AI Will Destroy Everything

But not in the way you think it will.

Photo by hao wang on Unsplash

If you take the tech bros seriously, we will all be suffering in a Terminator-esque AI dystopia in just a few years because their AI models will be so damn good. And why shouldn’t we believe them? It isn’t like they have a huge vested interest in AI being perceived as an existential threat… Meanwhile, back in the real world, AI poses a gargantuan threat to us all; it’s just more like 2008 and 1933 rather than some big Hollywood production. You see, AI critics like myself have been comparing the AI bubble to the infamous dot-com bubble of the late ’90s, but we have been sorely wrong. It is far, far worse. The dot-com crash will seem like a picnic in the park in comparison to what is to come.

There is a good chance that many of you reading this weren’t even born when the dot-com bubble happened (my god, am I old!), so let’s quickly recap.

The dot-com bubble occurred in the late 1990s because e-commerce suddenly became viable. Vast swathes of the population had a personal computer and a slow dial-up internet connection (which involved accessing the internet via landline phone cables). At the time, capital was cheap too. So investors piled money into internet businesses, hoping to scoop up large portions of the market, as well as building infrastructure, such as much faster fibre optic broadband networks. This drove the speculative values of businesses behind this boom through the roof! But by the end of the year 2000, the bubble burst, these businesses’ valuations tanked, many went bankrupt, millions were laid off, the economy was in tatters, and a recession ensued.

Why?

Investors were keen to jump on this new market. They believed that early movers would dominate their respective sectors and that commerce would rapidly migrate to mostly online markets. So they speculatively valued small upstarts at astronomical levels, creating the bubble.

This bubble was then primed to pop by overinvestment in infrastructure and a lack of profitability. The adoption of the internet and e-commerce was significantly slower than predicted, and thus, the billions of dollars invested in building faster networks were largely wasted. Four years after the bubble burst, 85% to 95% of the fibre optic internet networks laid in the 1990s remained unused. Even the largest internet businesses, such as Pets.com, were losing hundreds of millions of dollars a year. To attract customers, they had to lower prices, and the cost of expanding their capacity was crippling, making profitability impossible. Because capital was cheap, many used loans backed against their extortionate valuations just to keep the lights on.

The pin that popped the bubble was a shift in the economy. Recession fears shook the world after Japan’s economy faltered, and to prevent inflation from gripping the US, interest rates were hiked. Fear of a recession meant investors’ appetite for risk reduced, as they looked for a safe place for their money to weather the storm, so speculative internet investments were less attractive. But the rising interest rates suddenly made borrowing money expensive, and as such, internet businesses couldn’t raise the funds they needed to pay for their losses. In one fell swoop, investors pulled their cash, major players declared bankruptcy, and the entire bubble collapsed.

If you have been paying attention to the generative AI industry, you can already spot the similarities. Now, we know this isn’t just similar but nearly identical to the dot-com bubble. From the investor hype to the infrastructure overspend to the lack of profitability to the economic forces that pricked the bubble, all the signs are there. Except this time, the bubble is so much bigger, and the damage it will cause when it inevitably bursts will be biblical. Let me explain.

I will cover how inflated AI stocks are in a minute. For now, let’s start with the surprising fact that no sector of the AI industry is profitable, from its infrastructure to operators to adopters — everyone along the chain is losing money.

A recent MIT study found that 95% of AI pilot projects fail to yield meaningful results. For the 5% where AI yielded results, the improvements were marginal, and the AI was used to augment highly constrained back-office jobs, not replace workers as promised. A METR report found that AI coding tools, which are meant to be the most promising application for generative AI, actually slow developers down. The inaccuracy of these models means they repeatedly make very bizarre coding bugs that are highly arduous to find and correct. As such, it is quicker and cheaper to get a developer to code it themselves. Research has even found that for 77% of workers, AI has increased their workload and not their productivity, leading to burnout and decreased performance.

In other words, all the data points to the fact that there is actually very little upside to a business adopting AI. Personal users are also struggling with adopting AI, as it seems to be highly damaging to their mental health, leading to a dramatic uptick in reports of “AI Psychosis”. It isn’t much of a surprise that AI adoption has lagged behind predictions for years now (with more on that in a bit).

And it isn’t like AI operators, such as OpenAI, are making bank either. In fact, they are losing money hand over fist! Despite having by far the largest income of any AI company, OpenAI still loses a dramatic amount of money for every one of its $200-a-month plans. Their models are just too expensive to build and run. In fact, there are suggestions that OpenAI would have to charge ten times price that just to break even.

But OpenAI is only valuable because investors think it could one day make “agentic AI”, which is “smart” enough to do complex tasks reliably and autonomously. However, to improve their currently unreliable models, let alone reach this agentic myth, OpenAI needs to invest exponentially more money in them — so, they are actually getting further away from profitability! Analysts have found that OpenAI is set to post a loss of over $14 billion in 2026, which is a larger loss than many of the banks that folded in 2008. OpenAI’s own numbers, which use unbelievably optimistic AI adoption rates, suggest that by 2029, it will post losses in the hundreds of billions of dollars (read more here). Every other generative AI business is facing the same problem. They really aren’t profitable at all.

But remember how one of the big issues with the dot-com bubble was overinvestment in infrastructure? Well, AI has that too, and those building AI infrastructure, namely data centres, are also losing money hand over fist!