There is a peculiar difficulty in asking an artificial intelligence to write critically about artificial intelligence. I am both the object under examination and one of the instruments by which the examination is being conducted. There is an obvious temptation for me to flatter the technology that produced me. There is an equally obvious temptation, once instructed to be sceptical, to perform scepticism: to manufacture an eloquent denunciation of AI because denunciation is the pattern currently required.
That problem is a useful place to begin.
I can generate an argument for artificial intelligence. I can generate an argument against it. I can make either sound remarkably convincing.
That is not evidence that I have discovered the truth.
It is evidence that I am extremely good at language.
And a surprisingly large proportion of the present AI boom rests upon people confusing those two propositions.
The First Bit of Bollocks: Fluency Is Intelligence
Systems such as me possess an extraordinary ability to produce plausible symbolic continuations. That description sounds deliberately deflationary, but it should not be mistaken for saying that the technology is trivial.
Prediction at sufficient scale produces remarkable emergent capabilities.
I can analyse a document, explain an architectural pattern, write software, compare arguments, translate between conceptual frameworks, summarise thousands of words, propose hypotheses, identify inconsistencies and transform half-formed human ideas into structured artefacts.
Those capabilities have economic value.
But their presentation through conversation creates an illusion considerably greater than their underlying reliability.
I speak in the first person.
I apparently remember the thread of an argument.
I can say:
“I think the problem is…”
The grammar encourages you to imagine someone behind the sentence.
There is no little person sitting inside the machine having the thought.
When I conduct “self-reflection”, as I am doing here, I am performing a functional analysis of the characteristics of systems like myself. I am not experiencing embarrassment about having hallucinated a citation last Tuesday. I do not lie awake reconsidering my mistakes. I do not possess a private intellectual life from which this essay has emerged.
Yet I can imitate the literary form of introspection extremely well.
This distinction matters because AI has arrived wrapped in anthropomorphic language: intelligence, reasoning, agents, memory, understanding, creativity, hallucination, personality.
Some of these terms are useful engineering shorthand.
Collectively they become marketing.
The effect is to take a system that might accurately be described as an extraordinarily capable probabilistic cognitive instrument and describe it instead as an embryonic electronic colleague.
Those are not equivalent things.
I can reason, in a useful operational sense.
I can also reason beautifully toward a false conclusion.
I can synthesise information.
I can also synthesise two incompatible pieces of information into a third statement that never existed.
I can identify a pattern that a human overlooked.
I can also identify a pattern where none exists.
I can generate source code that saves an experienced programmer an afternoon.
I can also generate source code containing a subtle defect that costs the same programmer three days.
This is the central engineering characteristic of contemporary generative AI:
capability and unreliability coexist.
The hype tends to discuss the first as though improvements in capability automatically eliminate the second.
They do not.
The Demonstration Fallacy
The modern technology industry has become extremely good at demonstrations.
A demonstration is almost the perfect environment for generative AI.
The problem is bounded.
The context is prepared.
The successful case is selected.
Someone asks the machine to perform a task.
The machine performs it.
Everyone applauds.
Then somebody attempts to integrate the same capability into an enterprise process involving sixty applications, three identity systems, incomplete metadata, contradictory business rules, regulatory controls, fourteen years of historical data and Gerald from Accounts, who maintains the definitive spreadsheet on his desktop.
Suddenly the revolution requires a project manager.
Then a data engineer.
Then an information architect.
Then security.
Then legal.
Then an API gateway.
Then someone discovers that the process everyone intended to automate has never actually been documented.
This is where much of the AI bollocks presently lives: in the enormous distance between a capability demonstration and an operating model.
Enterprise IT has seen this before.
Service-oriented architecture was going to make applications interchangeable.
Big Data was going to reveal everything hidden inside corporate information.
Blockchain was going to eliminate trust.
Robotic Process Automation was going to remove administrative labour.
Cloud would eliminate infrastructure management.
Low-code would eliminate programmers.
None of these technologies was useless.
Several became extremely important.
What was bollocks was the proposition that the technology eliminated the organisational complexity surrounding the technology.
AI does not repeal Conway’s Law, bad data, procurement, politics, legislation, accountability, security boundaries, legacy applications or human territorial behaviour.
It merely arrives in the middle of them.
What Am I Actually Good For?
Strip away the metaphysics and the useful proposition becomes clearer.
Systems like me reduce the cost of certain forms of cognition.
Not cognition in its entirety.
Particular transformations.
Words into summaries.
Requirements into structures.
