Misbah Uraizee

How I’m currently thinking about AI at JRB: I’m still trying to decide on this but here’s where I’m currently at: Every employee is FORCED to use AI first daily. I don’t want most of them spending time learning Claude code but I’m considering signing up everyone for Cowork. Similar capabilities for them with easier ux. Realistically I think 5% of the will and should learn Claude code and build apps. Some already have. But most won’t Andi don’t think it’s how they should be spending their time. So it’s better to enable them. Here’s how: 1. All the softwares we use have AI capabilities. For ex richpanel for Cx, nectar for social and influencer, etc. no legacy softwares without it. Rip out any old ones that don’t and replace with an AI native one. 2. Designate 1 person in the org to be an AI lead. Goal them on getting AI adoption and usage. They do some internal FDE work and build our apps for people. 3. SEANs. I also have a SEAN we pay $10k/mo to that does the same but they have deeper expertise and a whole team. Prob most similar to what you are doing. Immensely valuable. To get employees using daily, a few things have to happen: 1. They cant worry about job security 2. They need to be trained on the tools consistently by the AI lead 3. They need tools brought in or built for them. Open to feedback or thoughts here! And for me personally I’m building tools like Matt is. But I’m doing it a nights, weekends, etc. building legit super valuable stuff for JRB but I can’t let it get in the way of my real job.

Linear’s CEO just described the biggest shift in product team structure since Agile. For decades, product work meant: PM defines requirements → designers create specs → engineers translate to code. The middle step, translation, absorbed 70% of the time and created most of the friction. Karri is saying that step is collapsing. AI agents don’t need handoff documents or sprint planning rituals. They need structured context about what matters, what constraints apply, and what success looks like. This inverts the leverage points. The person who captures customer intent clearly now has more impact than the person who translates it into implementation. And the person reviewing agent output becomes the quality bottleneck. Linear built their entire product around this bet: structured entities with clear ownership, context attached to work items, feedback connected directly to issues. It turns out the same system that helps humans coordinate also helps agents know what to do. The teams figuring this out first will have a structural advantage. Everyone else will still be writing Jira tickets that read like riddles.

BUILD THE WHOLE PRODUCT If you're a startup CEO, you should think deeply about what Frank Slootman says : "Build the Whole Product, or solve the Whole Problem as fast as you can". In 2026, the biggest winners will be companies who realize that fragmented experiences don't serve the customer well, and will solve the entire end to end problem for their customer. Customers are tired of stitching together five tools that each do 80% of what they need. They want one solution that does 100% of what they need. Fearless, visionary entrepreneurs will build a whole solution for their customer segment, even if it means that solution has to compete across multiple categories, including with entrenched incumbents. Now this doesn't mean they will solve the whole problem for EVERYONE from day one. They will choose very specific customer segments (size, geography, vertical, behavior, etc) and solve the entire problem for that segment, and do it 10x better than the customer could do by cobbling together several systems. And then they will expand concentrically from that initial segment. One of the best examples is Square, which took on decades-old incumbents in payment processing, hardware terminals and POS, and built a hardware + software system that solved the entire problem for micromerchants. Not just software, but also custom hardware that the team built from scratch, despite having zero hardware experience. Why? Because hardware was critical to deliver the whole solution. By doing so, they "compressed" the value chain across 3 industries, and instead of the customer needing to feed 3 profit pools for payments, hardware and POS, they only needed to pay one company, leading to a much lower Total Cost of Ownership. If you're an entrepreneur tackling a WHOLE problem and building a WHOLE product, please ping me. I'd love to connect and chat.

The real moats in 2025: specific workflows, proprietary data with real switching costs, distribution, and UX that makes AI disappear into the job-to-be-done. Simultaneously: we are early (only a % are using AI properly) so this is an amazing time to start a startup.