Nvidia‘s Nader Khalil and Sydney Sykes will lead the session “The Open vs. Closed AI Debate Is Just Getting Started” on the Builders Stage at TechCrunch Disrupt 2026, running October 13-15 in San Francisco. Khalil is Director of Developer Tech, where he leads open source and local AI, and Sykes is Global Head of VC Partnerships. The session frames the open-versus-closed question as a business decision rather than a philosophical argument about open source.

The article sets the context by noting how quickly open models have advanced. 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. Proprietary frontier labs are pushing capabilities forward, and the market question is shifting from whether open models can be useful to where each approach makes commercial sense. Nvidia itself rejects a simple either-or framing; at GTC, CEO Jensen Huang argued the future is “proprietary and open” rather than proprietary versus open.

That framing gets complicated when building a company around it. The article raises practical questions: if two models deliver similar results, does lower cost win? What if one gives more control over data? Does owning more of the stack create defensibility, or just infrastructure to maintain? And if the best model changes every few months, how tightly should a product tie to any one of them? These are the questions the session will unpack.

The speakers approach the topic from different directions. Before joining Nvidia, Khalil co-founded Brev.dev, an AI infrastructure company acquired by Nvidia in July 2024, built around simplifying access to GPU infrastructure across 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 into a single compute source. Sykes brings the venture ecosystem perspective, framing what developers need to build and what companies need to become investable, scalable businesses.

The article also addresses where competitive advantage actually lives. If competitors can access the same proprietary API, differentiation must come from elsewhere — proprietary data, workflow, distribution, customer relationships, product experience, or specialized technology. Choosing an open model doesn’t automatically create a moat either; it brings flexibility and control but also decisions around deployment, optimization, and infrastructure, with economics that shift by 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.

The session is aimed beyond AI engineers. Founders face choices that shape margins, fundraising stories, and product roadmaps; investors can gauge where value sits in the stack and distinguish genuine defensibility from a thin product layer on top of someone else’s model; line-of-business leads deal with procurement, security, data control, and the freedom to change providers later; developers and students can connect today’s technical decisions to the business models built around them. The article closes by repeating registration details: savings of up to $200 before prices rise on September 25 at 11:59 p.m. PT.