The AI Bubble Is About To Burst, But The Next Bubble Is Already Growing

Techbros are preparing their latest bandwagon.

Photo by Dynamic Wang on Unsplash

Speculation rules the world. It didn’t used to. But from the 1980s through to 2008, something changed. Investors realised that they could get far more return from hype than from any kind of legitimate business. This is the information age, after all, and information is easy to manipulate and commodify. This led to the dot-com bubble, the 2008 credit crunch, the 2016–2017 cryptocurrency bubble, the late 2020–2021 cryptocurrency bubble, and the 2022 NFT bubble, with the latest fad being the AI bubble. In fact, nearly half of the world’s private investment is being funnelled into AI, and AI speculation is the main driving force behind the S&P 500’s recent growth. But, just as the others did before their catastrophic failure, the AI bubble is showing signs of imminent bursting. However, the finance and tech bros have learnt their lesson and are developing the next bandwagon to ride off into the sunset with all our money, ready for when they inevitably need to jump ship. It’s just a shame that it’s even more of a dead end than AI.

So, it’s essentially common knowledge that the AI bubble is ripe for bursting. Things like the efficient compute frontier (read more here) and the Floridi conjecture (read more here) mean that the AI models we have now are about as good as they will ever be. Even if OpenAI spent trillions of dollars increasing the size of their models tenfold, they would only be slightly better. The recent release of ChatGPT-5 is a perfect example of this. It had significantly more data, training, and cash shoved into it than its little brother ChatGPT-4, yet it is only marginally better than ChatGPT-4.

This is a huge problem! Because, as they currently stand, generative AI models aren’t actually that useful or even remotely profitable.

An MIT report found that 95% of AI pilots didn’t increase a company’s profit or productivity. For the 5% in which it did, the AI was relegated to back-room, highly constrained admin jobs, and even then, there were only marginal improvements. A METR report found that AI coding tools 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. Logically, 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. As it stands, generative AI is far too error-prone to deliver meaningful increases in productivity or profitability in the vast majority of use cases.

In other words, for AI models to actually deliver on the speculation driving their massive investment, they need to become far, far better, which involves spending exponentially more money.

This is another huge problem, given that OpenAI, which has by far the largest customer base of any generative AI company, is still losing money hand over fist for every one of its $200-per-month plans. In fact, it seems they would need to sell it at more like $2,000 per month to break even (read more here).

Big Tech, backed by venture capital and investment banks, has spent hundreds of billions of dollars each year on AI for the past few years. Yet the technology is reaching its limitations and can’t improve, as well as being far, far away from profitability. It is a perfect bubble, with colossal amounts of money being used to bolster completely unfounded and outright false speculation. And now, with GPT-5’s disappointment, Meta’s restructuring and downplaying of its AI division, and interest rates threatening to rise, the very investors who helped inflate this bubble are warning that it will burst. Even Goldman Sachs, which has piled tonnes of money into the AI bubble, has warned that the AI bubble will likely pop soon, and when it does, it will also take the data centre bubble down with it, causing immense damage to not just xAI, Meta, Google, Anthropic and OpenAI, but also tertiary players like Amazon, Oracle and Nvidia who provide AI infrastructure.

In other words, when this bubble bursts, it will deal unbelievable damage to every weird tech bro and toxic finance guy you know.

Fortunately, they have a plan to sidestep this man-made economic apocalypse. Quantum computers. And they are all desperate for us to hop on the new bandwagon.

Sadly, I don’t have time to explain how a quantum computer works in great detail, but if you are interested, Veritasium’s old video is great. However, in layman’s terms, rather than using bits, which can be 1 or 0, a quantum computer uses qubits, which can be both. This means a quantum computer can, in theory, have exponential computing power, as it can receive a huge amount of inputs at once and spit out a huge amount of outputs at once too. In fact, a quantum computer recently solved a math problem in minutes which would have taken our best supercomputer longer than the age of the universe itself to solve. As such, quantum computing promises to deliver revolutionary advances in chemistry by calculating complex molecule structures and interactions, as well as in machine learning and AI, given that its exponential computing power could remove the current limitations of the technology.

In fact, some have even suggested that our brains are quantum computers, and that this means quantum AI could finally create machines with actual human-level intelligence that are far more efficient and cheaper to run than current models.

