Field notes from San Francisco: Greg Brockman's agentic AI future meets enterprise AI's reality
What OpenAI's Greg Brockman says is coming, and what Databricks' cofounder says is happening today
After three days in San Francisco, I left this morning on a beautiful note, with a cool breeze off the Bay and Karl the Fog tucked in over surrounding ridges and peaks, letting the sun freely shine and temporarily banishing the typical June gloom.
To be honest, though, I spent most of my Uber ride to the airport fascinated by the contradiction at the heart of the AI industry in this tech-obsessed city. Here, the world’s most sophisticated technology is being marketed through some of the most old-school channels imaginable: billboards, banners pulled by airplanes, pedicabs, and bus-stop ads.
It’s kind of adorably analog and seems to matter not whether anyone other than a narrow target audience knows what these companies do. Take the Databricks Data + AI Summit, which took over the Moscone Center and the surrounding blocks this week. Here's a company that makes little sense to most people outside enterprise tech, yet is reportedly seeking a valuation of $165 billion, has raised vast sums of capital, and seems perpetually on IPO watch. Everyone in enterprise AI seems to be able to tell you about the company’s data lakes and data unification and data engineering and data governance.
But the scene on the ground is all analog. The crowd (I’m told it was over 30,000) is an overwhelming sea of orange lanyards, streaming through the streets, on escalators and in ballrooms. Massive orange Databricks posters coat buildings like paint, while on the expo floor software buyers and developers compete in games for startup swag like kids playing Pin the Tail on the Donkey.
A morning at Databricks Data + AI Summit
I was at the Summit on Wednesday to meet Databricks co-founder Arsalan Tavakoli, who I’m interested in because he oversees the company’s global field engineering organization—I imagine an army of specialists working directly with enterprise companies who are desperate to drive AI adoption. If anyone knows what’s going on with customers on the ground, I think it’s Tavakoli.
One thing enterprises are struggling with in AI, he said, is that it turns out AI is only as good as the context surrounding it. Despite hopes that large models could work with messy or fragmented data, organizations still need high-quality information and a clear understanding of how their business data fits together.
The other challenge — which is very of the moment given this week's extraordinary showdown between Anthropic and the Trump Administration, with Anthropic taking Fable 5 offline after officials raised concerns about a potential jailbreak — is model choice.
“They’re like, do I bet the farm on Anthropic? On OpenAI? Is it Grok?” he said. “Okay, I bet on Claude, but now there’s the whole Fable thing.” The answer, he said, is not to bet on a single model, but to build systems that can route tasks to different models depending on the use case.
