What’s Next for AI? OpenAI’s Łukasz Kaiser (Transformer Co-Author)

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Previously titled “GPT-5.1 & the AI Frontier — Łukasz Kaiser (OpenAI, Transformer Co-Author)” — renamed by the publisher on Aug 2, 2026

The MAD Podcast with Matt Turck 1h 5m 1 speaker 8 chapters transcribed 1 month ago

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Why do some claim AI progress is slowing down despite recent breakthroughs?

Łukasz Kaiser 0:00

There is this thing that's happening in AI. And in AI, every week now a lot is happening. Fundamentally, if you look at AI progress, it's been a very smooth, exponential increase in capabilities. This is the overarching trend. It's not like pre-training fizzled out. It's just we found out a new paradigm that, at the same price, gives us much more amazing development. And this paradigm is still very new. I think one of the biggest things that I would say People kind of know on the inside and others don't is that already right now it's not about the progress. There are so many things chat or Gemini any LM can do for you that people just don't realize. You can take a photo of something broke and ask how to repair it, it may tell you.

Łukasz Kaiser 0:39

You can give it a college level homework and it will do it for you.

Matt Turck 0:43

Hi, I'm Matt Turk. Welcome to the Matt Podcast. My guest today is Lukas Kaiser, one of the key architects of modern AI who has quite literally shaped the history of the field. Lukas was one of the co-authors of the Attention Is All You Need paper, meaning he's one of the inventors of the transformer architecture that powers almost all the AI that we use today. He's now a leading research scientist at OpenAI, helping drive the second major paradigm shift towards reasoning models like the ones behind. GPD 5.1. This episode is a deep exploration of the AI frontier, why the AI slowdown narrative is wrong, the logic puzzles that still stump the world's smartest models, how scaling is being redefined, and what all of that tells us about where AI is heading next.

Matt Turck 1:23

Please enjoy this fantastic conversation with Lukash.

What low‑hanging technical improvements can boost AI performance now?

Matt Turck 1:27

Lukash, welcome. Thank you very much. There was a uh narrative, at least in some circles, maybe outside of San Francisco, throughout the year, that um AI progress uh was slowing down, that uh we had maxed out pre training, that uh scaling laws were hitting a wall. Yet uh we are recording this at the end of a huge week or couple of weeks with release of GPT five point one, GPT. GD5.1 CodX Max, G V D 5.1 Pro, as well as Gemini, Banana Pro, Groc 4.1, Almo 3. So this feels like a a major violation of of that narrative. What is it that people in Frontier AI labs know about AI progress that at least parts of the rest of the world seem to not understand?

Łukasz Kaiser 2:15

I think there is there is a lot to unpack there. So so so so I I want to go a little slower. Th there is this thing that's happening in AI. And in AI every week now a lot is happening. You know New model, coding, doing slides, self-driving cars, images, videos. You know, there there's it's it's a it's a nice field that that doesn't make you be bored for a long time. Um but through all of this, it's sometimes hard to see the fundamental things that are happening. And fundamentally, if you look at AI progress, it's been a very smooth Exponential increase in capabilities dis this is the overarching trend. And there has never been much to make me at least, and I think mo my colleagues in the labs believe that

Łukasz Kaiser 3:06

This trend is not happening. It's a little bit like Moore's Law, right? We we ha Moore's Law happened through decades and decades, and arguably you would say it's still very much going on, if not speeding up with the GPUs. But of course it did not happen as like one technology was bringing you there for 40 years. There were was one technology and then another and another and another and another. And and and this went on for decades, right? So so from the outside you see a smooth trend, but from the inside of course You know, progress is made through new developments in addition to the increase of computer power and better engineering and so so all of these things come together and in terms of language models

Łukasz Kaiser 3:50

I think there was a big pivotal point. I mean, one point was, of course, the Transformers w w when it started, but the other point was reasoning models. And That happened, I think oh, one preview was a bit a year and a month ago or something like that. So We started working on it maybe three years ago, but But y you you know, it's very recent.

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Chapters

8 chapters

1 Why do some claim AI progress is slowing down despite recent breakthroughs? 0:00–1:27 2 What low‑hanging technical improvements can boost AI performance now? 1:27–8:25 3 How do reasoning models differ from traditional pre‑trained LLMs? 8:25–12:29 4 What is chain‑of‑thought prompting and how is it trained with reinforcement learning? 12:29–17:24 5 How did the Transformer paper originate and what does “attention” really mean? 17:24–21:51 6 What challenges arise when scaling from Google Brain to OpenAI’s GPT‑5.1? 21:51–23:59 7 What is the future of pre‑training, GPU economics and model distillation? 23:59–27:59 8 How will multimodal reasoning and long‑running agents change AI applications? 27:59–1:05:25

Speakers

1 identified

Matt Turck 11 min

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Topics in this episode

OpenAI 4 Anthropic 1 ChatGPT 1 Gemini 1 Codex 2 GPU 2 Ilya Sutskever 1 Ray Kurzweil 1 Matt Turck 3 robotics 1 Gemini 3 2 RL 1

Source

Recorded26 Nov 2025

Publisheranchor.fm ↗

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Show 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.OpenAIWebsite - https://openai.comX/Twitter - https://x.com/OpenAIŁukasz KaiserLinkedIn - https://www.linkedin.com/in/lukaszkaiser/X/Twitter - https://x.com/lukaszkaiserFIRSTMARKWebsite - https://firstmark.comX/Twitter - https://twitter.com/FirstMarkCapMatt Turck (Managing Director)Blog - https://mattturck.comLinkedIn - 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

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