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Is AI investment a ticking time bomb for UK businesses?

ended 26. May 2026

Businesses, most recently Microsoft and Uber, are starting to work out the full cost of AI and are taking drastic action to reduce their costs. But in your experience, are everyday businesses that are reinventing themselves with, or leveraging themselves upon, AI fully aware of the true costs of token burn and underlying tokenomics? Is a whole economy being built on foundations few businesses will truly be able to afford? What percentage of SMEs would you say are modelling themselves on the real costs of AI?

3 responses from the Newspage community

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Editor
Mitali Deypurkaystha
AI Governance Strategist & CMO at AISensa
Big Tech is spending more than $700 billion flooding the market with cheap AI, according to Wall Street analysis, and many British businesses are behaving like they’ve been handed free samples without realising the dealer eventually comes back with the bill. As an AI strategist, one of the biggest parts of my job is often explaining where businesses should not use AI. Too many owners see cheap subscriptions and assume automation belongs in every workflow whether the return on investment exists or not. I’ve had to push back on companies wanting to force AI into processes where the gains simply weren’t there, especially when today’s bargain pricing is heavily subsidised and may not reflect the true long-term cost. AI will absolutely transform the economy, but too many SMEs are rebuilding their operations around tech whose future pricing and operating costs they barely understand. The danger is that by the time those costs rise sharply, businesses may already be too dependent to walk away
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Katrina Young
Futurist | Chief Technology Officer CTO/CDO Ethical AI & Digital Transformation Strategist at KYC Digital
Microsoft and Uber recalibrating AI spend is not just a cost story. It is an early signal that the operational economics of large-scale AI adoption are still being tested in real time. The deeper issue is maturity versus implementation speed. Many organisations are scaling AI into workflows before governance, tracking, oversight, and operational resilience are fully established. ROI calculations often focus on productivity gains while excluding verification labour, escalation handling, accessibility, compliance exposure, and long-term dependency risk. As an AI human-in-the-loop and digital transformation consultant, part of my role is assessing whether organisations are operationally ready for AI beyond the pilot stage. The hidden risk is removing too much human verification in pursuit of speed. That is where trust failures, bias risks, errors, and operational fragility begin surfacing.
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Colette Mason
AI Ethics Consultant at Clever Clogs AI
Only businesses using pay-as-you-go (API) billing understand the real cost of AI. Flat-rate subscriptions are heavily subsidised to bring in users, effectively hiding the true cost of compute. This is known as "tokenomics."

Let's look at what happens when you're charged per token. Imagine a 10-person firm where staff summarise just one 10-page PDF daily, roughly 220 PDFs per month.

Each summary uses 10k–15k input tokens and 500–2k output tokens. With input priced at £4/M and output at £20–£24/M, running one simple task costs:

Opus 4.6: £11.00–£22.00/mo
GPT-5.5: £11.44–£23.76/mo

One summary workflow costs about the same as one person's subs, before any other tasks are even touched, and this assumes the output is right first time and doesn't need a rerun. Add in 10 more tasks with reruns and it will cost a small fortune.

PAYG reveals the true AI price tag. Scale this across dozens of daily workflows, and the real cost of enterprise AI quickly becomes impossible to sustain.