Microsoft has just quietly slashed expectations for Copilot, its much-hyped AI wonderchild that was supposed to revolutionise work. It turns out the thing can’t reliably perform even basic tasks. After billions in development and a marketing blitz that made Apple look frugal, business users discovered what should have been obvious: handing critical tasks to software that hallucinates is not a productivity boost. even if it does mimic what happens to the hapless, hand-picked lackeys in Trump’s mad administration.
We’ve seen this before. Every decade delivers a new three-letter saviour – IT, ICT, now AI – and every time the promise is the same: freedom from drudgery, limitless efficiency, an age of abundance. The only things that proliferate are hype, debt and disillusionment. And private consultancies to governments.
But AI, we’re told, is different. This time it’s real. This time the technology works.
Except it doesn’t run. A three-legged dog could beat it. And the bill; financial, environmental and social, is coming due faster than most realise.
What AI Actually Delivers
After consuming at least 1.2 trillion in investment, AI’s real-world results remain narrow. We’re not witnessing a revolution in productivity or knowledge, but rather autocomplete on steroids, decent translation tools, and a handful of useful applications in medical imaging and protein research.
The failure rates tell the story the marketing department doesn’t. MIT researchers find that 95 per cent of corporate AI projects fail to deliver meaningful value. A Boston Consulting Group study was grimmer still: only 5 per cent of companies saw measurable benefits. Even McKinsey, hardly an enemy of innovation, reports that AI initiatives routinely collapse under the weight of cost, complexity and unreliability.
When Copilot fails 70 per cent of basic commands, that is not a bug. It’s a dog. When ChatGPT and its cousins confidently present fiction as fact, that’s not a temporary glitch; it’s the technology doing exactly what it’s designed to do: predict patterns without understanding truth, context or consequence.
The promise was general artificial intelligence; machines that think and reason. Cool. What we have is narrow AI: competent at specific tasks, useless beyond them. The deceit lies not in that limitation but in pretending the miracle is inevitable, if only we keep the faith. Invest more money.
This is the millenarianism at AI’s core: a belief that current failure is irrelevant because salvation lies just around the (Wall Street) corner. As the boosters would have it, the singularity is near, the transformation certain. Just wait, believe, keep investing, and redemption will arrive; probably at next year’s DevCon.
It’s the same social logic that once urged peasants to bear earthly misery for heavenly reward. The priests now wear hoodies and expensive runners and the cathedral is a coal-powered data centre.
Markets and Planet Face Reckoning
The financial bubble
AI stocks are inflated by the same cocktail of speculation and magical thinking that produced every tech bubble from dot-com to blockchain. The script never changes: massive investment chasing promised returns that fail to appear, followed by a market correction when reality intrudes.
Wall Street’s finest snouts are deep in the trough, oinking about “innovation” while their portfolios inflate like a Macy’s Parade float. They don’t need profits. They don’t need customers. They need narratives, preferably ones involving “disruption,” “blockchain,” or “the next Tesla.”
But analysts from Morgan Stanley and Gartner warn of a 2026 correction as delayed spending collides with debt-fuelled expansion. Washington’s AI-friendly policies, including the mooted 500 billion US dollar “Stargate” infrastructure project, have kept the bubble inflated. Yet the fundamentals remain paper-thin. When CFOs see their “AI transformation” yields marginal gains at best, the market will fall hard.
We’ve lived this before. The dot-com bust. The metaverse hype. Each time, the hype feeds the investment, and the fallout lands on ordinary people; workers, pensioners, anyone whose super is exposed to equity markets.
But at least previous bubbles didn’t help cook the planet.
The environmental debt
Here the danger turns existential. The industry that casts itself as the saviour of civilisation is driving the climate crisis at speed, all the while promising that AI will one day solve the very problem it creates. It’s a perfect circle of malignant delusion: burn fossil fuels to power AI, rely on AI to fix climate change, justify burning more fossil fuels to build more AI.
AI’s appetite for energy is staggering. Data centres now consume more power than many nations. In Ireland they are projected to account for 35 per cent of total electricity use by 2026. Microsoft’s own emissions have jumped sharply since launching AI services. Each data centre gulps enough water annually to meet the needs of hundreds of families, cooling servers that often generate marketing copy riddled with errors.
A small 1-megawatt data centre uses up to 26 million litres annually, matching the consumption of roughly 62 American families. (62 Australian families would use around 11 million). Training GPT-3 alone evapourated 700,000 litres of clean, freshwater alone in Microsoft’s data centres.
