The AI race is getting expensive—really expensive. Word is that a major AI company is staring down $140 billion in cumulative losses before it even sniffs its first dollar of actual profit. That's not a typo. We're talking about burn rates that would make even the most ambitious crypto project blush.

This kind of capital consumption raises some serious questions about the sustainability of the current AI infrastructure buildout. Sure, everyone's racing to build the next breakthrough model, but at what cost? When you're bleeding that much cash before reaching profitability, the pressure to monetize becomes immense.

It's a familiar pattern for anyone who's watched tech cycles play out. Massive upfront investment, sky-high valuations, then the scramble to justify those numbers with actual revenue. The difference here? The scale is unprecedented.

Makes you wonder how this plays into the broader narrative around AI tokens and decentralized compute. If centralized giants are burning through billions, maybe there's room for alternative models after all.