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Earlier this year, I analyzed Swedish job ads. I wanted to understand what was happening to the opportunities available to people starting their careers.
Using public data from Arbetsförmedlingen and JobTech, I examined selected occupations that typically require higher education. Between 2022 and 2025, ads with junior-related wording fell by 48.6%. Total ads in the sample fell by 40.5%, so much of the decline happened in an already weakening market.
The larger difference was between occupational groups. Junior-profile ads fell by 66.2% in the group I classified as more exposed to generative AI, compared with 36% in the comparison group.
These were ads containing words such as junior, trainee, or graduate. The measure misses entry-level roles advertised without those words. It also tells us nothing about who got hired or which employers used AI. The economy, interest rates and the tech cycle could all help explain the decline.
But I did show a pattern, and a similar pattern to what we’ve seen happen in the US for example with the “Canaries in the coal-mine” paper that was published last year.
Then I read this new working paper from Danmarks Nationalbank.
The researchers linked Danish firms’ responses about AI use to monthly employment and wage records. They followed companies that first reported using AI in 2023 and compared them with companies reporting no use in 2022 or 2023. They accounted for each firm’s earlier employment trend, its size, and developments within the same industry.
That gets us closer to what happens inside an organization after it starts using AI. It still doesn’t prove that AI caused the difference. Companies choose both their technology and their staffing strategy, and those decisions can be part of the same reorganization.
But by September 2025, employment at the adopting firms was about 11% lower relative to those earlier trends and the comparison group.
Eleven percent sounds like a lot but the authors offer a useful illustration which I really like and that I also see across a lot of the clients that I work with. Imagine a company that grows from 50 employees to 53, while its earlier growth suggested it would reach about 60.
More people work there. The company is still hiring. It has nevertheless fallen below the path it was on. (Estimating this is hard but I do like how they try to do this in the Danish study!)
What also changes the picture is the the size of the company. The decline was concentrated in firms with fewer than 100 full-time-equivalent employees. Their shortfall was roughly 15%. Larger adopters showed no statistically clear decline in total employment relative to their trend, although their employment mix shifted towards less AI-exposed occupations.
What interests me more is how the adjustment seems to happen.
The researchers found a sharp decline in employees with short tenure relative to trend, while longer-serving employees were retained to a greater extent. They interpret this as adjustment through reduced hiring rather than layoffs.
From an HR perspective, it is easy to imagine how this could play out. A manager decides the team can handle the workload without filling a vacancy. A planned position gets postponed. The next budget includes fewer additional hires because the existing team is expected to do more with AI.
Just as I found in my study, in Denmark they found that the shortfall was concentrated in occupations with tasks more exposed to AI. Young and university-educated workers also showed larger declines, although these groups overlap. The researchers cannot neatly separate age, education, and occupational exposure into independent explanations.
But if this holds true and if we hire fewer junior people because experienced employees can do more with AI, how do people get the experience we will later expect them to have?
That is my concern and worry, rather than a finding from the paper, but I think it’s a valide one. The researchers haven’t followed today’s missing hires through their future careers. But the question belongs in workforce planning now, alongside the savings from the positions we decide not to fill.
I’m also torn here. Keeping a task manual just so someone can learn it seems like an odd response to better technology. People should learn to work with the tools they will use. At the same time, handing someone an AI tool doesn’t automatically teach them to recognize when its output is wrong. We still have to design work in which people can develop that judgment.
There is some reassurance in the Danish results. The researchers find no clear wage response, and the measured effects among adopters are too small to show up clearly in employment across the economy. Denmark also has a fluid labour market, which may help people find opportunities elsewhere.
I don’t think we should assume every country or company will handle the adjustment equally well. The study covers early adopters over a limited period, and the firm survey excludes businesses with fewer than ten employees. It leaves plenty unresolved.
If you are CxO or in HR, I would start with a fairly ordinary exercise. Look at the positions you filled, the vacancies you chose not to replace, and the planned hires that never reached an advert. Ask the managers involved what changed. Lower demand, budget pressure, a different organization, or an expectation that AI would absorb the work?
Then look at which opportunities have disappeared for people at the beginning of their careers, and what opportunities we have created for them to learn instead.
Worth while thinking about when going into workforce planning for 2027 which I think a lot of us are about to enter now.
Sources: Johannes Sundlo, Vad händer med de juniora jobben, updated August 2026; Simone Maria Bonin, Saman Darougheh and Andreas Kuchler, AI Adoption and Firm Size: Employment Effects in Monthly Administrative Data, Danmarks Nationalbank Working Paper No. 223, 16 September 2026.