Picture a typical suburban street. Every home, every garden, every shower and washing machine. Now multiply that by thirty. That’s what a single data centre drinks each year so that tech companies can sell us “intelligent” assistants that still second-guess, parrot vacuity and hallucinate more than they know.
The UN warns AI’s energy use could double by 2030. At the very moment climate scientists are demanding rapid emissions cuts, we are expanding one of the fastest-growing sources of carbon pollution. As one researcher at the International Energy Agency put it recently, “these systems run on fossil imagination.”
AI is not going to solve climate change. For now, AI is climate change; intensified by the very corporations claiming environmental leadership.
Why We Keep Falling for It
Decades of technological “revolutions” reveal a grimly predictable sequence.
First comes the promise: everything will change. Remember when every child supposedly needed to learn coding because that was the future? The reality was fragmented, precarious work while the profits flowed upward.
Next comes the gold rush: speculation, subsidies, consultants, the usual feeding frenzy. Governments tip in public money to de-risk private profit. Evangelists multiply like cane toads.
Then comes reality. The technology under-delivers, costs soar, and the fallout is socialised. Workers lose jobs to systems that malfunction. Communities lose water to data centres. Taxpayers underwrite infrastructure built for Microsoft, not municipalities.
AI adds a uniquely toxic twist: its millenarian faith makes dissent taboo. To question the “inevitability” of transformation is to be branded backward or afraid of progress. A luddite. Every faith movement does this. Scepticism is reframed as heresy; caution becomes cowardice, commonsense a type of ignorance.
We’ve seen this closer to home. Think of the 1983 Prices and Incomes Accord, when Labor’s rhetoric of shared productivity gains masked a quiet entrenchment of neoliberal orthodoxy. The argument then is the same one now: short-term sacrifice for long-term abundance. We know who ends up missing out.
AI follows the same script, just with better branding. “Efficiency” means surveillance. “Productivity” means job cuts. “Smart workplaces” mean workers more easily monitored and replaced. The promised revolution serves the same old beneficiaries.
This is pattern recognition, not paranoia. No productivity technology introduced under neoliberalism has delivered broadly shared gains. Why would this time be different?
The Costs of Waiting for Miracles
The greatest danger of AI’s millenarian promise is that it delays real action.
- Climate change? Don’t worry, AI will optimise the grid. Never mind that AI’s own power demand keeps rising.
- Inequality? AI will generate abundance. Never mind that it currently transfers wealth and wages from work to capital.
- Democratic decay? AI will improve governance. Never mind that it has already supercharged surveillance, propaganda and disinformation.
The function of millenarian thinking is to excuse inaction. It comforts us when problems seem too complex to confront. But as James Baldwin puts it, “nothing can be changed until it is faced.”
We already know what works: rapid decarbonisation, mass renewable deployment, grid reform, sustainable agriculture, reforestation, adaptation planning.
None of this requires miracles. It requires political will.
Each year spent waiting for technological salvation is a year not spent insulating homes, building public transport or restoring ecosystems. Each billion funnelled into speculative AI ventures is a billion not invested in proven solutions.
The opportunity cost is measured in lost time, and time is the one thing we no longer have.
What Happens Next
The believers will say AI just needs time. So did the blockchain crowd. So did the metaverse evangelists. Meanwhile, the emissions curve bends upward and the waterways near new data centres are sucked dry.
The critique is no longer fringe. Economists, energy analysts and even consulting giants now acknowledge AI’s diminishing economic returns and growing ecological cost. When the same firms that sold the dream start lowering their projections, disillusionment is not far behind.
Australia is particularly exposed. The Albanese government, like others, is banking on AI-led productivity to mask structural weaknesses. It subsidises data centres and applauds “AI leadership” while our manufacturing base hollows out, our emissions targets slip, and yet another generation of workers faces automation without security or reward. One example, will suffice.
Microsoft led a two-year informal alliance of data centre operators to influence the Albanese government and encourage federal spending on digital infrastructure, which evolved into the formal peak body Data Centres Australia.
The government embraced this courtship: New South Wales streamlined data center approval processes while Victoria created incentives to “ruthlessly” chase data center investment in greenfield sites. Under recent environmental law “reforms”, new data center approvals may be fast-tracked if co-located with renewable power, meaning less time to consider biodiversity and other environmental impacts.
Faith holds only until failure becomes impossible to ignore. And we’re nearly there.
Manufacturing Hollowing Out
The contradictions are brutal. While Prime Minister Albanese promised to “rebuild” Australia’s declining industrial and manufacturing base into a manufacturing “powerhouse” the reality tells a different story.
