Homer Simpson may be the best AI commentator we have right now

On work, trust, power — and why London now faces one of the biggest transitions in its modern history

Artificial intelligence is becoming a bit like alcohol.

The cause of, and solution to, all of life’s problems.

Not my line. Homer Simpson’s. But it may be the most accurate description of this entire AI moment.

Public debate about AI swings constantly between two extremes. On one side: the evangelists. Productivity boom. Unlimited growth. A new industrial revolution. On the other: the alarmists. Mass unemployment. Social collapse. Machines replacing humans.

Most people are not living at either extreme. Neither am I.

But one thing feels increasingly clear: the next challenge in AI is not simply capability. It is legitimacy.

Partly because of this, I am chairing a new taskforce for the Mayor of London looking at AI, jobs and the future of work across the capital. We are intentionally keeping it practical, evidence-led and short-term. No mission creep. No speculative AGI debates.

The focus is on what is already changing inside organisations before it fully appears in unemployment statistics: hiring, training, progression, management and job design.

Because the labour market is already shifting.

Recent research from King’s College London found that nearly seven in ten workers are worried about the economic impacts of generative AI on jobs. More than half believe it will eliminate more jobs than it creates. Around one in five think it could contribute to social unrest.

Meanwhile, recent Institute for Fiscal Studies analysis and broader labour-market data point to a weakening entry-level jobs market, with graduate recruitment cooling sharply and younger workers increasingly squeezed between economic uncertainty and changing employer expectations.

I do not read this as anti-technology sentiment.

I think it reflects something deeper: a growing suspicion that the gains from this next technological wave will once again flow disproportionately to a relatively small number of people, firms and places.

After all, we have seen versions of this story before.

Broadband transformed economies. Smartphones changed behaviour. Social media reshaped culture. Platforms created extraordinary convenience and wealth. But the benefits did not spread evenly. Power, capital and influence concentrated in familiar places and among familiar groups.

It is valuable to understand the history because AI is arriving into societies where trust in institutions is already fragile.

One of the most striking features of the current AI race is how uneven public confidence is globally. In China and parts of Asia, optimism about AI remains relatively high. In Europe and the US, public attitudes are noticeably more anxious and sceptical.

The countries that deploy AI fastest may not simply be those with the best models. They may be the ones where citizens still broadly trust institutions to manage technological change.

Western democracies are trying to build transformative technologies at precisely the moment public trust in governments, media and large corporations is fraying.

That tension sits underneath many of the current arguments around AI.

Take the growing legal battles involving OpenAI and other frontier labs. The New York Times lawsuit against OpenAI may ultimately become one of the defining cases of this era — not simply because of copyright law, but because it asks a deeper question: can the internet’s collective knowledge, journalism and creativity simply be absorbed into AI systems without meaningful consent, attribution or compensation?

Even if courts ultimately permit much of it, the public may not automatically experience it as fair.

Societies can absorb astonishing amounts of technological disruption when people believe the rules are legitimate and the gains are shared. They become far less tolerant when change feels extractive, opaque or designed somewhere else by people they neither know nor trust.

And change is now happening extraordinarily quickly.

London is probably more exposed to this shift than any other part of the UK. City Hall analysis suggests around 46 percent of London workers — roughly 2.4 million people — are in jobs where generative AI could automate at least some tasks.

This is not some separate “tech sector” story. A whole-economy transition is unfolding across offices, hospitals, classrooms, studios and public services.

At the same time, the barriers to experimentation have collapsed.

I have never written a line of code in my life. Yet with the latest AI tools, I now find myself building small apps late at night much to the amusement of my family.

When the cost of trying something falls dramatically, the number of attempts rises dramatically too.

Many organisations are now living through a period of “shadow AI” — employees adopting tools informally long before governance, training or management structures have caught up.

Junior staff are quietly using ChatGPT before formal policies exist. Teachers are generating lesson plans late at night. Civil servants are using NotebookLM to analyse draft policy papers before meetings even begin. Small firms are automating work that previously required much larger teams or expensive outside agencies.

A junior consultant can now produce in twenty minutes what used to take half a day.

So, the question is no longer simply whether something can be built, it is who realises it already can.

One consequence of this shift is that where power sits inside organisations and labour markets is in flux.

Employers are beginning to redesign work around AI capability before the headline employment numbers fully show up. Many professions are quietly losing exactly the kinds of repetitive junior tasks through which people traditionally learned judgement and experience.

Labour markets rarely break all at once. They soften first around the edges.

Hundreds of applications. Automated responses. Or increasingly, no response at all.

Much of the anxiety around AI is really anxiety about disappearing stepping stones.

How does somebody become a lawyer without junior drafting work? Or an architect without early design work? Or a producer without assistant roles where judgement develops over time?

Those are not abstract questions. They are economic questions. Social mobility questions. Political questions.

London does, however, have real advantages here.

This city has repeatedly reinvented itself through creativity, openness and adaptability. It absorbed the decline of manufacturing. It built world-class strengths in finance, media, science, technology and the creative industries. Entire categories of work emerged that barely existed a generation earlier.

London is unusually exposed to this next transition — but also unusually capable of shaping it.

The goal should not simply be to host successful AI companies. London should aim to become a city where far more people feel capable of participating in the upside of technological change.

That means protecting entry routes into work. Rethinking training. Piloting AI-augmented apprenticeships. Helping smaller firms adopt these tools responsibly. Moving faster inside universities and public institutions.

Most importantly, it means taking public trust seriously.

Not fear. Not anti-innovation. Seriousness.

Because ultimately, the societies that succeed in AI may not simply be the ones with the smartest models. They may be the ones where people feel they still have agency inside the transition itself.

Otherwise, much as I enjoy taking life advice from Homer Simpson, artificial intelligence risks becoming less like a tool we shape — and more like a force that simply happens to us.