AI is already smart enough, but it lacks the right context to reach the level of AGI. Databricks CEO Ali Ghodsi talks about the current state of enterprise AI, how companies' databases are not built for an AI-native world, and why Databricks is not planning on going public just yet. He speaks with Ed Ludlow on "Bloomberg Tech."
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What Ali Ghodsi said
Written from the verified transcript and checked against it. Every figure links to the moment it was said.
Ali Ghodsi, CEO of Databricks, argued that AGI is already here but lacks context, which is why AI agents are not yet widespread in workplaces. He introduced Genie Ontology, an enterprise knowledge graph that infuses context into AI, and Genie One, an agent that computes answers live rather than reciting from documents, citing Novo Nordisk's use for real-time drug trial analysis. Ghodsi also discussed the need for lake-based architectures designed for agents, unifying databases and warehouses, with Prada as an example. He identified security approvals as the main constraint on AI adoption and Databricks' growth, though he said the company is growing faster than ever. On fundraising, he neither confirmed nor denied a $175 billion valuation round, citing investments in new products like Customer Lake and Lake Watch. He reaffirmed his stance that Databricks will go public but that 2026 is a poor year due to market instability from mega IPOs.
Key takeaways
AGI is already here; the constraint is context, not intelligence.
Genie One computes answers live instead of reciting from documents, unlike other agents.
Databricks is growing faster than ever, with security approvals as the main deployment constraint.
Ghodsi stands by his view that 2026 is a terrible year to go public, preferring to wait for calmer waters.
Databricks is investing in new agent-first products like Customer Lake and Lake Watch.
Numbers and commitments
Figure
What it refers to
Type
At
$175 billion
rumored valuation for Databricks fundraising round
This is a really big week, right? It's a really big deal. Databricks might not be a household name to a lot of people, but you would argue this is the biggest AI conference in terms of attendance and energy in the world. We'll go into all the nitty gritty of what you're announcing and what people will be discussing there. I want to start with the mile high big picture, and that's your argument that AGI is already here. Intelligence is not the constraint. What is the constraint? It's the context. These AIs are plenty smart. Anyone who's interacted with them, if you ask a big audience how many think this is super smart, smarter than many people you work with, most of the time all the hands go up. But yet we're not seeing them in the workplace. They're not everywhere. We don't have hundreds of agents working together doing work for you. We're all managing thousands of agents. That's just not reality. Why is that?
They just don't have the context. If you could take all the data, all the processes, everything that people are thinking and put them in the models, they could actually help us tremendously. But there's a big gap there. So we really want to infuse that context into the existing AIs, and when you do that, magic happens. That's why we launched Genie Ontology. That's exactly what it tries to do. The idea of an enterprise ontology, you hear people in industry and culture use that term. What is it and why is it important? It might be a confusing term, but it's a big web of all the knowledge and how the knowledge is interconnected. So it could be this person opened this document, this document was read by these other people, this document is about this particular information, and tagging and explanations of what that is and what the key elements are. And that not just for that document, but for everyone in the organization and everything they're doing. A lot of that information already exists; it's just not infused into the AI. What agents today do is they try to go one by one and find it. My analogy is it's like when Google invented the ten blue links. Imagine instead of giving you the ten blue links, it would go search one website, read it, then find another link, and so on. That would take forever, cost a lot of money, and wouldn't be very good. That's how agents do it today. An ontology is essentially like that index that Google had, but for the enterprise, computing it behind the scenes. Then when you're asking questions or having the agent do things for you, it can tap into that index.
The big difference with Genie One is that Genie One computes; other agents recite. The best way to explain this is with an example. I ask it all the time: Who are the new customers on the platform in the last 24 hours? Or which of our biggest customers churned in the last ten days? Other agents will recite; they'll try to find a document that has the answer, search through it quickly, and then give you that answer. But what if that answer does not exist in any document and you have to compute it live? You need numeracy for the AI to understand the data. That's what makes Genie One different. It can do that really fast because it uses the ontology. This is what Novo Nordisk uses Genie One for. They had empowered their scientists to use it, but before it took them a long time when they ran different experiments and wanted to know how an obesity study was going, how a drug was affecting different control groups, and what results they were seeing. Now anyone can compute that; it's not trying to recite it because that result doesn't exist. It computes it live in a fraction of the time. That's what they use it for.
You're making a bigger claim here than just product launches. It's that those systems built for human use, software built for humans, don't work as well as systems built for agents to use the software. You and I got into this a couple of weeks ago. That brings us to the lake. What are you trying to say needs to be rebuilt here?
You nailed it. The cost of creating software is almost zero; anyone can create software anywhere right now. But every piece of software you create needs a database behind the scenes. That's how software works, which is why we have big database companies. But these databases were created for humans. Agents are different. They want to move very fast and experiment. Sometimes they break the database or create thousands of databases, and you don't want to pay for thousands of databases. They also want to move fast, so you don't want to wait 20 minutes. That would slow them down significantly. That's what a lake-based architecture is. It helps them operate fast on the database where all the information is stored. If they want to ask more complicated questions, like a data science question about the data, they have to use a different database today, called a data warehouse. So there are two separate systems. We unified them into one database that's really fit for agents. Now agents can operate on the database and ask AI-like questions, like what the curve looks like. That's a game changer. My favorite example is Prada. They store all their cookies inside the lake-based system, and their agents can move very quickly to make KPIs available to leadership. It's a game changer; it lets them do things much faster with higher accuracy and lower cost.
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Ghodsi, A. (2026, June 16). Context Needed to Reach AGI, Says Databricks CEO [Interview transcript]. Bloomberg Technology. CEOInterviews.AI. https://ceointerviews.ai/interview/988507/
MLA
Ali Ghodsi. "Context Needed to Reach AGI, Says Databricks CEO." Bloomberg Technology, 16 Jun. 2026. Transcript, CEOInterviews.AI, https://ceointerviews.ai/interview/988507/.
BibTeX
@misc{ghodsi2026_988507,
author = {Ali Ghodsi},
title = {Context Needed to Reach AGI, Says Databricks CEO},
howpublished = {Interview transcript, Bloomberg Technology. CEOInterviews.AI},
year = {2026},
month = {jun},
url = {https://ceointerviews.ai/interview/988507/},
note = {Speaker-attributed transcript with timestamps}
}
