The World After Work: What AI Means for All of Us

I want to start with something I believe deeply, and that I think most people in the technology and business world are still dancing around rather than saying directly.

Artificial intelligence is going to eliminate an enormous number of jobs. Not gradually, not marginally, not in ways that will be comfortably offset by the new roles it creates on a comparable timeline. We are entering a period of structural displacement in the labour market that will be unlike anything the modern world has experienced, and the honest response to that is not reassurance. It is preparation.

I say this not as someone standing at a distance from the technology, watching it happen to other industries. I say it as a founder who has lived the shift from the inside. At Whoppah, AI has already transformed how we work. A task that used to take five minutes now takes thirty seconds. Customer queries that used to require a human are increasingly resolved by AI before anyone on our team is involved. Engineering cycles that required large teams are being completed by smaller ones, faster. These are not pilot programmes or experiments. They are the new normal. And we are a sixteen FTE company in a single category. Extrapolate that across every sector of the global economy and the picture becomes very clear, very quickly.

This is the conversation I think we need to be having. Not whether AI will change work, but what we do about it.

The Jobs That Are Going, and Why

To think clearly about this, it helps to understand which kinds of work AI is best at replacing and why.

AI excels at tasks that are repetitive, pattern-based, and high-volume. Data entry, basic analysis, content generation, customer support scripting, legal document review, financial modelling, code writing, translation, scheduling, compliance checking. The list is long and it cuts across almost every white-collar profession that has historically felt safe from automation. The factory worker lost their job to the robot. The knowledge worker is next in line, and the timeline is shorter than most people expect.

The reason is that large language models and the AI systems built on top of them are not just automating mechanical tasks. They are automating cognitive ones. They can read, reason, summarise, generate, and respond at a level that, for a wide range of everyday professional tasks, is already comparable to a competent junior employee. And they are getting better every few months, not every few years.

The jobs most at risk in the next ten years are not the ones that require physical dexterity in unpredictable environments. Those are harder to automate than people think. The jobs most at risk are the ones that involve processing information, following defined procedures, and producing standardised outputs. Which is, if you think about it honestly, a very large proportion of what the modern professional economy runs on.

I am not saying this to create alarm. I am saying it because clarity is more useful than comfort, and because the people who see this coming clearly are the ones who will be best positioned to navigate it.

The Uncomfortable Question of Livelihoods

If significant portions of the workforce face displacement over the next ten to twenty years, the question of how people sustain themselves becomes urgent in a way that most current policy conversations have not yet caught up with.

The historical answer to technological displacement has been retraining and transition. The agricultural worker became the factory worker. The factory worker became the service worker. And there is some truth to the idea that AI will create new categories of work that do not yet exist. It always has.

But there are two differences this time that I think make the old reassurance insufficient on its own.

The first is speed. Previous technological transitions unfolded over generations, giving labour markets, education systems and social structures time to adapt. The AI transition is unfolding over years. The gap between displacement and the emergence of new absorptive roles may be long enough to cause genuine social and economic harm to tens of millions of people.

The second is breadth. Previous automation waves tended to concentrate displacement in specific sectors or skill levels. AI is horizontal. It affects entry-level and mid-level knowledge work simultaneously, across industries, across geographies, across demographics. The cushioning effect of moving from one sector to another is reduced when the disruption is everywhere at once.

This is why I think Universal Basic Income, UBI, and more broadly Universal Basic Guarantee, UBG, which encompasses not just income but access to healthcare, education, housing support and retraining, are going to move from fringe policy ideas to mainstream necessity within the next decade. Not because they are politically comfortable, but because the alternative, which is large-scale structural unemployment without a social safety net capable of absorbing it, is far more destabilising.

Where the Money Comes From

The question I hear most often when UBI comes up is a simple one: who pays for it?

It is a fair question, and it deserves a serious answer rather than a hand wave.

The most logical source, in my view, is a direct levy on the productivity gains generated by AI. If a company replaces twenty employees with AI systems and retains the majority of that saving as profit, a portion of that gain should flow back into the social systems that support the people who were displaced. This could take the form of an AI productivity tax, a broader corporate tax reform that captures a greater share of technology-driven profits, or a sovereign wealth fund model in which governments take equity stakes in the AI platforms and companies generating the largest gains, and redistribute the returns.

The Nordic countries have demonstrated that high-productivity, high-tax economies can maintain both competitive private sectors and robust social safety nets. There is no fundamental economic reason why that model cannot be adapted for the AI era, provided there is the political will to do so.

There is also a generational wealth transfer argument worth making. An enormous amount of capital is concentrated in the hands of people and institutions that will benefit most directly from AI-driven productivity gains. A thoughtfully designed capital gains framework, combined with inheritance reform and sovereign AI funds, could provide meaningful UBI financing without crippling innovation or investment.

