NVIDIA’s Real Moat Isn’t Hardware — It’s 4 Million Developers
Last Updated on January 26, 2026 by Editorial Team
Author(s): JP Caparas
Originally published on Towards AI.
A fact-check of the ‘NVIDIA is dying’ thesis, and what the numbers actually show
I watched Theo’s video “Why NVIDIA is dying” yesterday, and I couldn’t stop thinking about it. The core thesis felt important enough to verify. So I spent a few hours digging through earnings reports, SEC filings, and technical benchmarks to separate signal from noise.

The article discusses the debate surrounding NVIDIA’s market position amidst claims of its decline, exploring the company’s robust financial performance despite losing some market share, the growing importance of inference in AI, and NVIDIA’s substantial advantages due to its extensive ecosystem and developer community built over two decades with CUDA, which competes against emerging players like Groq and Cerebras. The narrative suggests that while NVIDIA faces challenges from these competitors and hyperscalers, it is unlikely to “die” as its foundational technologies and developer support are deeply entrenched.
Read the full blog for free on Medium.
Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.
Published via Towards AI
Towards AI Academy
We Build Enterprise-Grade AI. We'll Teach You to Master It Too.
15 engineers. 100,000+ students. Towards AI Academy teaches what actually survives production.
Start free — no commitment:
→ 6-Day Agentic AI Engineering Email Guide — one practical lesson per day
→ Agents Architecture Cheatsheet — 3 years of architecture decisions in 6 pages
Our courses:
→ AI Engineering Certification — 90+ lessons from project selection to deployed product. The most comprehensive practical LLM course out there.
→ Agent Engineering Course — Hands on with production agent architectures, memory, routing, and eval frameworks — built from real enterprise engagements.
→ AI for Work — Understand, evaluate, and apply AI for complex work tasks.
Note: Article content contains the views of the contributing authors and not Towards AI.