By Matthew Shilts, Forbes Councils Member for Forbes Technology Council

At a time when AI has already morphed just about every corner of the enterprise, it might be time to rethink a long-held assumption: more experience equals more output.

It’s been tried and true for decades — years of experience correlate with productivity, reliability and insight. Today, with everything augmented by AI, that equation no longer holds up. In fact, junior developers with zero to two years of experience aren’t just holding their own; they often outpace their more seasoned counterparts. It’s a counterintuitive shift, but one that I’ve seen play out across agile, AI-first software teams.

We’re in a new world of AI-native development. The new truth is that success favors energy over legacy, openness over ego and velocity over hierarchy.

Micro-Teams Can Create Major Momentum

The emergence of micro-teams or “pods” — smaller, more agile groups supported by AI tools—has opened up a new kind of productivity. I’ve worked with productive pods staffed by recent graduates, including several with zero professional experience. And yet, they were outperforming veterans.

Why?

It’s because they come with no baggage, no bad habits. There’s no ego. They see AI as a teammate, not a threat.

As has been the case with many new technologies across generations, this wave of young developers is culturally primed to collaborate with AI tools. They don’t have the same hangups about job security or feeling like they’re "cheating" by using copilots and code assistants. Instead, they lean in. They’re concerned with getting things done faster and better, then iterating quickly.

The AI Learning Curve Is Practically Flat

The most powerful gift AI has given to development teams isn’t just acceleration — it’s compressing timelines. What once took weeks to learn now takes days, sometimes hours. Developers switching stacks or diving into unfamiliar frameworks no longer face a multi-week onboarding slog. AI can fill in the blanks and speed up the entire journey.

The context-switching tax that kept developers siloed in niche roles is basically gone.

The beautiful thing about this shift is that it opens up unprecedented agility. Micro-teams staffed with adaptable, fast-learning developers can chase priorities wherever they arise.

Faster Than Agile: Productivity In The AI Era

The traditional Scrum cycles that most developers are familiar with — two-week sprints and bi-weekly showcases—are starting to feel archaic. Very senior, “elite” teams have been releasing daily for years with good CI/CD. Now, less experienced teams, with AI assistance, are releasing daily or at a minimum of every week. Hold your teams to a higher standard. Expect EVERY team to operate like an elite startup team.

Key metrics are evolving to be even more important. Teams are now measuring:

• Frequency and depth of AI tool usage

• Code coverage and automated test quality

• Visual velocity — screens built, PR’s merged

In this new paradigm, output is no longer constrained by a developer’s hours but by management’s ability to absorb and direct the flow of work. This is “vibe coding” at scale, but with all the right checks and balances to ensure great outcomes.

Management Is The New Bottleneck

Ironically — as we just alluded to — with development speeding up, the slowest component is no longer engineering. It’s management.

The work is now starting to happen faster than we can absorb, handle and manage. Sometimes, it’s like dropping a race car engine into a sedan. You’d also better upgrade the brakes if you want to stay alive.

AI has forced a rethinking of management tools and processes. Quality control has to scale with productivity, meaning things like automated test coverage, human-in-the-loop verification and a deep understanding of software fundamentals are essential, not optional.

The Skills Paradox

All of this has combined to create an interesting twist: the faster things move, the more critical foundational knowledge becomes.

It can be dangerously tempting to assume AI reduces the need for technical expertise. But score one for more experienced developers, because the exact opposite is actually true. Developers without strong fundamentals are more likely to produce problematic code, misinterpret AI suggestions or fail to detect subtle bugs. Velocity amplifies both competence and error.

Returning to the metaphor of a racing engine in a sedan: If you don’t know what you’re doing, you’ll go off a cliff faster than ever.

Hiring for core software engineering skills such as data structures, architecture and systems thinking is more important now than ever before. AI doesn’t save you from bad instincts. It just makes the ride faster.

How To Rethink Seniority In An AI-Driven Environment

This transition hasn’t been smooth for everyone, of course. Some senior engineers resist the shift — not because they’re incapable, but because of cultural inertia, fear or outdated hardware.

When senior developers are equipped and motivated, they absolutely can thrive in this new landscape. But it takes transparency, tool upgrades and — let’s face it — a little social pressure from their faster-moving junior peers.

Lessons Learned From Building AI-First Teams

What separates the teams that succeed in this new paradigm from those who struggle? A few lessons we’ve learned stand out:

• Train managers, not just developers. Management needs to evolve to keep pace with faster, decentralized work.

• Invest early in testing and QA. Automation isn’t optional, so use it intelligently.

• Encourage experimentation. Let teams discover their best workflows, tools and techniques.

• Hire for fundamentals. AI accelerates impact, for better or worse. A solid foundation is essential.

Ultimately, it’s time for leaders in tech and beyond to reassess how they define talent and team structure. The person using AI is more important than AI itself, and that person might be fresh out of school, full of energy, open-minded and unconstrained by "the way it’s always been done."

It sounds obvious, but AI isn’t taking jobs. The people using AI are.

If you want to build a team that thrives in this new era, it might be time to look past résumés and start looking at potential, fundamentals and the willingness to learn fast.

‍

Originally published by Forbes: https://www.forbes.com/councils/forbestechcouncil/2025/08/25/rethinking-hiring-in-the-age-of-ai/