Whether it’s fielding basic customer complaints or generating sophisticated code, it seems like today’s AI tools can just about do it all. But as the technology keeps growing at a rapid clip, so have employees’ fears over its ability to make them redundant.

These concerns aren’t unfounded. Companies including Microsoft, Amazon and Salesforce have cited AI adoption as the reason behind over 50,000 job cuts in the United States in 2025. More recently, The Wall Street Journal reported that AI companies are asking subject-matter experts to train their models in everything from astronomy and psychology to video editing and financial investing. OpenAI has also apparently been asking contractors to upload projects from other jobs to train and prepare its AI agents for office work.

Will humans be left in the dust as AI takes over? I’d argue that history and simple economic analysis suggest otherwise, and that conventional wisdom about the technology annihilating jobs is almost certainly wrong.

A look at past technological advances

Let’s take it back to the 1980s, when CT scans became appreciably cheaper thanks to advances in medical imaging technology. The worry was that everyone employed in radiology departments would lose their jobs. But per the US Bureau of Labor Statistics, employment in this area didn’t fall – it actually grew, leading to a shortage of radiology technicians.

What happened was that more people could afford getting CT scans. More doctors recommended them, and insurance companies were more willing to pay for them. The resulting efficiency led to an uptick in CT scans, which created jobs, not killed them. It increased the need for human oversight, unearthed more complex cases (that subsequently needed treatment) and led to the emergence of new specialties like interventional radiology. 

Another example is transportation. Ride-sharing apps like Uber commodified mobility when they first burst onto the scene in the late 2000s, dramatically dropping the cost of a ride. You’d expect that many drivers, especially taxi drivers, would lose their jobs. Instead, lower costs exploded demand. People who typically walked or took buses started hailing rides for short trips, while new sectors like food delivery emerged. Globally, the number of ride-hailing drivers ballooned from thousands to millions. Sure, some taxi drivers had to become Uber drivers, but overall employment in the ride-sharing and taxi industry grew substantially between 2015 and 2023.

We can go back in history to the Industrial Revolution to see the same pattern. Until the 1870s, prior to the invention of something called the Bessemer process, buildings were limited to materials like wood, brick and iron, which couldn't support extreme heights or withstand environmental stressors without costs becoming prohibitive. 

The Bessemer process of producing steel cost-effectively changed that, allowing us to build much taller buildings. But it also led to fears over job displacement in traditional ironworking trades, which manifested in strikes, union resistance and shutdowns. Although there were some shifts in labour types – and steel production reduced the need for highly skilled ironworkers – new jobs sprouted and demand for overall construction labour increased.

Jevons paradox: efficiency in action

The idea behind this, known as Jevons paradox, was first articulated in 1865 by economist William Stanley Jevons. He noticed something about the use of coal in England: New steam engines made coal use more efficient, but instead of reducing the amount of coal required, it made coal cheaper and more versatile. Industries ramped up their consumption, deploying it for everything from powering factories and ships to heating homes. Demand for coal skyrocketed, and so did jobs.

Let’s apply this logic to AI and knowledge work. AI agents make non-routine tasks like coding prototypes, drafting reports and brainstorming campaigns cheaper and more accessible. This means that a small start-up can use AI to avoid shelling out significant resources on bespoke software, allowing it to tackle more projects, build more apps and input more features. Demand for coders doesn't drop. It rises because customised software is being used by more companies. Beyond writing code, coders will now do different things, such as testing code generated by AI, as well as providing oversight, integration and advanced tweaks.

You can see a similar trend in the graphic design industry. Prior to the invention of AI, graphic design was time- and cost-intensive. Now, companies can create ads quickly and easily. Graphic designers have to use AI to generate more ads within a shorter timeframe, but the need for human collaboration, good taste and a strong connection to the brand and audience will remain – things that AI has yet to fully replicate.

Pre-AI, you often had to shell out a substantial fee for basic legal documents. With AI making legal work cheaper, the demand for it will increase, and lawyers will have to assume a more advisory role rather than just executing legal documents. The same goes for mergers and acquisitions, where AI has brought down the cost of deal-making. Bankers will then act as advisers – not so much as writers of contracts and covenants. There will be more deals happening, and more work for bankers, but in a different capacity.

How to give yourself an edge

So, take heart. All is not lost, not by a long shot. 

That’s not to say that the transition to an AI-infused world will be easy, or that there won’t be pain in the form of layoffs and reskilling. Some may gripe that a few companies and individuals seem to be reaping the spoils of AI, while the rest of the population is left having to adjust to a new technology that, frankly, many of us didn’t ask for.

But what's clear is that AI will likely affect every job in one way or another, and employees must adapt as companies increasingly incorporate the technology into how they do business. McKinsey, for instance, is piloting an AI skills test as part of its recruitment process by asking applicants to use the firm’s AI tool, Lilli, to complete certain tasks. Candidates are evaluated on how they prompt the chatbot and what they do with the information it generates.

How can you ensure that you gain from AI? Here are some easy-to-implement tips:

1. Embrace it as a multiplier: Use it for drafts and ideation, then add your human edge. By using AI to improve your performance and get work done better and faster, you can free up time for higher-level tasks. 

2. Upskill in oversight: Learn how to manage AI agents – which can be extremely useful if you use them well – such as by taking courses on prompt engineering specific to your job or industry. You can even ask AI agents to summarise the latest knowledge about how to make the best use of AI for your context.

3. Spot opportunities: Ask yourself, “What in my field will be unlocked if this task gets 10 times cheaper?” Capture these opportunities, either in your existing job or by becoming an entrepreneur. Indeed, it’s not difficult to create custom AI agents that specialise in a particular job function.

4. Lead on AI adoption: If you’re running a team, normalise AI in workflows. Use it to share knowledge more quickly and solicit ideas from your employees on how to better use it.

It’s true that AI will eliminate some jobs and reshape many others. But it will also increase demand for products and services that were previously cost-prohibitive, creating opportunities that didn’t formerly exist. If you figure out how to use the technology to your benefit – rather than resist it – you’ll be better positioned to navigate the changes and uncertainty ahead.