The disappearing experience pyramid: How do you become senior without being junior?

The great junior job extinction

Microsoft's 2024 study of 200,000 user conversations with AI tools revealed something that should alarm every recent graduate: higher AI applicability correlates directly with education level. The more educated you are, the more likely AI can do parts of your job.

Previous waves of technology eliminated manual labor first. AI specifically targets the cognitive, entry-level work that college graduates have historically used as stepping stones to senior positions.

SignalFire's research confirms this shift: new graduates now account for just 7% of startup hires in 2024, down from 30% in 2019. The traditional entry point into professional careers is simply disappearing.

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When AI excels at "junior work"

Microsoft's study found that AI excels at exactly the tasks junior professionals have always performed to build expertise: information gathering, writing, and communication. These are the foundation of professional development.

A junior consultant traditionally researches markets, compiles data, writes initial drafts of presentations, and supports senior team members. AI can now handle these tasks faster and more consistently than human beginners.

"At Eli5, we've experienced this transformation firsthand," explains Kishan, our CTO. "We simply don't hire junior developers anymore because the value they traditionally brought, basic coding tasks and simple feature implementation, is now handled more efficiently by AI tools. A senior developer with AI can accomplish what used to require a team of juniors."

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The experience paradox deepens

This creates what economists call the "experience paradox," and AI has supercharged it. Workers cannot gain experience without jobs, and cannot get jobs without experience. Now, with AI automating typical beginner tasks, this cycle has become nearly impossible to break.

Wall Street provides the starkest example. Goldman Sachs and Morgan Stanley executives openly discuss cutting junior analyst hires by up to two-thirds. Deutsche Bank's Chief Strategy Officer bluntly stated: "the easy idea is you just replace juniors with an AI tool."

The financial sector shows particularly dramatic impacts because AI can analyze thousands of reports in minutes, work that previously required teams of junior analysts working around the clock.

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The competency compression effect

Software development shows what we call "competency compression." Nearly all developers (97% according to GitHub) now use AI coding tools. The basic tasks that junior developers used to learn from, like writing simple code and fixing bugs, are now handled by AI.

This creates a competency compression effect: new developers must understand AI tool capabilities, prompt engineering, code review and validation, and system architecture from day one. The gradual learning curve that allowed previous generations to develop expertise through increasingly complex assignments over several years has been eliminated.

UX/UI design faces similar disruption as tools like Figma's AI features, Adobe Sensei, and other platforms automate wireframing, layout generation, and basic visual design. Junior designers traditionally built skills through repetitive execution of design tasks, and AI now handles these assignments faster and more consistently than human beginners.

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Beyond the crisis: rethinking professional development

The elimination of traditional entry-level positions forces us to completely reimagine professional development. Career planning strategies must fundamentally shift from hierarchical progression to portfolio skill development. Young professionals now need to develop "AI-augmented" capabilities from the start, combining technical AI proficiency with uniquely human skills like emotional intelligence, creative problem-solving, and strategic thinking.

AWS research analyzing 283,000+ job postings identifies software development, data analysis, cloud engineering, cybersecurity, and data engineering as high-growth fields requiring AI collaboration rather than AI replacement.

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The retraining imperative

This affects more than just new graduates. The research shows that 40% of employers are planning workforce reductions where AI can automate tasks. Current professionals need to urgently assess their vulnerability and adapt.

Retraining for the existing workforce falls into two - or perhaps three - categories:

Profession switching: Moving to fields where human skills remain irreplaceable. Healthcare, skilled trades and human services show lower AI automation potential.

AI augmentation: Learning to work alongside AI tools in your current field. This means understanding AI capabilities, developing prompt engineering skills, and focusing on higher-level strategic thinking that AI cannot replicate.

Retire: For professionals later in their careers, early retirement may become the most practical option. The cost and time investment required for comprehensive retraining may not make economic sense for those who can afford to step away from the workforce entirely.

The most promising solutions combine AI proficiency with human-centric skill development. Large organizations are now providing universal prompt engineering training for all new hires, recognizing that future professionals must be "AI-augmented" rather than AI-replaced.

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The window for action is narrowing

AI adoption is happening at unprecedented speed. ChatGPT reached 1 billion users in four months compared to the personal computer's two-decade adoption timeline.

This acceleration provides little time for natural market adjustments that historically allowed displaced workers to retrain or move to emerging industries.

Economic modeling suggests the elimination of junior positions could create a "missing generation" of mid-career professionals by 2035-2045, with potentially catastrophic implications for organizational knowledge transfer and leadership development.

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The diamond-shaped future

The traditional pyramid is becoming a diamond: experts at the top, seniors in the middle, and AI handling what juniors used to do. This requires completely different career strategies.

Success in this new landscape means treating AI as a collaboration partner rather than a replacement threat. The organizations and individuals who proactively embrace this transformation will define the next phase of economic development.

The evidence suggests that outcomes depend heavily on implementation choices, policy responses, and market dynamics that remain fluid and responsive to deliberate intervention.

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The questions we must answer

As we navigate this shift, I’m struggling with questions like:

What will be the core tasks that humans will be doing? As AI handles routine cognitive work, human professionals must identify the irreplaceable skills that define their value: creative problem-solving, emotional intelligence, and complex stakeholder management.

What is the ability to consume and process information of the human operators? The bottleneck shifts from information gathering to synthesis and decision-making. Professionals must rapidly assess AI-generated outputs and make nuanced judgments that combine analysis with human intuition.

What about the black boxes that are built to alleviate repetitive and automated work? The risk isn't just job displacement, and also the gradual erosion of institutional knowledge. Organizations must balance automation efficiency with human comprehension of their own operations.

How do we discover what we actually want to do for work in this AI-transformed landscape? When traditional career paths disappear, the fundamental question becomes more important than ever: finding meaningful work that aligns with human interests and capabilities in a world where the old exploration methods apply less and less.

The answers to these questions will shape not just individual careers, and also the fundamental nature of professional work itself.

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Sources: Microsoft "Working with AI: Measuring the Occupational Implications of Generative AI" (2024), SignalFire startup hiring data, Goldman Sachs research, McKinsey Global Institute projections, AWS job posting analysis, GitHub Copilot adoption statistics