Databricks CEO on AI Pacing, Cyber Risk, and the Enterprise
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Why is lack of organizational context a barrier to AI adoption in enterprises?
As a business leader, there's a tragedy of the commons. If you want to stop,
if you
want to go slower, why
don't you go slower? Like I'm competing, I want to win. There's almost two camps. There's one camp which believes that this actually is an engineering problem, and there's others which actually believe you have to slow it down. Humans don't respond fast enough.
To the attacks that are happening, you need to automate all of those. And most organizations are actually not close to doing that. Is RSI and recursive self-improvement that the labs are doing leading us there? That's the big question.
Something Elon said, this is some elaborate 4D chess because on the one hand you're saying all of humanity will die. On the other hand, you're saying, hey, what do you want for your IPO allocation?
For the first time ever, a company at scale last week said that they're moving from the frontier models to GLM. Do you think that that's a trend or do you think that's just like a one off anecdote?
Um AI may already be smart enough for the enterprise. The problem is that it doesn't understand your company. Databricks CEO Ali Godzi joins A16Zs Martin Casado and Sarah Wei to discuss what's actually holding back AI adoption, and why the answer may have less to do with building smarter models and more to do with giving them the right context. They also debate the push to pace frontier AI, recursive self-improvement, and where the risks are real today. Ali argues that superintelligence remains far from what we currently see. While cybersecurity is an immediate problem as agents make attacks faster and harder for human security teams to keep up with. And they get into what Databricks has learned using AHI internally, from building an organizational ontology to managing token costs and choosing different models for different jobs.
Thank you for being here, Ali.
Super
excited.
So we obviously want to get to Databricks, but there is a broader conversation going on right now about AI. And Dario's weighed in, Jakob's weighed in, Elon's weighed in. But we want to hear what Ali Goetze thinks. In terms of, you know, if you called the topic, broadly speaking, pacing the frontier, et cetera. Um What is your strongest agreement with what's being out there, where do you disagree and maybe where is there nuance that's not being captured?
Yeah. H happy to cover it. And me and Martine argue a lot. So um I'm sure that's not gonna take long. Try to stay calm. But uh well, I I do think first and foremost that they're uh maybe we agree on this, that um uh leaders have responsibility to not freak people out unnecessarily unless there's really, really good reason. And I think, you know, there's always different people in society that are at different places, you know, in their mind space. So, you know, talking about these kind of existential risks and you know uh scenarios where all of humanity is gonna be wiped out. I think uh um is ir irresponsible. Uh like it can tip a lot of people over and it can cause a lot of like mental health issues.
Unless you have something that's gonna wipe people out.
Yeah, as I said. Yeah. If if there is a actual reason for it, then you know, that's a different story. But I think that right now the existential risk is close to zero. Um, so why freak everybody out? It's not actually needed. Uh there are risks, we'll get into it, that's probably where we disagree. Um, but first and foremost, I think that leaders should not freak everyone out. And I mean, you know, if there's like technical nuances in how we're doing AI research and so on, well, researchers can discuss that. You don't need to every time go on TV and or blast on Twitter to millions of people that, hey, you know, I think there's like this percent. percentage, ten percent risk that all of humanity is going to be wiped out.
I don't think that's like helpful for a lot of people. Actually I think causes a lot of harm for a lot of folks who who get stressed out and actually are not in the nuances of all of this stuff and what it means. So that I don't think we should do.
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Chapters
8 chapters
1
Why is lack of organizational context a barrier to AI adoption in enterprises?
0:00–8:43
2
Should AI development be paced or paused to mitigate emerging risks?
8:43–17:11
3
How do AI‑powered agents amplify cybersecurity threats for businesses?
17:11–26:26
4
What is an organizational ontology and how does it improve AI performance?
26:26–34:15
5
How can enterprises control AI token costs and choose the right models?
34:15–42:27
6
What are the most impactful real‑world AI use cases Databricks has enabled?
42:27–51:00
7
Is existential AI risk realistic or exaggerated according to industry leaders?
51:00–59:55
8
How are AI agents reshaping the future of data infrastructure and databases?
59:55–1:08:25