TechCrunch Disrupt 2026 is putting enterprise AI, security and new startup business models at the center of its AI Stage. The event runs from October 13–15 in San Francisco at Moscone Center, with the AI Stage presented by Google for Startups.
The agenda reflects how quickly AI has moved from experimental projects into the operating core of startups and large companies. Instead of treating AI as a single product category, the program breaks it into the problems founders are now facing: how to sell AI products, how to protect sensitive systems, how to price software when models are commoditizing, and how to build teams for work that barely existed before.
Enterprise AI Moves Beyond the Pilot
One of the headline sessions will focus on what happens after companies actually deploy Claude. Cat de Jong, Head of Applied AI, Anthropic, works with enterprises using Claude in critical workflows, giving the session a practical angle on where AI adoption succeeds and where it slows down.
The session is framed around a gap in the wider AI conversation. Many discussions stop at strategy, demos or early planning. This one starts with deployment and asks what can be learned from the organizations already putting AI into production.
That matters because enterprise AI progress is not only about choosing a model. It also depends on workflows, expectations, internal readiness and whether a company can move beyond pilots into repeatable value. The session is expected to examine why some deployments deliver quickly while others remain stuck after eighteen months.
For founders, that is a useful lens. Selling AI into large organizations requires more than a strong technical pitch. It requires understanding the conditions that make adoption possible once the product leaves the demo environment.
OpenAI Spotlights AI-Native Go-to-Market Work
OpenAI will be represented by Tara Seshan, Head of Productivity, OpenAI, in a session on what building AI native actually means. The focus is go-to-market work, where AI has changed the traditional stack and created a new discipline around GTM engineering.
The agenda describes GTM engineering as a role that did not exist two years ago and is now one of the fastest growing roles in the industry. It also notes that independent practitioners are building million-dollar businesses around this shift.
The broader point is that AI is not only changing how software is written. It is changing how companies find customers, support growth and organize the systems behind sales and marketing. An AI-native GTM plan is presented as something different from simply adding automation to an existing process.
Another session, led by Kareem Amin, Co-founder and CEO, Clay, will also examine the GTM engineer as an emerging job category. Together, the sessions suggest that founders need to think about AI as an operating model, not just a product feature.
Security Takes an Infrastructure-Level Turn
Security is a major theme across the AI Stage. Several sessions are built around the idea that traditional security approaches were not designed for AI systems making autonomous decisions inside sensitive enterprise environments.
Arsalan Tavakoli, Co-founder and SVP of Field Engineering, Databricks, will discuss what enterprise AI security requires in 2026. The session is expected to cover observability, governance and the architecture needed for deployments that enterprises can trust.
Ric Smith, President of Product & Technology at Okta, will focus on agent security. The session frames agentic AI as powerful but not originally built with security as a foundation. It will explore why enterprises are having to rebuild core parts of cybersecurity for this new environment.
AWS, Luta Security and 1Password will also be part of the security conversation. Chet Kapoor, VP, Security Services & Observability, AWS, Katie Moussouris, Luta Security, and Wendy Nather, 1Password, are listed for a session on cloud complexity and the infrastructure demands of securing AI in enterprise systems.
The repeated focus on architecture is important. These sessions are not framed as surface-level discussions about policy alone. They point to the technical foundations that determine whether AI systems can be monitored, governed and trusted when they operate at speed and scale.
SaaS Pricing and Video Intelligence Join the Agenda
The AI Stage will also look at whether the old SaaS model still works in the AI era. A session titled around rewriting SaaS will bring together Arvind Jain, Founder & CEO, Glean, Barr Moses, Co-founder & CEO, Monte Carlo, Cathy Gao, Partner at Sapphire Ventures, and Aaron Jacobson, Partner, NEA.
The core questions are practical: how to price AI products sustainably, how to build defensible moats as models become commoditized, and how SaaS companies can adapt rather than assume the old playbook still applies unchanged.
Video intelligence will be another focus area. Dean Leitersdorf, Co-founder and CEO, Decart, and Amit Jain, Co-founder and CEO, Luma AI, will discuss real-time inference, physical reasoning and what comes after visual AI moves beyond attention-getting demos.
That session broadens the AI Stage beyond enterprise software and security. It points to a frontier where generation, reasoning and real-time systems are beginning to overlap, raising new product and company-building questions for founders working at the edge of visual AI.
Why the AI Stage Matters for Founders
The AI Stage is designed for builders who are already confronting the second-order effects of AI adoption. The topics are not limited to model capability. They include pricing, security, workflow design, enterprise trust and the creation of new roles inside the technology industry.
TechCrunch says attendees will join 10,000+ startup, tech, and VC leaders, with access to other stages, Startup Battlefield, networking opportunities and the exhibition floor. The publication is also promoting a pricing window that can save up to $200.
For founders, the agenda shows how the AI conversation has matured. The central issue is no longer whether AI will matter to startups. It is how companies can build sustainably in an environment where AI changes the product, the buyer, the infrastructure and the team all at once.