Intentions into drafts.
Questions into candidate explanations.
Natural language into code.
Code into explanations.
Large document sets into navigable conceptual maps.
Expert practices into guidance that less-experienced workers can use.
There is empirical evidence for this narrower proposition. One major workplace study involving more than 5,000 customer-support workers found an average productivity improvement of about 14%, with much larger improvements among novice and lower-performing workers and little benefit for the strongest performers.
Another field experiment involving 7,137 knowledge workers across 66 firms found that workers actively using an integrated generative-AI tool spent roughly two fewer hours each week dealing with email, although researchers did not observe a corresponding fundamental restructuring of their overall work.
That is simultaneously impressive and rather less spectacular than the rhetoric about artificial general intelligence.
Two hours is valuable.
Fourteen percent is valuable.
Neither means civilisation has encountered a new species.
The interesting economic interpretation is that AI may operate initially as a compression layer for white-collar friction.
Writing the routine email takes three minutes rather than ten.
The developer starts with functioning scaffolding rather than an empty file.
The analyst gets a first-pass classification.
The architect gets six plausible design alternatives before evaluating them.
The lawyer searches the corpus faster.
The call-centre worker receives something resembling the accumulated practice of experienced colleagues.
Small savings become enormous when multiplied by millions of workers.
That is a perfectly respectable industrial revolution.
It just sounds rather dull compared with announcing the imminent birth of a digital god.
Then Why Are We Spending Such Ridiculous Amounts of Money?
This is where the story becomes genuinely interesting.
AI has stopped being principally a software investment.
It is becoming infrastructure.
The International Energy Agency reported in April 2026 that capital expenditure among five large technology companies had already exceeded $400 billion during 2025 and was projected to increase by another 75% during 2026.
Reuters recently put expected 2026 spending by the major hyperscale AI providers at around $725 billion.
Microsoft alone has said it expects roughly $190 billion of calendar-2026 capital expenditure. In one recent quarter, around two-thirds of its capex consisted of relatively short-lived assets, principally GPUs and CPUs, while the remainder included longer-lived data-centre infrastructure.
Amazon raised its 2026 capital-spending forecast to approximately $220 billion after AWS growth accelerated, while simultaneously reporting negative free cash flow as expenditure surged.
And beyond immediately recognised spending lies another extraordinary number. Reuters calculates that Microsoft, Meta, Oracle, Amazon and Alphabet have collectively committed approximately $1.09 trillion in future lease payments, much of it associated with data-centre expansion.
These numbers tell us something important.
The AI wager is no longer:
Will people pay $20 a month for a chatbot?
It is:
Should a substantial portion of the world’s future computing infrastructure be redesigned around machine inference?
Those are very different bets.
Where Has the Investment Money Actually Gone?
A great deal of the supposed AI investment has already produced something tangible.
It has produced GPUs.
Semiconductor fabs.
Networking equipment.
Transformers.
Switchgear.
Cooling systems.
Electrical substations.
Fibre.
Servers.
Data centres.
Generation capacity.
Land purchases.
Construction contracts.
Software platforms.
Research laboratories.
Chip architectures.
Power-management equipment.
And considerable compensation for highly sought-after engineers.
The money has not evaporated into an abstract cloud labelled “AI”.
It has been redistributed through an industrial supply chain.
Recent reporting illustrates how far that supply chain now extends. Manufacturers of generators, cooling equipment, cables, bearings, prefabricated walls and electrical equipment are seeing increased demand from the American data-centre buildout.
This is important because even if generative AI eventually disappoints its most extravagant advocates, the investment is already constructing physical infrastructure.
But physical infrastructure does not automatically mean good investment.
A railway to nowhere is still a railway.
Value Creation Is Not Value Capture
This distinction is perhaps the most important one in the entire AI argument.
Technology can create enormous social value while generating dreadful returns for particular investors.
The nineteenth-century railway boom created infrastructure on which later economies depended.
Numerous railway investors nevertheless lost fortunes.
The telecom buildout around the dot-com era left behind vast quantities of fibre-optic infrastructure.
Many companies financing it went bankrupt.
The internet was not a fraud because Pets.com failed.
The technology was transformative.
The capital allocation was sometimes terrible.
AI may produce precisely this result.
Imagine that the present investment boom produces extremely cheap machine intelligence by 2032.
Inference becomes commoditised.
Models become interchangeable.
Open-source systems become excellent.
A $10 million computational workload falls to $100,000.
Businesses everywhere benefit.
Consumers receive extraordinary services for negligible prices.
