Meet Gonçalo Gaiolas, the proven product leader bringing DeepL’s solutions to life

You can tell a lot about the quality of a leader by the comments that their former colleagues post about them on LinkedIn. 

When we announced earlier this month that Gonçalo Gaiolas has become DeepL’s new Chief Product Officer, those comments came flooding in. There were hundreds of them, from former colleagues working at a who’s who of leading tech companies, and they all had very similar things to say. Gonçalo has a deep understanding of product development and how it works in an era of rapid innovation. And he’s got an equally deep and precious understanding of the people that make it happen — how to build teams that live at the intersection of AI research, engineering and customer understanding, and are also creative, motivated and fulfilled by the challenge of bringing those elements together.

So how will DeepL’s product strategy fuse cutting-edge research with real customer needs, and deliver AI innovation that solves the challenges that matter for business? We asked Gonçalo to share his vision for what new solutions DeepL will build, how we’ll keep delivering on product quality and trust, and how we’ll build an experience of AI that works for employees throughout global organizations.

What made you excited about joining DeepL at this particular moment?

DeepL is at an incredibly exciting inflection point, and I knew I had to be a part of it.

There’s a real pioneering spirit here: DeepL has world-class research and a beloved product, and it’s poised to redefine how we communicate and get work done. Joining now, as we prepare to bring new products like DeepL Agent to market, felt like a chance to help shape the future of AI in a very tangible way. It’s the kind of opportunity where my work can impact millions of people’s daily lives, and that’s a big motivator for me.

There’s nothing better than taking an idea from a rough concept to a real product that delights users and solves a true problem. As a product leader, I get to do that every day. From strategy to early brainstorms to watching customers use what we build in their daily lives. It’s incredibly rewarding to hear someone say that our product transformed their work by allowing them to do something they couldn’t before. 

DeepL’s focus on quality and trust in AI also really aligns with my own values. This is a company that cares deeply about getting it right — not just getting there first — and that was important to me.

What would you say is your Chief Product Officer superpower?

I believe I have two, actually.

The first is enthusiasm. It’s the renewable energy that keeps teams moving, makes hard problems fun, and turns setbacks into momentum. I show up excited, I celebrate progress loudly, and that optimism tends to spread.

My other superpower is translation and connection across domains. I “grew up” across Engineering, Professional Services, Customer Success, and Community, and then came back to Product. That gave me a multilingual ability inside a company. I can talk tech with engineers, strategy with execs, UX with designers, value with customers. Bridging those perspectives keeps the seams from showing between what the business wants and what we are building. I pair that with a direct yet kind communication style. I will tell it like it is — always with respect — which builds trust.

The net result is that I’m a connector between ideas and execution, people and goals, and I bring the energy to keep everyone pulling in sync.

What makes AI product design unique?

AI product design is a different beast from traditional software design. You’re often dealing with a probabilistic system rather than deterministic software. The AI might not behave exactly the same way every time, and it can surprise you. That means as a product designer or manager, you have to embrace uncertainty and design for it. It also makes user trust paramount. Users need to understand what the AI is doing, and feel in control. That’s why we put a big emphasis on transparency and feedback in the user experience: showing source translations, providing explanations, letting users correct the AI. A concrete example is how we built DeepL Agent with features to allow humans to monitor and intervene – you can literally watch the agent step through tasks and stop it if needed. Designing those kinds of control points isn’t something you typically worry about in a normal app, but with AI it’s essential.

In my experience, successful AI product teams mix deep technical understanding with strong user empathy. We need to be comfortable collaborating really closely with research scientists and data scientists, and also thinking about things like ethical considerations and bias. We also have to imagine new interactions that users, and ourselves, have never seen before. 

In many ways, we’re the bridge between the lab and the market. We take the cutting-edge research and package it into a solution that delivers real impact for customers. Often that means lots of iteration: prototyping early, putting it in front of users for feedback, and refining. We’re not afraid to tweak or even reinvent how a research insight is applied if it means a better user experience.

AI product design is a challenging space that forces everyone to constantly learn. But that’s also what makes it fun. The playbook is being written in real time. If your team has the right mix of AI knowledge, creative design thinking, and focus on user trust, you can build some truly groundbreaking products.

Where would you like to see DeepL’s product portfolio 3-5 years from now?

I see DeepL solving two big human problems: how we understand each other across languages, and how we get work done in the age of AI. 

Today we are known for translation quality. Next, we’ll be known for a portfolio that helps people communicate, collaborate, and achieve outcomes without language or technical barriers. Picture a global meeting where DeepL translates, localizes, and subtitles in real time. Then the AI follows through by drafting follow-ups, updating project tools, and executing parts of the workflow, all inside one seamless DeepL ecosystem.

We’ll grow by taking on complicated and previously out-of-reach use cases: messy multimodal content, high-context collaboration, long-running and compliance-heavy workflows, privacy-sensitive deployments, real-time constraints. We will keep pushing into those edges until the hard things feel easy. As AI saturates everything, DeepL should feel like the reliable colleague you can trust and want to work with.

The big picture is that DeepL evolves from a single product to a platform of AI language services and agentic capabilities that reinvent how the world communicates and works. It’s a bold vision, and with our talent and focus, it’s well within reach.

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