If you're worried about an AI bubble, Daniel Newman thinks you're solving the wrong problem. Speaking at CES 2026, the Futurum Group CEO made his case that the artificial intelligence industry isn't facing a hype crisis—it's facing a hardware crisis. There simply isn't enough computing power on the planet to sustain what he calls a "multi-decade super-cycle."

The Skeptics Have It Backwards

"Those calling for a bubble really don't understand what's happening," Newman said in an interview clip he shared on X. His argument is straightforward: we're not in the late innings of AI mania, we're in the earliest stages of a fundamental technological shift that will take decades to play out.

The problem, according to Newman, is that we're about to hit an infrastructure wall. As AI systems evolve from chatbots that generate text into "agentic" systems capable of executing complex autonomous actions, the computing requirements will explode. Think less "write me an email" and more "manage my entire supply chain."

"We don't have enough turbines. We can't build what the demand is for what people are doing today," Newman warned. And that's just today's demand. As usage fully ramps up over the next five to ten years, he predicts the technology sector will face a critical hardware shortage that makes current concerns about market froth seem quaint.

Enterprise AI's Coming Out Party

Newman highlighted 2026 as the year when "enterprise AI will show its face." Translation: this is when corporate America stops experimenting with AI and starts generating real returns on all those billions in investments.

Right now, the AI story has been dominated by consumer-facing applications—chatbots, image generators, coding assistants. But Newman points out that the industry is currently utilizing only a tiny fraction of available trained data. The vast majority sits locked inside proprietary corporate systems, waiting to be deployed for drug discovery, manufacturing optimization, and supply chain management.

This shift matters because it represents a transition from the capital-intensive "build phase" of training massive models to the "monetization phase" of inference—where AI begins delivering measurable margin improvements and productivity gains. In other words, this is where companies start making money instead of just spending it.

The Numbers Are Already Wild

To understand the scale we're talking about, Newman offered a jaw-dropping statistic: Alphabet Inc.'s (GOOGL) Gemini AI model is now generating 10 trillion tokens daily. That's trillion with a T. Every single day.

On a more relatable level, Newman cited his own firm's experience. AI has compressed complex market research workflows that previously took six months down to just two weeks. That's not incremental improvement—that's a fundamental transformation of how work gets done.

"We are very early," Newman concluded. Despite the skeptics and the bubble-callers, he believes the era of significant AI monetization and durability is only just beginning. The challenge won't be finding uses for AI—it will be building enough infrastructure to power them all.

AI Investment Options

For investors looking to gain exposure to the AI sector, here are several technology-focused ETFs with their recent performance: