Why IBM Wants AI to Be Boring
episodePreviously titled “Why IBM Wants AI to Be Boring: AI as Infrastructure, Not a Friend” — renamed by the publisher on Aug 4, 2026
Transcript
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What is the main topic discussed in this episode?
Let's treat the thing not as a friendly robot, but let's treat it as a computational device. It's good, it's fast, it's cheap, trustworthy. Even the biggest, baddest, most expensive, and they're very expensive models still struggle with some quirky things sometimes. I don't necessarily want there to be the new god or something. Nothing is theoretically infinite.
Welcome, humans, to the Neuron AI Explained podcast. I'm Corey Knowles, joined as always by Grant Arby. How are you, Grant? Doing well, doing well.
Really excited for this one.
So who are we talking with today, Grant?
So today we are talking with David Cox, who leads AI model development at IBM Research. And we're talking about Granite 4.0, IBM's newest open model family and the bigger strategy behind hybrid architectures, long context efficiency, and enterprise deployment.
So we'll cover what Granite 4.0 is optimized for, why IBM chose to go this direction, and where these models fit alongside larger reasoning systems in your chain. On that note, David, welcome to The Neuron.
Thanks for having me.
David, before we get to Granite, I have to ask, when we met back in March, I was pretty impressed with IBM's strategy of treating models like tools, not entities. which is kind of divergent from some of the way that other labs treat AI models. But I have to ask, as the capabilities of the frontier models have evolved over the year, has your point of view changed at all on how much progress we're making or how much they can do? And how has it changed since we talked in March?
Yeah, I mean, obviously, that's a great question. The models keep getting better. That's always true. The top keeps getting higher. At the same time, also, we're able to pack more and more down into smaller models. So it's a great time to be in the field. But I don't think that changes the perspective, though, where, hey, we want to use these things to get work done. And the programming model, I used to be a computer science professor, so I'm kind of like one of those cranky, get off my lawn kind of people. But it's just like, the programming model shouldn't be I'm talking to a friendly robot. That has inherent problems, even if it's a really smart, friendly robot, or it seems to be. We want to have a way to get more deterministic.
We want to be in control of the interaction. And if you look at it, even the biggest, baddest, most expensive and they're very expensive models still struggle with some some, you know, quirky things sometimes. Like this is one of those things like what they're bad at and what they're good at. They like they can dazzle you if they're good at and then they can do something like, you know, face plant on something like kind of simple. So totally it's it's very expensive. It's very unpredictable. It just doesn't really do what we need to. I think that tool framing really gets us back to center.
I've always kind of thought it was interesting because I think there are probably tasks that we do that with as well. And different tasks, I would say, not necessarily the same ones even. And it's really interesting to kind of watch how all this has unfolded. On the side, when looking at the granite models, it refers to them as enterprise-ready granite. Can you tell me kind of what sort of constraints you've optimized for or what that looks like in practice?
The starting point is they have to perform as well as anything, right? State of the art performance. So we are, you know, right at the best, you know, we define the Pareto curve for sections of the Pareto curve of like tradeoffs of speed and efficient speed and performance. So that's like an uncompromising thing. But then you need to be able to trust them for enterprise use. It's not okay to go to a bank SVP for risk and say, here's a big pile of numbers I made. I'm not going to tell you how I made them or what data went into it, but maybe trust me. They want to know. They want to know that you didn't have something in there that was dangerous or that was not legally allowed to be in there.
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Chapters
5 chapters
1
What is the main topic discussed in this episode?
0:00–5:03
2
Why does IBM frame AI models as tools instead of friendly agents?
5:03–11:48
3
What enterprise constraints and trust requirements shaped Granite 4.0’s design?
11:48–15:38
4
How does open licensing and external auditing support enterprise adoption?
15:38–46:35
5
How can LoRA adapters and activated adapters change model behavior at runtime?
46:35–52:56
Speakers
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