The next phase of AI adoption hinges less on model breakthroughs and more on who controls the infrastructure behind them. Anthropic and OpenAI build the leading AI models. Nvidia sets the pace for the world’s AI hardware.

Wall Street analysts have raised expectations as Anthropic and OpenAI move toward public offerings and Nvidia climbs further beyond a $5 trillion market cap. As Forbes contributor Drew Bernstein notes, access to the computing power needed to build and run advanced AI systems is now “shaping how Washington, Wall Street and the global tech industry think about who controls the next era of compute.”

Investors are watching three leaders whose decisions increasingly shape the AI economy: Dario Amodei at Anthropic, Sam Altman at OpenAI and Jensen Huang at Nvidia. Their positions on computing power, supply chains and global partnerships often diverge. Bernstein points to the public clash between Amodei and Huang over chip export policy to China — each calling the other’s position “crazy” or “stupid.” Altman, meanwhile, has staked out a middle ground by supporting export controls while pushing for the AI spending needed to keep OpenAI ahead.

It’s the kind of strategic tension Forbes’ expert contributors say will shape the next phase of the economy, as rivalries and overlapping dependencies determine who leads from here.

Anthropic’s Strategy In An AI Infrastructure Crunch

Surging demand for Anthropic’s enterprise AI tools is pushing its valuation into the high hundreds of billions of dollars, making a potential IPO — perhaps as soon as October — increasingly likely, Forbes markets contributor Peter Cohan writes.

Under CEO Amodei, the company has positioned Claude as a stability‑ and safety‑focused system, an approach Cohan says “is grounded in the company’s rapid enterprise adoption,” helping Anthropic’s valuation and increasing pressure to prove its growth is durable.

Anthropic’s financial disclosures show annualized revenue at $1.4 billion, with more than 500 customers spending at least $1 million a year, Forbes contributor Jon Markman reports. That growth helped fuel a $30 billion Series G round, the second-largest private raise in tech behind only OpenAI's $40 billion last year.

"If you're trying to figure out where the AI money is actually going, this is a good place to start," Markman writes.

Anthropic's potential public listing is shaping up to be one of the most important AI offerings of the decade, Cohan writes, forcing investors to grapple with the economics of frontier‑scale model development.

OpenAI’s Lead Comes With New Pressures

OpenAI remains the company against which many AI competitors are measured. Under Altman, the company has pursued a two‑track approach: releasing new GPT versions quickly and weaving its technology deeply into Microsoft’s cloud.

That Microsoft partnership gives OpenAI privileged access to Azure’s large AI data centers, tying its growth to Microsoft’s spending plans and regulatory exposure. Markman notes that Microsoft’s move to end Claude Code licenses came after internal usage costs “ran past the annual AI budget months ahead of schedule,” a reminder that even top‑tier models face economic constraints.

OpenAI is running into the same structural pressures as Anthropic. Access to high‑performance hardware remains uneven, training costs continue to rise and governments are imposing stricter rules on where advanced chips can be deployed. Analysts expect OpenAI’s next models to demand significantly more computing power, putting the company into the same bottleneck as its rivals. Those pressures will also shape how investors evaluate a potential OpenAI IPO, which would give the market its first detailed look at the company’s own hardware costs.

Huang’s Strategy Puts Nvidia At The Center Of AI Infrastructure

If Amodei and Altman represent the demand side of AI, Nvidia's Huang represents the supply side, and analysts have recalibrated expectations around Nvidia’s dominance in the global chip environment. Huang has sharply criticized U.S. export controls, arguing they “gave Chinese companies the spirit, the energy, and the government support to accelerate their development,” a tension that shapes Nvidia’s global strategy, Bernstein writes.

Export rules have changed where Nvidia can sell its most advanced chips, but they’ve also triggered a rush of domestic buying. Cloud providers, government‑backed AI programs and large enterprises are scrambling to secure capacity ahead of any new restrictions. Nvidia has accelerated the pace of new chip releases and tightened joint development efforts with AI labs and cloud providers, reinforcing its position at the center of the AI hardware stack. As Cohan points out, Nvidia is one of the clearest market signals for the entire AI sector.

Beyond Anthropic, OpenAI And Nvidia

While Anthropic, OpenAI and Nvidia dominate, investors are increasingly watching other companies that play critical roles in the AI economy.

Google DeepMind is advancing its Gemini AI roadmap, positioning Google as both a rival to OpenAI and Anthropic and a major provider of the computing infrastructure needed to run AI systems. Meta continues to release more powerful open-source AI models, increasing competitive pressure on companies that charge for access to their technology.

AMD is gaining traction with its MI300 AI accelerators, which compete with Nvidia's chips for training and running large AI models. TSMC remains the critical manufacturer behind many of the industry's most advanced processors, while Broadcom provides networking technology that helps connect increasingly large AI computing clusters.

As AI adoption grows, control of manufacturing capacity, networking infrastructure and cloud computing resources may become as important as building the models themselves.

The Chip War Is Pulling AI Companies And Chipmakers Closer Together

The companies building advanced AI systems are becoming increasingly dependent on the chipmakers that supply the computing power needed to run them. Bernstein captures this dynamic in the clash between Amodei and Huang over export controls that Amodei says protect U.S. leadership and Huang argues accelerates China’s rise. It's “the most important industrial policy question of the decade,” Bernstein says.

Anthropic’s growth increases demand for Nvidia’s networks of AI chips, while Nvidia’s architecture roadmap shapes how quickly model companies can scale. For example, Markman highlights Anthropic’s move to secure more than 220,000 Nvidia GPUs through SpaceX, underscoring how difficult it has become for AI companies to obtain the computing capacity needed to scale their models.

The relationship is why analysts increasingly treat AI model companies and chipmakers as deeply interconnected businesses. The chip debate isn’t just geopolitical; it's a structural force shaping how these companies operate, invest and grow.

What Markets Are Watching Next

Wall Street analysts are watching a few catalysts that will shape the next phase of the AI economy. Cohan’s valuation analysis and Markman’s reporting on AI infrastructure point to the same conclusion: The winners in AI will be determined not only by better models, but also by access to computing power, distribution and favorable regulation.

Anthropic’s expected IPO filing will give investors their first real look at what it costs to build cutting‑edge AI models. OpenAI’s next big model, and how it chooses to price it, could swing the industry. And the best real‑time indicator of AI demand is how much Microsoft, Amazon, Google and Oracle spend on data center hardware that rises and falls with Nvidia’s latest upgrades.

Regulation is now a core input in valuation models. Export rules, government-backed AI investments and policies governing access to advanced computing power will shape where and how fast the ecosystem grows. Taken together, these forces will determine the next phase of the AI economy.