The rate of the AI buildout has driven capital expenditures higher for Wall Street leaders, but could the great infrastructure buildout be happening at an unsustainable pace? 

To date, the Magnificent Seven collective of S&P 500 megacap stocks has committed $700 billion to AI infrastructure projects, marking a steep acceleration from the $400 billion spent last year.

The scale of capex spending has only been increasing, with Google parent Alphabet (NASDAQ:GOOGL) tumbling nearly 7% in the wake of its Q2 2026 results, which included an upturn in capital expenditure expectations towards $195 billion to $205 billion, up from the $180 billion to $190 billion forecast in the previous quarter. 

The company linked the spending increase to pouring more money into AI infrastructure in a bid to meet booming demand. 

But could we see further headwinds emerge due to the workforce demands of such a widespread artificial intelligence buildout? 

Infrastructure Skills Shortages

Hyperscalers have been seeing a new wave of investor unease with the scale of spending that many companies are actively increasing at present. 

The Roundhill Magnificent Seven ETF (MAGS), which is largely comprised of hyperscalers, has erased much of its growth since September 2025 due to a cocktail of doubts over whether its stocks can meet their soaring price-to-earnings (P/E) ratios, and the prospect of addressing an ever-increasing skill shortage could be the latest issue to overcome. 

The data behind the AI data center buildout in the US is stark. The domestic data center industry would need 340,000 workers that don’t currently exist. For every 100 applicants who walk through the door, only 15 meet the minimum qualifications for modern data center roles. 

This comes as hyperscalers are already battling against a construction worker shortfall for their buildouts. Along with meeting new power demands, accessing chips, and winning permits, the bottlenecks that could stand in the way of Wall Street leaders living up to their lofty valuations appear narrower than ever. 

Worryingly, the workforce doesn’t appear to be keeping pace with the scale of the AI boom. Data suggests that 90% of data center operators now cite staffing shortages as a key constraint on expansion. 

Additionally, 53% of operators struggle to hire qualified staff, a figure that’s ballooned from the 38% recorded in 2018. With two-thirds of data center companies struggling to hire or retain qualified staff at all, one of the big emerging problems that hyperscalers face will be finding the workers capable of delivering on lofty industry expectations. 

Bridging Gaps

We’re seeing some innovative solutions from different companies when it comes to addressing AI skill shortages. 

One key approach has been to create developer ecosystems through investing heavily in AI startups and developer platforms to create community-driven solutions that reduce reliance on internal specialist teams. 

Alternatively, in the case of Meta (NASDAQ:META), the tech giant has opted to spend hundreds of millions of dollars in a bid to lure AI talent from its rivals, which has seen startups like OpenAI lose staffers to the Facebook parent company. 

More recently, OpenAI’s Jason Wei and Hyung Won Chung, both of whom also previously worked at Google, joined Meta’s superintelligence team last year. 

Controlling AI Talent

According to the World Economic Forum, the artificial intelligence boom will create around 170 million new jobs while displacing 92 million roles, leaving a net employment increase of 78 million positions. 

While this is likely to be welcome news among the workers who believe that their positions may soon be under threat, it underlines the scramble for talent that’s already reshaping the way hyperscalers are attempting to navigate their high AI buildout ambitions. 

With access to talent unlikely to keep up with the pace of capex in the years ahead, investors may need to scruitinize the fundamentals of Wall Street’s hyperscalers more than ever before. 

In an industry with weaker access to the talent that can support growth, all market leaders can’t reach their potential. This suggests that the ongoing period of stagnation among AI firms could see investors reevaluate their credentials, creating a new generation of winners and losers to track. 

What’s Next for Hyperscalers? 

There’s still plenty to be excited about the AI boom, but it’s becoming increasingly clear that the boom cycle surrounding the industry is giving way to companies that can demonstrate a higher level of resilience against ongoing market concerns. 

As investors become more wary of companies overpromising their AI buildout, the current high-tech earnings season for Q2 2026 could help to uncover those who are continuing to balloon their capex to levels that the workforce can’t keep up with. 

At a time when valuations are far in excess of revenues in AI, it pays to spend more time researching Wall Street’s brightest prospects and comparing them against peers that may be less likely to survive the industry’s growing headwinds.

Disclosure: On the date of publication, Dmytro Spilka did not hold (either directly or indirectly) any positions in the securities mentioned in this article. The opinions expressed in this article are those of the writer. Dmytro Spilka does not intend to make a trade in any of the securities mentioned above in the next 72 hours.

Benzinga Disclaimer: This article is from an unpaid external contributor. It does not represent Benzinga’s reporting and has not been edited for content or accuracy.