The AI job apocalypse is a myth. We need more human talent than ever before

London Tech Week’s focus on AI - from a £12 million investment in AI for SMEs to AI bootcamps for graduates and more - has reflected the pressure to compete in an AI-era.

As this digital revolution progresses, the job economy is changing, but the mantra that AI is taking our jobs is simply not correct and potentially fueled by an undercurrent of classicism.

When the Luddites famously started to break the first machines of the industrial revolution in 1811 in England, fearing for their job as textile artisans, the “Bourgeoisie” would describe them as “ignorant workers”, with no understanding of basic economics.

More than two hundred years later, with the rise of GenAI, it is no longer the blue-collar workers who fear for their job, but the white-collar workers. This time it is the “bourgeois” who live in the anxiety of an uncertain world.

Since ChatGPT introduced AI into the everyday lexicon, it has been clear that we would experience an unprecedented revolution. The rhetoric that immediately began to dominate social discourse has been that AI tools would render most jobs insignificant.

Furthermore, whilst other technological revolutions ended up being creative destruction, ‘this time it was different’.

But is that really the case? Or are we more fearful, more concerned about destroying the status quo, because this time it’s a different ‘class’ of people being impacted? This time it’s the desk workers, not the physical laborers, who risk losing jobs, and suddenly there is alarm.

AI is a human creation and still relies on humans to evolve. First, we have those who build the infrastructure, like data centers, which accounted for almost all of the United States’ GDP growth in the first half of 2025 (according to Harvard economist Jason Furman).

Then, we have those who train the models, which still need to be constantly retrained. Even if models are able to train themselves eventually, there is no consensus that human intervention in training will become obsolete, because human behavior and the entropy of organizations are in a constant state of flux and evolution.

And even when trained, AI constantly needs to also understand the “context” in which it is prompted to perform efficiently. AI then needs to be deployed. Managing security, defining guardrails for agents, understanding how to use AI and tracking agentic AI’s actions, all comes with inherent challenges.

The CIOs of the largest global corporations are already investing hundreds of millions of pounds to understand this. Startups based in San Francisco - a city I recently visited where 95% of out-of-home ads were about AI agents - are focused entirely on resolving these problems for large enterprises.

The fact that both Anthropic and OpenAI have launched their own consulting companies is proof that managing AI complexities in the coming years will be the biggest source of growth for all consulting and outsourcing companies of the world.

Software engineering is a job category where GenAI - perfectly trained on open-source code and GitHub repositories - can now code better than even the most experienced developers.

Additionally, developers in AI labs - with privileged access to “tokens” on Claude Code or OpenAI Codex - now develop 100% of the time without writing a single line of code. Nonetheless, when asked about their biggest challenges, all AI startups would point to recruitment.

A report by the UK's National Foundation for Education Research showed a 50% increase in tech job adverts between 2019/20 and 2024/25, with entry-level roles particularly affected. However, we’re now seeing a surge in demand driven by Gen Z, according to Employment Hero’s March Jobs Report.

This demand for AI expertise is reflected in a new Malt Tech Trends Report, which analyzed 1.2 million searches of tech freelancers in 2025. It reveals that AI is now the second most-in-demand skill, irrespective of company size, industry, or project type. More specifically, demand for freelancers with agentic AI expertise exploded by 5,800% in just twelve months.

Observer of the AI revolution, Andrew Ng, explains that if, for example, a team of 3 developers builds 10 times faster, then they need more designers or product managers to fuel the creative process. Doing more faster, with fewer people creates more work to fuel and execute the output.

More people are echoing the same rhetoric as Ng, calling out the phenomenon of ‘AI washing’, whereby companies have justified mass redundancies with AI disruption. In reality, in many cases, they were either adapting to geopolitical and economic uncertainties or had simply employed too many in the crazy post-COVID bull market.

Software engineering is a perfect example of a job category that has constantly evolved. Since the inception of computer science, programming has become progressively more about “natural language”. Whilst there were 50,000 developers worldwide in the 1960s, today there are almost 50 million. Undoubtedly, the eradication of barriers to entry to build software increases that number tenfold.

History, data, and observation shows us that the AI job apocalypse is not yet here. Still, the fear is real and needs to be understood. The reason every science fiction novel paints an inhospitable world and unattractive paradigm is because the human mind always fears change. We assume the worst.

AI transformation, like all transformations, will be a cultural change first. And it’s companies, not professionals, who are most at risk if they fail to adapt. If one thing will be different in this digital revolution, compared to the last (arguably comparable is the advent of the internet), it’s the rate of change.

CEOs will have to be imaginative, change org charts and processes, admit they are not omniscient, take risks, and invest in training. Schools and universities also face the challenge of teaching soft skills: how to adapt to live and work in a more uncertain world. Because we can only harness top-tier AI talent if we understand how to truly adapt to change.

Independent professionals - those who create their own roles - from freelance developer to fractional manager and strategic consultants - have already redefined work.

On average, freelancers spend 4 hours a week on upskilling and keeping up with the job market and already have the habit of switching from one client project to another. They were the first to adapt to AI and realize that a job is more than just a bundle of tasks.

As Jensen Huang, CEO of Nvidia, recently said, if someone were to observe him at work, we would conclude that his day consists of tasks like making hundreds of calls and sending emails. AI will replace, augment, and improve these tasks. But it will not take Jensen’s job.

This article was produced as part of TechRadar Pro Perspectives, our channel to feature the best and brightest minds in the technology industry today.

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