Monetizers vs manufactures: How the AI market could splinter in 2026

The AI market is tipped to splinter in 2026.

The last three months of 2025 were a rollercoaster of tech sell-offs and rallies, as circular deals, debt issuances, and high valuations fueled concerns over an AI bubble.

Such volatility may be an early sign of how AI investment is set to evolve as investors pay closer attention to who is spending money and who is making it, according to Stephen Yiu, chief investment officer at Blue Whale Growth Fund.

Investors, especially retail investors who are exposed to AI through ETFs, typically have not differentiated between companies with a product but no business model, those burning cash to fund AI infrastructure, or those on the receiving end of AI spending, Yiu told CNBC.

So far, “every company seems to be winning,” but AI is in its early innings, he said. “It’s very important to differentiate” between different types of companies, which is “what the market might start to do,” Yiu added.

He sees three camps: private companies or startups, listed AI spenders and AI infrastructure firms. 

The first group, which includes OpenAI and Anthropic, lured $176.5 billion in venture capital in the first three quarters of 2025, per PitchBook data. Meanwhile, Big Tech names such as Amazon, Microsoft and Meta are the ones cutting checks to AI infrastructure providers such as Nvidia and Broadcom.  

Blue Whale Growth Fund measures a company’s free cash flow yield, which is the amount of money a company generates after capital expenditure, against its stock price, to figure out whether valuations are justified.  

Most companies within the Magnificent 7 are “trading a significant premium” since they started heavily investing in AI, Yiu said.

“When I’m looking at valuations in AI, I would not want to position — even though I believe in how AI is going to change the world — into the AI spenders,” he added, adding that his firm would rather be “on the receiving end” as AI spending is set to further impact company finances.  

The AI “froth” is “concentrated in specific segments rather than across the broader market,” Julien Lafargue, chief market strategist at Barclays Private Bank and Wealth Management, told CNBC. 

The bigger risk lies with companies that are securing investment from the AI bull run but are yet to generate earnings — “for example, some quantum computing-related companies,” Lafargue said. 

“In these cases, investor positioning seems driven more by optimism than by tangible results,” he added, saying that “differentiation is key.”

The need for differentiation also reflects an evolution of Big Tech business models. Once asset-light firms are increasingly asset-heavy as they gobble up technology, power and land needed for their bullish AI strategies.

Companies like Meta and Google have morphed into hyperscalers that invest heavily in GPUs, data centers, and AI-driven products, which changes their risk profile and business model.

Dorian Carrell, Schroders’ head of multi-asset income, said valuing these companies like software and capex-light plays may no longer make sense — especially as companies are still figuring out how to fund their AI plans.

“We’re not saying it’s not going to work, we’re not saying it’s not going to come through in the next few years, but we are saying, should you pay such a high multiple with such high growth expectations baked in,” Carrell told CNBC’s “Squawk Box Europe” on Dec. 1.

Tech turned to the debt markets to fund AI infrastructure this year, though investors were cautious about a reliance on debt. While Meta and Amazon have raised funds this way, “they’re still net cash positioned,” Quilter Cheviot’s global head of technology research and investment strategist Ben Barringer told CNBC’s “Europe Early Edition” on Nov. 20 — an important distinction from companies whose balance sheets may be tighter.

The private debt markets “will be very interesting next year,” Carrell added. 

If incremental AI revenues don’t outpace those expenses, margins will compress and investors will question their return on investment, Yiu said. 

In addition, the performance gaps between companies could widen further as hardware and infrastructure depreciate. AI spenders will need to factor into their investments, Yiu added. “It’s not part of the P&L yet. Next year onwards, gradually, it will confound the numbers.” 

“So, there’s going to be more and more differentiation.”