Entry-Level Tech Jobs Aren't Dying. They're Growing Up.

January 27, 2026

Last September, I wrote about betting on people over pure AI automation while Salesforce celebrated cutting thousands of support staff. The thesis was simple: human expertise isn’t a cost center to optimize away. It’s a competitive advantage.

Three months later, Salesforce executives admitted they’d been “too confident” in AI’s ability to replace humans. Service quality dropped. Complaint volumes rose. Remaining employees spent their time correcting AI outputs instead of helping customers. The savings evaporated into secondary costs.

Now the same logic is being applied to entry-level tech hiring. The headlines are grim: junior hiring down 25% year-over-year. You see companies announcing they’ll hire “no new engineers.” The narrative is clear: AI is eliminating junior roles, and tomorrow’s talent pipeline is drying up.

This gets it backwards.

In my group, our last class of software engineering interns blew us away with their productivity and quality. They weren’t productive despite AI—they were productive because of it, combined with strong mentorship and the space to be ambitious. The future of entry-level tech work isn’t disappearing. It’s transforming. And the companies that figure out how to develop talent in this new landscape will have an enormous advantage.

The Old Model Is Already Gone

Three years ago, onboarding a junior developer meant starting them on small tasks: simple bug fixes, increasing test coverage, writing documentation. These were the training wheels—low-risk work that let them learn the codebase while contributing something useful.

Here’s the problem: AI does all of that easily now.

If your development strategy for juniors is still “start them on the grunt work,” you’re training them for jobs that no longer exist. Worse, you’re wasting their potential. The friction that used to slow down ambitious projects has been dramatically reduced. A junior developer with good tools, good taste, and good guidance can now tackle work that would have taken a mid-level engineer not long ago. In many ways, they’re better suited to how work actually happens in the age of AI.

What Actually Works: The Ideal Team Player

Our interns succeeded because they embodied the traits Patrick Lencioni calls the “Ideal Team Player”—Humble, Hungry, and Smart. What’s changed is how those traits manifest when AI enters the picture.

Hungry: Ambition is now affordable. In the past, hunger in a junior dev was often checked by their skill level. You might want to build the big feature, but you had to earn it with six months of bug fixes. AI changes the equation. Instead of spending months on boilerplate, our interns could attack complex problems immediately. The AI handles the boilerplate—they provide the drive. We need juniors who are hungry enough to act like founders, not gophers.

Smart: Judgment over syntax. Lencioni defines “smart” not as intellectual capacity, but as interpersonal intuition and judgment. This matters more than ever. When a junior can generate code in seconds, the bottleneck shifts from typing speed to decision-making. Knowing what to build, knowing when the AI is confidently wrong, and knowing how to ask the right questions of senior staff—that is the new “smart.”

Humble: “Showing your work” is the job. At our recent company meetup, even a senior developer was asking for more pair programming—not less. To learn efficiently, we have to move past status-update code reviews toward real debates about trade-offs. We need honest post-mortems about where both we and the AI failed. It requires a humility that prioritizes the best solution over the ego of the “author.”

The New Training Ground

If the old training ground was grunt work, the new training ground is decision-making under uncertainty.

We’ve shifted our focus to training people to ask good architecture questions before setting off to build. That means asking humans and asking the AI. It means learning to evaluate whether the AI’s suggestions actually fit the problem. It means understanding patterns deeply enough to know when to follow them and when to break them.

The tasks are more ambitious now, but they’re also more supervised. Not in a micromanaging way—in a “let’s talk through your thinking” way. The AI can write the code. The human needs to develop the judgment about whether it’s the right code.

This is closer to how senior engineers work: less time typing, more time thinking and communicating. We’re just starting people there earlier.

The “Small Giants” Opportunity

I’ve long believed in the concept of small giant companies—organizations that punch above their weight through efficiency and smart use of technology. What’s changed is that this is now possible for individuals, too.

We’re in a golden era for people who want to do big things with limited resources. A single person with taste, judgment, and the right tools can produce what used to require a team. But here’s the catch: taste and judgment aren’t downloaded from a model. They’re developed through experience, feedback, and mentorship.

The companies cutting junior roles entirely are making a short-term efficiency play that will cost them long-term. As AWS CEO Matt Garman put it: “How’s that going to work when ten years in the future you have no one that has learned anything?”

We’re betting the other direction. Not on AI replacing humans, but on AI amplifying what humans can do—including what new humans can do when we set them up for success.

Founders, Not Gophers

I mentioned earlier that we need juniors hungry enough to act like founders, not gophers. Here’s what that looks like in practice.

High agency has always been the strongest predictor of success. The people who advance aren’t the ones who wait for permission—they’re the ones who see a problem and start solving it. That was true before AI, and it’s even more true now.

What AI gives us is the ability to provide safeguards that allow more exploration and empowerment, not less. When a junior can prototype something quickly, we can let them try ambitious things without betting the whole project on it. When AI can catch obvious errors, we can give people more autonomy with less risk. The guardrails have gotten better, which means we can let people run faster.

In a recent post, I wrote about how the most human thing you can do in a world of artificial agents is choose how to use them well. That applies to how we develop people, too.

Agency without scaffolding is just abandonment. “Figure it out” isn’t mentorship. “Ask ChatGPT” isn’t training. But scaffolding without agency is just busywork. The goal is to create an environment where people can take real ownership of real problems—with enough support that they learn from the experience rather than just surviving it.

The Future Isn’t Fewer Humans

The companies celebrating AI-driven headcount reductions are optimizing for the wrong thing. They’re saving money today and hollowing out their talent pipeline for tomorrow. Salesforce learned this the hard way with their support teams. The same lesson applies to engineering.

The better question isn’t “how many juniors can we replace with AI?” It’s “how do we develop the next generation of senior engineers, architects, and leaders in a world where the training ground has shifted?”

We know the direction: more ambition, more mentorship, more visible learning, more space for humans to develop the judgment and taste that AI can’t provide.

The future of entry-level tech jobs isn’t disappearing. It’s just different. And the companies that figure out how to develop people in this new landscape will build something their competitors can’t easily replicate: a bench of talent that knows how to think, not just how to prompt.