Industry executives and experts share their predictions for 2026. Read them in this 18th annual VMblog.com series exclusive.
By Rob Biederman, Managing Partner, Asymmetric Capital Partners
After two years of unrelenting AI hype, 2026 may become the year of reckoning for artificial intelligence spending. The past few years have been defined by near-limitless enthusiasm – and capital – poured into AI infrastructure, foundation models, and the startups building on top of them. But as the frenzy gives way to pragmatism, companies will begin sharpening their pencils and asking hard questions about return on investment.
For now, the system is operating at maximum velocity. Compute buildout continues at a breakneck pace, with data centers rising across the globe and chipmakers running at full capacity. Every week brings new model releases, new tools, and new infrastructure announcements. Yet beneath this momentum lies a fragile equilibrium – one that assumes persistent demand growth, rising utilization, and premium pricing. Any deviation from that trend could have outsized ripple effects.
In 2026, we’re likely to see those assumptions tested. As enterprises move from experimentation to execution, CFOs will begin scrutinizing AI budgets with the same rigor they apply to other technology investments. The question will shift from “What can AI do?” to “What value is it driving?” Some companies will find that their AI initiatives deliver clear efficiency gains or new revenue streams; others will discover bloated spending with little measurable impact. That divergence could trigger a pullback – and potentially a correction – across parts of the ecosystem.
The industry’s interdependence amplifies this risk. A drop in AI demand wouldn’t just affect application-layer companies; it would reverberate through the entire stack. Model providers would see lower API consumption, cloud platforms could face underutilized infrastructure, and hardware suppliers might suddenly find themselves with excess inventory. In a system optimized for perpetual growth, even a small slowdown could have large consequences.
We’ve seen this movie before in other technology cycles. Cloud computing experienced a similar moment in the early 2010s, when enterprises paused to evaluate cost structures after an initial wave of migration. The difference today is scale – both in capital outlay and in public attention. The AI ecosystem’s current burn rate and dependency chains mean that any contraction will be felt more acutely and more broadly than in past corrections.
That said, a period of recalibration isn’t inherently negative. A temporary slowdown could actually strengthen the sector in the long run by forcing discipline, clarity, and focus. The companies that emerge stronger will be those that tie their AI investments directly to business outcomes, integrate the technology deeply into workflows, and resist the temptation to chase every new model or infrastructure upgrade.
Investors, too, will need to adapt. In the coming year, capital will flow more selectively. Funds will favor startups with proven customer traction and sustainable unit economics over those simply touting technical sophistication. Expect to see greater scrutiny of gross margins, cloud costs, and revenue concentration. The bar for what constitutes a “defensible” AI business will rise – and that’s a healthy development.
If 2023-2025 were the years of boundless optimism, 2026 will be the year of discernment. The industry will move from exuberance to efficiency, from experimentation to execution, and from theoretical promise to demonstrated value.
The correction, if it comes, won’t signal the end of the AI era – merely the end of its first chapter. What follows could be even more powerful: a period where artificial intelligence delivers on its transformative potential, not because it’s fashionable, but because it’s finally productive.
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ABOUT THE AUTHOR
Rob Biederman, Managing Partner, Asymmetric Capital Partners
Rob Biederman is the Managing Partner of Asymmetric Capital Partners, an early-stage venture capital firm backing high-growth technology companies. Prior to founding Asymmetric, Rob was the co-founder and CEO of Catalant Technologies, a leading platform for on-demand business expertise. He is a Harvard Business School graduate and author of Reimagining Work: Strategies to Disrupt Talent, Lead Change, and Win with a Flexible Workforce.
