It’s becoming clearer that we are in a perilous financial situation globally. Fears over an “AI bubble” are being cited by the Bank of England, the International Monetary Fund and the boss of JP Morgan, Jamie Dimon.
If you want a sense of how insane the narrative is around AI investments, consider this: Thinking Machines Lab, an AI startup, recently raised $2bn funding on a valuation of $10bn.
The company has zero products, zero customers and zero revenues. The only thing it made public to its investors was the resume of its founder, Mira Murati, formerly chief technology officer at OpenAI. If that’s not hubris meeting market exuberance, what is?
But narrative is crucial here because it’s what’s driving all this insane investment in the future of AI or so-called artificial general intelligence (AGI), and it’s important to examine which narrative you believe in if you are to protect yourself for what’s to come.
If I were to pick between the views of a politician such as UK prime minister Keir Starmer, and a writer such as Cory Doctorow, I’d put my bet on Doctorow. Contrast these two statements and see which you feel more comfortable with…
Doctorow suggests the AI bubble needs to be punctured as soon as possible to “halt this before it progresses any further and to head off the accumulation of social and economic debt”.
He suggests doing that by taking aim at the basis for the AI bubble – namely, “creating a growth story by claiming that AI can do your job”.
AI is the asbestos we are shovelling into the walls of our society and our descendants will be digging it out for generations Cory Doctorow
Claims about jobs disappearing to AI have been around since 2019 with Sam Altman, then leader of venture capital (VC) fund Y Combinator, speaking about radiology jobs disappearing in the future: “Human radiologists are already much worse than computer radiologists. If I had to pick a human or an AI to read my scan, I’d pick the AI.”
Fast forward six years to 2025 and look how that worked out. According to a recent report by Works in Progress, despite the fact that radiology combines digital images, clear benchmarks and repeatable tasks, demand for human radiologists is at an all-time high.
The report authors’ conclusions drive a horse and cart through the current AI/AGI narrative that if left unstopped will cause severe global economic pain: “In many jobs, tasks are diverse, stakes are high, and demand is elastic. When this is the case, we should expect software to initially lead to more human work, not less. The lesson from a decade of radiology models is neither optimism about increased output nor dread about replacement. Models can lift productivity, but their implementation depends on behaviour, institutions and incentives. For now, the paradox has held – the better the machines, the busier radiologists have become.”
Across other sectors too, the mythology around job losses is slowly being interrogated – for example, Yale University Budget Lab found no discernible disruption to labour markets since ChatGPT’s release 33 months ago.
The research goes on to state: “While this finding may contradict the most alarming headlines, it is not surprising given past precedents. Historically, widespread technological disruption in workplaces tends to occur over decades, rather than months or years. Computers didn’t become commonplace in offices until nearly a decade after their release to the public, and it took even longer for them to transform office workflows. Even if new AI technologies will go on to impact the labour market as much, or more dramatically, it is reasonable to expect that widespread effects will take longer than 33 months to materialise”.
Normal technology
In other words, AI is just, well, technology as we have always known it – or as experts Aryind Narayanan and Sayash Kapoor call AI, just “normal technology”.
Importantly in their paper, AI as normal technology – An alternative to the vision of AI as a potential superintelligence, they identify key lessons from past technological revolutions – the slow and uncertain nature of technology adoption and diffusion; continuity between the past and future trajectory of AI in terms of social impact; and the role of institutions in shaping this trajectory. They also “strongly disagree with the characterisation of generative AI adoption as rapid, which reinforces our assumption about the similarity of AI diffusion to past technologies”
A good example of AI as normal technology without all the hype, hyperbole and billion-dollar burn rate, is the City of Austin, Texas. Here, an on-premise AI system helped the local government process building permits in days instead of months.
According to David Stout, CEO of WebAI, this was done “with no spectacle. No headlines. Just efficiency gains that will outlast the market cycle. He said, “That’s the point too often missed in the frenzy. Mega-models attract headlines, consume billions in capital, and struggle to demonstrate sustainable economics. Meanwhile, smaller, domain-specific systems are already delivering efficiency gains, cost savings and productivity improvements. The smart play isn’t to abandon AI, but to pivot towards models and deployments that will endure”.
