Whether you've been listening to hedge fund manager Dr. Michael Burry, who made a name for himself by betting against the housing market right before its implosion during the Great Recession, or just looking at the market in general over the past several weeks, it's clear that investors are worried about an artificial intelligence (AI) bubble forming.
How could they not be when stocks like Tesla and Palantir Technologies are trading at monster valuations? But the artificial intelligence chip king, Nvidia (NVDA 6.69%), just reported strong earnings ahead of Wall Street estimates, while also guiding for higher revenue in the current quarter than Wall Street expected.
What's an investor considering AI stocks supposed to do with these seemingly conflicting data points?
During Nvidia's earnings call for the third quarter of its fiscal year 2026, CEO Jensen Huang refuted the notion of an AI bubble. Here's why.
Image source: Nvidia.
No way to deny that a massive transformation is underway
Before answering questions from Wall Street analysts, Huang pushed back against the idea of an AI bubble forming. He referenced three major platform shifts impacting the world, the first since the introduction of Moore's Law. Introduced by Gordon Moore in 1965, in simple terms, Moore's Law is the idea that computing circuitry will double its processing ability (thus cutting its price in half) roughly every two years.
The first big platform shift, according to Huang, is from central processing units (CPUs) to graphics processing units (GPUs). The computer system we all know today is built on CPUs, which are systems designed to compute tasks in a certain order. GPUs can simultaneously process multiple tasks and solutions, which is why they are significantly more powerful than CPUs.
Huang believes the world has only just begun the transition from computing built on CPUs, which represents hundreds of billions in cloud computing spend, to GPUs, although this trend has reached a tipping point.
The second transition is from classical machine learning to generative AI, which is essentially AI's ability to leverage large and complex datasets to create new content. OpenAI's ChatGPT can generate pictures, code, and even write television scripts. Huang noted that generative AI is already taking over search ranking, recommendation systems, ad targeting, click-through prediction, and content moderation.
NASDAQ: NVDA
Key Data Points
He cited Meta Platforms' second-quarter results. It reported a 5% increase in ad conversions on Instagram and 3% gain on Facebook, due to investments in generative AI technology.
Finally, Huang discussed agentic AI systems, which can independently make decisions based on large datasets. Huang sees agentic AI systems as "the next frontier of computing." Examples include Tesla's full self-driving software and legal assistants such as Counsel AI Corp.'s Harvey. Huang said:
The transition to accelerated computing is foundational and necessary. Essential in a post-Moore's law era. The transition to generative AI is transformational, and necessary supercharging existing applications and business models. And the transition to agentic and physical AI will be revolutionary, giving rise to new applications, companies, products, services. As you consider infrastructure investments, consider these three fundamental dynamics. Each will contribute to infrastructure growth in the coming years.
The reality is, we just won't know until it happens
Unfortunately, hindsight is always 20/20, and predicting the future is nearly impossible. This is why investors should avoid trying to time any of these market trends. The AI trade could also stay strong for several more years before a significant pullback.
The reality is that some data suggest we are in a bubble, while some data don't. Remember, history often rhymes but rarely repeats itself.
I have no doubt that AI will be a part of the world's future, although I am unclear to what extent. The path forward will also likely not be linear. Investors should maintain a long-term investment horizon and, at the very least, practice dollar-cost averaging, especially for companies trading at ultra-high valuations.
