“If you get rid of the base of the [organisational] pyramid, it becomes like a a diamond. Then where are those junior those middle managers going to come from and where the senior people going to come from? And too many companies are being shortsighted about that.” – Professor Erik Brynjolfsson – Stanford economist, Director of the Stanford Digital Economy Lab

The structural problem facing advanced economies is not simply that automation removes tasks, but that it selectively erodes the bottom rungs of professional labour markets while leaving demand for experienced judgement intact.¹ Entry-level, routine knowledge work is disproportionately exposed to generative AI, yet organisations still need mid-level managers and senior experts to coordinate, decide and lead.^1,6,13 The tension is straightforward: firms optimise costs by shrinking junior roles today, but they also depend on those same roles as the training ground for tomorrow’s decision-makers.¹

The pyramid under pressure: AI and entry-level work

Recent research led by Erik Brynjolfsson and colleagues at the Stanford Digital Economy Lab shows a striking decline in employment for young workers in occupations most exposed to generative AI.^1,6,11 In these roles, employment for people in their early twenties has fallen by around 16 % relative to less-exposed occupations, while older workers in the same fields have largely maintained or increased employment.^1,6,8,11 The pattern is consistent with AI systems taking over routine, well-specified tasks that previously justified hiring larger cohorts of juniors, especially in software development, call centres, customer support, paralegal work and some marketing and sales functions.^1,6,8,13 Senior workers remain because they supply judgement, context, client interaction and organisational memory – capabilities that current models cannot fully replicate.^3,9,13

Brynjolfsson repeatedly emphasises that the analytical unit is the task rather than the occupation.^1,3,9 Most jobs consist of bundles of tasks, and generative AI is automating specific components – coding small features, drafting emails, summarising documents – rather than entire professions.^1,3,9,15 That nuance matters, yet the aggregate effect at the bottom of organisational hierarchies is clear: the share of human effort devoted to routine execution is falling, and the first group to feel it is junior staff.^1,6,13 When organisations respond by cutting entry-level hiring rather than redesigning roles, they begin to hollow out the base of the pyramid that traditionally fed into middle management and senior leadership.¹

From pyramid to diamond: a changed organisational geometry

Professional services firms, large corporates and technology companies have historically adopted a pyramidal staffing structure: many junior employees, fewer mid-level managers and a relatively small group of senior partners or executives.^1,9 Juniors handled labour-intensive, repeatable tasks, generating leverage for experienced staff who focused on complex decisions, client relationships and high-value work. Over time, some juniors were promoted into mid-level roles, creating a natural pipeline of talent.^1,3

AI systems disrupt this geometry by absorbing a growing fraction of the routine work once done by the base.^1,6,9 In software engineering, junior developers used to spend years writing boilerplate code, fixing defects and implementing well-understood patterns under supervision.^1,6 Now coding assistants and agentic tools can generate plausible implementations directly from natural-language specifications, enabling a smaller number of more experienced developers to oversee much larger codebases.^1,6,12 In call centres, conversational models have progressed from supporting human agents to autonomously handling a substantial share of inbound queries, reducing the need for large cohorts of novice staff.^1,6,15 Similar trends appear in document-heavy fields such as law and compliance, where paralegal-style tasks are increasingly automated.^1,8,13

The immediate result is that firms can plausibly operate with fewer juniors while retaining or even expanding their cadre of mid-level and senior staff.^1,6,9 The longer-term consequence is more subtle and potentially damaging: without a broad base of early-career employees gaining experience, the future supply of people capable of occupying those higher-level roles shrinks.^1,2,7 The organisational pyramid begins to resemble a diamond – relatively narrow at the bottom, wide through the middle, and tapering again at the top – but that diamond shape is unstable if the mid-section is not continually replenished.¹

Career formation without apprenticeship

The traditional route into managerial and expert positions involved prolonged apprenticeship: juniors observed how decisions were made, absorbed tacit knowledge and gradually took on more complex responsibilities.^1,3,9 Much of this learning occurred incidentally through the performance of routine tasks – reviewing documents, preparing reports, shadowing meetings – that were economically necessary even if intellectually basic. AI-driven automation strips away exactly those tasks, leaving fewer natural opportunities for observational learning.^1,6,13

The structural risk Brynjolfsson highlights is that firms enjoy short-term cost savings while inadvertently destroying the mechanisms that produced their own human capital.^1,2,6 If junior positions vanish, aspiring professionals face a paradox: they are told that senior judgement, project management and domain insight are the safest skills, but they are denied the environments in which those skills were historically developed.^1,3,9 At the societal level, this becomes a coordination problem. Each individual firm has an incentive to reduce entry-level hiring and lean on AI substitutes; collectively, the economy needs a steady stream of workers acquiring experience to sustain future productivity and leadership.^1,6,7,13

