The AI Stage Gets Its Headliners

San Francisco is about to become the epicenter of the AI world again. From October 13–15, TechCrunch Disrupt 2026 returns to Moscone Center, and this year’s AI Stage — presented by Google for Startups — just locked in two of the biggest names in the game.

Anthropic and OpenAI are both sending senior executives to the stage. That’s not just a feather in the conference’s cap. It signals something bigger: the conversation has shifted from demo-day theatrics to the messy, unglamorous work of actually deploying AI inside real companies.

And honestly? It’s about time.

What Anthropic Sees After the Enterprise Deal Closes

Most enterprise AI panels are all about the pre-sales pitch. This one starts where the others end — after the contract is signed and Claude is running in production.

Cat de Jong, Head of Applied AI at Anthropic, works directly with the enterprises putting Claude to work across critical workflows. She sees patterns that never make it into press releases. Where deployments take off immediately. Where they stall. What separates organizations extracting real value from the ones still running pilots eighteen months in.

It’s a rare behind-the-curtain look at applied AI inside the world’s most closely watched AI company. If you’re building on top of frontier models, this session could save you from learning the hard way.

OpenAI’s Head of Productivity on the GTM Engineer Revolution

Here’s a wild fact: two years ago, the phrase “GTM engineering” didn’t exist. Today it’s one of the fastest-growing roles in tech, with independent practitioners building million-dollar businesses around it.

Tara Seshan, Head of Productivity at OpenAI, will trace how AI collapsed the traditional go-to-market stack and created an entirely new discipline in its place. The session promises to walk away with a practical understanding of what AI-native GTM actually looks like — and how it’s changing the way companies grow.

For founders still running outbound playbooks written in 2019, this one’s essential.

Security Sessions That Cut Through the Hype

AI is making autonomous decisions inside the most sensitive enterprise systems on Earth — at a speed and scale traditional security frameworks were never designed to handle. Two sessions on the AI Stage tackle this head-on.

The Agent Security Problem Nobody Is Talking About

Agentic AI is powerful, but it was never built to be secure. Ric Smith, President of Product & Technology at Okta, will get technical about what agent security actually requires at the infrastructure level. He’ll also explain why application-level permission models are fundamentally flawed — a claim that should ruffle some feathers in the boardroom.

Securing the AI Enterprise in 2026

This infrastructure-level session brings together Chet Kapoor from AWS, Katie Moussouris of Luta Security, and Wendy Nather from 1Password. Expect a candid breakdown of observability, governance, and the architectural principles that separate deployments enterprises can trust from ones they can’t afford to touch.

If your CTO has been losing sleep over AI sprawl, this is the session to send them to.

More Sessions Worth Your Time

The AI Stage roster goes deeper than the headliners. Here’s what else is on deck:

• The Video Intelligence Race: Dean Leitersdorf from Decart and Amit Jain from Luma AI discuss real-time inference and physical reasoning — what happens when generation crosses into genuine intelligence.
• Rewriting SaaS: Arvind Jain (Glean), Barr Moses (Monte Carlo), Cathy Gao (Sapphire Ventures), and Aaron Jacobson (NEA) debate whether the SaaS playbook is dead or just evolving. Pricing AI products sustainably when models are commoditizing is the core question.
• The GTM Engineer: Kareem Amin, Co-founder and CEO of Clay, goes deeper into how AI created tech’s next big job category — and what it means for growth teams.
• Enterprise assumptions: Arsalan Tavakoli from Databricks challenges what you think you know about enterprise AI security requirements in 2026.

Pricing, Tickets, and the Bottom Line

Disrupt 2026 isn’t just about the AI Stage. You’ll be surrounded by 10,000+ startup, tech, and VC leaders, with access to every other stage, Startup Battlefield, the exhibition floor, and enough networking opportunities to fill your calendar for months.

One practical note: the current pricing window is closing, and you can save up to $200 if you grab your ticket before it’s gone. If you’re planning to attend, don’t sleep on it.

The AI Stage at TechCrunch Disrupt 2026 is shaping up to be where the builders get specific. Whether you’re rethinking your pricing model, closing security gaps in your AI stack, or writing a go-to-market playbook that doesn’t exist yet — this is the room to be in.

The quiet failure mode nobody’s naming

Ask a security team a simple question: which agents can reach which systems? Watch the silence. Ask which agent triggered which downstream action three hops ago. More silence.

