What to expect from Nvidia GTC 2026: Jensen Huang sets the next phase of artificial intelligence
Nvidia is gearing up for another edition of GTC, the annual event that has cemented itself as a must-attend for anyone even remotely interested in the future of artificial intelligence. The conference takes place March 16 through 19 at the SAP Center in San Jose, California, bringing together nearly 20,000 in-person attendees who are eagerly waiting for the most anticipated moment on the tech calendar — the keynote from Jensen Huang, CEO of what is now the most valuable company in the world. And what is at stake this time goes far beyond new chips and jaw-dropping benchmarks. We are talking about billions in investments, strategic partnerships with major players, a massive bet on open models, and even self-driving demonstrations through the streets of San Francisco.
GTC 2026 promises to chart the course for the next phase of artificial intelligence, and the signals that leaked ahead of the official presentation show that Jensen Huang wants to position Nvidia not just as a hardware supplier, but as a central piece in virtually every layer of the AI ecosystem. From data center infrastructure to software frameworks and training platforms, the company has been expanding its footprint in a way no competitor has managed to replicate so far. The move is bold and calculated at the same time — and the GTC stage is where it all becomes official.
Over the past two years, the event has gone from being just a gathering of Nvidia loyalists to something more like the Super Bowl of the AI industry. There is even a three-hour pre-show this year featuring the CEOs of several Nvidia partner companies. The energy that takes over the SAP Center when Jensen Huang walks onstage is comparable to a college basketball game in the middle of March Madness — with cheers and celebrations at every new announcement 🏀.
AI as a 5-layer cake: Jensen Huang’s vision
Days before the official start of GTC 2026, Nvidia published a blog post written by Jensen Huang himself titled AI is a 5 Layer Cake. In the post, Huang argues that artificial intelligence depends on five fundamental pillars: energy, chips, infrastructure, models, and applications. According to him, all of these layers need to scale together to enable the massive buildout of AI across every sector of the economy.
The most interesting part of this analogy is that Nvidia positions itself strategically at the center of the stack. The company makes the chips that power the models, develops the software infrastructure that connects everything, and maintains partnerships with companies operating at the edges — both in energy generation and in end-user application development. It is as if Nvidia is both the filling and the frosting of this cake, linking most of the layers together.
This integrated ecosystem vision is key to understanding why Nvidia does not see itself as just a GPU manufacturer. Jensen Huang is building a complete platform, and the 5-layer cake concept works as a strategic map guiding every decision the company makes. For anyone following the tech industry, it is a useful framework for understanding how the different components of the AI revolution fit together 🎂.
Billion-dollar investments and heavyweight partnerships
This year’s GTC also brought important revelations about the massive investments Nvidia is making to fuel the next wave of artificial intelligence growth. Since last year, the company has invested in dozens of AI companies, deploying billions of dollars across the entire ecosystem. In the week leading up to the event, Nvidia announced a $2 billion investment in Nebius, a cloud company focused on AI, reinforcing its presence in the infrastructure segment.
Another highlight was the investment in the new startup from Mira Murati, former CTO of OpenAI. The company, called Thinking Machines, received Nvidia backing with more than 1 GW in chips, signaling that Jensen Huang’s company is willing to bet on new talent that could reshape the AI landscape in the coming years. Murati is one of the most respected figures in the field, and having Nvidia’s support right at the start of her new venture says a lot about the confidence the market places in her.
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These partnerships are not merely symbolic — they involve significant investments, technology co-development, and deep integrations that create a mutually beneficial relationship between the parties. For partner companies, having the Nvidia stamp means access to the most powerful hardware on the market and a mature software ecosystem. For Nvidia, every partnership is another distribution channel and another use case that validates its platform.
The big bet on open artificial intelligence models
If there is one topic dominating the conversations behind the scenes at GTC 2026, it is Nvidia’s strategic decision to go all in on open models of artificial intelligence. According to reports from Wired, the company is investing up to $26 billion in open source models, a move that completely rewrites the rules of the game in the sector.
