AI’s epic cash call is matched only by its ability to strangle revenue streams

As it was written, so it has come to pass. A while back, we suggested that given how good AI was at code analysis, it would become possible to clone large applications. And then where would we be? Here, that's where. An industrious individual has released source code and binaries for Claude-copied Microsoft Office and Adobe Creative Suite programs, disappointingly not calling elements of the latter Fauxtoshop. They're in pre-alpha states of incomplete bugginess, but with substantial functionality, which is proof of how insanely productive AI can be given a task needing zero creativity and wearing "This is what a proof of concept looks like" and "Unintended Consquesences" T-shirts. They could also add Karma Police to their wardrobe. It's clear that Microsoft and Adobe are facing some competition from what their rival describes as "clean-room reimplementations." It's also unclear that there is any law against it. You can't copyright functionality, the code has been generated in Rust with claimed clean-room conditions, and while there may be something in claiming that the works are substantially derivative, there's a big problem here. Companies that sell AI trained on the biggest theft of IP in history are going to need unmatched legal dexterity to claim that AI shouldn't be allowed to sully their own IP. Chutzpah used to be defined as a child on trial for murdering their parents begging for mercy on the grounds that they're an orphan. We'll have to update that. The post-codeclone world is going to be interesting. Everything becomes effectively open source, which closes down substantial revenue streams for big companies who had assumed lock-in. The motivation to build ab-initio alternatives may go away, in open source as well as closed. The push to subscription-only cloud-hosted products, where nobody can get their thieving AI prompts on binaries, seems an obvious counter move, but AI can control and scrape a web interface, which is all it needs. It's an arms race that won't be won by humans. Code cloning is just the latest case of AI being so disruptive it threatens its creators. Another is the doom loop that Microsoft-OpenAI acknowledged in internal emails: by siphoning away the ad revenue that pays for content, AI chokes off the data it depends on. It's deforestation by companies that built their business on other people's IP. The biggest potential point of failure is the money. How revenues will hold up against cheaper or free Chinese models is a concern, given the high importance assigned by Beijing to develop AI as a global influence vector. The models themselves are very efficient, in part because of export regulations on high-end silicon and something of a glut of older datacenters. This wouldn't be the first time restricting access to technology is an evolutionary driver with a poor outcome for those doing the restricting. A much bigger issue is: where will the money come from to build the infrastructure that is needed to support the claims AI is using to justify its growth. Gartner says that there's not enough return on investment to drive that growth, while a much more engaging SemiAnalysis podcast predicts total capex by 2030 of $11 trillion and asks where it's going to come from. If you're interested in understanding AI and aren't following SemiAnalysis, you're not interested in understanding AI, and while the outfit is relentlessly optimistic it's also wildly well-informed. In this case, the structure of the investment market for AI is picked apart. It is very weird. Neocloud is made up of startups with a voracious appetite for cash, but no investment grade status. Thus, Nvidia backstops them, guaranteeing to rent unsold capacity for a fixed price for long enough to guarantee enough revenue to attract other investment. Other cash-rich big tech companies do similar things. This backstopping, also known more wryly as "balance sheet as a service," effectively shifts more risk to investment-rated companies. It's an increasingly important capex enabler, but as SemiAnalysis says, it's necessarily limited, especially if AI itself has blown a hole in the enterprise. Or take the residual value of the older compute. Normally, the book value and revenue capabilities of datacenter chips depreciates over five years or so to some nominal value. That's not happening here, where chips of that vintage are suddenly producing the same or more revenue than they did when they were new. Such are the peculiarities of a supply-constrained market used to rapid obsolescence. Surely a good thing, though? No. It's another strange and hard-to-model factor in a market defined by strange and hard-to-model factors. Don't even mention AGI. Or models too dangerous to use. Or model collapse. Many investors do not like the strange and hard to model, including huge pools of institutional funds. There are models that can blend in risk-averse investors to speculative investments, but those who remember the clever models that blew up the global economy nearly twenty years ago are not yet all dead. The tl;dr of the answer to where do you get enough money to make AI work as promised, is: nobody knows. What happens if the capex has no cap to ex? See previous answer. It may be an infantile affliction to have animation clips come to mind when considering AI, but it can't be helped. Agentic AI? Mickey Mouse as the sorcerer's apprentice in Fantasia. Big Tech pushing out AI as fast as it can? Wile E Coyote sawing through the branch he's sitting on. AI investment market? Any of Caine's quests in the Amazing Digital Circus. We're all along for the ride, whether we like it or not. (R)