On The Obsolescence Of Interns In The Age of AI
Even in the era of LLMs, interns are great to have on the team.
Hiring a software engineering intern in the summer of 2025 was a controversial decision among the founder circles I run in.
We are a small startup. We are proud of our lean burn rate. And we exist in a moment where, for twenty dollars a month, you can have a coding agent that knows every library in existence and never sleeps.
An intern, by contrast, has short tenure with high onboarding overhead, likely knows very little about your tech stack, and requires time from your senior engineers to explain things.
We did it anyway, without regret.
Andrej Karpathy highlighted, in his “2025 Year in Review”, the concept of Jagged Intelligence. He notes that we aren’t “growing animals” that get smarter at everything simultaneously; we are “summoning ghosts” with spiky capabilities.
These models are genius polymaths in verifiable domains, but simultaneously “confused and cognitively challenged grade schoolers” when navigating novel environments.
This “jagged” profile creates the perfect spot for interns. The model performs at a genius level in the spikes: the syntax, the boilerplate, the error checking. The intern works in the dips: the product context, user empathy, trade-offs in conflicting goals.
Karpathy additionally notes that:
Regular people benefit a lot more from LLMs compared to professionals
What this all meant for us is that the traditional tax on hiring interns, namely the mentorship bandwidth, became much easier to accommodate.
In the old days (a year ago), if an intern didn’t understand a React hook or a complex backend pattern, they had to spend three hours hoping to find something related on Stack Overflow, or tap a senior engineer on the shoulder.
But this summer, our intern could just ask the agent and get an answer tailored to our codebase and norms.
We also had an assigned mentor, one of our lead engineers, to give guidance on “the dips.” They’d stand together at the whiteboard talking through problems and solutions. Discussing why we prioritized one feature over another, and things like why we would want to use a CDN in specific scenarios.
After a few of these sessions, as our intern worked with Claude to implement features, there was a profound learning: Claude always needed guidance on the things discussed at the whiteboard. And rarely about the actual code.
The thinking, the debating, the architectural trade-offs were the actual job! Those were the important things to learn from the internship. That’s the “engineering” in “software engineering”.
There is an old American folktale about John Henry, the “steel-driving man” who raced against a steam-powered drill. He won the race, but his heart gave out and he died with his hammer in his hand. It is a story about the futility of fighting mechanization.
When our intern started, they treated the agent as the competition. As something they had to prove they could out-code. But by the end of the summer, they conducted the machine like a maestro.
The kinds of tasks being assigned were the same kinds of tasks interns have always been given, with the same amount of scope and blast radius, but were completed with substantially increased throughput and confidence. The agent was an amplifier.
Where an intern in 2024 might finish one significant feature during a summer, ours finished several.
There is a postscript to this story. Our intern is back at college now, finishing their computer science degree.
As the internship wrapped up, they didn’t ask for a reference; they asked for more tickets. They wanted to know if they could keep working for us in between classes.
In any other era, we would have hesitated. As many learned during Covid, it can be difficult to make sure remote interns are set up for success. This would be both our only part-time and only remote team member.
But we didn’t hesitate. They’d gained our trust and we put our money where our mouth is. With the stipulation that Logic couldn’t get in the way of schoolwork, we happily said yes.