That silence is the real enterprise AI risk. Not a rogue agent going off-script — but the tangled web of connections between agents that nobody can see clearly enough to govern. Complexity is the insidious shadow lurking inside enterprises right now, and it needs a light shone on it.

The problem: enterprises don’t deploy a single agent and watch it run. They deploy fleets. Each one calling APIs, calling other agents, reaching into applications that were never built with a machine decision-maker in mind. That’s the failure mode that should keep you up at night: a windy, complicated system nobody can fully map.

Why complexity compounds faster than headcount

Add a second agent to a system, and you’ve added one connection. Add a tenth, and you haven’t added ten connections — you’ve potentially added dozens. Because now any agent might call any other, and each of those calls can trigger a call somewhere else.

Complexity doesn’t creep up with agent headcount. It compounds with the number of paths between agents. And nobody’s job is to draw that graph.

A support ticket that used to touch one system might now pass through four agents before a human ever lays eyes on it. Every one of those handoffs is a decision point nobody approved.

Where it actually breaks down

Permissions creep comes first. Somebody builds an agent to summarize support tickets, grants it broad API access because scoping it properly would’ve taken another sprint, and forgets about it. Six months later, that same agent has a path into the payments system. Nobody remembers signing off on that. Nobody did.

Ownership thins out the further the chain runs. Five agents touch one workflow, something breaks at step four, and now you’re asking who’s responsible for a link nobody was ever assigned to own. The org chart stopped at “deploy the agent” and never got to “name the human who answers for it.”

This is a story about governance infrastructure that hasn’t caught up with how agents actually behave: interconnected, cascading, multiplying faster than the processes built to track them.

The checklist trap

The instinct is to treat this like a checklist. Approve the agent. Log the agent. Move on. That’s the wrong instinct.

A checklist checks a single point in time. Complexity runs across a chain. You can’t govern a chain with a stack of one-time approvals any more than you can call a diet successful because you had a vegetable once.

What actually fixes the cluster

Fixing this starts with identity. Every agent needs to exist as its own entity — not a shadow permission borrowed from whoever deployed it. Its own name in the register. Its own scoped authority. A named human sponsor who answers for what it does.

That part is necessary. But it is nowhere near sufficient.

The harder piece is oversight that holds across the entire chain, not just at each individual link. You need to see what an agent did, what it set off downstream, and where that trail ends — in real time, not in a report someone pulls together once a quarter.

Get agent-level identity right and stop there, and you end up with a filing cabinet full of perfectly documented agents operating inside a system nobody can actually explain.

Oversight isn’t enforcement

And oversight by itself only tells you what already happened. Watching a chain isn’t the same as controlling it.

Enforcement is the piece most programs skip: the ability to stop an out-of-policy call before it executes, not just log it for someone to find in a review three weeks later. A dashboard that shows you an agent breached its scope five minutes ago is a monitoring tool. A system that stops the breach from happening in the first place is governance.

Enterprises serious about agent accountability need both. Most have only built the first.

Scale and accountability can grow together

We’re all running at blazing speed to ensure we’re not the ones left behind in the race we’ve found ourselves in. And we’re all too aware that there’s a cost to slowing down.

Every enterprise serious about agentic AI hits the complexity wall eventually. The ones that get past it are the ones who built enough visibility and accountability — so their fleet can keep growing without anyone losing the ability to answer one question: what is this system doing right now, and who’s responsible for it?

But don’t miss the point. Complexity isn’t a reason to pump the brakes. The enterprises getting this right aren’t slowing down. They’re building toward Human-Agent Harmony, where scale and accountability grow together instead of trading off against each other.

The real risk was never a single agent doing exactly what it was built to do. It’s a hundred of them doing exactly that, all at once, interacting in combinations nobody designed for. That kind of multiplication is what keeps enterprise AI stuck running pilots forever instead of running production.

Solve for complexity, and autonomy stops being the villain. It starts being the whole point.

Rory Blundell is CEO at Gravitee. Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. For more information, contact sales@venturebeat.com.

The Uncomfortable Gift of Failure

What if the most valuable thing you can give a child isn’t praise, protection, or the perfect educational toy — but the experience of failing? It sounds counterintuitive. Most parents spend their days trying to shield kids from frustration, disappointment, and the sting of getting something wrong.

Yet by swooping in at the first sign of struggle, we might be robbing them of something crucial. The very skills they’ll need to thrive in a world reshaped by artificial intelligence — curiosity, judgment, perseverance — are often forged in the messy moments when things don’t work out.

