You’ve heard the hype around artificial intelligence, but a growing number of voices warn that the party can’t last forever. The AI bubble burst is a scenario that science fiction author and journalist Cory Doctorow believes is not only possible, but likely. He argues that this so-called AI market crash won’t just be a financial event — it will leave a lasting scar on the workforce. According to Doctorow, when the bubble bursts, executives who replaced staff with AI will discover that rebuilding lost human skills takes a “really long time.” That’s a sobering message, and he’s bringing it directly to Australia.
Doctorow is heading Down Under to spread his core idea: that humans will take their jobs back. He’s scheduled to appear at Sydney‘s Festival of Dangerous Ideas from 22 to 23 August and at Melbourne‘s the Capitol on 25 August. His visit is a timely reminder that the AI investment risks aren’t just about lost capital — they’re about the irreplaceable value of human expertise. So before you assume the AI gold rush is here to stay, it’s worth listening to what Doctorow has to say about the crash that could change everything.
What Does the AI Bubble Burst Mean?
For Doctorow, the AI bubble burst isn’t just a prediction; it’s a structural inevitability. The immediate trigger is a collapse in what he calls circular investment. This is the money flowing into AI infrastructure that isn’t coming from real customer demand, but from a closed loop of speculative capital. AI companies use their funding to buy compute power, which flows back to the infrastructure providers, who then reinvest in even more capacity.
Why Circular Investment Is Fragile
This circular investment in the AI sector could be the first domino to fall. It’s fragile because it’s largely disconnected from sustainable end-user revenue. If the downstream applications don’t generate enough cash to justify the massive upfront costs for chips and datacentres, the entire loop breaks. The hype alone can’t sustain it forever.
The Role of Gulf Sovereign Wealth Funds
The AI bubble is kept afloat by massive investments from Gulf sovereign wealth funds and billionaires into new datacentres. This connection between Gulf sovereign wealth funds and AI is a key pillar of the current market. These investors are providing the fuel, but they are ultimately seeking returns. If they sense the AI funding bubble is bursting, they can pull back quickly, accelerating the downturn.
So, what does the AI bubble burst mean for you? It signals a potential market correction where hype outpaces reality. It doesn’t mean AI disappears, but it does mean the current ‘spend first, ask questions later’ era is on borrowed time. Understanding this cycle helps you see which parts of the AI landscape are built on solid ground versus speculative sand.
The Human Cost: Why Skills Are Lost Forever
While the financial risks of a potential AI bubble burst dominate headlines, a quieter but more permanent cost is unfolding. When experienced workers are replaced by AI systems, the knowledge they carry does not simply transfer into a database. It disappears. This type of knowledge loss is often invisible on a balance sheet, but its effects ripple through organisations for years.
Workers who are fired, pushed into early retirement, or retrained for entirely different professions take decades of accumulated know-how with them. That expertise includes subtle judgment calls, instinctive problem-solving, and the ability to handle unusual situations — things no manual can fully capture. Unlike a software update, human experience cannot be patched back in once it is gone.
Examples of Irreplaceable Skills
Think about roles that depend on tacit understanding. A senior mechanic who has diagnosed hundreds of engine failures knows where to look first based on sound alone. A project manager who has navigated a dozen supply chain crises can spot a weak link before it breaks. These workers build mental models over years of trial and error. When AI job replacement removes them, the organisation loses not just a position but a living archive of edge cases and hard-won solutions. That gap cannot be filled by hiring a junior employee and handing them a user manual.
Why Copyright Falls Short
Some argue that copyright reform could protect creative workers from having their work used to train AI without consent. But as author and activist Cory Doctorow points out, copyright law “won’t solve anyone’s problems.” He suggests that labour law rights over AI terms and creative output would offer a more practical path forward. The core issue is not just who owns the finished work — it is whether the worker still has a say in how their skills are deployed. Once that human capital walks out the door, no legal framework can bring it back. The expertise is simply lost forever, and no amount of legislation can reverse that.
Alternatives to Copyright: Labour Law Rights Over AI
The idea that expertise can simply walk out the door raises a bigger question: if copyright can’t keep human talent from leaving, what can protect the workers who stay? Cory Doctorow has a clear answer. He argues that copyright law “won’t solve anyone’s problems” when it comes to the fallout from an AI bubble burst. Instead, he proposes a practical shift toward labour law rights that give creative workers direct control over how their output is used by AI systems.
How Labour Law Rights Would Work in Practice
Under this model, your employment contract would spell out exactly how AI can interact with your work — both the work you produce and the data you generate while on the job. Instead of relying on complex copyright claims that can take years to resolve, you’d have clearer protections baked into your everyday employment relationship. If an employer wants to train an AI on your creative output, they’d need your explicit agreement, and you’d have a legal basis to negotiate terms or refuse entirely. This approach puts AI labour rights front and centre, making them a daily workplace reality rather than an abstract legal battle.
