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FollowingInvestor Parihapitiya released a capital flow chart for the AI market, pointing out that the fastest returns come from power and data center real estate.
Investor Chamas Parihapitiya posted a roadmap on the X platform that he predicted that funds would flow in the AI market, and told his followers that the fastest cash returns are in the power and data center real estate sectors. However, he also pointed out that lasting profits will belong to the "control framework" and the applications built on top of that.
In Palihapitiya's guide, where will the money flow first?
The founder of Social Capital has labeled his posts on the X platform as an "AI Investment Guide" as of August 2026. He divided the investment area into multiple levels, first dealing with what he called LPS, which stands for land, electricity and shell. This refers to the layout of the data center's physical infrastructure before installing any chips. This "remains the most obvious and fastest way to get a cash return," Palihapitiya wrote. He added,"At this level, a lot of value can be quickly integrated and traded. As data centers face increasing resistance, the value of land granted for power permits could soar. I am very optimistic about this floor." Parihapitiya said he and his partner Anita Verma-Larian have locked in nearly 6 gigawatts of electricity supply by 2029. He has previously said that access to land and chips approved for zoning gives its owners bargaining chips over all downstream participants.
Why does Palihapitiya think control frameworks and applications will be winners?
On top of concrete and power lines, Parihapitiya's choice is the control framework. "A modern control framework coupled with an open model will reduce your token consumption but maintain your performance," he said in a post posted in July. According to a glossary released May 25, a control framework is software built around an AI model that determines what the model can see, which tools it can call, and when to stop. Anthropic's Claude Code, OpenAI's Codex and Google's Antigravity are all control frameworks. In its documentation, Claude Code is referred to as the "proxy control framework around Claude." Palihapitiya said that "the control framework helps companies have their proprietary environment (what Alex Karp calls 'alpha')," which in his view includes their data, workflows, business rules, etc. His argument also extends to apps, which he believes will be another long-term winner. "Every company, with the right control framework, can now inject their 'alpha' into the software running their company," he wrote.
Do people agree with Palihapitiya?
Some industry people commented on Palihapitiya's post, and many expressed support, especially his views on the control framework. Qu Xiaoyin, founder of Tycoon AI, expressed more support for the control framework, pointing out that the control framework "can create profits regardless of whether the model is commercialized or not" because a suitable control framework can unlock large, long-term business tasks whose value exceeds the output of any single model. In response to another post highlighting the performance of various AI agents, Box CEO Aaron Levy said that the control framework "will become the most important variable in the AI technology stack," keeping pace with the original model's capabilities. In mid-July, Palihapitiya posted on the X platform questioning the current state of AI spending, asking whether the spending has paid off for others except a few companies that have already profited from it, and pointing out that buyers can now buy it at $0.50 per million cutting-edge tokens instead of the previous $56. It seems that his call for control frameworks is a response to his own complaint that if models become cheap and interchangeable, money will flow to the people who control data, workflows and ultimately top-level applications.