Technology like we have always known it to be – not the insane fantasy of “superintelligence” that is powering this dangerous bubble.
The question to ask is, given the prediction of at least a 33-month lag before any return on investment, however small, will the markets wait another 33 months for their returns to materialise?
Protracted crisis
A recent report on MarketWatch suggests the AI bubble is now ”seventeen times the size of the dot com frenzy and four times the sub-prime bubble”. MarketWatch quotes financial analyst Julien Garran, who previously led UBS’s commodities strategy team, who said “AI now accounts for over four times the wealth trapped in the 2008 sub-prime mortgage bubble, which resulted in years of protracted crisis across the globe”.
Warnings from the Bank of England in its semi-annual Financial Policy Committee report are equally stark: “Uncertainty around the global risk environment increases the risk that markets have not fully priced in possible adverse outcomes, and a sudden correction could occur should any of these risks crystallize.”
The bank also warned of “the risk of a sharp market for global financial markets amid AI bubble risks and political pressure on the Federal Reserve.”
What a sudden correction means is that a collapse of the AI investment bubble will take trillions of investment with it, impacting us all.
Even more worrying is the issue of debt financing among those competing in the AI race – that is, all the tech bros. It now appears, according to Axios, that these companies are turning to private debt markets and special purpose vehicles for cash, which means this kind of borrowing does not have to show on their balance sheets.
Meta, for example, recently sought $29bn from private capital firms for its AI datacentres. This off-book debt financing should ring more alarm bells that something is terribly wrong with the AI growth narrative.
After all, as pointed out by the Axios analysts, “If hugely profitable tech companies need to mask their borrowings to fund AI spending, it signals they’re not confident that they’ll soon get the returns needed to justify such investments. That suggests the very spending powering today’s earnings boom can’t last forever.”
Unit economics
To go back to Cory Doctorow’s argument, we are not in the early days of the web, or Amazon, or other dot com companies that lost money before becoming profitable: “Those were all propositions with excellent unit economics. They got cheaper with every successive technological generation and the more customers they added, the more profitable they became”.
AI companies do not have excellent unit economics – in fact they have the opposite, according to Doctorow: “Each generation of AI has been vastly more expensive than the previous one, and each new AI customer makes the AI companies lose more money”.
[Only] about 5% of tasks will be able to be profitably performed by AI within 10 years Daron Acemoglu
And if that’s not sobering enough for the VC and private equity firms, then the circular investing going on between these tech firms should be a huge concern.
Microsoft is investing $10bn in OpenAI by giving free access to its servers. OpenAI reports this as an “investment,” then redeems these tokens at Microsoft datacentres, which Microsoft books as $10bn in revenue.
Bain & Co says the only way to make today’s AI investments profitable “is for the sector to bring in $2tn by 2030,” which, according to the Wall Street Journal, is more than the revenue of Amazon, Google, Microsoft, Apple, Nvidia and Meta – combined.
Taking a closer look at US economic growth is surely more cause for concern.
According to Harvard economist Jason Furman’s analysis, GDP growth in the first half of 2025 was driven almost entirely by investment in information processing equipment and software. This spending was largely tied to the rapid expansion of AI infrastructure and datacentres.
While these tech sectors only made up 4% of total GDP, they contributed a staggering 92% of growth. Absent this investment, Furman estimates US GDP growth would have hovered around 0.1% on an annualised basis – barely above zero.
There is a lot riding on a technology that’s supposed to be godlike and all powerful but which, according to MIT Institute professor Daron Acemoglu, is far less likely to achieve the insane hyperbolic claims being made by the tech bros in an effort to win an unwinnable race.
Acemoglu estimates the 10-year effect of AI in the US will be that only “about 5% of tasks will be able to be profitably performed by AI within that timeframe,” with the GDP boost likely to be closer to 1% over that timespan. If that’s not a recipe for stock market collapse, what is?
Emperor’s new clothes
Going back to the AI booster narrative and how it’s driving things, Doctorow is again incisive: “The most important thing about AI isn’t its technical capabilities or limitations. The most important thing is the investor story and the ensuing mania that has teed up an economic catastrophe that will harm hundreds of millions or even billions of people. AI isn’t going to wake up, become super intelligent and turn you into paperclips – but rich people with AI investor psychosis are almost certainly going to make you much, much poorer”.
