What prompted me to write this article was the email notifications I received in short succession: within 48 hours in mid-May 2026, The Economist published two contrasting pieces: a cover story titled “Prepare for an AI Jobs Apocalypse.” Just two days later, a feature focused on the US productivity miracle, noting that this miracle is driven by an energy boom and tech adoption, AI hasn’t yet much to do with it.
These seemingly contradictory articles, published so close to each other in the same outlet, left me perplexed, but at the same time, I think this is an accurate reflection of the current employment context. On one hand, we have a continuous stream of doom-announcing information about AI’s impact on future employment. On the other hand, there’s a very convoluted picture of its impact on work productivity.
This is where we are right now. And for anyone working in People and Culture, the HR and organisational professionals whose job it is to shape how organisations attract, develop, and retain their people, this contradiction is central to their position, almost as a brief. Not just in these turbulent times, the ability to hold different perspectives is what constitutes a good People leader.
My main takeaway or thesis here is that many articles and pieces of information will be published, some of them will resonate within the LinkedIn space, will produce viral charts and buzzwords that will make the task of predicting AI impact on different jobs harder or impossible. But it is not even our job to predict whether AI will replace workers. This is not where the focus should go. We should focus on building conditions where AI amplifies human capabilities, not replaces them, because the only evidence of progress or (financially) positive outcomes comes from stories focused on this particular application.
Let’s consider specific points in the recent past that produced major industry buzz.
In March 2026, an Anthropic study resonated widely on LinkedIn and elsewhere, introducing a new measure called “observed exposure“, meaning it’s not just about what AI can do theoretically, but what it is actually capable of doing in real workplace usage. There was this infamous graph with pie charts, and the main takeaway that scared everyone was that AI is very far from reaching its theoretical limits; current coverage is only a fraction of what is feasible. This, of course, freaked everyone out.
On the other hand, a real broadly evident warning sign of the AI influence is the slowing down in hiring young workers in exposed positions, especially in IT. Specifically, there’s been roughly a 14% drop in job-finding roles for those aged 22 to 25 entering these high-exposure roles. This is a truly actionable signal; 14% is not a small number. This should be used as a starting point for evangelising and educating leaders about the future importance of a proactive approach, not just short-term activities.
Also, a major study from Gartner surveyed 350 executives in companies with more than $1 billion in revenue and found that AI layoffs are not producing the return on investment that was expected. The results are very striking: 80% of those surveyed who have piloted AI or autonomous technology reported workforce reduction, but the business cost savings from automation didn’t materialise in monetary ways. Helen Poitevin, Gartner VP Analyst, leading the research, states that “chasing value only through headcount reduction is likely to lead most organisations down a path of limited returns.” The article conveys a very strong sense that the path to choose is using AI as a form of people amplification, implementing technology to make workers more productive rather than outright replacing them.
The third point is that even the loudest “doomers” are now more toned down. For example, Dario Amodei in 2025 was forecasting that AI would wipe out 50% of entry-level tech, legal, consulting, and finance jobs within five years. But in May 2026, on stage with JP Morgan’s Jamie Dimon, he used a different framework. He mentioned Jevons’ paradox, the 19th-century observation that when something becomes cheaper, we use more of it, not less. I don’t like the term “cheaper cognitive work,” but perhaps “more accessible cognitive work” is something that would not lead to less demand. It would actually mean more demand for it, especially in places not yet invented.
So, what is the real job of the people function in this story? Let us focus once again on the Gartner findings and how good people practices are becoming central. This is basically the backbone of what workforce strategy should be: not a cost-cutting lever, but strategic shaping of the future of the organisation.
Skills amplification is a people discipline; it’s not an IT discipline. The companies that are extracting value from AI are not doing it through automation alone, but through roles redesign that adequately pairs human judgment with AI execution, skills development that builds AI fluency, especially for non-technical functions, change management practises, performance frameworks that reward AI-augmented output, not by inviting burnout (or burning tokens for trivial, non-relevant tasks).
