Jensen Huang once again emphasized that NVIDIA no longer wants to remain merely a chip supplier. At GTC, the company introduced an entire ecosystem—a unified platform that includes AI factories, agent-level control, robotics, and a graphics stack.
This serves as a warning to the market: if the leader in computing elevates itself to a higher level, many AI teams will find it increasingly difficult to profit from the thin layer built on someone else’s infrastructure.
This is especially evident in the collaboration with Thinking Machines Lab and Vera Rubin. Now, it’s not about a single device but about full control over the operational layer—both the process and the data.
What does this imply? In the coming years, cost will be determined less by the AI software itself and more by access to clients, data, deployment, and results—those aspects that cannot simply be wiped out with a platform update.
This presents a serious challenge: risks are much broader than those faced by typical startups. When several players control computing power, software, and access rules, individual teams have fewer opportunities for independent experimentation. Smaller teams will find it harder to build a stable business. Specialists are more likely to become temporary links between the platform and clients. This is a pain point of 9 out of 10 for the entire wave of AI teams that hoped to quickly build their own layer on top of someone else’s magic.
What should be done? It’s better to seek opportunities to have your own channel, your own data, client trust, and a work segment that cannot be destroyed with a single update from above. It’s important to create unique value and protect your niche from complete disruption by external interference.
Created with n8n:
https://cutt.ly/n8n
Created with syllaby:
https://cutt.ly/syllaby