I’m not an economist, so I did what we are all supposed to do now for our enlightenment. I gave the machines built by the tech bros all the same prompt: “What fable best encapsulates the current AI bubble?”
Gemini, Perplexity and ChatGPT were all in agreement with nearly the same explanation of why they all picked the same story: “The emperor’s new clothes remains the best classic fable to explain the AI bubble, as it encapsulates the collective willingness to believe in – and profit from – an imagined reality, until facts and external shocks eventually break the spell.”
Fossil consumption calculation process for use of fossil fuel in an integrated kraft pulp mill. Credit: Applied Energy (2025). DOI: 10.1016/j.apenergy.2025.126685
The pulp and paper industry consumes large amounts of energy. But despite stricter EU requirements for efficiency improvements, there has been no way to measure and compare energy consumption between different companies in a fair way. In collaboration with the Swedish Environmental Protection Agency, researchers at Linköping University, Sweden, now present a solution that has great potential to be used throughout the EU.
“Even if this would contribute to increasing efficiency by one or a few percent only, this involves so much energy that it can make a huge difference,” says Kristina Nyström, Ph.D. student at the Department of Management and Engineering at Linköping University.
Globally, the pulp and paper industry accounts for 4% of energy used by the industrial sector. Through its Industrial Emissions Directive, the EU has set efficiency requirements for the industrial sector to reduce climate impact. An important tool for this is to make comparisons between factories within an industry—so-called benchmarking.
“But this has not been possible in the paper industry, because the mills have been so different that comparable results have not been achieved,” Kristina Nyström explains.
Therefore, the Swedish Environmental Protection Agency, assisted by Linköping University and Chalmers Industriteknik and in consultation with the paper industry, has developed a calculation method to enable comparisons. The method, which is presented in an article published in the journal Applied Energy, has great potential to be used throughout the EU, according to Olof Åkesson, former Swedish Environmental Protection Agency employee, who initiated the project.
The solution is to divide paper production into standardized processes such as actual pulp production, dissolution of purchased pulp, drying of pulp or paper production. These processes are common to enough mills for comparisons to be meaningful. In this way, companies can discover what in their processes works less efficiently compared to others, where improvements can be made and which actions would be most beneficial.
In addition, this method allows for more measures to be included in the energy efficiency efforts. One example is that companies are credited with the residual heat from manufacturing that is used in the surrounding community, such as the heating of homes or greenhouses.
Should this method gain ground, it could contribute to a changed approach to energy efficiency. At present, public agencies’ demands for energy audits often focus on details, which risks significant efficiency measures being overlooked.
“The benefit of making the pulp and paper industry more efficient is that this can reduce the use of fossil fuels and release raw materials, biofuels and electricity for other purposes,” says Åkesson.
With the involvement of researchers, public agencies and companies in the pulp and paper industry, chances are high that the method was designed in a way that is useful in practice. The collaboration between organizations can serve as a model for other industries wanting to develop their own measurement methods, according to Nyström.
Several companies that tested the measurement method have been positive, and it now needs to be spread and tested on a larger scale, the researchers say. The Swedish Environmental Protection Agency is working to develop the model, now also in dialog with public agencies and the pulp and paper industry in Finland.
More information:
Olof Åkesson et al, A calculation method enabling energy benchmarking in the pulp and paper industry: Adopting a methodology that bridge the research–policy implementation gap, Applied Energy (2025). DOI: 10.1016/j.apenergy.2025.126685
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Paper industry could become more energy-efficient with a new measurement method (2025, October 16)
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Cutting planet-warming pollution to near-zero will take more than inventing new clean technologies—it will require changing how the world invests in them. That’s especially true for industries like aviation, where developing and adopting greener solutions is risky and expensive, according to a University of California San Diego commentary piece in Science.
The paper calls for smarter ways of managing investment risk that could help speed up the shift toward cleaner air travel and other hard-to-decarbonize sectors.
“The aviation sector—a fast-growing source of greenhouse gases—illustrates the broader challenge of industrial decarbonization: too little investment in technologies that could yield the biggest climate benefits,” said the paper’s co-author David G. Victor, professor of innovation and public policy at the UC San Diego School of Global Policy and Strategy and co-director of the Deep Decarbonization Initiative.
