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.
This isn’t a philosophical argument about openups 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.
Nader Khalil approaches the discussion from the builder and infrastructure perspective. Before becoming Nvidia’s Director of Developer Tech, where he leads openompany acquired by Nvidia in July 2024
Brev.dev was built around simplifying access to GPU infrastructure across different environments. Nvidia’s developer documentation described its tools as allowing developers to deploy AI software across public cloud, private cloud, and on-premises infrastructure without locking themselves into a single compute source.
Sydney Sykes, meanwhile, brings the venture ecosystem into the discussion as Nvidia’s Global Head of VC Partnerships.
Together, that creates room to examine the same decision from different directions: what developers need to build and what companies need to become investable, scalable businesses.
Sit front and center at one of the biggest debates in today’s world of AI. Register for your ticket before the savings of up to $200 end on September 25 at 11:59 p.m. PT.
Your model isn’t your moat — until it is
There is another uncomfortable question behind the open-versus-closed debate: Where does your competitive advantage actually live?
If competitors can access the same proprietary API, differentiation needs to come from somewhere else — proprietary data, workflow, distribution, customer relationships, product experience, or specialized technology. But choosing an open model doesn’t automatically give you a moat either.
You gain flexibility and potentially greater control, but you also take on decisions around deployment, optimization, and infrastructure. And the economics can change depending on the workload and scale.
Nvidia is investing heavily in that open ecosystem. Its Nemotron 3 Super, launched in March, is an open 120-billion-parameter model designed for agentic workloads, and companies are already combining it with proprietary models rather than treating the two approaches as mutually exclusive. That hybrid reality may ultimately be the most interesting part of the debate.
Register now to join the conversation on the Builders Stage in October. Savings of up to $200 end on September 25 at 11:59 p.m. PT.
What should you build on? Decide at TechCrunch Disrupt 2026
That’s what makes this session useful beyond an audience of AI engineers.
If you’re a founder, the choice can shape your margins, fundraising story, and product roadmap. If you’re an investor, understanding where value sits in the stack can help distinguish genuine defensibility from a thin product layer sitting on somebody else’s model. If you’re a line-of-business lead, it affects procurement, security, data control, infrastructure, and the freedom to change providers later. And for developers and students, it’s a chance to understand where the technical decisions being made today connect directly with the business models being built around them.
No one needs another abstract argument over whether open or proprietary AI is philosophically better. What builders need is a clearer understanding of the trade-offs.
Join Nader Khalil and Sydney Sykes on the Builders Stage at TechCrunch Disrupt 2026 and decide which side belongs in your AI strategy. Register now to save up to $200 before prices go up on September 25 at 11:59 p.m. PT.
