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Open or closed AI? Nvidia’s Nader Khalil and Sydney Sykes take on one of the decisions shaping next-gen startups at TechCrunch Disrupt 2026

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Why This Matters

As open-source AI models close the capability gap with proprietary frontier systems, startups face a pivotal and recurring decision about which approach to build on—one that shapes cost, control, and competitive advantage. This session at TechCrunch Disrupt 2026 signals that the open-vs-closed debate has become a core strategic question for the industry, not just a philosophical one.

Key Takeaways
Worth a Look

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You have an AI product to build. Do you choose a proprietary frontier model and get moving quickly? Build on an open model and gain greater control? Fine-tune your own version? Run locally? Use multiple models? Change strategy six months from now when the economics and capabilities shift again?

There may not be one right answer. But for founders, choosing badly can affect almost everything that follows — cost, infrastructure, margins, differentiation, speed, and control.

That decision is at the center of the session “The Open vs. Closed AI Debate Is Just Getting Started,” coming to the Builders Stage at TechCrunch Disrupt 2026, happening on October 13-15 in San Francisco. Led by Nvidia‘s Nader Khalil, Director of Developer Tech, and Sydney Sykes, Global Head of VC Partnerships, the session will discuss the trade-offs between open and proprietary AI and whether either approach can provide a lasting competitive advantage.

Image Credits:TechCrunch

This isn’t a philosophical argument about open source. It’s a business decision being made right now inside startups of every size. Dive deep into this AI debate with 10,000+ tech leaders at Disrupt by getting your pass. Register now and save up to $200 before prices go up on September 25 at 11:59 p.m. PT.

The AI gap is closing — the decision isn’t getting easier

Open models have advanced quickly. Nvidia said in July that 145 papers accepted at ICML 2026 cited its Nemotron open models and datasets, alongside research using other Nvidia open model families across robotics, autonomous vehicles, and biomedical research.

At the same time, proprietary frontier labs continue pushing model capabilities forward. The result is a market where the question is increasingly less about whether open models can be useful and more about where each approach makes commercial sense.

Even Nvidia rejects a simple either-or framing. At GTC earlier this year, CEO Jensen Huang argued that the future is not proprietary versus open, but proprietary and open. That sounds straightforward until you have to build a company around the decision.

If two models can deliver similar results, does lower cost win? What if one gives you more control over your data? Does owning more of the stack create defensibility, or just infrastructure you now need to maintain? And if the best model changes every few months, how tightly should your product be tied to any one of them? These are the questions Khalil and Sykes will unpack at TechCrunch Disrupt 2026. Don’t miss it — register for your ticket now to get $200 savings before September 25 at 11:59 p.m. PT.

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