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20VC · 3 min · Oct 26, 2026

Hidden reasoning makes model oversight harder

Satya Nadella says chain-of-thought can be unfaithful, and hiding it makes oversight harder. He argues trusted partners need inspectable traces, which could make open weights more attractive.

Satya Nadella

Satya Nadella is CEO of Microsoft.

Led Microsoft's cloud infrastructure and services transformation, and has been CEO since February 2014.

  • Harry Stebbings, Host · Founder, 20VC

    Built a venture firm on his podcast; its third fund closed at $400M.

About this segment

Satya Nadella says chain-of-thought can be unfaithful, and hiding it makes oversight harder. He argues trusted partners need inspectable traces, which could make open weights more attractive.

From “Satya Nadella: Whether to Trust Frontier Models | Why Companies Need to Own Their Own Intelligence”, Oct 26, 2026. Watch the full episode

“We know that the chain of thought itself can be unfaithful.”
Satya Nadellaat 4:37 · 20VC4:37

Takeaways

  1. Chain-of-thought can be unfaithful.
  2. Hiding it from trusted partners makes auditing harder.
  3. Inspectable traces could increase the appeal of open-weight models.

In this episode

Full episode
  1. 8:53Companies should keep the intelligence their AI use creates3:24

  2. 4:32Hidden reasoning makes model oversight harder2:53

  3. 15:47AI training budgets ultimately depend on GDP growth3:12Up next

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

  1. 4:44

    side, if we think about chain of thought, how reliable is that? It's not, right? We know that the chain of thought itself can be unfaithful. And in fact, it can be made worse when the model company says, well, it's unfaithful, and I'm going to hide it. And so, it becomes even more problematic. And that's where I think a little bit of the push for standardization and the COT, at least for trusted partners being available as is, so that it's inspectable, it's auditable, and I can check using multiple models and what have you, right? So, that's going to be one of the... In fact, that's one of the things that may even drive more open weights, quite frankly, Because if you can't trust, it's funny, closed trade models are more powerful, but if they kind of don't recognize the need to be more transparent, especially as their trusted partners use it, then we may have a problem.

  2. 5:41

    Can I ask you, when we think about that rise of open source, I completely agree with you in terms of closed being more powerful. Open source today is predominantly Chinese models. Is that a concern then when we think about safety being baked into pre-training, when we think about disclosures, as we said there, and then juxtaposed with the majority of open source being Chinese? Yeah, I mean, I think there are two sets of things.

  3. 6:01

    I do believe if you sort of step back, somehow AI safety not being there is not a good thing for China. It's not a good thing for the United States. It's not a good thing for the UK. So I think that there's a part of everyone, because this is not something that... Somehow an unaligned, open-weight Chinese model will probably attack even China. So it's not like you can sell, the rest think that this will only be used as an attack vector.

  4. 6:24

    So setting that aside, I'm pretty pleased with some of the progress even the open-weight models in the West are making. And obviously, Jensen's working on it. The other folks are working on it. We will have even the MAI models that will be not open-weight. I think of it more as licensed weights. Think of it that way. I want to turn this into just a simple, straightforward software business as opposed to a network effects intelligence business. So I think there will be different business models. And I ultimately trust in your ability to use those models and control those models is what's going to determine what gets used. As a venture investor, I have to predict where traffic will go today, between closed, open. When you think about the two different