The CTO of a $205 billion tech firm says artificial intelligence isn’t just automating code — it’s squeezing the middle out of the software engineering ladder.
It’s not just about job loss. It’s about job distortion. At least that’s how Pat Casey, Chief Technology Officer at ServiceNow, sees it. And with a company valued north of $200 billion and embedded in the heart of enterprise tech, Casey isn’t just guessing. He’s watching it happen live — team by team, title by title.
A New Kind of Pressure Is Squeezing the Engineering Pipeline
“The middle’s getting pinched,” Casey said, bluntly. And he didn’t mean in a vague metaphorical way.
Over the past 18 months, entry-level engineers have been leaning on tools like GitHub Copilot, ChatGPT, and Claude to get things done fast. Faster than before. It’s like giving a beginner a cheat code that instantly pushes them into the intermediate tier — skipping the hard, slow grind that used to define the early years of a software career.
But that acceleration comes with a catch.
“The middle tier — those folks with four to seven years of experience — are seeing their value proposition questioned,” Casey said. “And that’s not a morale problem. That’s a structural one.”
He’s not alone in thinking this.
Seniority Isn’t What It Used To Be
Once upon a time, experience spoke louder than speed. Now, with AI helping level up junior talent almost overnight, it’s the engineers who can go beyond autocomplete who are truly standing out.
And that’s created a new challenge for managers trying to make sense of performance.
“I can’t tell anymore if someone’s productive because they’re brilliant or just good at prompt engineering,” one tech VP told Business Insider off the record. “And that’s throwing a wrench in our promotion pipeline.”
Casey echoed the same concern, adding: “How do you now distinguish your stars when everyone’s shipping at the same pace, using the same AI tools?”
For tech leaders, the traditional barometers of excellence — clean code, quick turnaround, low bug counts — are getting fuzzier.
AI’s Career Impact Is Playing Out Like This
Here’s what’s actually happening inside large tech orgs right now, according to Casey and insiders BI spoke with:
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Entry-level engineers are being onboarded faster and getting productive quicker thanks to AI assistants.
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Mid-level engineers are struggling to differentiate themselves in a sea of AI-boosted juniors.
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Senior engineers are evolving into AI strategists — evaluating tool effectiveness, refining workflows, and acting as system architects.
And somewhere in all this, a quiet question lingers: Is the ladder broken?
Table: How AI Is Shifting Engineering Roles at Big Tech Firms
| Role | Pre-AI Responsibilities | Post-AI Shifts |
|---|---|---|
| Entry-Level Engineer | Write basic code, debug | Use AI to generate and debug code faster |
| Mid-Level Engineer | Handle feature work, review code | Struggle to prove added value |
| Senior Engineer | Lead architecture, mentorship | Direct AI usage strategy, system design |
| Engineering Manager | Resource planning, team leadership | Redefining performance metrics |
Some managers are quietly rethinking how many mid-levels they really need.
Productivity Is Up — But So Are Expectations
Here’s the twist: AI hasn’t lowered the bar. It’s raised it.
With tools that complete whole functions in seconds, engineers are expected to do more, not less. Faster. Cleaner. Smarter. There’s less tolerance for sloppy work and more demand for cross-disciplinary thinking — especially the kind that can’t be outsourced to a chatbot.
“It’s not that jobs are disappearing,” Casey explained. “They’re just morphing in ways people didn’t expect.”
And it’s not just engineers. Product managers, QA analysts, even tech writers are feeling the squeeze. Everyone in the product pipeline now has to understand AI — at least a little.
That’s changed the vibe in hiring, too.
What Hiring Teams Are Looking For Has Shifted — Quietly
Forget ping pong tables and React knowledge. Recruiters at top firms are scanning résumés with different eyes now. Soft skills like judgment, abstraction, and cross-team leadership are gaining weight.
And then there’s the unspoken AI litmus test: Can you work with AI without letting it do all the thinking for you?
One recruiter at a cloud security startup shared: “We used to give take-home assignments that tested your raw coding. Now we test how you use tools. It’s not about knowing Python — it’s about knowing when not to use AI.”
Basically, AI hasn’t replaced engineers — it’s replaced mediocrity.
The Next Five Years Could Be Weird
Nobody really knows where this goes next.
Casey admitted as much. “This is a reformation moment. We’re rewriting the rules of technical career growth on the fly.”
One scenario? Engineers split into two camps: those who manage and direct AI-assisted workflows, and those who specialize in building the tools themselves — the underlying models, the datasets, the infra. The rest? Some might drift into irrelevance if they don’t adapt.
And this shift isn’t just happening at startups. It’s rumbling through giants like Microsoft, Meta, Oracle, and Google — all companies that have rolled out internal AI copilots in the last year alone.
Some engineers are embracing the shift, retraining themselves, picking up AI ethics or system thinking on the side. Others are anxious, worried their once-stable careers are suddenly on shaky ground.
One-Sentence Paragraph
Nobody knows if the ladder gets rebuilt or replaced.
