Sir Tim Berners-Lee is best known for his early work. Whilst at CERN, he invented the World Wide Web and wrote the HTTP protocol. No biggie. However in his recent memoir, This is for everyone, what captivated me was learning of his latest efforts; co-founding The Open Data Institute (ODI), and developing the Solid protocol with his team at MIT. The mission is to empower individuals to own their own data, and to create trust in data more broadly.

To most, the idea of "getting Big Tech to hand over all our data" is something of a pipe dream. We'll likely to roll our eyes & move on.

“They would never do that. Dream on, Tim.” ~ the average defeatist

Yet, how refreshing to see in Sir Tim a leader who challenges that attitude, and speaks of bold humanitarian goals not as something farfetched or out of reach, but as real, tangible, achievable targets. In this article, we'll explore why you should care, what owning your own data looks like, and how we can make it a reality.

(I have not included a full list, but encourage you to read about Sir Tim's other great work, such as leading the coalition which runs the Alliance For Affordable Internet (A4AI), who promote universal & affordable Internet access, particularly in low- and middle-income countries.)

What are most of us missing?

Born in the same year as Steve Jobs and Bill Gates, Sir Tim was amongst a cohort in the right place, at the right time, to play a lead role in the technological revolution. What's unique about him is that his efforts were not driven by financial gain.

It is difficult to imagine focusing the majority of your energy, time and effort into humanitarian goals that seem so out of reach. However, it’s that willingness to proudly and unapologetically be a dreamer that most of us lack. It’s easier to dismiss such goals as ”too idealistic”. …

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Last year, there was a marked change in the programming capability of the latest Large Language Models (LLMs). AI-generated code became professionally usable, and in combination with the release of tools such as Claude Code, Agentic Engineering was set to take off in the work place.

How has it played out?

Ideation, rate of iteration, initial design, debugging and Mean Time To Recovery (MTTR) have all improved. Since Thariq Shihipar's tweet on the effectiveness of HTML outputs from these tools, I've been approaching problems differently too, reducing decisions to multiple-choice, then often refining these choices toward an optimal solution.

One of the wonderful things about LLMs is that they do not get bored. Where I fatigue two hours into fixing a sticky bug, AI just keeps going. It's immensely satisfying, too, turning round a task in an afternoon that previously would have taken me a week.

However, it's messy. Engineers are handing out 10,000 line PRs like they're going out of business, the most successful SaaS providers are grappling with demand, and Junior Engineers are not being given the opportunities to learn. One METR study highlights a bizarre phenomenon where experienced engineers believed they had completed tasks faster using AI tools, when in fact doing so had slowed them down. Clearly, it's sub-optimal to reach for these tools as your default.

Do we even need engineers?

Like many others, I wondered whether I'd be in a job following …

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