I'm not Ed Zitron, and I shouldn't claim to match his expertise. If you really want to do a deep dive into how truly fucked the AI industry is, go check out his blog at whersyoured.at. Anyway, this is a brief summary of what I have learned from Zitron and my own research:
The current generation of AI companies is fucking toast, and they might even know it, but founders and venture capitalists are still trying to escape so they're pretending they have a future.
To explain, as briefly as possible, the above: startups are funded through venture capital, where an investor sinks a giant pile of money in exchange for an ownership share of the company (yes, this is exactly like Shark Tank). Venture capitalists do not actually have any interest in owning pieces of startups; what they actually want is to get their shares bought out. Failing that, they'll settle for dividends and walk away from bankruptcies. Ultimately, there's three ways for venture capital to get a return on their investment: the startup can go public (start selling shares to the public), they can start being profitable, or they can go bankrupt, liquidate, and get sold off for parts.
I know OpenAI is making noises about an initial public offering (where a company offers shares to the public for the first time). I will be extremely surprised if this winds up happening. The reason is that an IPO requires disclosure of the company's financials to auditors, and if the auditors discern weird shit in your financials, they say so. If they don't say so, they go to prison. No AI company wants to disclose anything, because:
Their balance sheets are a disaster. We are talking about an industry that, collectively, has spent almost two trillion (with a t) United States doll hairs on building infrastructure to support their product, and which has, collectively, revenue measured in the hundreds of millions. For perspective: one hundred million seconds into the past is around four months ago. One trillion seconds into the past predates human habitation in the Western Hemisphere (by about ten thousand years). So even if there were zero externalities attached to AI, the industry will collapse under its own weight once they exhaust the willingness of VC to keep writing enormous checks.
Because of the above, it is probably structurally impossible for the current generation of AI companies to ever turn a profit, at least on honest books.
Which leaves one outcome, once the merry go round stops: liquidation. Here's the thing: the models these companies use to answer your inane questions or pretend you have a girlfriend are an asset that can be sold. Somebody will end up owning it.
Which means, alas, that the proponents of AI are probably right that something that looks like AI will be here to stay. But it won't be what we're using now. Which is mostly a good thing.
However (and this is where we move into my research rather than Ed's) a significant part of the problem is that AI cannot work as advertised. It cannot and will not ever be able to reason.
Not going to go deep on the cognitive science here, but: there's not really a consensus on what "reasoning" is, but most scholars would probably agree that it needs to include evidence reform and model reform.
Evidence reform is when you realize that the way you are gathering evidence for your model of the world cannot answer the question you're interested in. For example, if you want to find out what flavor of pie was America's favorite, and you went out and observed the purchasing patterns at a thousand diners that sold pies, you would, if you were reasoning correctly, realize that all you're getting is information about people's preferences as to the pies diners offer. People might prefer a pie that only appears in home kitchens. So you have to change how you gather evidence to begin with.
Model reform is when you realize that there is a factor affecting your observations that you did not include in your model of the world. For example, you run your diner pie survey and learn that by a huge margin the favorite pie of Americans is pecan pie. Then, as you're reviewing your data, you realize that every single one of the diners you visited was in Mississippi, where pecan pie is a local specialty. A factor you did not consider (location of your sampling sites) has affected your observations, and you will need to reform your model to reflect this.
AI, in its current form, can do neither of these things. Without getting in the weeds, the AI is not aware of anything that it hasn't been told. It can find patterns in the things it's been told that humans haven't discerned yet, but it cannot recognize a missing piece. Both evidence reform and model reform involve seeing and recognizing that you have incompletely described the world.
So, to sum up: AI is probably not going to replace most workers permanently. Executives are already bumping up against its limits and realizing they need to bring people back in.
The hype surrounding AI is the last burst of energy a dying patient has before they go into the final decline. Don't mistake it for a new lease on life.
I won't say that AI is never useful or that it never will be useful. But I will say that the current structural assumptions around AI are not playing to the strengths of the tool. But playing to the strengths of the tool would mean that Sam Altman could only make money selling copies of the OpenAI model to academics who do machine learning work, and that would not keep him in the lifestyle he would like to be accustomed to.
Anyway, good luck out there, and push back against the hype.