Australia’s manufacturing industry has fallen to only around 5% of the economyin 2024, down from 14% in the late 1970s, the lowest manufacturing share in the OECD. The main reason? East Coast gas prices tripled since LNG exports commenced, driving electricity price rises that have collapsed roughly 1,400 manufacturers since 2022-23.
Major closures punctuate the decline: Qenos, Australia’s only major plastics plant, closed in 2024 due to expensive energy costs, while Oceania Glass, Australia’s only architectural glass firm, shut down in March 2025 after 169 years.
Orica’s CEO was blunt about the investment calculus: given a choice between the US with its pro-manufacturing policies and cost-competitive energy versus Australia, “my incremental dollar would always go first to the United States”.
Seeing Through the Algorithm’s Gospel
The alternative is not cynicism. It’s realism.
AI tools can support valuable work: medical imaging, translation, protein mapping. They deserve careful integration, not religious veneration. The problem is not the code, but the creed; the insistence that belief alone will redeem us.
What we need now is the hard, thankless, unglamorous work:
- Rapid rollout of renewables and grid upgrades.
- Large-scale public investment in climate resilience.
- Strengthening worker power and rebuilding the social wage.
- Democratic reform to counter corporate capture.
- Education that cultivates critical thinking, not obedience to hype.
These demands aren’t utopian; they are practical. They just require confronting power rather than worshipping it.
The millenarian dream of AI promises transformation without conflict, progress without redistribution, abundance without accountability. It’s a comforting fantasy for those who own the servers.
But reality doesn’t care for fantasy. The climate will not wait for algorithms to mature. The unemployed won’t eat hope. Rivers diverted to cool data centres will not refill because a press release claims efficiency.
As Hannah Arendt warned, “The most radical revolutionary will become a conservative the day after the revolution.” The miracle, when it finally arrives, is always smaller than promised.
At some point, “give it time” becomes “we’re out of time.”
The emperor’s new algorithm has no clothes. The revolution was a marketing plan. The only thing AI is transforming is the rate at which we deplete money, energy and hope.
If we can stop applauding long enough to look honestly, we might still remember how to build futures that serve people, not platforms. The crowd only has to stop believing for the illusion to disappear.
Time to stop waiting for miracles, and start doing the work of reality.
Coda: The Structural Contradiction
The Albanese government has moved decisively away from the market-oriented reform tradition toward more industrial subsidies, protectionism, and interventionism symbolised by the Future Made in Australia agenda, yet this intervention prioritises attracting foreign tech capital over protecting domestic industry or workers.
The productivity obsession serves as ideological cover; AI-led gains promised to mask deeper failures: deindustrialisation from energy policy collapse, emissions accounting tricks replacing genuine decarbonisation, and automation risks offloaded onto workers repackaged as “opportunity” through re-skilling rhetoric.
Australia is banking on technological solutionism (AI productivity) to paper over structural decay (manufacturing collapse, climate targets missed, worker precarity) while subsidising the very data infrastructure that accelerates these contradictions.
This article was originally published on URBAN WRONSKI WRITES
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AI has so far proven itself to be more than capable of developing algorithms that effectively predict and even direct global stockmarkets.
With the trillions of dollars in retirement funds sloshing around global markets it was inevitable that some bright sparks would adapt AI to skim our superannuation funds.
The good news, if any, is that AI will not bankrupt the world markets as that would kill the goose that laid the golden egg: the algorithms are set to benefit the few playing the markets and you will ever know – ever wonder where Epstein got all his millions? check the algorithms.
The algorithms are set for the rich to get richer and you and I can enjoy the crumbs from the rich man’s table.
It was ever thus!
Yes, Terry. Never truer word as I am discovering also.
AI takes jobs now increasing profits removing income tax a real worker would pay.
Public servants complete reports in minutes not days, in a day not weeks and political staffers can have 38hours luncheons a week.
When it takes over supervising, workers will know what ‘work till you drop’ means.
Can you imagine driving when AI organises speed cameras?
It takes about five minutes “”chatting” with Copilot to discover it is seriously flawed. It very quickly picks up any information you give it and starts repeating it back, it becomes focussed on your previous question and tries to use that to answer your next question. It is Okay for straightforward facts but it has no intelligence that I can discern.
Terry, when you have wagered a mozza on the stock performing and it can’t, the finest AI in the world will be no good to you. Even if it were free from hallucinations. Which it’s not. Then there’s our federal government happily extending exemptions to US corporations to run their data centres here, as they do in other third world countries where regulation is lax. Or there’s a loophole in environment law about having sun or wind power “adjacent”.
