Ali Ghodsi
Co-founder + CEO, Databricks · Former adjunct professor, UC Berkeley AMPLab
“I think we have AGI. I think we have artificial general intelligence. We really have it. We absolutely have it. It's like anyone who says we need to get to AGI, that's like, it's false premise to start with. We already have AGI. I came to the United States in 2009 at UC Berkeley, not far away from here, and I was in an AI lab. It was called Amp Lab. And back then, the definition of AGI we had — we already have satisfied that. So for 30, 40 years, we had a definition of AGI. We've already hit that. Now we're changing it and moving the goalpost.”
“I think the LLM is a commodity. People are not saying that, but it is a commodity. Like you can get gas from this gas station, you can get gas from that gas station. It doesn't matter. Just compare price. LLMs have become that way. So they're a commodity. So it's not about that. It really comes down to your company. What data does your company have that's special that your competitors don't have? Can you leverage that and can you build AI that really understands that data?”
“There's like three paradigms or three kind of camps. The first camp is this quest for super intelligence camp — all the Frontier Labs. It's really still being a lot of it comes from the scaling laws mentality which is whoever has the most GPUs and the most data is going to win the quest for super intelligence. The second camp are the people that created the original technology — Rich Sutton, who created reinforcement learning, Yann Lecun, the founding fathers. They say that first camp is not going to work. They're like, that's just autoregressive next token prediction. They say it's 20 years out. Third camp, which is what we are in: I don't think we need super intelligence right now. We have AGI. So we just need to make it useful inside the enterprise.”
“I am very long on agents. I think I'm very long on speech as an interaction. I think keyboards are basically going to disappear completely. We haven't actually nailed speech. I know it feels like we have, but we haven't because you're still using your keyboard. So as long as you're using your keyboard, we haven't nailed speech. But I think we're this close to completely eliminating keyboard. I do think coding is a little bit overhyped. I don't know if I would short it. I mean, I think it's still the future. So I think that's one of them. I think automating customer service and support is a little bit overhyped.”
“The paradigm we live in today with AI is there's still problems. The biggest problem is that you bake a model and that's where it's learned everything it needs to learn and then you freeze it and then you launch it and then maybe you give it some context, but that's it, it's frozen. So therein lies the problem that we need an AI that really can sort of continue learning while it's using the desktop and clicking around. So I do think this problem is hard to nail. We haven't really nailed computer use yet.”
Ali Ghodsi
One-line summary: Co-founder + CEO of Databricks; former UC Berkeley AMPLab researcher; one of the most-cited voices on enterprise AI deployment economics. Tracked here for his Dec 2025 'we have AGI / LLMs are a commodity / three camps' framing.
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Speaker-attributed claims extracted from diarized sources. Each bullet mirrors one entry in quotes: frontmatter — keep them in sync.
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On agi-timeline-decade-of-agents, three-camps-of-ai:
"I think we have AGI. I think we have artificial general intelligence. We really have it. We absolutely have it. It's like anyone who says we need to get to AGI, that's like, it's false premise to start with. We already have AGI. I came to the United States in 2009 at UC Berkeley, not far away from here, and I was in an AI lab. It was called Amp Lab. And back then, the definition of AGI we had — we already have satisfied that. So for 30, 40 years, we had a definition of AGI. We've already hit that. Now we're changing it and moving the goalpost." — 2025-12-23-bg2-databricks-glean-enterprise-ai (2025-12-23)
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"I think the LLM is a commodity. People are not saying that, but it is a commodity. Like you can get gas from this gas station, you can get gas from that gas station. It doesn't matter. Just compare price. LLMs have become that way. So they're a commodity. So it's not about that. It really comes down to your company. What data does your company have that's special that your competitors don't have? Can you leverage that and can you build AI that really understands that data?" — 2025-12-23-bg2-databricks-glean-enterprise-ai (2025-12-23)
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On three-camps-of-ai, agi-timeline-decade-of-agents:
"There's like three paradigms or three kind of camps. The first camp is this quest for super intelligence camp — all the Frontier Labs. It's really still being a lot of it comes from the scaling laws mentality which is whoever has the most GPUs and the most data is going to win the quest for super intelligence. The second camp are the people that created the original technology — Rich Sutton, who created reinforcement learning, Yann Lecun, the founding fathers. They say that first camp is not going to work. They're like, that's just autoregressive next token prediction. They say it's 20 years out. Third camp, which is what we are in: I don't think we need super intelligence right now. We have AGI. So we just need to make it useful inside the enterprise." — 2025-12-23-bg2-databricks-glean-enterprise-ai (2025-12-23)
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On ai-coding-productivity-paradox:
"I am very long on agents. I think I'm very long on speech as an interaction. I think keyboards are basically going to disappear completely. We haven't actually nailed speech. I know it feels like we have, but we haven't because you're still using your keyboard. So as long as you're using your keyboard, we haven't nailed speech. But I think we're this close to completely eliminating keyboard. I do think coding is a little bit overhyped. I don't know if I would short it. I mean, I think it's still the future. So I think that's one of them. I think automating customer service and support is a little bit overhyped." — 2025-12-23-bg2-databricks-glean-enterprise-ai (2025-12-23)
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On agi-timeline-decade-of-agents:
"The paradigm we live in today with AI is there's still problems. The biggest problem is that you bake a model and that's where it's learned everything it needs to learn and then you freeze it and then you launch it and then maybe you give it some context, but that's it, it's frozen. So therein lies the problem that we need an AI that really can sort of continue learning while it's using the desktop and clicking around. So I do think this problem is hard to nail. We haven't really nailed computer use yet." — 2025-12-23-bg2-databricks-glean-enterprise-ai (2025-12-23)
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