So far in 2026, Nvidia has already committed more than USD 40 billion in equity investments in artificial intelligence companies. The data, revealed by CNBC on May 9, 2026, marks a turning point: the company that for decades manufactured GPUs for gaming is now one of the largest corporate investors in the global AI ecosystem. The question tech professionals in LATAM should ask themselves is: what does this move say about where the market is heading?

In this article we analyze why Nvidia took this direction, in which sectors and companies it's betting, what it implies for the region's tech ecosystem, and what concrete signals it sends about where the money is moving in AI.

Why Nvidia went from manufacturing chips to investing in AI companies

The short answer is: because GPUs are the foundation, but the AI value chain has many floors on top. Nvidia saw that dominating the hardware was not enough to capture all the value that the AI revolution generates.

The company already executed this logic before: it created CUDA (its software ecosystem for GPUs) so that developers would be locked into its hardware. Now, with the equity investments, it's building dependence at the ecosystem level: if the most important AI companies are financed by Nvidia, they have more incentives to build on Nvidia infrastructure.

According to CNBC, Jensen Huang, CEO of Nvidia, described the strategy as "building the nervous system of the AI economy". It's not rhetoric: it's a deliberate play to control the key nodes of the value chain.

Which sectors Nvidia is betting on

The investments are not concentrated in a single segment. Nvidia is distributing capital across the entire chain:

Infrastructure and compute

Data center companies, cloud compute providers specialized in AI, and startups of alternative hardware (chips for inference, not just training). The logic: control where the models run, not just how they are built.

Foundational models

Nvidia invested in startups that develop their own LLMs and vision models, including companies in Europe and Asia that could become regional alternatives to OpenAI or Anthropic. Diversifying the ecosystem of models also diversifies the demand for GPUs.

Vertical AI applications

Health (imaging diagnosis, genomics), manufacturing (automated visual inspection), robotics, and autonomous vehicles. They are sectors with intensive compute needs and high barriers to entry.

Agents and automation

Startups that build on the autonomous agent platform, which is the next big vector of GPU consumption after model training.

To have context on how other big AI companies are moving their capital and infrastructure in this period, you can read the analysis of how the new AI models are changing the rules of the game for users and companies in CoderLibrary.

What it implies for the LATAM tech ecosystem

The impact in Latin America is indirect but real, and manifests in four dimensions:

Access to computing infrastructure

The companies Nvidia invests in usually expand their compute capacity globally. More GPU computing providers in the cloud means more options and lower costs for AI startups in LATAM, which historically paid premium prices to access infrastructure.

A demand signal for tech profiles

When USD 40 billion goes to AI companies, those companies hire. And increasingly, they hire remote talent in LATAM. The most sought-after profiles coincide exactly with the sectors where Nvidia is betting: AI Engineering, ML Ops, AI Agents, robotics.

New verticals of local application

The sectors where Nvidia concentrates its investments (health, manufacturing, agriculture) have enormous markets in LATAM. Companies and professionals in the region who are ready to apply AI in these verticals will have growing demand.

The "Nvidia network" effect

Companies financed by Nvidia tend to prioritize integrations with the Nvidia ecosystem (CUDA, NIM, TensorRT). Knowing this technology stack will be a differential advantage for developers who want to work with the most advanced companies in the sector.

What concrete signals it sends about where the money is moving in AI

When the world's most valuable company in AI infrastructure moves USD 40 billion in equity investments, it's giving very clear signals:

• Generative AI is no longer the peak: agents are the next cycle. The largest concentration of investments is in companies that build autonomous agents and AI robotics systems.

• The battle for inference will be bigger than that of training. The cost of training models has already dropped a lot. The next big business is scalable and cheap inference. Nvidia is positioning its H200 and B200 chips for this market.

• Verticalization is inevitable. Generic foundational models already exist. The value now is in adapting those models to specific industries with proprietary data and concrete use cases.

• Alternative hardware will grow, but Nvidia will capture part of that market too. The investments in alternative chips (TPUs, specific inference chips) show that Nvidia understands the market won't be a GPU monoculture forever.

According to the analysis of McKinsey on the state of AI in 2025-2026, 72% of organizations have already adopted AI in at least one business function, and AI spending is projected to surpass one trillion dollars globally by 2028. Nvidia is positioning itself to capture a significant portion of that spending.

Recommended Coderhouse courses

To work in the areas where Nvidia's capital is flowing, these are the most directly relevant training programs:

• AI Agents Course: autonomous agents are the sector with the greatest concentration of investments in Nvidia's ecosystem. Learning to build them positions you in the area of greatest projected growth.

• AI Engineering Course: to develop the technical skills needed to build and scale AI systems in production, the profile most in demand by the companies in Nvidia's portfolio.

• AI Automation Career: to master end-to-end the construction of automation solutions with AI, from workflow design to deployment in real enterprise environments.

Frequently asked questions

Why does Nvidia invest in AI companies if it's a chip manufacturer?

Because the goal is to capture value across the entire chain, not just in the hardware. By investing in AI companies, Nvidia creates ecosystem dependence: those companies have more incentives to use Nvidia infrastructure, generating sustained demand for its chips and software platforms like CUDA and NIM.

Is USD 40 billion a lot for corporate investments?

It's extraordinary. For context: the world's largest venture capital fund (SoftBank Vision Fund) has capital commitments of around USD 100 billion total. That a single industrial company commits USD 40 billion in equity investments in less than a year is unprecedented in recent corporate history.

Do these Nvidia investments go to LATAM companies?

As of May 2026, Nvidia's known investments are concentrated in companies from the United States, Europe, and Asia. However, the impact in LATAM is real: more capital in those companies means more demand for remote talent in the region and more access to technologies developed with that capital.

What is CUDA and why does it matter in this context?

CUDA is Nvidia's parallel programming platform that allows its GPUs to be used for scientific and AI computing. Most deep learning frameworks (PyTorch, TensorFlow) are optimized for CUDA. The companies Nvidia invests in build on this ecosystem, which perpetuates its dominant position in the AI compute market.

Is Nvidia's monopoly in AI sustainable?

It's an active debate. AMD, Intel, and startups like Cerebras or Groq are gaining ground in specific niches. However, Nvidia has the advantage of the software ecosystem (CUDA) that took decades to build and that is very hard to replace. The investments in alternative chips show that Nvidia is aware of the risk and is diversifying proactively.