How AI Is Rewriting the Career Ladder

Not long ago, a junior software developer might cut their teeth writing boilerplate code under a senior engineer’s guidance. Now, AI can handle much of that rote coding. At Google, for example, over a quarter of new code is generated by AI systems. Law firms report a similar shift: AI models can now draft contracts that used to be the thankless work of first-year associates. 

In field after field, generative AI is swiftly filling the role of the entry-level employee, churning out first drafts, summaries, and analyses.

This should be a win for efficiency. After all, Bill Gates calls this the era of “free intelligence.” But even with broader access to AI, closing the digital divide may not make up for the decline in entry-level work. Those tedious tasks were also teaching tools. 

/When AI Becomes the New Apprentice

If an AI chatbot handles the market research and an AI coder scaffolds the app, how does the human rookie learn the subtle skills and hard lessons that come from doing it the long way? Senior professionals, too, face a dilemma: Why spend time mentoring a junior on work an AI can do in seconds? 

Historically, each technological leap has forced such trade-offs. The Industrial Revolution’s machinery reduced the need for artisan apprentices; the rise of professional management in the 20th century saw MBAs leapfrog shop-floor experience. 

/High Agency or Hollow Hype?

Tech optimists argue that this disruption isn’t necessarily a bad thing. If AI handles the drudgery, newcomers can tackle more creative and strategic projects right away. In theory, a highly “agentic” young professional armed with AI can accomplish tasks in year one that used to require five years’ experience. 

Think of a 22-year-old financial analyst using GPT-4 to generate a full market report, or a rookie marketing coordinator deploying an AI image generator for a campaign – work that would have been far above their pay grade a decade ago. AI boosters note that intelligence is now on tap, which makes human initiative and agency the true differentiator. 

As tech entrepreneur Andrew Yeung put it, “High agency is about actively going after what you want without waiting for the circumstances to be perfect.” 

/Beyond the AI Echo Chamber

But how realistic is this vision, here and now? Outside the tech bubble, AI adoption in the workplace remains far lower than the breathless headlines suggest. In late 2024, a Pew survey found 81% of U.S. workers said they do not use AI in their jobs. Over half had rarely or never used even AI chatbots at work, and nearly a fifth hadn’t heard of them. 

A recent survey highlights the gap between C-suite leaders and employees on AI adoption. For example, only 45% of employees felt their company’s AI rollout was successful, versus 75% of executives. Just 57% of workers believed their employer even had an AI strategy, while 89% of execs insisted they did.

The hype around “high agency” collides with workplace realities. C-suite executives may be eager to roll out AI initiatives, but employees on the ground often aren’t feeling empowered – they’re feeling bypassed. In one 2024 study, 53% of workers admitted hiding their AI use from managers for fear of looking “replaceable.” Others quietly resist: around 41% of Millennial and Gen Z employees even confessed to sabotaging their company’s AI efforts by refusing to use AI tools or trust AI outputs. 

/The Apprenticeship Paradox: Eroding the Path to Expertise

Even if we could snap our fingers and have full AI uptake tomorrow, we’d face a more profound paradox. If AI takes over the beginner work, where do beginners come from? Leaders are fretting about a looming skills gap in fields like law, consulting, media, and tech. 

Junior work has long been the proving ground for judgment, patience, and experiential knowledge. Now those low-risk training arenas are shrinking. We can already glimpse the consequences. Seasoned experts will retire in the coming years – but if no one invested time in training their replacements, expertise could “age out” with them. 

/From Lost Apprenticeships to AI-Native Careers

The solution isn’t to bring back the old apprenticeship model, it’s to invent new ones that match the pace and logic of AI-native work. If foundational tasks are disappearing, then foundational learning must be built in by design: structured mentorship, AI-enhanced simulations, and early-career roles that emphasize judgment over repetition. 

Institutions at every level have a role to play. Employers must treat junior talent as more than productivity hacks. Schools must teach students how to think with AI, not just how to prompt it. And policymakers must ensure that the benefits of this shift don’t accrue only to the already-skilled or well-connected. 

AI may rewrite the rules of work, but only we can decide whether the next generation gets to climb or get left behind.