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AI’s infrastructure boom has already been running at a staggering pace, and this week it received a further $500 billion vote of confidence from Wall Street’s biggest names.
Nvidia signed memorandums of understanding with six of the world’s largest asset managers and investment banks, including Blackstone, KKR and Apollo Global Management, to raise the capital to fund what it is calling “AI factories.”
While not guaranteed, the deal signals that demand for AI infrastructure is showing no signs of slowing. VCs are backing that same buildout by funding the semiconductors that power it.
Global funding for AI and machine learning semiconductors reached $14.1 billion in the first half of the year, according to PitchBook’s latest AI Report, on track to surpass last year’s annual total by almost 50%. Both Q1 and Q2 this year are the two highest quarters on record for investment. Deal count is also projected to land above 2025’s figure.
“AI is not just a tool, it redefines technology,” Sriram Viswanathan, founding managing partner of deep-tech VC firm Celesta Capital, said. “If you accept the notion that everything is going to change, the question is, what is the tip of the spear? That is semiconductors, because that powers everything.”
That conviction is rooted in the belief that AI’s growth trajectory faces an increasing number of bottlenecks that can’t be solved by software improvements alone. Hardware, on the other hand, offers something increasingly rare elsewhere in AI: real defensibility.
The scale of the semiconductor opportunity is hard for any investor to ignore. According to a base-case estimate from McKinsey in March, the industry could reach $1.6 trillion in revenue by the end of the decade.
It is also a market that is becoming increasingly valuable as AI faces more constraints, according to Viswanathan. The demand for computers significantly outweighs the supply. AI data center demand is set to exceed capacity by 2027, and one-fifth of planned projects could be delayed due to a lack of grid connections.
He argues that AI faces limitations at every layer of the advanced computing stack, from the processor and the power that feeds it to memory and the network that connects the chips. These constraints are where VCs are finding opportunities for returns, leading to significant deals. |
