AI Commoditizes Entry-Level Work While Amplifying Senior Value
Everyone’s asking the wrong question about AI and product teams.
The debate splits into two camps: one believes product managers will code their way to replacing engineers, the other thinks engineers will own strategy and eliminate PMs. Both narratives miss what’s actually happening. AI isn’t replacing entire functions. It’s splitting each function into three tiers, and only one of them is shrinking.
The same pattern across three functions
Look at what’s happening to engineers first. Employment for software developers aged 22-25 has declined nearly 20% from its peak in late 2022, according to Stanford’s Digital Economy Lab. Computer engineering graduates now face a 7.5% unemployment rate, one of the highest across majors.
What’s getting automated? Boilerplate code. Unit tests. API maintenance. The tasks companies used to assign to junior developers to help them learn.
Now look at designers. Many studios used to hire junior designers specifically for wireframing. Those roles are shrinking. The State of AI in Design Report found that 89% of designers improved their workflow with AI this year, but the improvements concentrate on production work: variant generation, copy filling, visual polish.
Product managers see the same split. McKinsey found that generative AI improved PM productivity by 40%, but the gains came from automating tactical work: user stories, performance reports, backlog maintenance, PRD drafting. The strategic work, the judgment calls, the stakeholder dynamics? Still human.
Three tiers emerging
The pattern is clear across all three functions. Entry-level tactical work gets commoditized. Senior strategic work gets amplified. Mid-level roles transform into something new.
Tier 1: Commoditized Junior engineers who write boilerplate code. Junior designers who create wireframes. Junior PMs who draft user stories. AI tools handle these tasks faster and often as well as someone in their first year on the job.
Marc Benioff announced Salesforce will hire “no new engineers” in 2025, citing AI-driven productivity gains. Anthropic CEO Dario Amodei warned that AI could wipe out half of entry-level white-collar jobs. But AWS CEO Matt Garman pushed back, calling the idea of replacing junior developers with AI “one of the dumbest things I’ve ever heard.”
The debate reveals the tension: AI can do the tasks, but those tasks serve a purpose beyond just getting work done. They’re how people learn.
Tier 2: Transformed Mid-level roles aren’t disappearing. They’re morphing into hybrid positions that require both AI literacy and deep domain expertise. These are the people who know enough to guide AI tools effectively and spot when AI outputs miss the mark.
The new job descriptions reflect this: “AI Orchestrators,” “Product Engineers,” “Full-Stack PMs.” People who move fluidly between strategy and execution, using AI as a force multiplier rather than a replacement.
Tier 3: Amplified Senior roles gain value. System architecture. Product vision. Strategic design decisions. User empathy. These capabilities matter more, not less, in an AI-enabled world.
Here’s why: the more skilled you are at your craft, the better results you get with AI. A senior engineer can spot architectural flaws in AI-generated code that a junior might miss. A seasoned designer knows when AI-generated variants sacrifice usability for aesthetics. An experienced PM can tell when AI-drafted requirements miss the strategic context.
The hollowed-out career ladder
This creates a systemic problem nobody wants to talk about: if entry-level roles commoditize but senior expertise remains valuable, how does anyone become senior?
You need plenty of seniors at the top. AI tools handle grunt work at the bottom. But there are very few juniors in the middle learning the craft.
This threatens the long-term talent pipeline across product, engineering, and design. Companies benefit from AI productivity gains today while quietly eroding their ability to develop senior talent tomorrow.
The skills that make someone valuable at Tier 3 develop through years of doing Tier 1 work. You learn system architecture by first building components. You develop product sense by first writing user stories and seeing what ships versus what sits in the backlog. You understand good design by first creating wireframes and getting feedback.
Remove the learning ground, and you cut off the path to expertise. The question of whether curiosity beats tenure in this environment isn’t academic: it determines whether companies can develop the senior talent they’ll need.
What this means in practice
The conversation shouldn’t be “will AI replace PMs or engineers or designers?” It should be: “How do we structure learning when tactical work gets automated?”
Some companies respond by raising the bar for entry-level roles. Junior positions now require AI literacy plus traditional fundamentals. That solves the immediate hiring problem but makes it harder for people to break into the field.
Other companies may be experimenting with new mentorship models: pairing early-career people directly with senior staff and using AI tools to accelerate their learning rather than replace their contributions.
The companies getting this right recognize that AI doesn’t just change how work gets done. It changes how expertise develops. They’re designing career paths that acknowledge both the productivity gains from AI and the human judgment that AI amplifies but cannot replace.
But commoditization is only part of the story. The bigger question is what teams are actually building with all this newfound speed. Shipping 3x faster sounds great. Whether that speed translates to customer value is a different question, one I’ll tackle next.
Read next: The Feature Factory Problem AI Amplifies