The news shook the industry: Anthropic and Samsung are advancing in the development of a custom AI chip. Behind the headline there's a strategic move that could change prices, availability, and the rules of the game for developers and companies. Here we analyze what the attempt to reduce Nvidia dependence implies.

For years, training and running AI models meant, in practice, buying Nvidia hardware. That concentration generated bottlenecks, high prices, and waiting lists. The agreement between Anthropic and Samsung is a clear sign that the big AI companies are looking for alternatives, and that has consequences that reach the individual developer.

What was announced

As reported by TechCrunch in early July, Anthropic would be in negotiations with Samsung to develop a custom chip oriented to AI workloads. The sector's declared objective is to reduce dependence on a single GPU provider and gain control over costs and supply. Media like The Verge have been covering this trend of AI companies toward their own silicon.

Why AI companies want their own chip

• Cost: AI hardware is extremely expensive; designing their own chips optimized for their models reduces the expense at scale.

• Supply: depending on a single provider generates waiting lists and operational risk.

• Optimization: a chip designed for a specific architecture can be more efficient than a general-purpose one.

• Bargaining power: having an alternative improves the position against any provider.

It's not the first sign in this direction. Other giants are also advancing in their own silicon, as the case of Arm and its first own chip for data centers shows.

What changes for developers

The direct impact is not immediate, but the direction is favorable. More competition in hardware tends to lower the cost of computing, and cheaper computing translates into more accessible APIs and models that are more economical to run. For the LATAM developer, who usually operates with tight budgets, this can expand access to powerful models.

The relevance of open source models and alternatives also grows: when hardware stops being the only bottleneck, more options flourish. We see it with models like the ones this article analyzes about Kimi K2 and open source models for coding.

What to watch from now on

How to prepare as a developer

The strategic lesson is clear: AI infrastructure is diversifying, and those who understand how the models work —not just how to call them— have an advantage. Training in the fundamentals lets you adapt to any change of hardware or provider.

• The Introduction to Artificial Intelligence Course, to understand the complete landscape of the AI ecosystem.

• The AI Engineering Course, to work at a technical level with models and infrastructure.

• The AI Agents Course, if you want to build applications that leverage these models.

Stay up to date: follow the evolution of AI hardware closely, because it defines the cost and access to the technology you use.

Frequently asked questions

Why does Anthropic want its own chip instead of using Nvidia?

To reduce costs at scale, ensure the supply of computing, optimize the hardware for its own models, and gain bargaining power. Depending on a single provider generates waiting lists, high prices, and operational risk, and a custom chip diversifies that dependence.

Is this going to lower the price of AI APIs?

Not immediately, but the direction is favorable. More competition in hardware usually reduces the cost of computing over time, and that can translate into more economical APIs and models, something especially relevant for developers and companies with tight budgets.

What does this mean for developers in LATAM?

Potentially, more access to powerful models at a lower cost. A more diverse and competitive hardware ecosystem tends to democratize access to AI computing, which benefits those who operate with limited resources, as usually happens in the region.

Does Nvidia stop being relevant?

No. Nvidia remains dominant and its technology is the current standard. What changes is that serious alternatives start to appear, which reduces dependence on a single provider and opens the door to more competition and innovation in AI hardware.