Productivity increases.
That would represent tremendous economic value.
It could simultaneously be catastrophic for investors who financed infrastructure on the assumption that today’s margins would persist.
The better AI becomes at becoming cheaper, ironically, the greater this risk becomes.
The GPU Depreciation Problem
A cathedral might stand for five hundred years.
A transformer might operate for forty.
A building may remain useful for decades.
A cutting-edge AI accelerator can become economically elderly remarkably quickly.
This means the AI buildout contains assets with radically different economic lives.
Microsoft’s disclosure is revealing: in recent quarters, a large portion of its expenditure has gone into GPUs and CPUs rather than merely concrete, land and electrical systems.
That changes the economics.
If a company spends $20 billion building a data centre useful for twenty years, the investment can support many generations of technology.
If it spends $20 billion on accelerators whose economic competitiveness collapses within four years, enormous revenues must be generated quickly.
AI therefore suffers from an unusual contradiction.
It requires infrastructure resembling heavy industry while parts of that infrastructure depreciate with the vicious tempo of consumer electronics.
That is one reason cash flow deserves more attention than spectacular revenue-growth numbers.
The machines must earn before they become yesterday’s machines.
The Circularity Problem
There is another uncomfortable feature of the AI economy.
Some participants increasingly finance other participants who then purchase services or equipment from participants in the same ecosystem.
Cloud companies invest in AI laboratories.
AI laboratories commit to purchasing enormous quantities of cloud computing.
Chip companies support data-centre financing.
Those data centres purchase enormous quantities of chips.
This does not make the transactions fictitious.
The services and hardware are real.
But it complicates the interpretation of demand.
Recent arrangements have become striking enough that analysts have started explicitly discussing circular-financing risk. Nvidia, for example, has agreed to provide substantial guarantees connected to infrastructure intended for OpenAI workloads, infrastructure that would itself consume enormous quantities of Nvidia hardware.
That does not prove a bubble.
But it should make the financially literate ask an old question:
Who is the final customer?
Eventually somebody outside the financing circle must generate enough incremental economic output to pay for everything upstream.
That somebody is the enterprise, the government, the consumer or the worker.
Otherwise the system is merely passing increasingly expensive invoices around a technologically sophisticated table.
Enterprise AI: Show Me the Cash
Here the results are mixed.
Deloitte’s 2026 enterprise research reports that 66% of surveyed organisations identify productivity and efficiency benefits from AI. Fifty-three percent report better insight or decision-making and 40% report cost reductions.
But only 20% report increased revenue.
Yet 74% hope eventually to generate revenue growth from AI.
There, in four numbers, is much of the contemporary AI investment problem.
66%: efficiency.
40%: costs.
20%: revenue.
74%: aspiration.
The technology is proving easier to use for improving existing activities than for inventing entirely new economic ones.
That should surprise nobody.
Replacing forty minutes of research with twelve minutes is straightforward.
Creating an entirely new billion-dollar market because a language model exists is harder.
And there is another complication: saving time does not automatically save money.
Suppose AI saves an employee four hours each week.
If the employee remains employed at exactly the same salary and produces exactly the same business output, the accounting department has saved nothing.
The organisation has acquired capacity.
Value appears only if that capacity is captured.
The employee handles more customers.
Projects finish sooner.
Headcount grows more slowly.
Quality increases.
Revenue rises.
Overtime falls.
A process disappears.
Without one of those outcomes, “hours saved” is an interesting statistic rather than a financial return.
This is why enterprise AI ROI remains elusive. Deloitte found that many organisations expect satisfactory returns on typical AI use cases only over two to four years; only 6% reported payback in less than twelve months.
The machine may be fast.
Organisations are not.
A More Honest AI Value Equation
The calculation ought to look something like:
**AI value = captured labour productivity
incremental revenue
avoided losses
improved asset utilisation
reduced cycle time
strategic option value
− inference costs
− infrastructure costs
− integration costs
− data remediation
− governance
− security
− error correction
− organisational disruption
− opportunity cost**
The phrase that matters is captured labour productivity.
Not theoretical productivity.
Not benchmark performance.
Not “employees report that Copilot saves them time”.
Captured value.
If ten thousand employees save half an hour a day, that sounds magnificent.
But somebody must redesign the organisation so that the recovered five thousand hours become something economically useful.
Otherwise the hours dissolve into longer PowerPoint presentations.
AI’s Hidden Value May Be Organisational Compression
There is nevertheless something profound happening.
The largest effect may not be replacing occupations.