This idea took center stage in a recent conversation I had with Rachele Harmuth, Chief Product Officer at CrunchLabs, the education company founded by former NASA engineer and YouTube star Mark Rober. We talked about how to prepare kids for a future that’s impossible to predict.

AI Changes What Kids Need to Learn

For generations, education has been about accumulating knowledge. Memorize the dates, learn the formulas, master the coding language. The assumption was that these skills would translate into a career. That assumption is crumbling.

AI can now recall facts, execute processes, and even write code faster than any human. So what’s left for our children to learn? The answer, Harmuth argues, isn’t a new set of technical skills. It’s a different way of approaching problems.

By the time today’s eight-year-olds enter the workforce, the technology around them will be almost unrecognizable. Trying to guess which specific skills will be valuable is a fool’s errand. A better investment is teaching them how to think.

Think Like an Engineer

CrunchLabs boils this down to a simple mantra: “Think like an engineer.” This doesn’t mean every kid should build bridges or work for NASA. It’s about a mindset — a process for tackling the unknown.

• Define what you’re trying to accomplish.
• Brainstorm possible solutions.
• Test one.
• Figure out what went wrong.
• Adjust and try again.

This process is the foundation of curiosity and creative confidence. And at its heart lies something that makes most adults deeply uncomfortable: embracing failure.

Making Failure Less Threatening

Why is failure so scary for kids? Harmuth made a brilliant observation during our talk. Kids already understand the value of failing when they play video games. Nobody picks up a controller and expects to beat a hard level on the first try. You die, you restart, you learn the pattern, you try again. A failed attempt isn’t a judgment on your character. It’s just how you learn to play.

But step into the physical world, and the reaction changes. A drawing doesn’t look right, and a child declares they’re bad at art. A bike falls over, and suddenly they’re not athletic. A build fails, and the conclusion is “I can’t do this.” The problem isn’t the failure. It’s the meaning we attach to it.

Mark Rober’s videos are a masterclass in counteracting this. He shows off incredible inventions, but he also shows the failed prototypes and dead ends. The willingness to display imperfection is part of what makes engineering feel accessible to kids. It normalizes the struggle.

CrunchLabs applies this philosophy across its products. The Build Box introduces younger kids to hands-on physics. The Hack Pack lets older kids dive into robotics. Even the CrunchLabs Mysteries books turn reading into a hands-on STEM adventure where kids must solve puzzles and build devices to progress. The formats differ, but the lesson is consistent: try, fail, learn, adjust, repeat.

Resilience Is a Teachable Skill

My favorite part of the conversation was Harmuth’s take on resilience. We often treat it as an innate trait — you’re either born with grit or you’re not. Harmuth disagrees. She sees resilience as a muscle, built from specific, learnable skills: problem-solving, perseverance, and adaptability.

The good news? You don’t need to buy a fancy STEM kit to build this muscle. A child learning a skateboard trick, struggling with a piano piece, or trying to build a backyard fort is doing the same mental heavy lifting as a kid working on an engineering project. Something doesn’t work. They have to figure out why, make an adjustment, and decide what to try next.

This habit becomes even more vital as AI gets better at generating answers. The ability to recall information will be less valuable than the ability to ask the right questions, evaluate an answer’s logic, and figure out what to do when there’s no obvious solution.

Skills for a Future We Can’t Predict

Let’s be honest: none of us know which careers will thrive in 2040. Many jobs today’s kids will have don’t exist yet. Two decades ago, “YouTube creator” wasn’t a plausible career aspiration. Today, it’s a dream job for millions.

Preparing a child for one specific version of the future is a losing bet. Preparing them with portable skills — curiosity, judgment, problem-solving, perseverance — is a much safer wager. These traits serve an engineer, an artist, a teacher, or someone in a profession we haven’t even named yet.

Years ago, I bought a CrunchLabs Build Box for my granddaughter. I remember the frustration when a project didn’t come together perfectly on the first try. Looking back, I suspect those failed attempts were at least as valuable as the finished product. Getting the answer right was only part of the experience. Learning how to get there mattered more.

Parents naturally want to fix things. We see frustration and want to make it disappear. Sometimes that’s exactly what a child needs. But other times, giving them the space to struggle for a little longer is the more valuable response. It’s how they learn that they can handle it.

We can’t prepare our children for every problem they’ll face. But we can help them become better at working through problems. Even if they never become engineers, learning to think like one might be the most useful gift we give them.