Nvidia encounters a key 2026 issue threatening $20 trillion ambitions

NVIDIA’s 2026 wake-up call

You’ve heard about Nvidia’s incredible run. The company behind many of the world’s most important AI chips has already crossed the $5 trillion market-cap milestone, and I/O Fund lead analyst Beth Kindig still sees a possible path toward $20 trillion by 2030.

But Nvidia now faces a more complicated 2026 setup that could make the ride less smooth than before. Investors need to watch the shift toward inference, the rise of custom chips, and the uncertainty around Nvidia’s next-generation Rubin rollout.

The moat that made Nvidia strong

For years, one of Nvidia’s biggest advantages was CUDA, its software platform for GPU-accelerated computing. It became a powerful developer ecosystem that helped make Nvidia hardware the default choice for many AI training workloads.

That software moat created high switching costs for many engineers and companies. Analysts have estimated that Nvidia represented about 90% of AI training workloads, helping support its pricing power, margins, and stock-market rise.

Why the game is changing now

Here’s the big shift: AI is moving from training to inference. Training is when you teach a model once. Inference is when it answers millions of questions every day, like ChatGPT replying to you.

Inference doesn’t need CUDA as badly. New open-source tools let companies run AI on different chips without Nvidia’s software. That means Nvidia’s famous lock-in starts to loosen right when inference becomes the bigger business.

Your coffee maker analogy

Think of training like brewing a new recipe. You need a fancy kitchen with every tool. That’s CUDA. Now think of inference like pouring coffee all day. You just need a simple, fast machine.

Once a recipe is set, you don’t need the fancy kitchen anymore. Big tech companies realize this. They’re building their own simple coffee makers, custom chips that cost way less to run at scale. That’s a problem for Nvidia’s pricing power.

Little-known fact: A single Nvidia Rubin GPU has 288GB of HBM4 memory, while the new Groq LPU inference chip has only 500MB of SRAM.

Custom silicon, friends turn rivals

Your favorite cloud giants, Amazon, Google, and Microsoft, are now designing their own AI chips. Why? Math. NVIDIA’s chips have super high profit margins above 70%. If you spend $50 billion a year, making your own chips saves billions.

Google’s new TPU Ironwood is built specifically for inference. Amazon has Trainium. Even Meta is joining in. These were Nvidia’s best customers. Now they’re becoming long-term competitors, chipping away at Nvidia’s market share one workload at a time.

Little-known fact: Analysts project Nvidia’s share of the AI inference chip market could fall dramatically from over 90% today to somewhere between 20% and 30% by the year 2028.

The $20 billion Groq surprise

At GTC 2026, Nvidia highlighted Groq-based LPU technology as part of its broader push into AI inference. Reuters reported that Nvidia agreed to a non-exclusive license for Groq’s technology and hired key Groq executives, while financial terms were not publicly disclosed.

The Groq LPU is designed for low-latency inference and uses high-speed on-chip SRAM to support fast token generation. Pairing LPUs with Rubin GPUs shows Nvidia is expanding beyond GPUs alone to build a more specialized inference stack.

Rubin’s timing risk lands at a sensitive moment

NVIDIA says Rubin is in full production and that Rubin-based products are expected from partners in the second half of 2026. At the same time, TrendForce has reported shipment-delay risks tied to HBM4 validation, interconnect changes, higher power consumption, and more advanced liquid-cooling needs.

Even a modest delay could matter because custom silicon from Google, Amazon, Microsoft, Meta, AMD, and Broadcom-linked programs continues to improve. For investors, the risk is that slower Rubin availability gives customers more reason to diversify their AI chip supply.

Valuation looks tempting

Here’s what keeps some investors interested: Nvidia’s earnings have continued to climb, while its valuation multiple remains below some recent historical averages. I/O Fund cited Nvidia at about 40.7 times earnings versus a three-year median of 55.29, making the stock look cheaper relative to its own recent history.

But a lower valuation does not automatically make a stock the best opportunity. The real question for investors is whether Nvidia can still compound capital faster than other AI-related stocks over the next year.

How big is inference getting?

How big is inference becoming? By 2026, it’s expected to cover two-thirds of all AI computing. That’s a massive market, well over $50 billion just for inference chips. NVIDIA wants a big slice, but so does everyone else.

The good news is that Nvidia still leads in performance. The tricky part is that inference workloads are more repetitive and cost-sensitive. Customers will shop around for the best deal, not just the fastest chip. That puts pressure on Nvidia’s prices and profits.

OpenAI’s $1.4 trillion gamble

OpenAI’s compute ambitions remain enormous, but the numbers need careful framing. Reuters reported in February 2026 that OpenAI expected about $600 billion in compute spending through 2030, while Sam Altman had previously discussed a much larger $1.4 trillion ambition for 30 gigawatts of compute infrastructure.

The risk is that infrastructure spending could grow faster than OpenAI’s revenue and customer demand. If the industry builds more data-center capacity than the market can profitably use, chipmakers and cloud providers could face weaker pricing power.

Startups are raising big money

It’s not just big tech challenging Nvidia. On February 24, 2026, multiple AI chip startups announced major funding rounds, with reports indicating that more than $1 billion flowed into companies competing in AI acceleration.

MatX was founded by former Google hardware engineers, while SambaNova was founded by Stanford professors and a former Oracle executive. These startups are targeting opportunities in training, inference, and edge AI as the market becomes more competitive.

Where smart money is moving

The I/O Fund, which called Nvidia’s rise years ago, is now finding bigger wins elsewhere. Their portfolio is up 33% this year from stocks like Bloom Energy (up 1,100%) and an optical networking stock (up over 620%).

They still hold some Nvidia, about 5% of their fund. But they’re not betting the farm on it anymore. The lesson is that even great companies can become average investments if better opportunities exist. Sometimes the smartest move is knowing when to look elsewhere.

Want to see where Nvidia is heading next despite all this? Take a look at its latest AI breakthrough from the mega conference.

NVIDIA is still the leader

NVIDIA remains the king of AI chips. Nothing in these slides says the company is failing or collapsing. The $20 trillion path is still there, but it’s more back-loaded toward later years.

For 2026, expect a bumpier ride. The moat is weakening, rivals are creeping in, and delays add uncertainty. NVIDIA will still post amazing numbers. But if you’re looking for the absolute best return on your dollar this year, you might find it in smaller, hungrier AI stocks instead.

Want to see how big money is still positioning around Nvidia? Take a look at the Tesla tycoon’s 1M-share move; it’s aimed at steadying investor nerves.

If you found this slideshow helpful for understanding Nvidia’s 2026 challenge, give it a thumbs up or drop a comment below. I’d love to hear your take on where AI stocks go next.

This slideshow was made with AI assistance and human editing.

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