So, some more thoughts about AI. Obviously I've been testing with local AIs and paying attention to the advances that are going on in the field and… I don't think it's happening. LONG RANT (TM) time.
INTRODUCTION
Okay, what's the thing that all the AI companies are, implicitly or explicitly, promising? AI is going to reach the point where it is more capable at anything than a human being could possibly be and, thus, it will replace every single human being in employment and either bring about a utopia or dystopia.
And here's the thing, it's not going to happen and there several reasons why it won't.
THE DETERMINISM PROBLEM
One of the things I've been researching is how to train an AI. I do weird stuff sometimes and it'd be nice to have an AI specifically tuned/trained for the types of things I do rather than trying to fit a generalist square peg into the round hole of strange things I tend to do when I'm bored or curious. The first thing you learn when studying this is that AI is explicitly non-deterministic.
What does "non-deterministic" mean? Well, "deterministic" means that, if you give it the same input, you'll get the same output every time. Computer code, for example, is deterministic. Non-deterministic, obviously, means the opposite of that. In fact, every training guide I've read is explicit that if you train an AI to the point where it spits out the same answer every single time to a given query, you've actually broken it. You want to back off while it's still a bit fuzzy in order to preserve what makes it AI.
The thing is, that's a problem in an unbelievable number of areas. I mean, sure, there are some fields in which close is good enough or where multiple answers could be correct, but most fields you at least need to be able to give an accurate answer, the same accurate answer, every time! Even 1%-2% off can be completely unacceptable in a huge amount of applications.
Now, this can be mitigated to some degree by the use of systems of AI that check each other but, even then, accuracy is too low for a great deal of common applications currently done by humans or even some done by deterministic software.
THE COMPUTE PROBLEM
One of the things I don't see talked about a lot is that it's taking exponentially more computing power to train and run each generation of AI. There are early generation AIs (circa 2020, 2022, or even sometimes 2024) that can run on your personal computer but, right now, even Moonshot's recently released Kimi K3 AI, which is an order of magnitude lighter weight than Sol, Fable, or Mythos, requires whole server racks to run just a local instance.
Meanwhile, the world is running short of computer parts. I'm sure you've noticed the price of video cards and, lately, memory for computers spiking. NVidia is trying to sell more powerful chips that don't even exist yet and may not even be past the concept stage and every major company has been spending exponentially more money trying to suck up all the chips that exist in an effort to gain a little bit of ground on the competition.
Every chip maker in the world is working at maximum capacity right now and I'm not even confident they'll be able to produce enough for the next generation, much less the one after that. Moreover, the constrained supply and exploding demand is creating the next problem which is…
THE MONEY PROBLEM
The late 2010s and early 2020s were a really interesting time money-wise in that a lot of big companies, tech companies in particular, had enormous piles of cash and nothing to spend it on. There weren't enough promising start-ups to grab and they didn't have enough promising R&D opportunities to funnel money into, so they ended up with huge balance sheets based on their incredibly profitable core businesses.
But here's the thing, if you're a business that doesn't show good potential for growth, no matter how consistent or profitable your core business is, your stock price falls. Most people buy stock on the promise of future growth, not current profitability. Don't get me wrong, profitable companies are great to invest in and can pay really nice dividends but only growth stocks will return above the 7% long-term average of the market.
Because of this, in order to maintain their stock price and keep their investors happy, those tech companies needed to find a place for that cash to grow, something that would show they still had growth potential and were worth a P/E ratio of 15-20 like a growing tech giant and not a P/E ratio of under 10 like a legacy company. AI fit the bill perfectly.
Eventually, though, AI started to cost too much, the piles of cash that were huge less than a decade ago are starting to dwindle and, increasingly, the AI boom is being funded by debt. That's not too big of a problem because, while interest rates are high relative to recent numbers, they're still way low compared to their long-term trend, so you can fund data center build-outs by borrowing money on fairly favorable terms.
The problem is that it looks like inflation is becoming an issue, and when inflation goes up, interest rates go up, and borrowing that was affordable at 4% or 5% might not be at 7%. Up to this point, the companies involved in AI have been borrowing pretty much at the maximum limit of how much money people are willing to give them. Even a small decline in their ability to raise capital or borrow money is going to be enough to tip things over to where they can no longer repay debt as it comes due.
I'm not sure we're there or if/when we ever will get there, but there's a huge risk, particularly given the level of circular financing in the industry, that even a small negative shift in the broader economic conditions could cause the whole thing to topple in on itself. It's honestly a bigger concern than it should be right now.
THE CLOSED SYSTEM PROBLEM
In addition to all of that, one of the biggest problems we have is that neither of the two main frontier labs is public.
Google is a public corporation, it has to release details of what it's doing, what it's spending, what its plans are, and some information about its products. The same is true of Microsoft and Meta. Anthropic and OpenAI are not, which means they can pretty much choose not to release… well, whatever information they want.
Seriously, no one knows anything more about the model weights, the training, the guardrails, the finances, or anything else other than what they've chosen to release publicly. Legally, there are very few requirements for what they have to do and, quite frankly, they haven't actually done all that much. What we know about the American AI models largely comes from the public disclosure of Anthropic and OpenAI's corporate partners and the occassional leak of information to journalists from sources within the company who may or may not have their own axes to grind.
