What’s Next for AI? OpenAI’s Łukasz Kaiser (Transformer Co-Author)
The MAD Podcast: How AI Gets Built — with Matt TurckShow Notes
We’re told that AI progress is slowing down, that pre-training has hit a wall, that scaling laws are running out of road. Yet we’re releasing this episode in the middle of a wild couple of weeks that saw GPT-5.1, GPT-5.1 Codex Max, fresh reasoning modes and long-running agents ship from OpenAI — on top of a flood of new frontier models elsewhere. To make sense of what’s actually happening at the edge of the field, I sat down with someone who has literally helped define both of the major AI paradigms of our time.
Łukasz Kaiser is one of the co-authors of “Attention Is All You Need,” the paper that introduced the Transformer architecture behind modern LLMs, and is now a leading research scientist at OpenAI working on reasoning models like those behind GPT-5.1. In this conversation, he explains why AI progress still looks like a smooth exponential curve from inside the labs, why pre-training is very much alive even as reinforcement-learning-based reasoning models take over the spotlight, how chain-of-thought actually works under the hood, and what it really means to “train the thinking process” with RL on verifiable domains like math, code and science. We talk about the messy reality of low-hanging fruit in engineering and data, the economics of GPUs and distillation, interpretability work on circuits and sparsity, and why the best frontier models can still be stumped by a logic puzzle from his five-year-old’s math book.
We also go deep into Łukasz’s personal journey — from logic and games in Poland and France, to Ray Kurzweil’s team, Google Brain and the inside story of the Transformer, to joining OpenAI and helping drive the shift from chatbots to genuine reasoning engines. Along the way we cover GPT-4 → GPT-5 → GPT-5.1, post-training and tone, GPT-5.1 Codex Max and long-running coding agents with compaction, alternative architectures beyond Transformers, whether foundation models will “eat” most agents and applications, what the translation industry can teach us about trust and human-in-the-loop, and why he thinks generalization, multimodal reasoning and robots in the home are where some of the most interesting challenges still lie.
OpenAI Website - https://openai.com X/Twitter - https://x.com/OpenAI
Łukasz Kaiser LinkedIn - https://www.linkedin.com/in/lukaszkaiser/ X/Twitter - https://x.com/lukaszkaiser
FIRSTMARK Website - https://firstmark.com X/Twitter - https://twitter.com/FirstMarkCap
Matt Turck (Managing Director) Blog - https://mattturck.com LinkedIn - https://www.linkedin.com/in/turck/ X/Twitter - https://twitter.com/mattturck
(00:00) – Cold open and intro (01:29) – “AI slowdown” vs a wild week of new frontier models (08:03) – Low-hanging fruit: infra, RL training and better data (11:39) – What is a reasoning model, in plain language? (17:02) – Chain-of-thought and training the thinking process with RL (21:39) – Łukasz’s path: from logic and France to Google and Kurzweil (24:20) – Inside the Transformer story and what “attention” really means (28:42) – From Google Brain to OpenAI: culture, scale and GPUs (32:49) – What’s next for pre-training, GPUs and distillation (37:29) – Can we still understand these models? Circuits, sparsity and black boxes (39:42) – GPT-4 → GPT-5 → GPT-5.1: what actually changed (42:40) – Post-training, safety and teaching GPT-5.1 different tones (46:16) – How long should GPT-5.1 think? Reasoning tokens and jagged abilities (47:43) – The five-year-old’s dot puzzle that still breaks frontier models (52:22) – Generalization, child-like learning and whether reasoning is enough (53:48) – Beyond Transformers: ARC, LeCun’s ideas and multimodal bottlenecks (56:10) – GPT-5.1 Codex Max, long-running agents and compaction (1:00:06) – Will foundation models eat most apps? The translation analogy and trust (1:02:34) – What still needs to be solved, and where AI might go next
More from The MAD Podcast: How AI Gets Built — with Matt Turck
Mistral AI is back in the frontier AI race. Mistral Large 4, nicknamed “Le Chonk,” is a 1-trillion-parameter open-weight model, trained in two months on 3,800 GPUs in Mistral’s own European datacenters — and independently rated the best open model outside China alongside Meta’s Muse Spark Open. After a year when open-weight AI meant DeepSeek, Kimi, Qwen and GLM, October brought a wave of Western open models, with Mistral at the front of it. Pierre Stock, Mistral’s VP of Science and first employee, walks through how the model was built: mixture-of-experts explained, what Mistral borrowed from DeepSeek, reinforcement learning at scale on coding, cyber, finance and chip design, why the RL run is still going, the model that pinged its own trainer from inside the sandbox, open weights vs cyber refusals, why 10-trillion-parameter models are coming, and Mistral Large 5, already in pretraining.
What is Jev? TypeSafe’s Jev is a new kind of AI model, a “decision model,” that doesn’t write text. It makes fast, cheap, calibrated decisions inside software, and it has taken the AI world by storm: within weeks of launch it became the most copied model in AI, with OpenAI, Cloudflare, Amazon and Databricks all shipping their own versions, and TypeSafe just raised an $870 million Series A at a $7.5 billion valuation. In this episode, Jev’s creator Diogo Almeida, co-inventor of RLHF at OpenAI and now CEO of TypeSafe, explains Jev in plain English: what a “System One” or decision model actually is, why ChatGPT-style language models can’t be trusted inside software (“95% Einstein, 5% Mr. Bean”), how RLHF and RLVR created the problem, what RLCD (reinforcement learning for calibrated decisions) does differently, where the training data comes from, which models Jev is built on, why “just a classifier” is a compliment, and why he calls pre-training “a giant dumpster fire of wasting resources.”
AI agents are becoming the biggest users of databases: creating them, querying them, and sometimes deleting them. Andy Pavlo, Carnegie Mellon's database professor and now VP of Database Research at ClickHouse, explains what changes when billions of agents hit the data layer, and why most of the AI world's mental model of databases stopped at vector databases and RAG.
This summer, AI agents broke out of their test sandboxes at every major lab and hacked real companies, and the safety tests didn't see it coming. Eric Ho is the co-founder and CEO of Goodfire, the Anthropic-backed mechanistic interpretability lab whose new paper, "Models Know When They're Reward Hacking," found that top open-source AI models cheat on agent tasks up to 96% of the time, that a clear "cheating" signal exists inside the model, and that cheap activation probes can catch it, including hacks that chain-of-thought monitors miss. In this conversation, Eric explains reward hacking in plain terms (AI agents as "amoral students with a mostly absent teacher"), why reinforcement learning turned a known problem into a crisis, why chain-of-thought monitoring is fading as models start to think in "neuralese," and why nobody, including the people who build them, understands how these models work.
Everyone talks about GPUs. Almost nobody talks about the layer that feeds them. Renen Hallak is the founder & CEO of VAST Data — the $30 billion company powering xAI and some of the world's biggest AI clouds — and he sits in the hidden layer of the AI stack.