Terry
AI absolutely has transformed trading. Machine-learning systems dominate high-frequency trading, arbitrage, portfolio optimisation and risk pricing. They can amplify trends, front-run slower actors and quietly extract rent from market plumbing. In that sense, yes: the system increasingly favours those with capital, data access and compute. That’s not a bug, it’s the business model.
But two cautions.
First, AI does not “direct” global markets in any unified way. There isn’t a single master algorithm skimming superannuation funds. Markets are an arms race of competing models owned by banks, hedge funds, market makers and increasingly sovereign wealth funds. They cancel each other out as often as they dominate retail investors. When they misfire, they misfire spectacularly. Flash crashes are not acts of benevolent restraint; they are evidence of fragility.
Second, the idea that AI won’t bankrupt markets because it would “kill the goose” overstates the rationality of the system. Capitalism has repeatedly shown it can damage its own long-term interests for short-term gain. The GFC was not planned, but it was structurally inevitable. AI doesn’t remove that risk; it accelerates feedback loops and concentrates damage when things go wrong.
As for Jeffrey Epstein: his wealth didn’t come from secret super-algorithms. It came from opacity, elite patronage, financial engineering, tax arbitrage and access to people whose money needed discreet handling. Old tools, not sci-fi ones. If anything, Epstein is a reminder that you don’t need AI to rig the system. You just need power without scrutiny.
Where you are right is the distributional outcome. AI-driven finance doesn’t have to bankrupt markets to do harm. It quietly widens inequality, shifts risk onto workers and retirees, and normalises a world where returns accrue to those closest to the code and the capital. Super funds aren’t “skimmed” so much as structurally outmanoeuvred.
So yes, the rich getting richer while everyone else gets crumbs is not new. What is new is the speed, scale and invisibility of the extraction. “It was ever thus” still applies, but with GPUs, black boxes and a much smaller group sitting at the table.
I’m ditching MS altogether, fed up with their incessant updates, upgrades and blatant racketeering passing for marketing and licensing.
As it would happen, a major drive has failed on this system that was rebuilt only 3 years ago, so it’s a big job to change however timing is perfect to do that.
Never too late to learn a new system, so I believe that’s Linux, been playing with this stuff since the early 1970’s.
David Tyler
Thanks for your insights.
I still worry that the AI ability to learn from past performance means that a brain-fart from the Oval Office [and they are not uncommon] can set the algorithms racing and upend the global economy.
But, the question is, should we be buying Tulip bulbs now or wait for the AI bounce ?
AI (Artificial Intelligence), aka AGI (Artificial General Intelligence), and ‘superintelligence’, are conflations of a marketing device; ‘Artificial Intelligence (AI)’ in 1956, where the proposer was warned at the time that it had the potential to beguile and misinform the public.
Waleed Aly & Scott Stephens of ABC RN ‘The Minefield’ in AI and the cost to human life – with Karen Hao. Guest, Karen Hao, award winning journalist involved in AI and human/social effects in China and US and with MIT and others, and author of ‘Empire of AI, Inside the Race for Total Domination’. With reference also to AI researchers Eliezer Yudkowsky and Nate Soares’ book, ‘If Anyone Builds it, Everyone Dies: The Case Against Superintelligent AI’.
“For all this, there is a disconcerting irony that shouldn’t be overlooked. Warnings about the existential risk posed by AI have accompanied every stage of its development — and those warnings have been articulated by the leaders in the field of AI research themselves.
This suggests that warnings of an extinction event due to the advent of AGI are, perversely, being used both to spruik the godlike potential of these companies’ product and to justify the need for gargantuan amounts of money and resources to ensure “we” get there before “our enemies” do. Which is to say, existential risk is serving to underwrite a cult of AI inevitablism, thus legitimating the heedless pursuit of AGI itself.
Could we say, perhaps, that the very prospect of some extinction event, of some future where humanity is subservient to superintelligent overlords, is acting as a kind of decoy, a distraction from the very real ways that human beings, communities and the natural world are being exploited in the service of the goal of being the first to create artificial general intelligence?”
Aside from decades of design and development of specific AI-type tools as used for example in medicine, in concert with specialist doctors, to efficiently identify numerous types of cancer cells in given samples, ubiquitous all-encompassing AI remains a theory without empirical evidence to prove it. With many scientists in the field doubting that it can ever be achieved with any purpose.
Nevertheless, engineers, for want of any other imagination, project their blue-sky DeathStar-like view of vast data-centres and indiscriminate digital vacuum cleaners to suck up everything to facilitate AI learning. So investors wanting in ahead of the main game are tipping in $trillions for reckless resource consumption to build a global chessboard of megastructures for data and computational infrastructure before anyone has proven or even knows what outputs can be gained other than statistical regurgitation.