I’m sure you can see where this is going. Every tech bro and finance guy deeply invested in AI is now buying into the speculation that quantum computers could solve all of AI’s problems and so are throwing their money and weight behind it.

AI giants Google, Microsoft, and Amazon are currently developing their own quantum computers. Nvidia is developing quantum computer hardware and software platforms. OpenAI recently hired some of the best photon-based quantum computing physicists in the world. Even Musk has started to float the idea of quantum computing for his AI ambitions. But it isn’t just the big players; the smaller quantum computing outfits are beginning to see huge amounts of investment and watching their value skyrocket. Take Quantinuum, a rather small quantum computing research startup, which recently raised $600 million, doubling its value to $10 billion.

This seems to be their escape pod from the AI bubble — to force money and hype into this technology, which promises to solve all the problems the AI world is facing. So is this just replacing one bubble with another? Are these tech giants and their backers pouring billions into false promises again? Or can quantum computing actually solve the problems the AI industry is confronted with?

Well, sadly, quantum computing isn’t what it seems.

For one, the hardware is still miles from being fully functional. A true universal and legitimately operational quantum computer is still 10 to 20 years away. They are insanely hard to build and even harder to operate. This timeline could be sped up with some seriously large investments, but as fusion has shown, nothing is guaranteed. .

Truth be told, the hardware isn’t the issue. It is the software.

In most situations, a quantum computer is actually far slower than a normal supercomputer. It is only in these very specific, complex tasks, like calculating factorials, that it can outperform them. However, due to the peculiarities of qubits, these computers can’t run standard code or algorithms. They require specific algorithms, which is the problem.

Remember how, when you open the box of Schrodinger’s cat, you fix its superposition as either dead or alive? This is known as collapsing the quantum wave function. Well, when you read a qubit, you do the same, and by fixing its state, it makes it just like a regular computer, either a one or a zero, which renders this entire exponential computational thing moot. Instead, the computer needs to run an algorithm that uses quantum wave interference to whittle down the qubits to the useful ones before reading; that way, when we do read it, we get a useful answer and can harness this exponential computing power.

However, it is incredibly hard to figure out these kinds of algorithms, and they can only be used to solve very specific kinds of multi-nodal complex tasks.

We already have a few of these algorithms capable of finding factorials or modelling quantum physics, but that is about it. We have yet to find any that work for chemistry simulations or for neural networks that power AI, and many researchers think there might not even be any relevant algorithms for these applications. They point to the fact that the kind of data used in AI training is very unstructured and that the actual maths being calculated during training isn’t suitable for a quantum computer.

So, even if Big Tech can expedite the delivery of genuinely usable quantum computers, the current science says that they won’t make any difference to AI. In fact, we have found so few quantum algorithms that the majority of this technology’s promised upside seems totally unfeasible.

If it’s hard to get your head around this topic — which, let’s face it, it’s quantum physics; it’s always stupidly hard — there’s a channel run by a quantum computing PhD graduate called Looking Glass Universe. This video explains how quantum computers work and their limitations very well.

Yet again, it is all hype, no trousers.

Even the notion that our brains are quantum computers, the very idea that sparked the whole quantum AI movement, has been pretty much debunked with recent studies.

But that doesn’t matter. Reality no longer matters. This notion is in the zeitgeist. The misinformation about what quantum computers can do is out there and thriving. And these knobheads are ready to commodify that.

If Big Tech and its backers can grow the quantum computer bubble fast enough, all they will do is delay the AI bubble bursting. Sooner or later, the hype will die down after promised gains don’t materialise and the zeitgeist starts to align with reality. All those hundreds of billions of dollars, which should have gone to increase workers’ pay to at least keep up with inflation but were instead syphoned into this money pit, will disappear, and we will have virtually nothing to show for the effort. It’s pathetic, it’s sad, and it will hurt all of us, apart from those billionaires at the top, because they will have already extracted their money before it all goes tits up.

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Sources: The Register, The Guardian, Quillet, PC Gamer, Fortune, Viva Technology, AWS, Quantum Foundry, BBC, Fortune, Wiley, MIT, Stanford, Will Lockett, Quantinuum