The piece outlines a new approach that could help guide a coalition of research and development (R&D) programs alongside investors and airlines seeking to deploy new technologies to curb carbon emissions from the aviation industry.
“Despite all the chaos in global geopolitics and climate policies these days, there are large and growing pools of capital willing to take risks on clean technology,” Victor said. “What’s been missing is a framework to guide that capital to the riskiest but most transformative investments.”
He added that investors and research managers tend to focus on familiar, lower-risk projects like next-generation jet engines or recycled-fuel pathways.
“But getting aviation and other hard-to-abate sectors to near-zero emissions means taking on bigger risks with technologies and new lines of business that will be highly disruptive to the existing industry. Investors and airlines need to find smarter ways to encourage and manage these disruptive investments,” Victor said.
In the article, Victor and co-authors call for a more realistic framework to guide both research funding and private investment.
They propose a tool called an Aviation Sustainability Index (ASI)—a quantitative method to assess how different technologies or investments could help decouple emissions from growth in air travel.
The approach is designed to help investors distinguish between projects that only modestly improve efficiency and those that could significantly transform the sector’s climate impact.
The authors note that while roughly $1 trillion is expected to flow into aviation over the next decade, most of that money will simply make aircraft slightly more efficient. Few investors, they argue, have clear incentives to back the kind of breakthrough technologies—such as hydrogen propulsion, advanced aircraft designs, or large-scale sustainable fuel systems—that could substantially reduce emissions.
“Cleaner flight is possible, but it requires changing how we think about both risk and return,” Victor said. “We need new institutions, incentives, and partnerships that reward innovation, not just incrementalism.”
The commentary, written by a multinational team of scholars, also highlights a broader lesson for climate policy: global decarbonization goals such as “net zero by 2050” sound bold and ambitious. But when it becomes clear that they can’t be met, these goals make it harder to focus on the practical steps needed today to drive change in real-world markets.
Ultimately, the paper argues for action that begins now. By developing better tools to evaluate climate-friendly investments and by rewarding companies willing to take calculated risks on breakthrough technologies, governments, investors and industry leaders can accelerate real progress toward decarbonization.
The paper was co-authored by Thomas Conlon of University College Dublin, Philipp Goedeking of Johannes Gutenberg University of Mainz (Germany) and Andreas W. Schäfer of University College London.
Citation:
Researchers chart path for investors to build a cleaner aviation industry (2025, October 16)
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part may be reproduced without the written permission. The content is provided for information purposes only.
Imagine you’re walking your dog. It interacts with the world around you—sniffing some things, relieving itself on others. You walk down the Embarcadero in San Francisco on a bright sunny day, and you see the Ferry Building in the distance as you look out into the bay. Your dog turns to you, looks you in the eye, and says, “Did you know this waterfront was blocked by piers and a freeway for 100 years?”
OK now imagine your dog looks like an alien and only you can see it. That’s the vision for a new capability created for the Niantic Labs AR experience Peridot.
Niantic, also the developer of the worldwide AR behemoth Pokémon Go, hopes to build out its vision of extending the metaverse into the real world by giving people the means to augment the space around them with digital artifacts. Peridot is a mobile game that lets users customize and interact with their own little Dots—dog-sized digital companions that appear on your phone’s screen and can look like they’re interacting with the world objects in the view of your camera lens. They’re very cute, and yes, they look a lot like Pokémon. Now, they can talk.
Peridot started as a mobile game in 2022, then got infused with generative AI features. The game has since moved into the hands of Niantic Spatial, a startup created in April that aims to turn geospatial data into an accessible playground for its AR ambitions. Now called Peridot Beyond, it has been enabled in Snap’s Spectacles.
Hume AI, a startup running a large language model that aims to make chatbots seem more empathetic, is now partnering with Niantic Spatial to bring a voice to the Dots on Snap’s Spectacles. The move was initially announced in September, but now it’s ready for the public and will be demonstrated at Snap’s Lens Fest developer event this week.
Snap’s latest Spectacles, its augmented reality smart glasses.
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