TechCrunch Disrupt 2026
Let's be honest: AI hype has gotten tired. Everyone's talking about it, but few are getting specific about what breaks when you actually deploy it at scale. That's exactly why the AI Stage is back at TechCrunch Disrupt 2026—and why this year's lineup feels different.
October 13–15, Moscone Center, San Francisco. Presented by Google for Startups. 10,000+ startup, tech, and VC leaders in attendance. But the real story isn't the crowd size. It's what these three sessions demand you confront.
The Enterprise Isn't Broken. Your Assumptions About It Are.
Here's the uncomfortable truth: AI is now making autonomous decisions inside the most sensitive enterprise systems in the world, at speeds traditional security frameworks never designed for. You can't bolt cybersecurity onto AI. You have to rebuild it from the infrastructure up.
Arsalan Tavakoli, Co-founder and SVP of Field Engineering at Databricks, is leading this conversation because Databricks sits at the intersection of massive data pipelines and real-time AI inference. The session breaks down what enterprise AI security actually requires in 2026—observability, governance, and a new architectural principle: separating deployments enterprises can trust from ones they can't afford to touch.
The old model failed. Application-level permissions don't work when your AI agents are making decisions at machine speed. You need infrastructure-level controls. That means real-time observability into what AI systems are actually doing, governance frameworks that define when and how autonomous decisions get made, and strict architectural boundaries that prevent untrusted AI from touching sensitive systems.
It's not a software problem. It's an infrastructure problem. And most companies are already behind.
The Video Intelligence Race: Real-Time, Reasoning, and What Comes Next
Visual AI has moved past attention-getting demos. We're now in the era of real-time inference and physical reasoning. That's the distinction that matters.
Dean Leitersdorf, Co-founder and CEO of Decart, and Amit Jain, Co-founder and CEO of Luma AI, are leading this conversation because both companies have crossed the threshold from demo-stage demonstrations to production-grade systems. The question isn't whether AI can generate video. It's what happens when generation crosses into genuine intelligence.
Real-time inference means your system doesn't just recognize what's in a frame—it understands what's happening, predicts what comes next, and acts accordingly. Physical reasoning means the AI understands spatial relationships, physics, and causality in ways that go beyond pattern matching.
This isn't about generating a cool video clip anymore. It's about building systems that understand the physical world well enough to make autonomous decisions within it. That's where the real opportunities—and risks—lie.
The GTM Engineer: How AI Created Tech's Next Big Job Category
GTM engineering didn't exist two years ago. Now it's one of the fastest-growing roles in tech, with independent practitioners building million-dollar businesses. That's not hype. That's what happened when AI tools became powerful enough to automate entire go-to-market workflows.
Kareem Amin, Co-founder and CEO of Clay, is leading this conversation because Clay has been at the forefront of this shift. GTM engineering represents a fundamental rethinking of how companies reach customers. It's not about marketing campaigns or sales plays. It's about building AI-native systems that understand customer behavior, predict buying signals, and execute go-to-market strategies at scale.
The implications are massive. Companies that figured this out early are now reaping the rewards. Those that didn't? They're scrambling to catch up. AI-native GTM isn't a nice-to-have anymore. It's table stakes.
And for founders trying to figure out what it actually means to have a go-to-market plan in an AI-native world? This session is mandatory.
The Pricing Question Nobody Wants to Answer
AI models are becoming commoditized. That's the reality. And when models become commoditized, pricing models break.
This is the question that keeps startup founders up at night: how do you price AI products when the underlying technology is becoming a commodity? The AI Stage is tackling this directly, because pricing strategy is no longer just a business question. It's a technical one.
If your AI model can be replicated by any major cloud provider, what's your competitive advantage? The answer isn't in the model itself. It's in how you deploy it, how you integrate it, and how you deliver value to customers.
Why This Matters Now
TechCrunch Disrupt 2026 isn't just another conference. It's where the builders shaping this next wave get specific. Whether you're rethinking your pricing model, closing the security gaps in your AI stack, or building the go-to-market playbook that doesn't exist yet—this is where the real work happens.
The AI Stage is where you come to stop talking about AI and start building with it.
Register today before the current pricing window closes. Save up to $300 on your ticket. Because if you're serious about what's next for AI, you can't afford to miss this.
Register for TechCrunch Disrupt 2026
Sources: TechCrunch, "Discover what's next for AI, from the SaaS reckoning to the agent security gap, at TechCrunch Disrupt 2026," July 29, 2026.
