Problems Worth
Solving.

If you've ever traveled with someone who has a personal lifestyle consultant or travel concierge, you already know the gap. They don't just book flights and hotels. They know your preferences before you ask. They tell you which end of the street to start from. They adjust the plan in real time when something changes. They know the local etiquette before you embarrass yourself. They find the restaurant that's actually good, not the one with the most Instagram posts.

That level of service has always been reserved for people who can pay for it. Until now, there was no technology capable of replicating it at scale.

Technology has a pattern: it takes what only the privileged could access and makes it available to everyone. Wealthy people had drivers — Uber gave everyone a driver on demand. Wealthy people had chefs and restaurants delivered to their door — DoorDash and meal kits democratized that. Wealthy people had financial advisors managing their portfolios — Robinhood and Betterment opened that to anyone with a smartphone. In every case the core value wasn't the luxury wrapper — it was the access.

I've been using AI extensively to plan my own trips — finding recommendations based on my preferences, asking for hyperlocal suggestions, getting cultural context before arriving somewhere new. And it's genuinely better than Google. But it still fails in ways that matter. The restaurant it recommends has better SEO than the restaurant that's actually good. The shop it suggests is closed on the day you show up. The timing is wrong. The crowd situation is wrong. The match to what you actually wanted is close but not quite right.

The gap between "AI that knows about travel" and "AI that knows about your trip, right now, in the specific neighborhood you're standing in" is enormous. And nobody has closed it yet.

What I'm thinking about building: A hyperlocal, hyper-personalized, real-time AI travel companion. Not a trip booking tool. Not a recommendation engine. Something that functions the way a well-connected local friend would — one who knows your preferences, adjusts to your constraints in real time, understands the cultural context of wherever you are, and gets smarter about you with every trip you take.

The moat isn't the AI model. Every competitor has access to the same foundation models. The moat is the depth of user understanding that compounds over time — preferences, travel style, past mistakes, what you loved, what you hated, how you actually move through a city.

Experimentation teams are sitting on some of the most valuable behavioral data in the world — and almost none of it gets shared. Every A/B test that runs, every hypothesis that gets validated or killed, every pattern that emerges across industries and device types stays locked inside a single company's Confluence page or Notion doc. The collective knowledge of thousands of experiments exists nowhere in aggregate.

I've run experimentation programs for over a decade. The dirty secret is that most teams are solving the same problems, testing the same hypotheses, and making the same mistakes — completely independently of each other. There's no infrastructure for learning across organizations.

The insight I keep coming back to is GoodUI's original model: contribute data, get access to aggregated patterns. It worked because the incentive was right — you give something, you get something back that's more valuable than what you gave. But GoodUI was manual, static, and limited in scope.

What I'm thinking about is an AI layer on top of anonymized experiment data — connected to platforms like Optimizely and Adobe Target — that identifies themes, surfaces tested solutions, and organizes findings by industry, device type, and traffic model. The social mechanic is the moat: the more teams contribute, the more valuable the intelligence becomes for everyone. It compounds.

The free tier is the contribution model. The paid tier is depth — segmented insights, pattern matching against your specific context, hypothesis generation based on what's already been proven.

There's a category of legal dispute that the system was designed to handle without lawyers — small claims court. Disputes under $10,000. Landlords keeping deposits. Contractors who didn't deliver. Products that never arrived. The process exists specifically so ordinary people can navigate it themselves.

The problem is that "navigate it yourself" still means understanding filing procedures, writing demand letters, knowing what evidence to present, and showing up prepared in front of a judge. Most people have no idea how to do any of that. So they either abandon the claim — letting the other party win by default — or they spend more on a lawyer than the claim is worth.

The economics of this are broken in a very specific way: the dispute is too small for a lawyer to take seriously, but large enough that the person filing genuinely cares about the outcome. That gap is the opportunity.

An AI copilot that guides someone through the full small claims process — drafting demand letters, organizing evidence, preparing arguments, understanding jurisdiction-specific rules — could charge $50 to $100 per case and be the most cost-effective legal help that person has ever accessed. No retainer. No hourly rate. Just a tool that knows the process and walks you through it.

This follows the same democratization pattern as everything else: access to competent legal guidance has always been gated by whether a lawyer finds the case worth their time. That gate is artificial. The knowledge exists. It just needs to be packaged differently.

Most high performers are terrible at documenting their own value. Not because they're not doing great work — but because the work happens in Slack threads, in meetings, in decisions that never get written down anywhere. By the time review season arrives, six months of wins have evaporated. What's left is a vague sense that things went well and no concrete evidence to back it up.

This is one of the most widespread and quietly expensive problems in white collar work. People leave money on the table in negotiations. They get passed over for promotions they deserved. They walk into conversations with their manager underprepared, unable to articulate their own impact in the terms that matter — revenue influenced, time saved, problems prevented.

The insight is simple: the gap isn't performance, it's documentation. And documentation is exactly the kind of structured, recurring task that AI handles well.

What I'm thinking about is a tool that runs in the background — embedded into the workflows where work actually happens — and builds a running record of accomplishments in real time. Not a journal you have to remember to update. Something that surfaces wins as they happen, frames them in quantifiable terms, and gives you a ready-made case when it's time to ask for more.

The individual use case is clear. The enterprise angle is also interesting — companies spend significant money on performance management tools, almost all of which are designed to serve the manager's view, not the employee's. A tool that helps employees articulate their value serves retention, internal mobility, and compensation equity in ways that HR should care about.

Running a company — even a small one — means holding an enormous amount of context across radically different domains at the same time. Marketing performance. Product decisions. Cash flow. Competitor moves. Team dynamics. Strategic direction. Each of these has its own tool, its own dashboard, its own data format. None of them talk to each other. The founder is the integration layer, manually pulling signal from a dozen disconnected sources and trying to synthesize it into decisions.

This is not a productivity problem. It's an architecture problem. The tools were built for specialists — marketers use marketing tools, developers use dev tools, finance uses finance tools. But founders aren't specialists. They're generalists who need the 10,000-foot view across everything, updated in real time, without having to become an analyst to get it.

The idea is a connected operating layer — pulling from Stripe, Google Analytics, and other core platforms — that an AI synthesizes into a coherent picture of what's actually happening in the business. Not raw data. Interpreted signal. What's working, what isn't, where the bottleneck is, what competitors are doing, what needs attention this week.

The deeper layer is strategic memory. A system that knows your leadership style, your business model, your past decisions, and uses that context to make the intelligence more relevant over time. Less generic dashboard. More like a well-briefed advisor who was in every meeting and remembers everything.

The market for founder productivity tools is crowded with point solutions. Nobody has built the integration layer that makes them coherent. That's the gap.