The post from Robyn Agoston about AI transformation and why it would fail really resonates here. AI transformation is usually very focused on specific topics and on recruiting for specific roles, putting too much pressure on individuals, which sets them up for failure. If you have one particular person in a specific role, they could potentially be too junior, too siloed in one domain, maybe usually in IT, and with little authority or oversight to actually change the operating model. AI transformation is not a cause that could be solved with one position or one department. This is a capability that should be forever calculated in the org balance sheets. It’s something that is owned in a way, very aligned with the language of human capital that people leaders are using to justify people expenses in front of finance and executive teams. So, if the People functions are not claiming ownership of the human side of AI transformation, or better said, amplification, someone else will do it, and they will do it much worse or much more siloed, without results.
A good starting point to tackle and test the clout and strategic direction needed could be focusing on the early career problem as a hiring strategy. We mentioned this drop in job finding rates for young workers, and the question of the hiring managers they are potentially answering now, “Can we actually save some money by not hiring graduates if Claude can do what they can do, maybe even better?” The answer for most companies is no, because you are not hiring graduates for their first year of work; you are hiring them for the future value that they will create in their third, fifth, or tenth year, and to work on specific tasks that AI can’t or should not work on. So, cutting the entry-level pipeline is a very expensive form of short-termism, and this is something that we, people professionals, should be loud about.
Also, we should focus on something that Sam Altman called ‘AI washing’. He acknowledged the obvious, that there are many instances of AI washing where people are blaming AI for layoffs that would otherwise occur. And then there are some real displacements by AI in different kinds of jobs. Using very clear language on the reasons is crucial, especially as we are currently observing the reshaping of the social contract. Showing human values is the best long-term reputation strategy.
To conclude, focusing on a specific group, for workers in vulnerable roles, the apocalypse is not there. Let us all work together in order to build a system to prevent it from happening or lessen the blow. Building skills that complement AI rather than overlap with it, like judgment, orchestration, taste, and the ability to evaluate AI outputs. Financial resilience is still relevant, and also staying informed and curious.
If you’re currently experiencing job transformation and amplification, your value is deeply connected to being and becoming the kind of worker who knows how to direct AI rather than be displaced or directed by it. Also, there is this risk that should be mentioned: the intensification of work. When AI completes tasks faster, more tasks are added, and the cognitive load, “AI brain fry,” creeps up. How to manage it is the next step for workers in this modern world.
If you are entering the workforce, you are canaries in the coal mine. Across the US this graduation season, students booed commencement speakers who told them to embrace AI, from the University of Arizona to the University of Central Florida, which directly signals the emotions about AI that the youngest generation joining the workforce have. If you are part of this group, look hard at whether your skill set is inside or outside of the task bundle AI is rapidly covering, and build cross-domain expertise early. Don’t wait for a ‘junior analyst’ or specific position because we don’t know if it will be like that. And, of course, get hands-on with AI tools.
For people leaders, it’s amplification, not replacement, and treating AI amplification as an investment, not a cost, should be the main focus. Winning our seat at the table once again for change management, learning, and role design is relevant, and this is something that we will not just use AI as a substitute for hiring. Refuse the binary, because both things can be true at the same time. AI is transformative; it can have positive consequences, but it can also have very negative consequences. Most of AI productivity is not there yet. Maybe it would never be in the way it is professed. The apocalypse hasn’t arrived, but there are real groups and more and more people who are struggling and feeling pressure right now. Layoffs are happening, but they are not producing a return on investment to justify them. So, sitting with the complexity is hard and sometimes not very flashy. This post is not aimed to be a simplified LinkedIn post, but focused on slower, more deliberate, and more honest work ahead of us, which is exactly what people practice should or has always been.
So, the question for the next 12 months is not, “Will AI take my job?” but rather, “Can we make the case loud enough that amplification is better and beats replacement?” In the coming months, I will be sharing real examples of job redesign and AI amplification that are actually working, as inspiration for what this can look like in practice.
About the author:
Ivana Curic is a People & Culture Leader at the intersection of Talent Experience and the future of work. Over the past decade, she worked with global teams across Europe, designing hiring and people processes, employer branding, and talent journeys, more recently, exploring how AI is reshaping job search, hiring, and human decision-making.
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