Beyond direct investments, there are rumors that Nvidia will unveil something called NemoClaw during GTC, described as an open source AI agent platform aimed at enterprises. If confirmed, NemoClaw would represent yet another step by Nvidia toward offering complete software tools — not just hardware — to the enterprise market.
Jensen Huang has made it clear on multiple occasions that he believes in the power of openness as an engine for innovation. The logic is fairly straightforward: the more developers, researchers, and companies have access to AI models without proprietary restrictions, the more those people will need computing power to run, train, and fine-tune those models. And guess who supplies that computing power? Exactly. The open models strategy directly feeds Nvidia’s core business, creating a virtuous cycle that benefits the entire chain.
In practice, this means Nvidia is investing heavily in partnerships with labs and organizations developing open source models, offering specific optimizations so these models run at peak performance on its GPUs. The CUDA ecosystem, which was already a brutal competitive advantage, gains a whole new layer of relevance when combined with models that anyone can download, modify, and deploy. Jensen Huang understood that the future of artificial intelligence will not be dominated by a single closed proprietary model — it will be built by thousands of specialized models, fine-tuned for specific tasks, running on infrastructure that demands cutting-edge hardware 🧠.
It is worth pointing out that Nvidia’s strategy with open models is not about competing directly with the major frontier AI labs like OpenAI or Anthropic. The goal is to keep developers building within the Nvidia software ecosystem, which naturally drives demand for more chips. It is a smart play that combines purpose with profit in a way that makes sense for everyone involved.
Self-driving: Jensen Huang cruises through San Francisco in a Mercedes
Another point that grabbed a lot of attention ahead of GTC was the autonomous vehicle demonstration on the streets of San Francisco. Nvidia released a video showing Jensen Huang taking a 2-and-a-half-hour ride through the city in a Mercedes equipped with the Alpamayo self-driving system, developed on Nvidia’s platform.
The demonstration ties into the larger narrative of the event. Jensen Huang argues that the same artificial intelligence infrastructure that trains chatbots and generates images can be applied to solve complex real-world problems like urban mobility and industrial robotics. Nvidia has been steadily expanding its presence in the autonomous vehicle space, where its chips and software platforms are increasingly used by automakers building self-driving systems.
This is the kind of long-term vision that sets Nvidia apart from companies that are just riding the hype wave. The practical application of AI in the physical world — in cars, factories, and robots — is perhaps the most ambitious chapter of the story Jensen Huang is writing 🚗.
The impact of AI beyond GTC: what else is happening in the industry
While Nvidia dominates the spotlight with GTC, the world of artificial intelligence continues to move at breakneck speed on several other fronts. Some recent stories show how the technology is reshaping entire industries, creating opportunities, and raising legitimate concerns.
The end of programming as we know it
A feature story in the New York Times Magazine painted a detailed picture of how AI coding tools are transforming the software developer profession. The author, Clive Thompson, interviewed more than 70 developers at companies like Google, Amazon, Microsoft, and smaller startups. The consensus is that tools like ChatGPT and Claude are making programmers significantly more productive, but they are also changing the role of these professionals — from writing code line by line to supervising, reviewing, and correcting AI-generated code.
The bigger concern revolves around entry-level positions, which have traditionally served as a gateway into the industry. If AI can generate basic code competently, what happens to junior programmers? While many experts believe human engineers will remain essential for systems architecture, problem-solving, and oversight, the article argues that AI is already redefining what it means to be a programmer.
Atlassian lays off 10% of workforce to invest in AI
Atlassian, the cloud software company, announced it is cutting approximately 1,600 employees, around 10% of its workforce. CEO Mike Cannon-Brookes explained that the layoffs are part of a restructuring to redirect resources toward AI development and enterprise sales. The company’s stock has dropped more than half this year — and roughly 84% from its 2021 peak — with investors worried about competition from generative AI tools like Claude and other coding assistants.
