Snowflake, Databricks and the Model Makers: The Battle for the Agentic Client and AI Backend
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What is the core premise behind the “intelligent client” and AI backend?
Agentic AI is being misread as a series of separate battles, for example, Snowflake versus Databricks, co-pilots versus agents, model makers versus app vendors, et cetera. We think the real story is that the biggest opportunity in software is converging around who owns the new intelligent client and the AI backend that makes it useful. Full. The new client, you should think of that as the agent-based system of engagement. Snowflake's co-work and Coco, for example, or Databricks Genie, think Microsoft Co-Pilot. Google's got Gemini Enterprise. ChatGPT, of course, has Codex, and of course Cloud Co-Work with its momentum. And there, of course, there are others. But that client, we don't think, can deliver the business outcomes that people want without a new back end, what we call a system.
system of intelligence. Now that represents a model of the enterprise in terms of its business rules and very importantly, that tacit knowledge, that tribal knowledge that people always talk about. We don't think you can build one without the other. Now, we frame this premise at this breaking for this breaking analysis using Clay Christensen's integrated innovation concept and Jensen's extreme co-design, and we apply it to enterprise software. That is why Snowflake is the focal point for this breaking analysis, but not the whole story. Snowflake is not just competing with Databricks anymore. In the same strategic arena as Microsoft, Google, OpenAI, Anthropic, Salesforce, SAP, ServiceNow, we'll put Solonus in there, and of course there are others.
They're all trying to define where business users, builders, and agents get work done. And where the enterprise context that powers that work gets built. Now the key premise again here is that the system of intelligence that backend does not only ingest data from pipelines and catalogs, et cetera, it does that, but it also learns from business users and builders through the agentic client. Here we're talking about skills, artifacts. You hear about semantic views. Query history is an important input. The actions that agents take, and of course the human reasoning traces, they all become inputs that provide direct feedback. into the intelligence layer. So we think a key success factor is having the tightest feedback loop between the agentic client and the enterprise intelligence back end.
And that requires deliberate software engineering that tightly couples the pieces of the stack. And once again, George Gilbert and I dig into the emerging AI software stack. And we'll connect the dots from what we learned at Snowflake Summit and of course Microsoft Build, which was last week, also in San Francisco. And we'll set up the Databricks Data and AI Summit that's coming in mid June. George, once again, welcome. Thank you for your time. Good to be with you, Dave. All right, let's get into it. Alex, bring up the first slide. George, you you came up with this concept in previous episodes that we shared this, that there are more AI j more agents than fleas on a camel. But you're calling out Brett Taylor uh on this, um on this slide, who's both he's the chairman of Open AI, he's also the CEO of Sierra.
Uh and and you put a butt on this slide. What's the but?
Well it's the Um there's this this theme that came out of Y Combinator that vertical agents are gonna be ten times bigger than vertical SAS and You know, this is the the services software story that all the work that or m much of the work that humans can do can be captured in agents. And if we can just bottle bottle that knowledge up, we can turn these um specialized agent companies into giant companies. And that's where I'm like the to make the point, there are more of these than there are fleas on the average camel. The the problem the This is the but is we're just building more silos, the same silos in it with a new technology that we've been building for 60 years. And the point of agents and what agents are driving for the for the new um data infrastructure is that we have.
end to end infrastructure and visibility across how the enterprise work it works so that you can she you can achieve business outcomes that they're not specialized silos that you can like onboard a customer you can do a a bank can do a know your customer uh process um and
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Chapters
6 chapters
1
What is the core premise behind the “intelligent client” and AI backend?
0:00–4:51
2
How does the “system of intelligence” capture enterprise rules and tacit knowledge?
4:51–8:21
3
Why is Snowflake now competing with Microsoft, Google, OpenAI, and others?
8:21–11:39
4
What role does the feedback loop between the agentic client and the intelligence layer play?
11:39–41:59
5
How is the emerging AI software stack organized across data platforms, governance, and agents?
41:59–51:52
6
What are the nine maturity layers of the system of intelligence and how do they evolve?
51:52–57:39