Infosys, the Indian technology and consulting company, appears in Brynjolfsson’s narrative as a counterexample.¹ Rather than sharply cutting junior recruitment in response to AI, it continues hiring young staff but redesigns their work. Routine coding and documentation are delegated to AI tools; juniors focus earlier on project management, systems thinking and broader contextual understanding.¹ Learning that once occurred by osmosis is replaced with explicit training, often supported by AI itself as a teaching aid.¹ The strategic bet is that human taste, judgement and leadership will remain scarce and valuable, so preserving the pipeline into those capabilities outweighs immediate savings from eliminating novice roles.^1,5,16

Strategic myopia and distributional risk

Brynjolfsson’s broader economic work suggests that the same dynamic operates beyond individual firms in the form of skill-biased technical change.² Technologies that complement highly skilled workers while substituting for less-skilled ones tend to widen wage and income gaps, an effect documented historically with earlier waves of computerisation.^2,3 Generative AI threatens to accelerate that pattern by disproportionately eliminating entry-level white-collar roles while expanding the productivity of incumbents who already possess experience and decision rights.^1,2,6,8

If organisations underinvest in junior development, they will ultimately face a shortage of experienced managers and specialists, but the transitional damage may be borne by cohorts of young workers who struggle to enter professional careers at all.^1,8,13 Brynjolfsson warns that this could echo the policy failures of globalisation: aggregate gains in productivity and wealth coinciding with concentrated losses for specific communities, generating political backlash and social instability.^1,10,13 In the AI context, he argues for deliberate investment in education, retraining and apprenticeship-style pathways to avoid repeating that mistake.^1,7 Proposed mechanisms include public funding for skills development, more flexible labour-market institutions and the use of AI itself to support job matching and personalised training.^2,7,16

Redesigning roles around agents, not eliminating people

A recurrent theme in Brynjolfsson’s interviews is that future knowledge work will revolve around defining questions and evaluating answers, with AI agents handling much of the execution.^1,6,9,12 He divides projects into three stages: define, execute, evaluate. AI excels in the middle once a problem is clearly specified, but humans remain essential for identifying the real problem and judging whether the solution is correct or useful.^1,6,12,17 This framing suggests an alternative to removing junior staff: train them early to manage fleets of agents, formulate valuable questions and interpret outputs, rather than simply carrying out pre-defined procedures.^1,6,12

Under such a model, junior roles would be reconfigured rather than erased. Instead of spending years on manual data cleaning or repetitive coding, new entrants would learn to orchestrate AI tools across workflows: decomposing tasks, specifying constraints, monitoring for errors and integrating results into decisions.^1,6,9,12 They would acquire the meta-skills – problem framing, stakeholder communication, risk awareness – that constitute the foundation of senior responsibility, while agents supply the routine labour.^1,5,16 This path preserves a developmental ladder even as the nature of rungs changes, aligning organisational needs for future leadership with technological realities.

Debates, objections and alternative trajectories

Not all economists agree that hollowing out entry-level roles will persist or that the organisational pyramid must fundamentally change. Some argue that new categories of junior work will emerge around AI maintenance, data curation, prompt engineering or human oversight, effectively replacing traditional routine tasks with technologically mediated ones.^2,3,9 Others note that if AI substantially lowers the cost of producing certain services, demand elasticities may lead to expanded employment even at junior levels, as happened with radiology when cheaper imaging increased the total volume of scans.^1,3,9 Brynjolfsson himself acknowledges this mechanism, highlighting that roughly half the economy may see rising employment when prices fall, depending on demand curves.¹

However, the entry-level job data for generative-AI-exposed occupations suggest that, at least in the current phase, displacement is outpacing creation for young workers in affected fields.^1,6,8,11,13 The diamond-shaped organisational concern arises specifically where demand does not expand enough to justify large numbers of juniors, yet mid-level and senior roles remain necessary. The debate therefore centres less on whether AI will ultimately create new work – historical evidence suggests it will – and more on whether current corporate decisions around hiring, training and role design are aligned with long-term capability needs.^2,3,6,9

