Can Nvidia stay king of AI chips? Jensen Huang says next boom is just beginning – Firstpost

Nvidia chief executive Jensen Huang has a message for investors worried that the artificial intelligence frenzy may be slowing: the biggest wave of AI spending has not even arrived yet.

The world’s most valuable semiconductor company on Wednesday forecast stronger-than-expected quarterly revenue, unveiled an $80 billion stock buyback programme and laid out an aggressive roadmap for new AI chips, as Huang argued that Nvidia’s growth story is far from over.

But despite another blockbuster earnings report, Nvidia’s shares slipped 1.6 per cent in extended trading, reflecting growing doubts on Wall Street over whether the company can maintain its overwhelming dominance in the face of rising competition from rivals and even its own customers.

The debate now confronting investors is no longer whether AI is transforming the technology industry. It is whether Nvidia can remain at the centre of that transformation.

Nvidia projected second-quarter revenue of $91 billion, plus or minus 2 per cent, beating analyst expectations of $86.84 billion, according to LSEG data. First-quarter revenue climbed to $81.62 billion, ahead of forecasts of $78.86 billion, while adjusted earnings came in at $1.87 per share versus estimates of $1.76.

Its data centre business — the engine powering the global AI race — generated $75.2 billion in quarterly revenue, comfortably ahead of expectations.

For years, Nvidia’s graphics processing units, or GPUs, have been the backbone of artificial intelligence development. The chips train and run the world’s most advanced AI models inside massive data centres operated by companies such as Microsoft, Amazon and Alphabet.

That dominance helped Nvidia become the defining corporate winner of the AI era, propelling its valuation past the $4 trillion mark and making Huang one of Silicon Valley’s most influential executives.

Yet the very companies that fuelled Nvidia’s rise are now trying to reduce their dependence on it.

Major cloud firms are pouring billions into building custom AI chips tailored to their own systems, while rivals including Advanced Micro Devices and Intel are aggressively targeting the booming AI inference market — the business of running AI models after they have been trained.

Industry analysts increasingly see inference as the next major battleground in artificial intelligence, with lower-cost and more energy-efficient chips likely to gain importance as AI services become mainstream.

Huang, however, argued that the market opportunity ahead is far larger than investors currently appreciate.

Speaking during Nvidia’s earnings call, Huang said spending from AI-focused cloud firms is accelerating rapidly and could soon rival the traditional “hyperscale” giants. He added that Nvidia expects to grow faster than the broader capital expenditure increases by major cloud companies.

“We should be growing faster than hyperscale capex,” Huang said.

The Nvidia chief also pointed to the company’s next-generation “Vera” processors as a major new growth driver. According to Huang, the chips could open access to a fresh $200 billion market opportunity beyond Nvidia’s existing AI accelerator business.

Nvidia expects Vera-related sales to contribute roughly $20 billion in revenue by the end of the current fiscal year. Huang said those estimates are separate from the company’s earlier projection that its flagship Blackwell and Rubin AI systems could generate more than $1 trillion in cumulative sales between 2025 and 2027.

“I expect Vera to be the second largest sales contributor beyond Blackwell and Rubin chips,” Huang told analysts.

Still, Nvidia’s rapid expansion is creating fresh challenges.

Huang acknowledged that demand for the upcoming Vera Rubin platform — which combines Nvidia’s next-generation AI and central processing technologies — may outstrip supply throughout the product’s lifecycle.

“My sense is that we’ll be supply-constrained through the entire life of Vera Rubin,” he said.
The warning highlights a broader concern hanging over the AI industry: whether the supply chain can keep pace with explosive global demand for advanced semiconductors and memory chips.

To avoid production bottlenecks, Nvidia sharply increased its supply commitments to $119 billion during the fiscal first quarter, up from $95.2 billion in the previous quarter.

The company also disclosed $30 billion in cloud computing agreements aimed at supporting research and development, underscoring the enormous infrastructure demands required to stay ahead in the intensifying AI race.

With inputs from agencies.