None of this is simple. All of it is necessary. And I think the founders, technologists and business leaders who are honest about the displacement AI will cause have a responsibility to be part of the conversation about how society absorbs it, not just the beneficiaries of the productivity it generates.

What Skills Will Actually Matter

If the landscape is shifting this dramatically, the practical question for any individual is clear: where should I invest my time and energy in terms of skills and capabilities?

My honest answer, shaped by what I have seen work and fail across two startups and fifteen years of building technology-driven businesses, is this.

The skills that will compound in value over the next ten to twenty years are the ones that AI cannot easily replicate, not because AI lacks raw capability in those areas, but because they require something AI does not have: genuine human context, accountability, and the ability to operate with incomplete information in unpredictable social situations.

Critical thinking and judgment. AI is very good at generating options. It is not good at deciding which option is right given a specific set of values, constraints and relationships that it does not fully understand. The ability to evaluate information rigorously, identify flawed reasoning, and make sound judgments under uncertainty will become more valuable as AI floods the world with plausible-sounding but sometimes wrong outputs. The person who can tell the difference is enormously valuable.

Communication and human connection. The ability to inspire, persuade, listen deeply, build trust, and navigate conflict is not going to be automated. It will become more important, not less, precisely because so much of the transactional layer of human interaction will be handled by machines. What remains is the relational layer, and the people who are genuinely excellent at it will stand out.

Creative direction and taste. AI can generate. It cannot originate in the deepest sense. Not yet at least. It cannot decide that the brief is wrong, that the category needs reframing, or that the market is looking for something nobody has thought to ask for yet. The creative director, the strategist, the person who can look at everything AI produces and shape it into something genuinely meaningful, that role is not disappearing. It is becoming the central one.

Emotional intelligence and leadership. As organisations become leaner and AI handles more execution, the humans who remain will need to work together more effectively, navigate more ambiguity, and lead through more uncertainty. Emotional intelligence, the ability to understand yourself and others well enough to build productive relationships under pressure, will be one of the most sought-after capabilities in any organisation.

Entrepreneurial thinking. The ability to identify an unmet need, assemble resources around it, test a hypothesis quickly, and adapt based on what you learn is becoming accessible to far more people as AI lowers the cost and complexity of building. The person who combines entrepreneurial instinct with AI fluency is extraordinarily well positioned in the decade ahead.

What We Get in Return

I want to be honest that I hold both of these things simultaneously: a clear-eyed view of the disruption ahead, and genuine optimism about what becomes possible on the other side of it.

Because what AI gives back, to the individuals and organisations willing to use it thoughtfully, is time. And time, redirected toward the things that humans do best and find most meaningful, is an extraordinary gift.

I have felt this personally over the past two years at Whoppah. The hours previously consumed by tasks that AI now handles have been redirected toward thinking more deeply about strategy, spending more genuine time with individual team members, understanding our customers more carefully, and investing in my own development as a leader and as a person. The quality of the decisions that come out of that space is higher. The relationships are stronger. The work is more satisfying.

Scaled across society, the same principle applies. If AI handles a growing share of routine cognitive labour, and if we design our economic systems to ensure that the resulting productivity gains are broadly shared, then what we get in return is the possibility of a richer, more purposeful life for a much larger proportion of the population. More time for creativity, for family, for community, for learning, for rest, for the things that make life actually meaningful rather than merely busy.

That is not a utopia. It requires deliberate choices about how we design tax systems, social safety nets, education and technology governance. But it is a genuinely achievable outcome if we approach it with the same ambition and rigour we apply to building the technology itself.

How to Position Yourself Now

If I were advising someone early in their career today, or someone mid-career facing an uncertain transition, the framework I would offer is simple.

Become genuinely fluent in AI tools, not just familiar with them. Understand what they can do, where they fail, and how to direct them effectively. This is the foundational literacy of the next era, the equivalent of learning to use the internet in the 1990s. The people who mastered that early built enormous advantages. The same dynamic is playing out now.

Invest relentlessly in the human skills that AI cannot replicate. Communication, leadership, judgment, creativity. These are not soft skills. They are the hardest skills, and they will be the most valuable ones.

Build things. The cost of starting something, testing an idea, reaching an audience, and iterating based on real feedback has never been lower. AI has democratised access to capabilities that used to require large teams and significant capital. If you have an idea, there has never been a better time to act on it.

Stay curious and stay uncomfortable. The people who thrive in rapidly changing environments are not the ones who found the right answer and stopped learning. They are the ones who maintained a genuine appetite for the new, who were willing to change their minds, and who treated uncertainty as an invitation rather than a threat.

And finally, think about contribution, not just career. The next twenty years will raise profound questions about what kind of society we want to build with these extraordinary tools. The people who engage seriously with those questions, who bring both technological literacy and genuine human values to the conversation, will shape the outcome for everyone.

That feels like the most important work of our generation.

I intend to be part of it.