AI spend will jump 49.5% in 2026, says Gartner
Worldwide AI spending will increase 49.5% in 2026, to $2.7 trillion, and grow another 36.2% in 2027 as AI usage continues to expand, Gartner predicts in the latest quarterly update to its IT spending forecast.
But there’s been no diversion of money from other areas of IT to feed AI, said Gartner Distinguished VP Analyst John-David Lovelock. CIOs got some net new money for AI back in 2024, and a little in 2025, he said, “so there wasn’t a diversion, and now more of their spending is about rebranding than diversion.”
By 2030, “every dollar is going to be an AI dollar in one way or another,” he said.
Instead of buying just a laptop, companies now buy a laptop with AI chips in it, while enterprise software now has AI embedded in it. And instead of doing a project about business strategy, they now do a project about how AI might change business strategy.
“In one way, it’s kind of rebranding,” he said. “Net new spending is going more towards AI, and existing spending is being transformed towards AI.”
It’s similar with hyperscalers: “The hyperscalers haven’t diverted one dime away from their cloud. So AWS, Google, Microsoft, Meta are all continuing to build out their cloud infrastructure at the same rate they were in 2022.”
The AI buildout “is the largest infrastructure project humanity has ever undertaken,” he observed.
Gartner updates its spending forecasts quarterly. In its latest report, it predicted AI infrastructure spend will grow 51.2% this year, and AI software 60.2%. Spend on AI agents and assistants, which it breaks out separately from other software, is forecast to increase by 77.3%, and AI security spend to almost double.
As AI development platforms prove themselves, Gartner sees spending on them accelerating: It has increased its 2026 growth forecast for them from 28% in its May report to 39%, attributing the change to the development of custom AI applications by enterprises and software and service providers.
AI model providers are seeing a “small but growing opportunity” for domain-specific language models aligned with enterprise use cases, upping the 2026 growth rate for generative AI models to 117%.
Semiconductor surprise
But what surprised Lovelock was the semiconductor spend.
“Semiconductors for the last 30 years has been a nice, easy market to work through,” he said. “Total spending has been very incremental. It’s followed a nice long-term evolutionary curve. Back in 2015, we said it would be about a trillion dollars by 2030, and we got real pushback on that back then, saying we didn’t understand Moore’s law and fabrication and all the rest of it. Well, turns out both myself and the people giving the criticism were wrong, because with AI, chips are now going to push $2 trillion by 2030.”
The culprit is memory.
“We were ready for the run-up in the chips that went into the heart and soul of the servers. However, as the AI servers got more functional, they required more memory and, of course, more storage, which is also memory,” he said.
People often mistake the reason for the substantial rise in memory prices, he said: “It’s very often written that there is a memory shortage, which is not putting the blame or the explanation in the right place. There is a massive over-demand towards memory, and that’s pushed these chip prices incredibly high, and that has spiked that semiconductor spend, now doubling 2030 spend from what we thought it was before AI.”
His message to CIOs is straightforward: “The next two years, next three years, have at least three major transitions in them,” he said. “If you think you know what will happen with AI based on what is happening with AI, you’re wrong. The technology is changing too quickly for anybody to have assurances or certainty on what can be done and at what price point. This is going to be continually on your plate, and your balance between risk and reward has got to be your number one priority.”
This article originally appeared on CIO.com.
Source:: Computer World
