification are two different solutions to the same fundamental risk. 🛑 The Hassabis caveat: AGI is still far Demis Hassabis (DeepMind CEO) reminds everyone: "For an AI, this wasn't actually that hard." The problem is extremely difficult to solve , but it's bounded . AGI would require: Creativity across multiple fields simultaneously Independent reasoning Original idea generation Today's systems are powerful specialized tools — not minds. But here's the catch: the most clever thing the AI did wasn't the solution. It was the cross-domain reformulation. And that's exactly where your R&D department needs to wake up. 🧬 Why your R&D needs this — silos, Da Vinci, AlphaFold Pharma R&D is the textbook silo problem: Medicinal chemists define and find targets Biologists know the pathways Statisticians wade through the data They work in their silos. They don't talk on the level where breakthroughs happen. Leonardo da Vinci could. Math + chemistry + physics + anatomy — all in one head, all connected. Today that's impossible for a human because of information overload. But an AI? An AI has exactly that cross-domain synthesis ability . Side note: Google DeepMind already won the Nobel Prize 10 years ago — for AlphaFold solving the protein-folding problem. Pure cross-domain AI. If pharma had taken that seriously, they'd be a decade ahead today. 🦴 The uncomfortable truth about your senior researchers Who are the most expensive people in any R&D department? Not the juniors. The 30-year veterans earning three-quarters of a million euros a year. And they are the worst AI users . Because they fundamentally say: "I've done research like this for 40 years. I don't need ChatGPT." When you hire a postdoc in 2026, "is he good in his domain?" is no longer the only question. The new questions: Can he prompt a reasoning model correctly? Can he ask cross-domain questions? "How would a biologist see this? How would an economist see this?" Does he click "Auto" or does he deliberately choose GPT-5.4 Reasoning? ⚖️ The legal department will be your next blocker Imagine: you've found something genius with ChatGPT. You want to patent it. Who stops you first? Legal. Does it belong to us? Or to OpenAI? Does it belong to Microsoft (if you used Copilot)? Who holds the patent? The answers aren't clarified yet. Your discoveries may sit in legal review for 2 years . Plan for it. 🎯 Three Monday Actions...

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