2024 12 27 The AGI Sham

The apparent leap in the capabilities of large language models has been made possible by venture capitalists, persuaded by earnest but misguided entrepreneurs like Sam Altman, that given sufficient resources, the models could be evolved into generalized artificial intelligence. In other words, with enough money, OpenAI could build you a machine capable of the same cognitive tasks as a human employee that doesn’t need a 401k or health care and will work a 168 hour week without complaint without a paycheck. In other words, we will make you richer.

These investors and researchers now find themselves in one of Zeno’s paradoxes. Each new investment of cash has yielded an incremental improvement in the performance of the models, but building the next bigger and better model will require a substantially higher investment.

The breakneck pace of this investment is driven by the belief that whoever crosses the AGI finish line first will control the labor economy, but none of these clowns are any more clear on where that line is than they have ever been. Admirably, I suppose, the researchers who are subsisting on these vc dollars cling to the hope that with just a few more rounds of funding, they might make the breakthrough needed to cross it.

Eventually one of the funders is going to decide that being the first to abandon ship is preferable to sinking aboard it. This will be followed by a mutiny of investors demanding returns. Costs will need to be cut, demands will be made to reduce resource consumption rather than racing ahead at full steam. Casual users lured in by cheap subscription pricing will be forced to decide whether they can continue to afford them. Companies who have hired developers too inexperienced to work without assistance from a model will have to decide whether to foot the bill for subscriptions. Developers who have paid for access themselves will face either learning to program or spending more of their salaries paying an LLM to do it.

Once the signs of this looming collapse appear, speculators will become alarmed. Companies like Nvidia, whose business exploded with the demand for hardware needed to train modelsi, will be faced with peril when the first cloud providers signal they’ll be cutting back GPU orders.

The economy which has supplied this massive pipeline of cash- most of which was converted to heat exhausted from datacenters -will have to face the reality that the finish line never existed, that there will be no AI revolution this year, or next, and that we will have to continue teaching and rewarding people to do work.

It hasn’t been all for nothing; some of this new technology has great potential in reducing human toil, like sorting the stones from a conveyor belt of dried beans too fast for any human, translating text in ancient scrolls, or identifying photos which include your cat. There is value in it, but not the kind of value which would transform the labor economy and elevate venture capitalists to baron status.

Once this spell cast on investors by the AGI pied pipers has broken, and they begin to understand that every additional billion spent will yield increasingly finer improvements to the LLMs of today, they will begin to look for other opportunities. Many companies whose business models hinge on the appearance of AGI finish line will implode.

Since I’ve already speculated a great deal, so I may as well offer my final prediction, which is that people like Roger Penrose and Miguel Nicolelis who have argued that intelligence is not computable are correct and that the lure of the piles of money you would be rolling around in were you to find a way to compute it was too strong for them to be troubled by those arguments. In a Penrose vs Altman showdown, I know who my money is on. I don’t believe that the idea of creating artificial, generalized intelligence is impossible, just that it cannot exist on the substrate of a Turing machine.

We have only the most nascent understanding of how intelligence arises from human consciousness. The idea that the same phenomenon could occur spontaneously by computing a bunch of tensor products using a bunch of graphics card reveals just how wishful is the thinking of millionaires dreaming of being billionaires can be when they fall under the sway of a con man who dazzles them with parlor tricks.