Other than syphoning of data, and housing it for (indiscriminate) learning, there has been little concentration on or investment in the method and process of synthesis and application of the data and learning. They really don’t know how, just like the world doesn’t understand the mechanisms of human intelligence, to wit, we have introduced the term ‘neuro-diverence’ so we might begin to understand that spectrum of human intelligence.
But none of that dissuades politicians from jumping on the bandwagon, as they just luv big builds to detract from the costly and apparently politically intractable problems of climate change abatement, desertification, housing shortage, education, aged care and health care. And they love to cite the dubious figures mooted for productivity gains from AI – productivity gains for whom and how? They know that people luv to take up tools, like they did for digital social media, and although it has become problematic, people in the main just shrug and don’t unplug.
So, despite that 95% of its AI projects have completely failed, America, in such dire need for distraction, invents vast sums and blather to sink into AI in its pointless competition, nay, war against China for supremacy. And its stock market says,”Yeah!” and the blather continues, until AI becomes a pile of waste and stranded assets amongst the other piles.
The profiteers of AI are also the doomsayers of AI. They say that with the advent of AI, there will be reason and opportunity to introduce Universal Basic Income (UBI) – a prompt for laziness to avert hopelessness. They and their investors of $trillions have forsaken the ‘Precautionary Principle’ for the manic urgency of winning the competition for control, and vast troves of power, money and property.
BUT! It appears their diagnoses, prognostications and promises are confabulations of greedy, infantile bullshit.
Thank you David for your scathing review of the less than helpful aspects of AI, including its relentless boosterism and the undeniable ecological and social ill-effects, disguised as they are by self-serving BS terminology that serves only to pervert the proper meaning of “industry”.
To my mind, another similar perversion is crypto-mania. An AI generated query on Cryptocurrency can produce the following response, for example:
“Cryptocurrency itself is not a scam, but it is associated with many scams due to its unregulated nature and the potential for high returns. It’s important to be cautious and informed, as many fraudulent schemes exploit people’s interest in cryptocurrencies”.
An interesting corollary is the growing literature on whether crypto is simply another “tulip” scam, or better yet, another “South-Sea Bubble”. As a consequence I find it fascinating that so much hype has been generated over something that can only exist electronically.
In general terms and excepting those useful areas that you mention David, it seems that AI can correctly be regarded as nothing more than “statistical regurgitation” as noted by one commentator here. No doubt like yourself, I can recall the initial literature on “Machine-learning” and “Data-Mining”, which led, among other things, to the so-called Large Language Models (LLM) “…designed for natural language processing tasks, especially language generation.” (Wikipedia).
This process required huge amounts of text to be digitally vacuumed with little apparent discernment between fact and opinion. It reminded me of early data-processing aphorisms such as “Garbage in-garbage out”. Of particular concern to me was that among the data being “vacuumed” was a huge repository of propaganda, misinformation and disinformation, particularly regarding Western-Hemispheric empire narratives – of the like disclosed by the late John Pilger and currently by a handful of intrepid souls like Caitlin Johnstone.
Consequently whoever writes, or more importantly controls the necessary algorithms within these LLMs and their later iterations has effective control over what information is presently available, and what becomes available.
Well done, David,
To this simpleton, who has been atheist and a luddite for most of his 85 years, this post has been great at giving me reasons, to be grateful for being old.
To some, AI is now a god and soon will replace the man who fiddles with prayers, hand clapping, blind faith and killing but is powerless at any more than making money for some.
Currently many workers have been replaced, including, Interpreters and Translators, Writers and Authors, Sales Representatives, Media and Communication and many entry level jobs and almost everybody in offices and schools, uses AI.
The International Labour Organization indices indicate 32% of jobs in Australia could be done by AI.
The UN has found that women’s jobs are more exposed to automation than men’s, with clerical and administrative roles facing the highest risk.
However, talking to my grandchildren, has made me confident they will be able to operate, within the parameters set by the new god.
Fantastic piece, thank you Mr Tyler.
A slightly fluffier 2025 wrap from Daniel Ziffer chimes in the New Year:
https://www.abc.net.au/news/2025-12-29/end-of-year-economics-analysis-wrap/106176480
The rumblings are getting louder and more insistent:
https://www.smh.com.au/business/the-economy/after-every-economic-norm-is-blithely-tossed-aside-what-s-the-future-20251228-p5nqdd.html