Wow! According to the recent job report from Washington, companies added 162,000 jobs in August, and unemployment remained at 4.1%. This makes the hiring situation appear reasonably healthy.
But I don’t believe the numbers. Most likely, they will be revised downward next month, but even if they aren’t, they don’t reflect reality. When I talk with recruiters and, even more importantly, the job seekers and recent graduates, I hear a very different story. Hiring is slow, finding a job is difficult, and many companies are reluctant to add headcount.
The government numbers are not necessarily wrong on the surface. They reflect what they were designed to measure: employment. However, they do not measure the demand for talent within corporations. Those numbers were once close enough that the distinction did not matter, but today it does.
Maybe the most significant is what kinds of jobs were added. Of the 162,000 jobs added in August, approximately 59,000 were in food services and drinking places (perhaps attributable to the World Cup and mostly temporary jobs), and another 42,000 were in local government education (seasonal additions as school starts). Together, those two categories accounted for roughly 62% of reported job growth. There was slight growth in health and aged care employment, but at a much slower pace than previously. Meanwhile, employment in information services declined, while professional and business services showed essentially no growth. The majority of the jobs added are not high-paying, nor are they careers for most people.
It seems that corporate America is barely hiring. The unemployment rate tells us how many people are actively looking for work, while payroll employment tells us how many jobs are filled. Neither answers the question that matters most to us in talent acquisition: How much demand exists for external talent?
The Low-Hire, Low-Fire Economy
Other labor-market indicators suggest that hiring has weakened even while layoffs remain relatively low. Economists have described this as a “low-hire, low-fire” economy, but I suspect something more fundamental is occurring. Companies may be discovering that they can reduce their dependence on labor without conducting large layoffs.
Consider what happens when someone retires or resigns. Increasingly, the first question is not, “Who should replace this person?” It is, “Do we need to replace this person at all?” Can an AI agent do this job, or can an AI agent make another employee more productive? When a recruiter departs, the company may automate more sourcing, screening, scheduling, and candidate communication rather than hire another recruiter.
When the number of layoffs is fewer than 500, there is usually no layoff announcement. There may not even be a formal workforce-reduction program. It is a sort of trickle-out process that slowly reduces the headcount with little visibility. Yet, with repeated reductions across hundreds of positions, a company employing 20,000 people can gradually become one employing 18,000.
This rather slow encroachment of jobs being taken over entirely or enabling fewer employees to do more because of AI agents may turn out to be one of AI’s most significant effects on employment
The Job That Never Existed
Most discussions about AI ask how many jobs AI will eliminate, which occupations will disappear, and when the layoffs will begin. Those may be the wrong questions because AI does not have to eliminate someone’s existing job to reduce employment. It simply has to eliminate the need to hire the next person.
Historically, organizations added employees when the workload exceeded the existing workforce's capacity. AI changes that equation. Before approving additional headcount, executives can ask whether the existing team can perform the work using AI, whether an agent can perform part of it, or whether the workflow can be redesigned or eliminated altogether.
If so, traditional measures of job loss tell us only part of the story.
The Entry-Level Problem
Today, the most real pain is being felt at the bottom. Entry-level jobs have always served two functions: they produced work and trained workers. Junior accountants reconciled accounts, junior lawyers reviewed documents, junior programmers wrote simple code, and junior recruiters sourced candidates and conducted initial screens.
Much of that work was repetitive, but it was also how people learned. The traditional development process moved from routine work to experience, and from experience to judgment and expertise. AI is increasingly capable of performing the routine work that this apprenticeship provided.
This creates a problem. Employers demand people with judgment, critical thinking, communication skills, business acumen, and experience, while simultaneously automating the work that used to allow people to learn and gain that experience. We are dismantling the apprenticeship system embedded in white-collar work without building anything to replace it.
That makes this as much an L&D problem as a recruiting problem. If AI takes over much of the novice work, organizations will need to create developmental experiences deliberately through simulations, apprenticeships, rotations, mentoring, scenario-based learning, and AI-assisted practice. The future corporate university may therefore become less about delivering courses and more about creating environments where people can gain experience. This is an area where very little is being done, but one that requires serious thought and experimentation. The question is, who pays for it?
We Need Different Measures
We need more talent intelligence and deeper data than we currently have to measure what is happening. Unemployment and payroll growth are useful, but so are hiring rates, entry-level hiring, changes in total corporate headcount, trends in internal mobility, productivity growth per employee, and the percentage of vacated positions that organizations actually replace.
That last measure could become especially revealing. If 100 employees leave an organization and only 70 are replaced while output remains constant or increases, we are observing one of two things: is this increased output due to AI, or is it simply because most people were not contributing much? We need much better ways to measure productivity.
The biggest change is that workforce planning is shifting from jobs to work. Organizations are using more types of workers than ever. Most modern workforces are composed of regular/permanent employees, contractors, temporary and part-time workers, automation, platforms, and AI agents.
The question for talent leaders and corporate executives is no longer how many people the organization needs to hire. It is what work needs to be done, which portions require humans, which can be performed by machines, and how the organization will develop the human capabilities it still needs.
We keep waiting for AI to produce a massive wave of layoffs so that we can measure its effect on employment. We may be looking in the wrong place. The more important AI employment story may be the millions of jobs that companies quietly decide never to fill.