Łukasz Kaiser

Research Scientist, OpenAI · 1 appearance on the record.

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scientistauthorengineeracademic@lukaszkaiser ↗LinkedIn ↗

Łukasz Kaiser is a senior research scientist at OpenAI, where he focuses on frontier models and reasoning architectures. He previously worked at Google Brain and is a co-author of the seminal paper "Attention Is All You Need".

39statements → 18claims → 3claims resolved → 100%fully supported → 3.77/5average certainty → 2.18/5average debate potential → 4.2/5argument clarity · the sources → 6said about them ↓

3 supported 0 partly supported 0 contradicted 1 not yet assessed 14 not checkable as stated how the 18 claims stand · each chip opens the sources

2 predictions · 16 assertions · 1 opinion · 16 insights · 4 disclosures · every statement was checked. The predictions and assertions are the 18 claims: statements the public record can support or contradict. 3 are resolved, 1 is not yet assessed, and 14 name no date, number or outcome precise enough to check. Everything else (opinions, insights, what ifs, disclosures) can never be settled by the record, so it carries no assessment.

The record, in short

What the tape says about how Łukasz argues and how the claims held up. Everything they said, and everything said about them, is in the tabs below.

Their most notable supported claim

Assertion Supported
Kaiser: Translation industry grew and translators earn more post-Transformers
“The translation industry has grown considerably since then. It has not shrunk. There's more translations to be done. Translators are paid more.”
Łukasz Kaiser Nov 26, 2025 ▶ 1:01:04 What’s Next for AI? OpenAI’s Łukasz Kaiser (Transformer Co-Author)

Argument clarity: do they answer the question? how? →

4.2 / 5 directness 4.5 · coherence 4.2 · precision 3.9 · compression 3.7

redirected or did not address 1 of 12 assessed questions (8%). Watch them ▸

This is a score against a rubric. It is not a rank. Every host question → answer exchange is scored with names hidden on directness, coherence, precision and compression, 1–5 each, on meaning alone: disfluencies are ignored, and only raw unedited episodes count. This is the score that measures thought. Every scored exchange, scores shown → · The rubric and its checks →

How they sound: speaking style how? →

251 words/min while actually speaking · 3.7 um and uh per 1k words

Measured by listening to the audio itself: 10,001 words across 1 episode of raw-level tape, transcribed verbatim with every um and uh kept, each one attributed only where the alignment onto our timed stream is unambiguous. These are measurements of speaking style. We do not rank them: across this corpus, fluency and argument quality are nearly uncorrelated (ρ≈0.2), and smooth talking does not signal clear thinking. How it's measured →

Everything Łukasz Kaiser said on the MAD Podcast that made the record, most notable first. Filter by type, assessment or year in the ledger →

