Companies are still hiring for the previous generation of work

Artificial intelligence has changed how knowledge work gets done far more quickly than most hiring processes have adapted.

For years, recruitment has been built around identifying technical expertise. Engineers were evaluated on programming languages, frameworks and coding exercises. Recruiters looked for years of experience in specific technologies. Career progression often rewarded specialization because specialization created leverage. The harder a skill was to acquire, the more valuable it became.

That model made sense for the world we were operating in but it no longer makes sense today.

AI has dramatically reduced the cost of execution. A developer can write more code than ever before. A junior engineer can navigate unfamiliar technologies with much greater confidence. Tasks that previously required years of accumulated experience are becoming increasingly accessible.

This means that expertise moves somewhere else.

Technical skills are no longer enough

One of the biggest misconceptions surrounding AI is the idea that everyone suddenly becomes equally capable. They do not!

What changes is the baseline.

When almost every engineer has access to the same coding assistants, the same language models and the same information, technical execution becomes less effective at differentiating people. The gap increasingly comes from something much harder to teach.

Can someone make good decisions when there is no obvious answer?

Can they distinguish between solving the user's problem and simply building another feature?

Can they navigate ambiguity, challenge assumptions and make sensible trade-offs?

These are not new skills. They have always existed.

What has changed is their relative importance.

The best professionals are becoming multipliers

One of the most interesting effects of AI is that it raises everyone's productivity while making exceptional people even more exceptional.

The same assistant that helps a junior developer write better code also allows a senior engineer to review architecture faster, mentor more people and make decisions across a much larger scope. Technology amplifies capability.

This is why organizations should be careful about assuming AI will close the gap between average and exceptional performers. If anything, the opposite is happening. Access to powerful tools is becoming universal, but the ability to use those tools with judgment remains highly uneven.

The multiplier is no longer knowledge, it is judgment.

Hiring for potential becomes more valuable than hiring for certainty

This creates a difficult challenge for recruiting teams.

Many of the signals companies have relied on for years are becoming weaker predictors of future performance. Technical tests remain useful, but they tell us less than they used to. Memorizing syntax is less valuable when AI can generate it instantly. Solving isolated coding exercises reveals little about how someone approaches complex decisions or collaborates with a team.

As a result, hiring becomes more human than ever.

Curiosity, learning velocity, communication, ownership and what Dave often describes as “agency” become significantly stronger indicators of long-term performance than simply checking whether someone knows a particular framework.

The irony is that, just as AI makes technical work easier to automate, evaluating people becomes considerably harder.

Great companies will build different teams

The organizations that benefit most from AI will not necessarily hire fewer people but for sure they will hire differently.

They will build teams around people who can learn quickly, adapt continuously and apply technology to solve real problems rather than simply execute predefined tasks. Technical excellence will remain essential, but it will increasingly be expected rather than celebrated. The real differentiator will be the ability to combine that technical foundation with judgment, communication and an instinct for creating business value.

Artificial intelligence is not changing the importance of talent.

It is changing how we recognize it.

The companies that understand this shift first will have a significant advantage, because they will stop recruiting for yesterday's definition of performance and start building teams that are prepared for what engineering work is becoming.

About Dave García

Dave García is Co-founder of Pensero, the AI-era engineering performance and Agentic Deployment Intelligence platform.

Pensero brings together real signals from GitHub, Jira, AI coding tools, agents and the systems engineering teams already use to understand how work actually happens. By connecting delivery, quality, collaboration and AI usage, it helps leaders measure performance objectively, understand the ROI of their AI investments and guide their AI transformation, deciding where work is best performed by humans, augmented by AI or delegated to agents.

About Carles Font

Carles Font is Founder of Q-tech.

For more than 25 years, Q-tech has helped technology companies build exceptional engineering organizations through executive search, leadership advisory and talent strategy. Working alongside startups, scaleups and large enterprises, Q-tech helps organizations identify, attract and develop the people who will shape the next generation of technology.