Big Tech Will Spend $600 Billion on AI but Here’s Who’s Really Winning.

As Big Tech commits roughly $600 billion to artificial intelligence infrastructure, investors are increasingly finding that the strongest returns may come from the broader AI value chain rather than the hyperscalers driving the spending.

“AI has been kind of in the news for the last two years straight,” said Christian Magoon, CEO and founder of Amplify ETFs, in an interview with AInvest. He added that the investment theme has shifted “from FOMO to a little bit of FUD—fear, uncertainty and doubt—on whether all this investment will really pay off.”

Artificial intelligence has dominated markets for the past two years, fueled largely by the Magnificent Seven and their outsized weightings in the S&P 500 and Nasdaq 100. But the narrative is evolving as those same companies ramp up capital expenditures tied to data centers, chips and cloud expansion.

Magoon said roughly $600 billion is geared toward building out what he described as the “future of AI,” including data centers and infrastructure. That spending surge, he noted, has weighed on some hyperscalers’ near-term cash flow as investors question the timing of returns on that capital.

“You see that they’re kind of falling off a little bit because they’re investing so much capex into the future for kind of an uncertain return,” he said, referring to companies such as Alphabet and Amazon.

At the same time, performance leadership within the AI ecosystem has broadened. According to Magoon, networking companies, switching firms and businesses involved in power and cooling data centers have begun to outperform some of the more heavily owned hyperscalers and chipmakers.

That shift has implications for how investors gain exposure to AI. Rather than concentrating exposure in the largest names through traditional market-cap weighting, Amplify offers the Amplify Bloomberg AI Value Chain ETF (AIVC), which employs an equal-weight approach across companies participating in the AI ecosystem.

“With our Bloomberg AI value chain ETF, we took a different approach,” Magoon said. “Instead of market cap weighting the AI names, we’ve actually equal weighted all these names.”

The equal-weight structure, he said, is designed to reduce company-specific risk and avoid overexposure to a handful of mega-cap stocks that dominate broad indexes. AIVC includes exposure not only to U.S. technology leaders but also to global semiconductor manufacturers such as Samsung and SK Hynix.

Magoon said AIVC returned about 41% last year, ranking as the second-best performer among roughly 10 AI-focused ETFs available in 2025. This year, he said, it has gained about 6% even as the Nasdaq and S&P 500 have been flat to down.

Still, he cautioned that if the Magnificent Seven regain dominance and their AI investments generate strong returns, strategies that are not overweight those names could underperform. “Momentum in these large company stocks works until it doesn’t work,” Magoon said.