Assertion Not checkable as stated
Kaiser: Pre-training between GPT-4 and GPT-5 focused on reducing costs
“The pre-training part in that timeframe was mostly about making things cheaper. Not making things better.”
Łukasz Kaiser Nov 26, 2025 ▶ 40:36 What’s Next for AI? OpenAI’s Łukasz Kaiser (Transformer Co-Author)
Insight
Kaiser: Reasoning models are the second major milestone after Transformers
“One point was, of course, the Transformers when it started, but the other point was reasoning models.”
Łukasz Kaiser Nov 26, 2025 ▶ 3:53 What’s Next for AI? OpenAI’s Łukasz Kaiser (Transformer Co-Author)
Assertion Not checkable as stated
Kaiser: Pre-training scaling laws still hold across OpenAI and Google
“What scaling clause says is that your loss will log linearly decrease with your compute. We totally see that and clearly Google sees that and all other labs.”
Łukasz Kaiser Nov 26, 2025 ▶ 4:35 What’s Next for AI? OpenAI’s Łukasz Kaiser (Transformer Co-Author)
Insight
Kaiser: Pre-training science is plateauing, but compute scaling still improves loss
“Pre-training, as I said, I think it has reached this upper level of the S-curve in terms of science, but it can scale smoothly. Meaning if you put More compute. You will get better losses if you do things right, which is extremely hard, and that's valuable.”
Łukasz Kaiser Nov 26, 2025 ▶ 33:07 What’s Next for AI? OpenAI’s Łukasz Kaiser (Transformer Co-Author)
Assertion Not checkable as stated
Kaiser: Model hallucinations are dramatically lower than two years ago
“There was these things called hallucinations. It's still with us to some extent, but dramatically less than two years ago.”
Łukasz Kaiser Nov 26, 2025 ▶ 41:53 What’s Next for AI? OpenAI’s Łukasz Kaiser (Transformer Co-Author)
Insight
Kaiser: Test-time compute increases AI capabilities faster than pre-training
“Using more tokens to think increases your capability, and it increases it, given the computation, way faster than pre-training, right?”
Łukasz Kaiser Nov 26, 2025 ▶ 46:49 What’s Next for AI? OpenAI’s Łukasz Kaiser (Transformer Co-Author)
Assertion Not checkable as stated
Kaiser: No frontier AI model can solve a specific first-grade math exercise
“I took one exercise from this math book and none of the frontier models is able to solve it.”
Łukasz Kaiser Nov 26, 2025 ▶ 48:11 What’s Next for AI? OpenAI’s Łukasz Kaiser (Transformer Co-Author)
Insight
Łukasz Kaiser: AI pre-training expands stored knowledge rather than generalization
“Pre-training is a little different, right? Because it increases the data together with your increase in model size. So it doesn't necessarily increase generalization. It just uses more knowledge.”
Łukasz Kaiser Nov 26, 2025 ▶ 51:47 What’s Next for AI? OpenAI’s Łukasz Kaiser (Transformer Co-Author)
Prediction Not checkable as stated
Kaiser: OpenAI aims to create an AI intern by late 2026
“I think that's what OpenAI says is they say, you know, we say we'd like an AI intern by the end of next year.”
Łukasz Kaiser Nov 26, 2025 ▶ 55:58 What’s Next for AI? OpenAI’s Łukasz Kaiser (Transformer Co-Author)
Assertion Not checkable as stated
Kaiser: AI progress has been a smooth exponential increase in capabilities
“Fundamentally, if you look at AI progress, it's been a very smooth exponential increase in capabilities.”
Łukasz Kaiser Nov 26, 2025 ▶ 2:46 What’s Next for AI? OpenAI’s Łukasz Kaiser (Transformer Co-Author)
Insight
Kaiser: Reasoning yields far greater AI capability gains per dollar than pre-training
“With the new paradigm of reasoning, you can get much more gains for the same amount of money because it's on this like lower and like, there are just discoveries to be made and these discoveries unlock insane capabilities.”
Łukasz Kaiser Nov 26, 2025 ▶ 4:56 What’s Next for AI? OpenAI’s Łukasz Kaiser (Transformer Co-Author)
Insight
Łukasz Kaiser: Reasoning models require verifiable data, excelling in math and coding
“So currently, and current for at least the Most basic ways we use it currently, it needs to be fairly verifiable. So there is an, is your answer correct or not? You prepare data for that. You can do that in mathematics, coding very well. You can do this in sci…”
Łukasz Kaiser Nov 26, 2025 ▶ 13:49 What’s Next for AI? OpenAI’s Łukasz Kaiser (Transformer Co-Author)
Prediction Not checkable as stated
Łukasz Kaiser: Next-gen reinforcement learning will operate on general data
“I do believe the era of tomorrow will be broader. It will work on general data and maybe then it will expand to like domains that, that go beyond where, where it shines today.”
Łukasz Kaiser Nov 26, 2025 ▶ 16:37 What’s Next for AI? OpenAI’s Łukasz Kaiser (Transformer Co-Author)
Insight
Kaiser: Math reasoning in AI models transfers to generic web searching
“If you learn to think for math, you can, you will sometimes do some, you know, some strategies are the transfer very much like look up on the web and see what they say and use that information. So some of these things are very generic and they start to transfe…”
Łukasz Kaiser Nov 26, 2025 ▶ 18:51 What’s Next for AI? OpenAI’s Łukasz Kaiser (Transformer Co-Author)
Opinion
Kaiser: AI reasoning in visual domains is currently very undertrained
“I think, especially thinking in the visual domains is very under trained, I believe.”
Łukasz Kaiser Nov 26, 2025 ▶ 19:09 What’s Next for AI? OpenAI’s Łukasz Kaiser (Transformer Co-Author)
Assertion Not publicly verifiable
Kaiser: ChatGPT uses a secondary model to summarize raw reasoning steps
“So in the current chat GPT, you will see a summary of the chain of thought on the side. So there is another model that takes the full chain of thought and shows you a summary because the full ones are usually not very nice to read.”
Łukasz Kaiser Nov 26, 2025 ▶ 19:50 What’s Next for AI? OpenAI’s Łukasz Kaiser (Transformer Co-Author)
Assertion Not checkable as stated
Kaiser: The eight Transformer paper co-authors were never in one room
“I don't think all eight of us were ever in the same physical room.”
Łukasz Kaiser Nov 26, 2025 ▶ 24:34 What’s Next for AI? OpenAI’s Łukasz Kaiser (Transformer Co-Author)
Insight
Kaiser: AI tech labs are more similar than people think
“I think in, in general, the tech Labs are more similar to each other than people think. There are some differences, but I think if I look at it from the world, you know, from the university in France, the difference between this university and any of the tech …”
Łukasz Kaiser Nov 26, 2025 ▶ 30:22 What’s Next for AI? OpenAI’s Łukasz Kaiser (Transformer Co-Author)
Insight
Kaiser: Reinforcement learning reasoning works better on larger pre-trained models
“Pre-training has always worked. And the beautiful thing is it even stacks with RL. So if you run this thinking RL process on top of a better model, it works even better. Than if you run it on top of a smaller model.”
Łukasz Kaiser Nov 26, 2025 ▶ 37:15 What’s Next for AI? OpenAI’s Łukasz Kaiser (Transformer Co-Author)
Insight
Kaiser: Interpretability of large AI models faces fundamental complexity limits
“So the understanding of what the models are doing on a higher level has progressed a lot, but then it's still an understanding of what smaller models do, not the biggest ones. But it's not so much that these patterns don't apply to bigger models. They do. It's…”
Łukasz Kaiser Nov 26, 2025 ▶ 39:09 What’s Next for AI? OpenAI’s Łukasz Kaiser (Transformer Co-Author)
Assertion Not checkable as stated
Łukasz Kaiser: AI models still struggle with multimodal and sequential reasoning
“The models are just, they're starting, like you see the first example they manage, so they've clearly made some progress, but they have not yet learned to do good reasoning in multimodal domains, and they have not yet learned to use one reasoning in context to…”
Łukasz Kaiser Nov 26, 2025 ▶ 50:23 What’s Next for AI? OpenAI’s Łukasz Kaiser (Transformer Co-Author)
Assertion Supported
Kaiser: Translation industry grew and translators earn more post-Transformers
“The translation industry has grown considerably since then. It has not shrunk. There's more translations to be done. Translators are paid more.”
Łukasz Kaiser Nov 26, 2025 ▶ 1:01:04 What’s Next for AI? OpenAI’s Łukasz Kaiser (Transformer Co-Author)
Disclosure
Kaiser: OpenAI began working on reasoning models around three years ago
“So we started working on it maybe three years ago”
Łukasz Kaiser Nov 26, 2025 ▶ 4:06 What’s Next for AI? OpenAI’s Łukasz Kaiser (Transformer Co-Author)
Assertion Not checkable as stated
Kaiser: AI coding tools recently became how many programmers work
“But I think it's the recent few months when the transition happened from, you know, people using it sometimes, but rarely, to now basically this being how a lot of people work in coding.”
Łukasz Kaiser Nov 26, 2025 ▶ 7:42 What’s Next for AI? OpenAI’s Łukasz Kaiser (Transformer Co-Author)

Show 15statements(15 left)

The other half of the tape: Łukasz Kaiser's own voice is left out of every number here. Other people bring the name up 6 times in 2 episodes on the MAD Podcast. every mention, with the transcript →

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Appearances (1)

EpisodeDateSpeaking time
What’s Next for AI? OpenAI’s Łukasz Kaiser (Transformer Co-Author) Nov 26, 2025 49m
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