For Labor Day 2025, I offer a different lens on AI and careers.

Last month, I watched a client’s HR director explain to their leadership why they were eliminating four middle management positions and hiring three junior analysts. “We’re pairing each analyst with AI tools,” she said matter-of-factly. “The AI handles scheduling, performance tracking, and routine decision-making. We get the same oversight at a fraction of the cost.” These weren’t abstract future projections; they were immediate, profitable business decisions happening right now.

Every week I see debates about AI’s impact on jobs. The question is not if, but who gets displaced first and when. The reality is that AI will displace jobs faster than industries can create new ones. Most people fixate on entry-level automation. I think the real story sits higher up: middle management is walking into a buzzsaw they do not see coming.

The Numbers Don’t Lie

According to a global Pega survey, 78% of executives believe increased AI use will sharply reduce middle management. Gartner estimated robo-bosses would take on 69% of managers’ workloads by 2024. Bloomberg reports AI could replace 53% of market research analyst tasks and 67% of sales representative tasks, while managerial roles face only 9–21% automation risk. MIT’s State of AI in Business 2025 asked, “Would you assign this task to AI or a junior colleague?” For quick tasks like emails, summaries, and analysis, 70% chose AI. The headline risk looks lower for managers, yet the economics point the other way. Companies will cut entire layers of management.

While studies from Harvard (Hosseini and Lichtinger, 2025) and Stanford (Brynjolfsson et al., 2025) document sharp employment declines for young college graduates in AI-exposed junior roles, with the Stanford analysis pinpointing a 13% relative drop for workers aged 22-25 in the most exposed occupations, driven by automation rather than augmentation.

This pattern doesn’t spell doom for all juniors. Instead, the Harvard study’s U-shaped heterogeneity by education level, where mid-tier graduates (from “strong” and “solid” institutions) suffer the largest relative declines (around 8-10%) while elite (top-tier) and low-tier graduates are less affected, mirrors broader management risks.

AI levels the playing field by augmenting low-cost juniors with capabilities that rival seasoned routine work (as evidenced by Stanford’s finding of employment growth in augmentative AI applications) and preserving irreplaceable elite expertise, effectively squeezing the ‘solid but routine’ middle, exactly the domain where many middle managers sit, handling oversight that AI-augmented teams can now bypass.

Note that the charts and aggregates in both studies do not distinguish between middle and senior roles, potentially understating how these dynamics are flattening hierarchies by promoting capable juniors past obsolete mid-level positions.

While these studies highlight short-term junior pains, they overlook how AI acceleration is targeting the middle next.

The Middle Management Trap

The issue isn’t that AI is automating middle management. Consider the math: A middle manager costs $90,000 annually. A junior analyst costs $70,000. Add $10,000 in AI tools, and you get deeper analysis, more availability, and zero conflicts for less. The “experience premium” justifying management salaries evaporates when AI provides institutional knowledge and decision support in real-time.

DoorDash exemplifies this shift. Its algorithmic tools manage 200,000 drivers and 340,000 restaurants with no direct supervisors. This model is spreading rapidly.

In 2024, Big Tech reduced hiring of new graduates by 25%. However, these are structural eliminations. When companies can augment junior analysts with AI capabilities equivalent to seasoned managers, traditional management tracks become obsolete.

The pattern shown in the Harvard study may not hold as firms move from substitution to complementarity. Promotions are already rising for remaining juniors inside adopters, which hints at redesigned entry paths rather than a permanent lockout.

The elimination of middle management positions doesn’t just displace current managers, it forces a complete reimagining of how careers progress and when they end.

The Retirement Acceleration

As AI technologies improve and standardize, the most substantial change will happen at the retirement age, which will come from economic obsolescence, not worker choice. As junior employees paired with AI demonstrate that they can handle traditional middle management responsibilities, companies will stop promoting existing workers and instead hire new graduates for direct, fast-track programs to senior roles. Current middle managers, unable to compete with AI-augmented juniors and blocked from advancement, will find themselves in career dead ends.

