AI has changed performance faster than companies have changed the way they measure it

Every technological shift eventually forces organizations to rethink how they evaluate people.

The Industrial Revolution changed how physical work was measured. The internet changed how businesses thought about distribution and communication. Artificial intelligence is changing something equally fundamental: the relationship between effort and impact.

For years, companies have relied on proxies to evaluate performance. In engineering, we looked at code written, projects delivered or the opinion of a manager. In recruitment, we relied on years of experience, previous employers or technical interviews. None of these methods were ever perfect, but they worked reasonably well because producing valuable work required a significant amount of human effort.

Artificial intelligence changes that assumption.

When one engineer can produce ten times more code with the help of AI, or when another can automate tasks that previously required entire teams, traditional measures become increasingly difficult to interpret. More activity no longer tells us who is creating more value. In many cases, it simply tells us who is generating more output.

Activity has never been the same as contribution

One of the easiest mistakes organizations can make is confusing visible work with meaningful work.

This is not a new problem. Companies have always measured what was easiest to count rather than what was most valuable to understand. Sales teams counted calls before they measured conversations. Marketing teams counted impressions before they measured influence. Engineering teams counted commits, tickets or story points because they were available, even if everyone understood they only explained part of the picture.

Artificial intelligence makes those limitations much more obvious.

If generating thousands of lines of code takes minutes instead of days, counting lines of code becomes almost meaningless. If documentation can be created automatically, producing more documentation says very little about the quality of the decisions behind it. The relationship between effort and output has fundamentally changed, and performance systems built for a different era inevitably begin to fail. Therefore, the challenge is understanding contribution.

Meritocracy needs better signals

One of the reasons meritocracy becomes controversial inside growing organizations is that performance often becomes a matter of perception.

Managers naturally have incomplete information. Teams work on different problems with different levels of complexity. Some people create visible outcomes, while others quietly remove risks that never become apparent. Over time, politics begins to fill the gaps left by imperfect information.

Artificial intelligence creates an opportunity to improve this.

Not because AI can decide who deserves a promotion or who should receive a higher salary. Those decisions should always remain human. But AI can help organizations understand work with a level of depth that was previously impossible: It can analyze complexity, collaboration, review processes, delivery patterns and the context surrounding technical decisions in ways that would have required an enormous amount of manual effort only a few years ago.

Fairer organizations will make better decisions

This has implications that go well beyond engineering.

If organizations become better at understanding contribution, they also become better at recognizing potential, rewarding exceptional performance and identifying where people need support. Compensation becomes more closely connected to impact. Promotions become easier to justify. Career development becomes less dependent on visibility and more dependent on the value someone consistently creates.

That is ultimately what meritocracy was always trying to achieve.

Artificial intelligence does not eliminate the need for human judgment. If anything, it makes that judgment even more important. Leaders still need to understand context, coach people, build trust and make decisions that no algorithm should ever make.

What changes is that those decisions no longer need to rely exclusively on intuition.

The future belongs to organizations that understand how work creates value

Throughout this series we have argued that artificial intelligence is changing the economics of knowledge work. Knowledge has become more accessible. Execution has become significantly cheaper. The characteristics that define exceptional talent are evolving, and the organizations that adapt first will gain an important advantage.

Meritocracy is part of that transformation.

The companies that outperform over the next decade will not necessarily be those using the most AI. They will be the ones that develop a clearer understanding of how value is actually created inside their teams. They will recognize the difference between activity and contribution, between visibility and impact, and between producing more work and producing work that genuinely moves the business forward.

Artificial intelligence does not remove the human element from leadership. On the contrary, it raises the standard for it.

The organizations that embrace that challenge will build stronger teams, make better decisions and create cultures where performance is understood rather than assumed. In the long run, that may become one of the most important competitive advantages AI can offer.

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.