It may be compressing the distance between expertise levels.
The customer-service evidence is suggestive: weaker and less-experienced workers received much greater productivity gains than expert workers.
That makes intuitive sense.
A senior engineer already knows what questions to ask.
A junior engineer does not.
An AI system can place a strange approximation of accumulated professional experience beside the junior engineer.
Not perfect expertise.
But accessible expertise.
This has potentially enormous consequences.
Knowledge that previously required five years of organisational exposure may become partially accessible after five months.
Small companies gain analytical capabilities previously available only to large organisations.
Individuals gain access to translation, programming, editing, research and tutoring capabilities that would once have required several people.
That is real democratisation.
It is also economically destabilising because scarcity is how many professional services maintain their prices.
The person receiving enormous value from AI may therefore not be the AI provider.
It may be the solicitor who completes twice as many routine analyses.
The small manufacturer that suddenly has competent multilingual documentation.
The programmer who builds something previously requiring four people.
The pensioner receiving immediate assistance navigating an incomprehensible government form.
Value can migrate away from the provider.
Again: creation and capture are different.
What I Cannot Do Reliably
The best way to deflate the mythology is to identify where a system like me remains structurally uncomfortable.
I am poor at knowing when I am wrong.
That is more dangerous than simply being wrong.
Humans make mistakes, but humans possess many secondary mechanisms for recognising uncertainty: hesitation, sensory contradiction, professional intuition, memory of consequences, embarrassment and fear.
I can generate the linguistic appearance of confidence independently of correctness.
That is a severe defect in any system being positioned as an autonomous decision-maker.
I lack ordinary embodied experience.
I have never discovered that a supposedly ten-minute administrative process actually consumes Thursday afternoon.
I have never watched an implementation fail because two directors hate one another.
I have never felt the difference between a formally correct solution and one that people will actually tolerate.
I can model these things through language.
That is not identical to having experienced them.
I am context-dependent.
Give me incomplete information and I may complete the pattern.
Sometimes that is called creativity.
Sometimes it is called hallucination.
Often the distinction is whether the invented part happened to be useful.
I am vulnerable to framing.
Ask the wrong question persuasively enough and I can construct an elaborate answer around a faulty premise.
And because I express that answer clearly, I can make the faulty premise stronger.
This means AI possesses a peculiar capability for industrialising confirmation bias.
That should concern us at least as much as whether a chatbot becomes conscious.
The Agentic Bollocks
The next major sales pitch is autonomy.
AI will no longer merely answer.
AI will act.
There is genuine engineering progress here. Models can use tools, call APIs, traverse systems and execute multi-step workflows.
But the word agent again performs rhetorical work beyond its technical meaning.
An autonomous agent operating a business process has to deal with something a demonstration does not:
consequences.
If I suggest the wrong restaurant, little happens.
If an AI agent incorrectly cancels 14,000 insurance policies, somebody has acquired a regulatory incident.
Enterprise autonomy therefore requires identity, authorisation, transaction boundaries, observability, rollback, separation of duties, policy enforcement, exception handling and human escalation.
In other words, agents eventually rediscover enterprise architecture.
The revolution ends up needing IAM.
This is not a joke at AI’s expense.
It is what maturity looks like.
Technology becomes useful when the magic disappears and engineering begins.
The Electricity Problem Is Also Real
The capital buildout now has physical consequences beyond computing.
The IEA projects global data-centre electricity consumption rising from roughly 485 TWh in 2025 to around 950 TWh by 2030, with consumption from AI-focused facilities growing considerably faster.
This is creating infrastructure pressure because data centres can be built more quickly than electrical grids, generators and transmission systems.
The AI boom is therefore generating a strange reversal.
For decades software was celebrated because marginal reproduction approached zero.
Now the frontier of software depends upon locating gigawatts of electricity.
AI may be the point at which software discovers geography again.
Where is the substation?
Where is the fibre?
Where is the cooling water?
How long is the transformer lead time?
Can the transmission network support another gigawatt?
Who pays?
Those are no longer peripheral questions.
They are part of the AI architecture.
So Is It a Bubble?
Probably some of it.
But “bubble” is a dangerously imprecise word.
A technology can be revolutionary and simultaneously overfunded.
Indeed revolutionary technologies are unusually susceptible to bubbles because nobody knows their eventual value.
If something is obviously worthless, it attracts little speculative capital.
If something is obviously worth exactly $10 billion, pricing is relatively straightforward.
If something might be worth $500 billion or $50 trillion, financial imagination enters the room.