Critics argue that this overlooks the experience premium and institutional knowledge that seasoned managers bring. But this misses the fundamental shift: as AI systems accumulate organizational memory and decision-making patterns, they become the institutional knowledge. Client relationships and cultural nuance remain human domains, but the operational wisdom that justified middle management salaries gets encoded into algorithms.

The risk that boards won’t trust 35-year-old executives overlooks the fact that we already live in this world. Zuckerberg was 23 when he became a billionaire, Sam Altman is currently 40, and the average age of the youngest CEOs is 39. AI provides the analytical rigor that once required decades of experience. Just as automation flattened manufacturing hierarchies, AI will flatten corporate structures entirely. The result: a compressed career arc, where workers either reach the C-suite by 40 or exit the corporate world entirely by 50.

The Henry Ford Parallel

Greg Satell offered a timely frame this week. He highlights Henry Ford to show how displacement, productivity, and reinstatement shape labor shifts, and he argues that “it is ecosystems, not inventions, that determine the future.” I build on that lens here to push for designed human AI collaboration rather than market drift.

Henry Ford provides a clear model for how technology reshapes work. Automation on his family farm displaced his labor, which sent him to work for Thomas Edison. Higher productivity gave him leisure time, which he used to tinker, experiment, and imagine new possibilities.

Reinstatement proved transformative. Ford grew prosperous enough to start his own company and pioneer an industry that created millions of new jobs. Workers left family farms, where labor was no longer needed, to work in factories. Productivity gains raised incomes and helped families educate children for the high-tech industries of today.

AI compresses Ford’s decades-long arc into a handful of years. The displacement phase that once took generations now unfolds in quarters. New projections from the World Economic Forum and major consultancies suggest that artificial intelligence could eliminate a large share of entry-level white-collar roles within the next decade.

You cannot grasp the impact of the automobile by studying the engine alone. Second- and third-order effects matter most. Improvements in transportation and logistics transformed retail, urban planning, and manufacturing, creating real economic value.

Further reading: Greg Satell, “Here’s How To Think About Artificial Intelligence, Jobs And The Economy.”

The companies that win will design intentional human AI collaboration ecosystems rather than swap humans for machines. Build roles that blend human judgment with AI capabilities, not human-only domains or AI-only processes.

Designing the Post-Management Ecosystem

The shift ahead is not a purge; it is an upgrade to how organizations work. Henry Ford did more than replace horses; he built roads, supply chains, safety standards, and new careers. Treat AI the same way. We are not only automating management tasks; we are designing new structures for decisions, accountability, and growth.

The leaders out front invest in what I call cognitive apprenticeships: direct partnerships between AI-augmented juniors and senior executives that shorten the distance between insight and action. One client replaced rotations with a two-year C-suite shadow program for high-potential employees. Cost rose by 40 percent, yet the program produced executives ready for complex decisions two to three years faster.

The goal is not to hunt for tasks AI cannot do. Build new pathways to influence that do not depend on a ladder that no longer exists. Budgets already point to flatter, AI-integrated structures; our choice is whether to design them with intent.

Practical design moves:

• Pair every junior analyst with a senior sponsor and explicit decision rights.
• Assign AI tools to the work, not to the role, and track outcomes over headcount.
• Create entry paths in integrator apprenticeships, model QA, data lineage, safety reviews, and customer workflow mapping.
• Add human checkpoints where judgment, trust, and ethics matter most.

The transition is here. Organizations that codify apprenticeships and outcome ownership will build stronger leaders faster and with more purpose.

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Chris Hood is an AI strategist and author of the #1 Amazon Best Seller “Infailible” and “Customer Transformation,” and has been recognized as one of the Top 40 Global Gurus for Customer Experience.

To learn more about building customer-centric organizations or improving your customer experience, please contact me at chrishood.com/contact.