Why the organisational base still matters

The underlying message in Brynjolfsson’s warning is not nostalgic protection of obsolete jobs but a pragmatic assessment of capability formation.^1,2,6 Advanced technologies, including AI, are general-purpose tools whose economic impact depends on complementary investments in human capital, organisational redesign and institutional adaptation.^1,6,16 If firms treat AI purely as a substitution technology – a way to remove headcount at the bottom – they may gain short-term margin improvements but weaken their ability to innovate, coordinate and exercise judgement over time.^2,4,5

Conversely, organisations that explicitly preserve and reconfigure junior pathways, teaching novices to manage agents, frame problems and develop domain expertise, are more likely to sustain a robust pipeline of mid-level managers and senior leaders.^1,5,16 At the economy level, such choices affect whether AI becomes primarily a force for shared prosperity or for increased concentration of wealth and power.^4,6,8,13 The geometry of the organisational pyramid is therefore not merely a staffing diagram; it is a reflection of how societies choose to invest in human potential under conditions of rapid technological change.

 

References

1. “Stanford’s Top AI Economist: The Next 10 Years Will Be the Best AND the Worst in History” – Silicon Valley Girl – Jul 21, 2026 – https://www.youtube.com/watch?v=72duHF7iZiU

2. What AI Is Really Doing to Jobs Right Now – Stanford … – 2026-05-27 – https://www.youtube.com/watch?v=HpgjB4ZA7E0

3. The Jobs Equation-Erik Brynjolfsson – https://www.theatlantic.com/sponsored/google-2023/the-jobs-equation-erik-brynjolfsson-qa/3872/

4. New Interviews: Erik Brynjolfsson Speaks Out on Jobs and AI – 2017-09-05 – https://ide.mit.edu/insights/new-interviews-erik-brynjolfsson-speaks-out-on-jobs-and-ai/

5. Economist: The AI Risk Almost Nobody Is Talking About | Erik Brynjolfsson – 2026-06-14 – https://www.youtube.com/watch?v=TW0oaz_CF3E

6. Why Erik Brynjolfsson is a ‘mindful optimist’ about AI – Time Magazine – 2026-04-24 – https://time.com/partner-content/charter/why-erik-brynjolfsson-is-a-mindful-optimist-about-ai/

7. Stanford Digital Economy Lab’s Erik Brynjolfsson – 2026-07-21 – https://sloanreview.mit.edu/audio/creating-shared-prosperity-with-ai-stanford-digital-economy-labs-erik-brynjolfsson/

8. Congressional Testimony of Erik Brynjolfsson – https://www.congress.gov/116/meeting/house/109981/witnesses/HHRG-116-SY15-Wstate-BrynjolfssonE-20190924.pdf

9. Top AI economist who found ‘significant and disproportionate impact’ on entry-level jobs finds link between robots and minimum wage hikes – 2026-03-04 – https://finance.yahoo.com/news/top-ai-economist-found-significant-215609544.html

10. Erik Brynjolfsson on how AI is rewriting the rules of … – 2025-03-26 – https://www.hbs.edu/managing-the-future-of-work/podcast/erik-brynjolfsson-on-how-ai-is-rewriting-the-rules-of-the-economy

11. Erik Brynjolfsson: Will Technology Replace Human Jobs? – 2022-05-25 – https://www.youtube.com/watch?v=JCwIwCK8jpI

12. Addressing AI’s Impact on Employment: New Research – 2026-02-26 – https://www.linkedin.com/posts/erikbrynjolfsson_canaries-interest-rates-and-timing-more-activity-7426769858417147904-QlbU

13. This AI expert says the job apocalypse isn’t coming, even if you’re a coder – here’s why – 2026-03-30 – https://www.zdnet.com/article/ai-job-apocalypse-not-happening-for-coders/

14. AI could widen the wealth gap and wipe out entry-level jobs, expert says – 2025-08-05 – https://www.npr.org/2025/08/05/nx-s1-5485286/ai-jobs-economy-wealth-gap

15. Interview with Erik Brynjolfsson – 2017-03-07 – https://www.youtube.com/watch?v=fbE9xXfb0PA

16. Generative AI at Work* | The Quarterly Journal of Economics – 2025-04-08 – https://academic.oup.com/qje/article/140/2/889/7990658

17. Gener(AI)ting the future – https://www.capgemini.com/wp-content/uploads/2024/10/Erik-Brynjolfsson-23Oct2024-Conversations-for-tomorrow_Edition_9_Report.pdf

18. NBER WORKING PAPER SERIES – https://www.nber.org/system/files/working_papers/w31161/w31161.pdf

19. Artificial Intelligence and Jobs: Evidence from Online … – https://jadhazell.github.io/website/AI_And_Jobs.pdf