How tiny teams can have an outsized impact with AI

Like many of you, I’ve been paying closer attention to how large language models (LLMs) are impacting our work. They’re a transformational technology, and we’re only starting to understand what that scale of change might look like in our work. In news product, most experiments so far have focused on summaries, search, and conversational experiences. A good all-encompassing example is Time’s Person of the Year feature, which combines summaries, chat, and audio into one experience.

These experiments are a good start. They give us a chance to collect usage data and learn what readers actually want from AI; and on our own platforms, rather than through third-party remixes that increasingly strip out attribution and clicks. But I think the biggest shift, at least for now, is happening internally. AI gives product and editorial teams a low-risk way to extend their capabilities and move faster. As larger publishers grapple with lawsuits, brand risks, and internal inertia, smaller newsrooms have a real chance to move first. There’s more upside, and less to lose. When I sat down to prepare this newsletter, I wondered how Product Notes had changed over time. With almost 40 issues published, that’s a lot of content to analyse. So I opened Google’s AI Studio, where their latest Gemini models are freely available, and asked it to write a Python script that could scrape the NPA website and pull out the newsletter content. From there, I fed it back into the model to surface themes and trends.

According to the analysis, AI is the fourth most frequent topic—behind newsletters, but ahead of technical tooling. Seems I’m in good company.

Doing this job reminded me of the XKCD comic “Is it Worth the Time?”, which charts how much time you should spend automating a task based on how often you do it. That comic just turned twelve, and while the principles have held up well, I wonder if the numbers are increasingly shifting to the low-end. If it used to take five hours to build something and now you can do it in five minutes with the help of AI, the maths changes. Technological advances have made automating things like scraping the newsletter archive feasible within 30 minutes.

Of course, it’s not just about scripting or scraping or writing SQL. The more interesting question is how AI can help product teams build, test, and iterate faster. Recently, I’ve been experimenting with tools like Vercel’s V0, Lovable, Stackblitz Bolt, and Val Town’s Townie. These are prompt-driven interfaces for building front ends. So far these tools are mostly marginal gains: good for green field ideas, basic internal tooling and early prototyping, but you’re not likely to be able to use them to ship something much more useful. It’s still early days, but the pace of improvement is fast and the competition is intense.

At the FT, we’ve been exploring how AI could assist in the editing process—flagging errors or inconsistencies before a story is published. To prototype some ideas, I used v0 and Lovable to generate sample interfaces and see how they might plug into our own text editor infrastructure. It’s been a good way to test possibilities and get clearer on implementation paths.

Better results though have come from ideas I’ve got from colleagues that I’ve been able to quickly prototype in code using other methods. More modern, development-tuned LLMs with large context windows like OpenAI’s GPT-4.1 have made ‘vibe coding’ something really enjoyable, rapid, and productive. In just a few hours I managed to put together an app that transcribed live German radio into English and made it searchable via a web interface.

One of the advantages of using AI for code is that it’s easy to verify: does the output run? Does it do what you asked? While there are risks—especially if you’re building without understanding the underlying logic—for prototyping and lightweight tooling, it’s remarkably effective. A half hour of back-and-forth with an LLM can take you further than you might expect.

Product managers are often expected to be generalists, working across domains without always having deep technical skills. But that balance is shifting. These tools extend what a generalist can accomplish, letting you bring more ideas to life without waiting for engineering time or even full specs. That, I think, is where the most immediate opportunity lies.

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