Developers Videos

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You hear a lot of predictions right now that AI will make programming obsolete, that developers will disappear, that college majors like computer science are already outdated.

They're not.

But the field is shifting. Not in what we learn, but in what that knowledge gets used for.

Computer science has always had two layers. One is technical: languages, frameworks, tools. And the other is structural: how humans coordinate to turn logic into value.

And the technical layer is changing fast. Every few months there's a new model, or a new API, or a new tool that reshapes what coding means.

In three years, students won't be writing as much raw code. They'll be describing behavior, verifying results, and orchestrating agents.

But the structural layer, how humans define problems, communicate intent, and decide what should be built, that part barely changes at all.

Teams still need people who can reason about systems, manage trade offs, and translate messy human goals into precise machine actions.

AI accelerates execution, but judgment, clarity, and deep thinking remain human.

So if you're studying computer science today, don't panic. But don’t stay static either.

Keep learning the fundamentals: logic, systems, data structures, architecture. And then also build the new muscle: prompt clarity, workflow design, AI integration.

The best developers in three years won't just know how to write code. They'll also know how to shape systems, human and machine, that produce better outcomes faster, with less friction.

AI is not the end of computer science. It's just the next layer of abstraction.

Learn to work with it, and you'll still be at the center of how technology gets built.

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There's a quiet trend happening in tech right now of hiring fewer junior people.

Companies are looking at AI, seeing how much work it can automate, and deciding they can get by with a smaller, more senior team. And it does make financial sense on paper.

But strategically, it's a mistake.

Organizations don't just need output. They need fresh perspective. And when you stop bringing in new people, you stop bringing in new questions. You lose curiosity.

And curiosity is what keeps a company from quietly freezing in place.

Junior developers bring more than entry level skills. They bring unfiltered thinking. Why do we do it this way?

Those are questions that veterans stopped asking years ago.

They surface assumptions. They push at boundaries. And they see gaps that process heavy teams often overlook.

And yeah, AI can automate simple tasks that used to train juniors, but that just means the training has to evolve.

Instead of teaching syntax and setup, teach reasoning, debugging, and architecture.

Those are the skills that make AI an amplifier, not a crutch.

And forward thinking teams are already doing this, pairing early career engineers with AI to explore, test, and build systems end to end.

It's faster learning, and it injects new energy into the organization.

And without that, senior teams become echo chambers, technically excellent, but strategically stale.

AI doesn't make junior developers obsolete.

It makes their contribution, curiosity, adaptability, fresh eyes, even more essential.

If you want your company to keep evolving, keep hiring people who still ask why.

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AI is changing how code gets written

But the bigger shift is how people learn.

Junior developers can now produce functional code in minutes, which sounds great until you realize they might not understand what they built.

Traditional mentorship models do not work at machine speed.

Historically, mentoring meant pairing, code reviews, and long apprenticeships through repetition. AI collapses that loop.

Developers get instant answers, sometimes correct and sometimes dangerous, and they move on before context catches up.

If mentorship still relies on “watch me do it” or “read the diff,” it misses the real failure points: reasoning, structure, and judgment.

The new role of a development manager is to teach context engineering.

Not just what a function does, but why it exists, where it fits, and how it interacts with the system.

AI can generate patterns. Humans must define boundaries.

Mentorship shifts from syntax correction to constraint definition.

What are we optimizing for? What is safe to delegate? What requires human review?

Replace tutorials with reasoning walkthroughs.

Pair programming becomes pair prompting.

Code review expands into process review.

Documentation becomes mentorship at scale.

Every test and comment teaches the next developer or model what good looks like.

The teams that adapt fastest are led by people who mentor through structure, not status.

They build shared mental models and cultures where AI is a partner, not a shortcut.

In an AI accelerated field, mentorship is not about passing down answers. It is about teaching how to ask better questions.

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If you’re a student right now or you’re early in your career you’ve definitely noticed that things feel tighter. There’s fewer openings there’s higher expectations. Job descriptions ask for experience that you don’t have and AI seems to be moving faster than the traditional career ladder.

You’re not wrong. Entry level hiring has tightened across many industries and there’s lots of reasons for that. Part of that’s the economy. Companies are hiring more cautiously and running leaner.

Part of it is structural automation. AI are absorbing the mechanical repeatable tasks that used to serve as training grounds. That combination makes early roles harder to find not because the work is gone but because of being distributed differently.

The good news is that capability still compounds. The people who move ahead learn two things quickly how to use AI as an execution partner and how to think clearly about what they’re trying to accomplish.

You don’t need every framework or every tool. You need to know how to define a problem structure your approach and use AI to move faster through the learning loop from idea to test to feedback to refinement.

Those habits stand out. They signal that you can adapt reason and deliver. And that’s what hiring managers actually notice.

So if you’re still in school or you’re just getting started start building capability now. Use AI for research planning debugging writing anything that expands your ability to think and produce at a higher level. You’re not just doing tasks anymore. You’re learning how to design better ways of doing them. The path into the market is narrower but it is faster once you’re on it. So build these habits now so you can grow into the next layer of work not wait for it to come back.

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I met something like 50 new people in the last few weeks. It’s been really amazing. Last week I went to a meetup, and I met a bunch of solo founders, people that are building small businesses.

And it’s interesting how many of them start with domain expertise, not really technical know-how. They understand a specific industry, and they’ve worked inside a particular process. They’ve experienced a problem enough times, know exactly what was broken and what a useful solution would be.

And even a year ago, turning an idea like that into a software business might have involved raising money, finding a technical co-founder, or hiring a development team before you can really even find out whether or not that idea worked.

And AI’s lowered the barrier dramatically. People who understand the problem can now build a product, and test it with real users, and start a business in way less time and with way less money than was practical even a year ago.

And we’re gonna see a lot more businesses created this way. The person who deeply understands the problem can increasingly become the person who builds a solution.