Why Creative Workers Should Diversify Protections
Doctorow warns creative fields not to put all faith in copyright to safeguard their work. Copyright is slow, expensive, and often fails to address the speed at which AI companies iterate. By adding creative workers protection through labour law, you gain a second line of defence. It’s a practical hedge: if the AI bubble burst reshuffles the industry, you’ll have employment-based rights that stick with you regardless of who owns the copyright on any given piece of work. This isn’t about abandoning copyright — it’s about recognising that labour law AI protections can cover ground that intellectual property law simply cannot reach. For anyone earning a living from creative or technical skills, diversifying your legal safeguards is just smart career management.
Government Strategy: Wait for the Crash and Build on Open Source
If individual creators need to rethink their legal protections, what about the governments that are supposed to care for them? Author and activist Cory Doctorow offers a surprising answer: don’t rush to invest in AI right now. Instead, wait for the ai bubble burst and then build on what survives.
Doctorow argues that governments should sit on the sidelines during the current frenzy. Pouring public money into speculative AI ventures now, he says, is like buying shares just before the market turns. The smarter move is to save your resources and prepare for the aftermath. When the ai bubble burst leaves promising models stranded or underfunded, governments can step in and build on open-source AI foundations. That approach gives you control, transparency, and independence from the big players who currently dominate the field.
Why Waiting Is a Strategic Advantage
The logic is simple. Private companies are racing to capture the market, spending billions on proprietary systems. Many of those systems will collapse or become obsolete after the shakeout. If a government invests now, it risks backing a losing horse. If it waits, it can take the best surviving technology — often open-source AI that the community has already refined — and adapt it for public needs. This is not a passive strategy; it is a deliberate one. It means using the crash to reset the balance between public interest and private profit.
How Australia Is Responding
Australia is already thinking along these lines. Prime Minister Anthony Albanese has promised to ramp up protection against AI companies that refuse to pay for the content they use. That commitment signals a broader shift in government AI policy: moving from passive acceptance of whatever tech giants offer to active defense of local creators and industries. If the ai bubble burst accelerates, that stance could become a blueprint for other nations. The message is clear: don’t throw money at the hype. Prepare for the crash, protect your people, and build on open foundations when the dust settles.
Signs of the Bubble Burst and Counterarguments
If the hype is real, where is the crash? Watching the AI space right now feels a lot like watching a distant storm. You can see the clouds gathering, but you are not sure if they will hit your street or pass by. The ai bubble burst remains a hot topic, and the signs are there for anyone who cares to look. Yet for all the warnings, the exact timeline and the specific triggers remain frustratingly uncertain. Some AI market indicators point to a slowdown, while others suggest the run is far from over.
Potential Timeline and Warning Signs
You might start noticing the pressure in a few key areas. First, investor fatigue is real. When every startup claims to be an AI company, the market gets crowded and noisy. Profitability becomes the question nobody wants to answer. When the money dries up, the weaker players fold. Second, the technology itself has limits. It still makes mistakes, needs huge amounts of data, and costs a fortune to run at scale. Those operational realities act as a natural brake. The skepticism AI bubble crowd argues that these cracks will widen, and the correction is a matter of when, not if.
Counterarguments: Why the Bubble Might Not Burst
On the other side of the argument, plenty of voices say the bubble is not really a bubble at all. They point out that real-world adoption is still climbing. Businesses are using AI tools to cut costs, improve customer service, and automate tedious tasks. The demand is genuine, not just speculative. The infrastructure is being built slowly but steadily. A gradual slowdown is more likely than a sudden pop. In this view, the hype deflates, but the technology keeps growing underneath. That is not a crash — it is a normal market correction.
If you want a thoughtful look at what comes next, Doctorow’s new book is titled ‘The Reverse Centaur’s Guide to Life After AI’. It offers a practical framework for understanding the post-AI economy without the panic. It suggests that the real value lies not in the AI itself, but in the human decisions and open systems that surround it. That is a perspective worth keeping in mind as the storm clouds gather or pass by. The future might not be as dramatic as the headlines suggest, but it will be different. Prepare for that difference, not the disaster.
Frequently Asked Questions
What exactly does Cory Doctorow mean by the AI bubble bursting?
Doctorow describes the AI bubble burst as the moment when overhyped valuations and unsustainable investments collapse, leaving only a few truly useful tools standing. It mirrors past tech bubbles where speculation outpaced practical value. For you, this means a shakeout that separates reliable AI from vaporware.
How can governments protect against AI companies during the bubble?
Governments can enforce stricter antitrust rules and demand transparency in funding sources before awarding contracts. They should avoid pouring public money into unproven startups and instead require independent audits of claims. This practical step helps prevent taxpayer losses when the AI bubble burst hits.
Why will it take a long time to recover lost skills after the bubble bursts?
When overhyped AI firms fail, many specialized engineers and researchers leave the field, taking niche knowledge with them. Rebuilding that expertise requires new training programs and a stable market—both of which take years to develop. You will see a slow, careful rebuilding rather than a quick fix.
