A bit of AI fun: Are hyperscalers overinvesting in their buildout?
It’s the question on every investor’s mind: are the hyperscalers spending too much on their infrastructure buildout such that they won’t generate a satisfactory return on capital?
Investors are furiously debating whether the Hyperscalers are overspending.
Recently, ‘Lloyd’ commented on a Livewire blog post about the subject, stating the following:
“The last six months of debt issuance have highlighted the issue. The volume of straight corporate debt being issued is unprecedented for these companies. Hyperscalers (including Alphabet, Amazon, Meta, and Oracle) issued roughly $194 billion in bonds through just the first half of 2026—a massive 79% jump over 2025. Alphabet has even gone so far as to issue a 100-year bond and price an $84.75 billion equity raise to protect its cash position.”
I particularly wanted to pull out the final sentence;
“Alphabet has even gone so far as to issue a 100-year bond and price an $84.75 billion equity raise to protect its cash position.”
We know that Google’s parent, Alphabet, did indeed issue a rare ‘Century Bond’ this year, and the company also priced an upsized $84.75 billion equity capital raise in early June 2026 – the largest corporate equity raise in U.S. history – to aggressively fund its expanding artificial intelligence (A) infrastructure, data centres, and global compute capacity.
So, I asked Google’s own Large Language Model (LLM), Gemini, to fact check whether Lloyd’s statements were accurate.
Here’s Gemini’s answer to that prompt:

It would appear that consumer and enterprise customers of agentic AI will need to start spending big for the hyperscalers to generate a decent return on capital.
Given the ongoing hallucinations of LLMs, I suspect these will be commoditised rather than become premium tools that consumers will be willing to collectively pay trillions of dollars for over time.