Atlassian has been investing in its own AI products, including the Rovo platform, which now has around 5 million monthly users. But Cannon-Brookes acknowledged that AI is reshaping the types of skills and roles the company needs. This move reflects a broader trend in the tech industry, where companies are cutting staff while ramping up investments in artificial intelligence.
AI money is already influencing American elections
According to a Washington Post report, artificial intelligence companies and investors are pouring large sums of money into the 2026 U.S. midterm elections. Groups backed by companies like OpenAI and Anthropic have already directed more than $185 million toward races across the country, with striking results: in recent primaries in Texas and North Carolina, all 20 candidates who received AI-related funding won their races, with just one exception.
Much of the spending is directed at candidates who could influence AI regulation, reflecting a growing political battle over whether regulation should happen at the federal or state level. The surge in spending also comes at a time of growing public skepticism about AI, particularly regarding the expansion of energy-hungry data centers.
McKinsey scrambles to fix AI system flaws after hacker breach
The Financial Times reported that hackers using an AI agent managed to access millions of internal messages and details from Lilli, the internal AI platform at consulting giant McKinsey. Cybersecurity startup CodeWall said its automated agent gained read and write access to the system in just two hours, uncovering 46.5 million chat messages, tens of thousands of user accounts, and hundreds of thousands of AI assistants and workspaces.
McKinsey said no client data was compromised and the vulnerability was quickly patched. The incident, however, highlights the growing security risks as companies deeply integrate AI tools into their operations — and shows how AI agents can be used both to build and to attack corporate systems.
80% of doctors now use AI professionally
Here is a number that really stands out: according to a recent survey from the American Medical Association, 80% of physicians in the United States now use AI professionally, double the figure from 2023. The average number of use cases per physician rose from 1.1 in 2023 to 2.3 in 2026, with the most common applications centered on summarizing medical research and documenting clinical encounters.
Some additional highlights from the study:
- Physicians are more confident in AI: in 2026, more than three-quarters of doctors believe AI improves their ability to care for patients, up from 65% in 2023. The biggest expected benefits are in diagnostic accuracy and workflow efficiency.
- Cautious optimism: 40% of physicians have balanced attitudes, equally excited and concerned about AI, citing patient privacy and the integrity of the doctor-patient relationship as their top concerns.
- Worry about skill loss: 70% of physicians see AI as a tool to automate tasks that contribute to burnout. However, 88% are concerned about the potential loss of skills, especially among professionals with 10 years or less of practice.
What GTC 2026 signals for the future of artificial intelligence
Looking at the full picture of what was presented and discussed at GTC 2026, it is clear that Jensen Huang is playing the long game. Nvidia is not just reacting to the artificial intelligence boom — it is actively shaping the direction this technology will take in the years ahead. The bet on open models is perhaps the strongest signal of this strategy, because it indicates the company believes in a decentralized future for AI, where the value lies in infrastructure and tools, not in exclusive control over models.
This has deep implications for the entire tech industry, from startups just getting off the ground to giants like Google, Microsoft, and Meta, which need to recalibrate their own strategies in response to Nvidia’s moves. The 5-layer cake concept, the billion-dollar partnership investments, the NemoClaw platform for AI agents, and the self-driving demonstrations form a cohesive package that reinforces one central message: Nvidia wants to be indispensable at every stage of the AI value chain.
For anyone following the artificial intelligence market, GTC serves as a compass pointing where the wind is blowing. And this year, the wind is clearly blowing toward more openness, more accessibility, and deeper integration between hardware and software. Open models are no longer a fringe experiment — they are a core part of the strategy at the most valuable company on the planet. Jensen Huang has managed to turn what could have been just another tech conference into an event that sets global trends, and this year’s announcements confirm that Nvidia intends to hold that leading role for a long time to come.
The message is clear: if you want to build the future of AI, chances are you will need to go through Nvidia at some point along the way 🚀.
