The Two Biggest AI Companies Are Headed to TechCrunch Disrupt 2026

Anthropic and OpenAI, two of the leading companies in artificial intelligence, will speak at TechCrunch Disrupt 2026, a major technology conference running October 13–15 at Moscone Center in San Francisco (TechCrunch, 2026-08-27).

Cat de Jong, Head of Applied AI at Anthropic, will present a session called "What Anthropic Sees When Enterprises Actually Deploy Claude." The title suggests she will talk about what really happens when companies start using Anthropic's AI tool, Claude, in their daily work — not just what looks good in a demo. Tara Seshan, Head of Productivity at OpenAI, will present "What Building AI Native Actually Means." That title pushes past the popular phrase "AI-first" to ask a deeper question: what does it actually take to build a company around AI, rather than just adding it on?

The AI Stage is presented by Google for Startups. It is one of six themed stages at Disrupt 2026, and three of those six are focused on AI (TechCrunch, 2026-08-26). The broader agenda includes speakers from Google, Amazon, and Replit alongside OpenAI, Anthropic, and Databricks.

Two more sessions round out the business-focused lineup. Arsalan Tavakoli, Co-founder and SVP of Field Engineering at Databricks, will present "The Enterprise Isn't Broken. Your Assumptions About It Are." Ric Smith, President of Product & Technology at Okta, will present "The Agent Security Problem Nobody Is Talking About." Smith's session stands out because it puts identity and access management — the systems that decide who is allowed to do what inside a company's digital systems — at the center of the AI conversation. That is an increasingly important topic as AI agents, which are programs that can act on their own to complete tasks, start operating inside corporate networks.

Beyond the AI Stage, the conference includes a Builders Stage focused on practical strategies for growing startups (TechCrunch, 2026-07-01). There is also a Real World AI Stage with sessions on robots, automated factories, and extinct animals (TechCrunch, 2026-08-05). That stage covers physical, hands-on applications of AI rather than software alone.

The conference is expected to bring together 10,000 founders, investors, and business operators.

The session titles themselves reveal something worth pausing on. De Jong's focus on "when enterprises actually deploy" implies a gap between what companies selling AI show off and what happens in real use. Tavakoli's challenge to "your assumptions" pushes back against a habit in the AI world of treating older business data systems as a problem to work around, rather than the foundation they actually are. Seshan's need to redefine "AI native" suggests the phrase has been used so often it has lost its meaning. And Smith's "problem nobody is talking about" points to a real blind spot: when AI agents operate inside a company, they create new security risks around identity and permissions that go well beyond the data-leakage worries people usually discuss.

For anyone attending or following the event, the focus is on practical experience rather than new product launches. The Anthropic and OpenAI sessions center on what happens when companies actually use AI and how organizations change because of it, not on whose model scores highest on tests. That is a shift from last year's conference season, where new AI model releases and demonstrations dominated. The inclusion of Databricks, which handles large-scale data, and Okta, which handles digital identity, alongside the two AI companies sends a clear message: the infrastructure that supports AI matters as much as the AI itself.

In my view, the fact that Disrupt 2026 has dedicated half its stages to AI tells us the technology has grown up. It is no longer a niche topic for specialists. It has become a concern that touches every part of how companies build products, manage security, and run their operations. For the 10,000 people expected to attend, the real question is no longer whether AI will affect their work. It is how to close the gap between what AI promises in a demo and what it actually delivers when put to use.