Building Cato’s London R&D Site in the Age of AI

When people think about building a new R&D site, they often focus on the visible elements: office space, hiring plans, organizational structures, and growth targets. Those things matter. But over the past year, as I’ve helped establish Cato’s new AI hub in London, I’ve come to believe that the real work starts somewhere else: culture.

We’re building an engineering organization during one of the most significant technological shifts our industry has experienced in decades. AI is transforming how software is developed, how teams learn, and how engineering organizations operate.

Building a new site has always required careful thought about people, processes, and values. But today it also requires answering a more fundamental question:
What kind of engineering organization do you want to build when the way engineers work is changing so rapidly?

Culture Comes First

One of the biggest lessons I’ve learned is that culture is not something you announce. You can’t create it through presentations, company emails, or posters on office walls. Culture emerges through hundreds of small decisions that teams make every day: how people collaborate, how feedback is delivered, how technical disagreements are handled, how success is recognized, and how people support one another when things don’t go according to plan.

When you’re building a new site, those decisions matter even more.

In established organizations, culture already exists. New employees adapt to it. In a new location, every hire, every team interaction, and every leadership decision contributes to defining what that culture will become.

One example stands out from the early days of the London site. We spent significant time discussing how technical disagreements should be handled. We wanted engineers to challenge ideas openly, while keeping discussions focused on the problem rather than the person. Those conversations may seem small, but they helped establish norms that continue to shape how the team collaborates today.

The impact became visible surprisingly quickly. As new engineers joined the team, many were able to onboard far faster than we expected. We introduced several architecture-focused AI skills developed by Cato’s Developer Experience team. This tool helps engineers and new hires navigate a large and complex codebase. Combined with a culture that encouraged curiosity and open discussion, these tools significantly shortened the path to productivity.

What surprised us most was not that AI made engineers faster. It was that it changed onboarding itself. Engineers who, in the past, might have spent months building enough context to make meaningful changes were able to understand key architectural decisions and contribute to production systems within weeks.

That experience reinforced an important lesson: the value of AI in engineering is not simply automation. Its real value is accelerating learning and helping talented people become effective much faster.

More broadly, it offered a glimpse into how engineering itself may evolve over the coming years.

What AI Changes – and What It Doesn’t

Perhaps the most fascinating aspect of building a team today is watching the engineering profession evolve in real time.

AI tools are already changing how software is written, reviewed, tested, and maintained. Tasks that previously consumed hours can sometimes be completed in minutes. New workflows are emerging constantly.

For engineering organizations, this creates an opportunity to focus more energy on higher-level challenges: understanding customer problems, designing resilient systems, making sound architectural decisions, and coordinating effectively across teams.

The onboarding experience in London offered a practical example of this shift. AI helped engineers navigate complexity more efficiently, but it did not eliminate the need for understanding. Engineers still needed to learn why systems were designed the way they were, evaluate trade-offs, and make responsible decisions.

That’s why I believe some engineering skills are becoming even more valuable: system design, product thinking, communication, collaboration, and technical judgment.

The engineers who will thrive in the coming years won’t be the ones competing with AI. They’ll be the ones who learn how to combine human insight with increasingly powerful tools.

Building a Community in London

London provides a unique environment for building that kind of engineering organization. The city attracts exceptional talent from around the world. Engineers, researchers, data scientists, and product leaders bring experiences from startups, global technology companies, academic institutions, and every stage in between. That diversity creates an environment where different perspectives challenge assumptions and generate new ideas.

At the same time, building a successful site requires more than attracting talented individuals. It requires creating an environment where those individuals can do some of the best work of their careers.

My hope is that the London team becomes known not simply as another office within a global organization, but as a place where talented people can tackle meaningful technical challenges, learn from one another, and contribute to shaping the future of both networking and AI-driven security.