But there is one thing we do hear from them all the time; how dangerous their products are.
Speaking of… stop me if you've heard this one, AI is so powerful it could doom all of humanity. Sam Altman and Dario Amodei rarely miss an opportunity to opine on how their products could be used to destroy humanity and, usually when they're trying to raise another round of funding or trying to distract from an embarrassing news story, you can find them giving interviews to friendly or non-tech savvy reporters about how we're on the cusp of reaching the point where AI, specifically their AI, could end the world as we know it.
How plausible is that? It's unclear, largely because they don't release any actual information. It's fairly clear that their models are very good at hacking and, from general use, we can see that they're also very useful for programming and mathematical proofs, though it remains to be seen if they're cost-effective, but if there's any evidence that any AI is impossibly good at any other task I haven't seen it.
What the closed system does is make it so that their opinions are the only ones on the topic that can be informed by actual facts and, let's face it, they have every incentive to exaggerate the capabilities of the product that they desperately need to sell to the public for hundreds of billions of dollars or face financial ruin. That deserves a bit of detail in its own category so…
THE DOOM PROBLEM
I'll admit the possibility that, at some future point, AI or some other technology may advance to the point that the machine-pocalypse happens, but you have to admit there's a far more likely explanation for all the talk of it today.
You see, AI companies have a problem: they're spending trillions of dollars on a product that's not currently making enough money to pay the interest on all of that debt. More to the point, in order to get to where they have a product that can make enough money to pay the interest on the debt, it looks like they're going to have to spend hundreds of billions, probably even trillions, more!
How do you get someone to give you money in that scenario? Well, you clearly can't sell it with your actual product, but what if your product was potentially so powerful as to wipe out humanity? What if it was even just on the cusp of that level of capability? That's obviously something that could be extremely profitable if put to more constructive uses.
Of course, this kind of talk is a double-edged blade because, as much as greedy investors want to fund the most powerful, capable thing, the public hears that kind of talk and turns against the product. We're already seeing this in local opposition to data centers and national demands for tighter regulation.
More to the point, the fact that AI companies have to resort to this kind of talk, making fantastical claims about their product instead of making more reasonable predictions, tells me that they might be in a little bit of trouble.
WHAT'S NOT A PROBLEM
Look, it's very clear that AI is actually really useful… at certain things.
What it's genuinely good at is statistical analysis. I mean, heck, the whole reason it can mimic human-like speech patterns is because it has statistically analyzed billions of words produced by humans and produced a statistical model that can then create something that sounds like that.
It's also really good at tasks like programming and mathematical proofs for the same reason. If it can absorb a ton of information about something and then run through a training gamut where it can receive quick confirmation on whether it did well or poorly in a repeating cycle, it's likely that it can be trained to do a thing! And AIs trained this way have already produced impressive code and developed several mathematical proofs that have bedeviled humans for decades or even more.
Where it gets really fun is when we can use that to look for patterns in things we've never found patterns in before. After training on known data sets, an AI was produced that could quite reliably find breast cancer in a lab five years before it was detectable by other methods. That's really cool!
WHY THAT'S STILL A PROBLEM
But here's the thing, in order for an AI to be useful in the ways I described above, it generally has to have a reasonable amount of training from someone who knows what they're doing and the results themselves are generally only particularly useful for a similarly skilled individual.
Now, this isn't bad, I think AI, particularly Generative AI, is still going to be extraordinarily useful in science and will power a not insignificant number of breakthroughs, but that's not great if you're an AI company.
You see, if you're an AI company, you're pouring an absolutely absurd amount of money into creating this thing and you desperately need to make that money back at some point. The best way to do this is to have as many potential customers as possible but, if the product is only really useful to someone who is fairly experienced in a given field, that's a much tighter limit on the number of potential customers than a product that anyone can use for anything.
That's a big problem because if they can't get enough money to repay all the money they've borrowed, or at least make the interest payments, the AI companies are going to go under eventually. Getting enough money means making it useful enough to enough people that the collective amount of money they are willing to pay covers the cost of development and operation. The more the amount of money put into development (and operation as the models get more complex) increases, the more you need to remove any limits on the factors involved in making money.
CONCLUSION
I mean, I think it's fairly clear that there are cases where AI can perform certain tasks significantly faster and sometimes even better than a human being. Unfortunately, all of these cases have the same two issues which are (1) they still need to be overseen by someone with significant background/education in the task and (2) none of them have the kind of widespread demand that will allow the AI companies to pay back the now trillions of dollars that have been invested in creating the product. None of what we've seen so far is even close to the "AI will replace all human labor" or "AI will overwhelm humanity and bring about the apocalypse" situations that you hear so many people talking about.
And look, maybe one day we'll have computer programs so advanced that they will be able to match or even outmatch human beings on a wide range of things, but it seems unlikely both that we're on the cusp of it now or that the current AI technology will get there anytime soon. As far as I can tell, the claims in that direction seem to be mostly motivated by a need to raise money, keeping the current companies in business in the hopes that a few more months will allow them to discover a use case that could generate enough revenue to recoup the trillions of dollars they now owe from their efforts so far, rather than any genuine analysis of the product itself and the trend of its capabilities over time.
As usual, let me know if you think I missed anything or if you think I'm wrong about something important, I'm always happy to hear things I haven't thought of.











