Laura Rosenberger

Category: Observations

  • Building websites for agents, not humans

    I've built two websites recently. And I fell into a bit of a trap – I built them with humans in mind, not agents. How old fashioned of me!

    My target customer is consumer brands who need help with AI – getting up to speed with it, understanding what's possible, integrating it into their workflows. This isn't something that people in those teams would have previous experience with.

    For the person tasked with figuring this out, where are they going to look for answers?

    Most likely via ChatGPT or Claude.

    So I should have been building my websites with Claude in mind (rather than that person in my target company), to maximise my chances that my content will be surfaced in those chats.

    I've been following the guidance in this brilliant post by Tom Osman around creating llms.txt files, structured data, and comprehensive meta data.

    And asking Claude Code to integrate it for me, naturally.

    The web is going to serve two distinct purposes: a web for humans and a web for agents. Trying to make one website serve both would be a shame, so I hope the web will split in two. The human web can double down on beautiful experiences, thoughtful interfaces, content that's genuinely worth reading and stumbling upon. The agent web can be plain text, structured answers, Markdown, and can be where the mass-generated SEO blogs we've all learnt to scroll past can live. One is where you browse and discover, the other is where you pull down what you need.

  • AI Champions Are Being Set Up to Fail

    There's a new role quietly appearing across businesses: the AI champion. It's rarely a formal hire. It's usually someone already in the company (a generalist, a special projects person, someone who's enthusiastic and open-minded) who gets handed the AI brief on top of everything else they're doing.

    It's a crucial role. Arguably one of the most important in any business right now. And for the individual, it's an incredible opportunity to become the person who actually figured this out.

    But the support is basically zero.

    There's no playbook. No established methodology. No 'project management 101' equivalent for AI transformation. If you're a project manager, you've got university courses, certifications, Jira, Asana, decades of best practice to draw on. If you're an AI champion, you've got… a ChatGPT login and a lot of Googling.

    What strikes me most is how isolated these people are. Every AI champion I've spoken to is working it out alone, inside their own company, reinventing the wheel. They're not learning from each other. They're not sharing what's working. There's no community or infrastructure connecting the person figuring it out at a £10m consumer brand with the person doing the same thing at a £50m one.

    That feels like a massive missed opportunity, for the individuals and for the businesses betting on them.

    I don't have the answer yet. But I've been thinking a lot about what support for AI champions could actually look like, and I think it starts with getting these people in the same room.

  • The Imagination Gap

    AI is asking you to imagine something you've never seen. And it's hard to be what you can't see.

    I was speaking to the CEO who said 'we lack the imagination to even know what's possible', and it really stuck with me.

    Unless you're really into AI, it really is hard to see what to do beyond 'get ChatGPT licenses for everyone' because how are you supposed to know what's possible when very few people in this space are pushing the boundaries?

    The way I'm trying to bridge this 'imagination gap' for consumer brands is by taking inspiration from the businesses and the individuals who are operating right at the edge.

    Here are 3 sources of inspiration for me at the moment:

    'Companies as Code'
    If AI can do extraordinary things when given a set of 'skills' – essentially just well-structured text files – what happens when you codify your business in the same way? Your processes documented, your data accessible, your operating system written down in a form that AI can act on. So you don't just aim to be a consumer brand that uses AI, but a consumer brand whose operations are built to run like software.

    AI driven software development
    I spent time with a group of AI engineers who weren't just using AI to write code. They'd built entire systems where AI writes the code, tests it, catches its own mistakes, and iterates until it works. Their focus wasn't on building software. Instead, they'd built the machine that builds software. What's the equivalent for a non-technical business?

    See also – compound engineering.

    Claude Code for knowledge work
    Claude Code for non-technical use cases has been having a moment (and OpenClaw is Claude Code in a trench coat). If you want to really geek out, explore the Claude Code+Obsidian combo. Claude Code has become the default way in which I work and am building my consultancy business.

  • The Rise of the AI Operator

    Ops teams are always under pressure to cut costs, but they rarely get prioritised for dev time or allocated budget for new tools. They're stuck firefighting with no space to build something better.

    If you're an ops person who learns to build automations or AI tools – even small ones – you can create tools with massive leverage. And you're uniquely positioned to do this well. You're in the weeds so you see exactly what's causing drag and with AI, you can build something quickly to fix it (or at least make it less bad).

    The impact compounds as you move through the operation, continually improving it, fixing bottleneck after bottleneck.

    If you don't have these skills in your team, you're stuck doing everything manually, hiring linearly with growth.

    There's going to be a divergence where two similar businesses look identical from the outside, but one needs half the people to run the same operation.

    The role of 'AI operator' doesn't really exist yet as a job title. But it will. And the companies who figure out how to build or hire or train for it are going to have a structural cost advantage that's very hard to compete with.

  • What is left once Claude Code can write the code

    More and more people are looking at Claude Code for non-technical use cases. Claude Code handles the code, but what does a human still need to do?

    A fractional COO friend has been watching some Claude Code webinars and is ready to start playing with it.

    One of his first projects is to use it for invoice generation. We spent a few minutes on whether Claude Code could do this. Yep, it certain could code something. But what else does it need to do this well?

    Accurate timesheet data. Up-to-date client information and rates. Expense policies. Receipts. Knowing where the data lives and whether there are processes behind it so that you can trust it to be correct and up to date.

    All of this was his bread and butter as an operator, and it's easy to forget this doesn't come easy to everyone.

    Once Claude Code handles the coding, what remains is right in an ops person's wheelhouse.