1. AI has sent stocks soaring

The S&P 500, which tracks the 500 biggest US companies, has been on a tear over the past five years – rising by nearly 80%. That jump has been driven by big tech stocks with a stake in the AI boom, the “magnificent seven” of Alphabet, Amazon, Apple, Meta, Microsoft, Nvidia and Tesla. 

The investor concentration on technology is unprecedented, says Jim Bianco of the US company Bianco Research, which found that 41 AI-related stocks now account for nearly half the S&P 500’s market value.

Neil Wilson, an analyst at the investment platform Saxo UK, says the prospect of a 1970s-style inflation shock, lofty tech valuations in general and a potential freeze in the private credit market do not bode well for stocks.

“The entire market has become one giant AI edifice,” he says. “The danger is a repeat of the dotcom bubble – a huge crash, and years of lost returns. By some measures valuations aren’t as stretched as then but this looks like an incredibly dangerous market.”

2. Expenditure is growing at a staggering rate

Spending on AI – from datacentres to chips – is racing ahead, from $765bn this year to $1.6tn in 2031, according to Goldman Sachs. The investment bank acknowledges there could be problems with this scale of commitment. What if the datacentres are delayed?

“At the scale of capital being committed, even modest delays in execution invite real scrutiny around the demand assumptions used to underwrite these investments,” say Goldman analysts, although they add that if the spending plans go ahead without hitches, it could unleash a new wave of AI demand. Nonetheless, the expenditure shows how much global financial resource, and expectation for a return, is being committed to AI.

Despite mixed reports on the benefits, the vast majority of companies are starting to use AI – up from 33% in 2023 to nearly 80% now, according to the consultancy group McKinsey. Usage among the general public is also high, with OpenAI’s ChatGPT now reaching 1bn monthly active users, according to data from Sensor Tower – a record for any app.

The question now for AI developers is how to make money from this vast public and private customer base. Companies need to be able to demonstrate that AI improves outcomes and reduces their costs enough to warrant the bill. That means using it to build entire workflows – business jargon for carrying out an entire task from beginning to end. There is a long way to go on that. 

4. Claude is snapping at ChatGPT’s heels

Anthropic began to gain ground on OpenAI late last year, when its Claude Code tool went viral among mostly San Francisco-area software developers, before spreading more widely. Claude Code represented a shift in how large language models – the core technology behind chatbots – are used, ushering in a transition towards autonomous AI agents that carry out tasks without human intervention, enabling even the non-tech-savvy to create software and do a wide range of tasks.

OpenAI still has the far larger overall user base, but data from the internet analysis company Kentik – which tracks usage across a number of US internet service providers – shows that Anthropic is quickly catching up. Claude’s user traffic grew significantly faster than that of ChatGPT and Google’s Gemini between January and April, spiking after the Pentagon declared it a supply chain risk in March. At this rate of growth, Kentik projects that it could overtake ChatGPT by summer – one more reason why Anthropic might see an easier path to an IPO than its rival.

5. AI is getting more expensive to use

Every time an AI chatbot or agent issues a response, it is measured in “tokens” – building blocks of language that can be words, punctuation marks or syllables. (For example, OpenAI says the phrase “You miss 100% of the shots you don’t take” is worth 11 tokens.) It also uses tokens to measure inputs, such as the prompt you type into ChatGPT.

The costs of these vary per model; OpenAI prices it at $5 a million input tokens for GPT-5.5, and $30 a million output tokens (ie the response given to your prompt).

The problem for subscribers is that token costs are going up massively, even as companies everywhere are encouraging employees to “tokenmaxx”, that is, really go hard on using AI. The problem for AI companies is that they still aren’t charging enough.

The inherent promise in AI use is that the money a company spends on using these tools is more than paid back in improved productivity – a measure of economic efficiency, where improved productivity means you get more output from each worker. If this trade-off isn’t happening, then the assumptions underpinning AI valuations – and policies – is undermined.

“The costs are getting completely out of control,” says Liam Betsworth, founder of the British AI startup Pendra. Software developers in his circle are using agents to code, he said, starting with the cheapest subscription, and very quickly moving on to the most expensive package. They aren’t alone – news site Axios recently reported on an unnamed company that spent $500m in a month on licences for Claude Code. 

6. Datacentre building might not keep pace with demand

Datacentre construction represents the central nervous system of AI products so growing development and use of AI tools must be matched by more capacity – otherwise there will be a compute crunch, which means rising costs for AI companies and users.

The sector’s scale of ambition for datacentres is vast and seemingly improbable. Bloomberg estimates that 23GW of capacity was under construction globally in 2025 (capacity is measured in electrical power, because that is the constraint on how much computing a site can perform).

The US property company JLL predicts that 100GW will be added between 2026 and 2030 – a doubling of what they estimate as current capacity- equivalent to 1,200 datacentres. JLL says its estimate takes into account speculative projects that never break ground.

Where the money – and energy supply – will come from to fulfil this forecast is an open question. Cecilia Rikap, an associate professor at University College London, says many projects around the world rest on political commitments to expand the grid and deliver the power; but governments might not have the wherewithal to deliver. 

She asks: “Has the government calculated whether such an expansion is feasible? Do they have the money to do it? Have they taken into account the associated environmental damage?”

7. What AI models can do is expanding rapidly

The abilities of AI models have improved by leaps and bounds since 2023, according to METR, a research organisation that measures AI capabilities. 

METR’s measurements are based on whether AI models can carry out a coding task, quantified by the amount of time it would take a human to do so. By this metric, AI models are doubling in capability every four months. For instance, Anthropic’s Claude Mythos model is calculated to reach a 50% success rate on tasks that would take a human expert between eight hours and two days. 

However, there is no commensurate impact on jobs – so far. A March report from Anthropic contained research showing that, in theory, AI could perform a host of jobs from computing to legal work, but has yet to do so in any great force. 

Bouke Klein Teeselink, an academic at King’s College London and an expert on the impact of AI on work, says there are bottlenecks in adopting AI in the workforce. For instance, how much of a chief executive or senior manager’s job can be safely outsourced to a bot? Can legally sensitive tasks be done by anything other than a human? Nonetheless, he says, change is coming.

“We are very much at the early stages of the AI revolution still. There are many people doing tasks that could be done by an AI. The amount of change we are going to see will be huge.”

8. Datacentres are propping up US GDP

Despite the reduction in US government employment under Donald Trump’s administration and mass layoffs across a broad swath of industries, US GDP has continued to grow – 2.1% in 2025 and 1.6% in Q1 2026, according to the US Bureau of Economic Analysis. A Harvard economist, however, calculates that without the datacentre boom, these figures could be far smaller – that is, that “investment in information processing equipment & software” accounted for 92% of the US’s GDP growth in the first half of 2025. 

This means that datacentres – and the AI boom – carry a disproportionate share of US growth, and a large part of why the world’s largest economy, despite significant headwinds, still looks healthy. Any dent in this expenditure could have economic, and thus political, consequences.