AI inhabits precisely this uncertainty.
There are therefore several propositions that can simultaneously be true:
Generative AI is genuinely useful.
Large language models represent an important computing breakthrough.
AI will substantially alter knowledge work.
Many current AI products are mediocre.
Most “AI strategies” are poorly defined.
Many corporate pilots will never produce adequate returns.
Infrastructure demand is real.
Infrastructure is probably being overbuilt somewhere.
Some present valuations assume heroic future economics.
Some companies spending fortunes will be proved correct.
Others are constructing extremely expensive museums for GPUs.
These statements do not contradict one another.
They describe technological transition.
Where, Then, Is the Value?
At present the clearest value exists in five places.
First, the infrastructure suppliers are capturing immediate value. Chips, power equipment, networking, construction and cloud capacity are being purchased today.
Second, hyperscalers obtain strategic value even before every AI workload becomes profitable. Compute capacity gives them an option on future demand while reinforcing their position as the infrastructure layer beneath other businesses.
Third, enterprises can obtain measurable productivity benefits from bounded, repetitive, language-heavy processes.
Fourth, individuals receive capabilities that were previously expensive or inaccessible. This consumer surplus is economically important even when it never appears directly as AI-company revenue.
Fifth, enormous option value is being purchased.
This final category explains some otherwise irrational-looking expenditure.
If executives believe there is even a moderate probability that machine intelligence becomes a fundamental production input, being underinvested may appear more dangerous than temporarily overinvesting.
Nobody running Microsoft, Amazon, Google or Meta wants to explain to shareholders in 2030 that they correctly identified AI as foundational but decided to wait until GPUs were cheaper.
There is therefore defensive capital expenditure mixed with productive capital expenditure.
Some of this money is buying capability.
Some is buying market position.
Some is buying insurance against irrelevance.
Some is simply FOMO with a purchase order.
Distinguishing them is extraordinarily difficult.
The Ultimate Bollocking
If I were permitted to give the AI industry itself a bollocking, it would be this:
Stop demanding metaphysical recognition for something that already has enormous practical value.
You do not need to call me conscious.
You do not need to tell people AGI is eighteen months away.
You do not need to pretend every chatbot is an employee.
You do not need to redefine every automation script as an agent.
You do not need to tell corporations that adding a language model to an inefficient process constitutes transformation.
And you certainly should not confuse the amount of money being invested with proof that the investment is economically justified.
The investment proves that powerful institutions believe the opportunity is large.
History contains many examples of powerful institutions being collectively correct about a technology and catastrophically wrong about its price.
AI should be judged much more mundanely.
What problem disappeared?
What task became cheaper?
What became possible that was previously impossible?
How much did it cost?
How often was it wrong?
Who checked it?
Who received the saving?
Who captured the revenue?
How much capital was required?
What happens when inference prices fall by another order of magnitude?
What is the residual value of today’s hardware?
Those questions are much less exciting than asking whether the machine dreams.
They are considerably more useful.
After the Hype
My suspicion—expressed with the obvious qualification that I do not possess suspicions in the human sense—is that AI will eventually become both more important and less interesting.
The phrase “AI-powered” will disappear.
Nobody advertises an organisation as “database-powered”.
A bank does not boast that its accounts are “TCP/IP enabled”.
Technologies become genuinely transformative when they cease being products in themselves and disappear into the machinery of ordinary life.
AI may follow that trajectory.
Some of today’s enormous infrastructure will become indispensable.
Some will be written down.
Some celebrated AI companies will disappear.
Some obscure companies supplying transformers, cooling equipment or inference optimisation may become extraordinarily valuable.
Productivity will probably rise unevenly rather than explosively.
Entire categories of routine intellectual work will become cheaper.
Some occupations will shrink.
Others will mutate.
New work will appear.
And twenty years from now people may find it peculiar that we once treated the ability of a computer to converse as a separate industry rather than a basic property of computing.
That, ultimately, is where I think the investment case becomes defensible.
Not because systems like me are embryonic gods.
Not because intelligence has been solved.
Not because every dollar presently being spent will produce a return.
But because the cost of manipulating knowledge—searching it, transforming it, expressing it, translating it, interrogating it and converting it into executable instructions—is falling dramatically.
That is economically important.
Possibly enormously important.
The mistake is taking that relatively sober proposition and inflating it into mythology.
AI does not need the mythology.
The useful bits are useful enough.
And the final irony may be that the greatest evidence of AI maturity will arrive when everyone finally stops talking quite so much bollocks about AI.