Closing the Gap Between Prototype and Product Success

We keep seeing the same pattern in conversations around AI, pipelines, and “authority”. A lot of it is describing the problem. New terms. Big ideas. Long explanations of how things should work. And most of it is brilliant. But there’s a gap we can’t unsee: Talking about the problem is not the same as proving the solution. It comes down to something very simple: → what actually ran → what it had access to → what was removed or denied And whether you can show that — not just describe it. In practice, it’s less about what can be described, and more about what can be verified. Curious — can you actually verify what your pipeline ran, end-to-end?

AI slop. Or just… early stage AI. These are clips from my last experiment, wrong prompts,bad outputs,bad videos.I kept them, put them together, Because this is where AI is right now.Started around 2022 .... and already moving this fast.Imagine what comes next. sound: Phillip Glass - ankhten

The more I'm using AI, the more I feel using analog tools like my pen and paper. It helps me think through and slow down before I get to Codex or Claude.

AI is starting to reshape credit markets in real time—driving new dispersion trends, influencing risk assessment, and creating differentiated opportunities across sectors. As infrastructure scales, understanding these shifts will be key for investors navigating the evolving landscape. Insightful takeaways—looking forward to exploring the full analysis.

You don’t have to buy an AI thematic fund to be long AI... owning broad equity and IG credit indices already gets you there, in size. AI‑related and hyperscaler issuance has, by some street estimates, approached one‑fifth of TTM DV01‑weighted investment‑grade supply, once you adjust for longer average duration. Many institutional credit allocators have yet to explicitly update their benchmark‑exposure and risk assumptions to reflect this new supply regime. The quality and sector profile of major IG benchmarks has shifted meaningfully in recent years, but allocator mandates and risk frameworks often adapt with a noticeable lag. Hyperscalers that were reliable free‑cash‑flow machines for the past decade are now seeing much of that cash absorbed by AI and data‑center capex, with several banks and brokers projecting hyperscaler AI capex to exceed one trillion dollars in 2027. A significant share of that spend is likely to be debt‑funded, and issuance has already shifted toward longer tenors, compounding interest‑rate sensitivity. The implication for passive credit holders is that large, AI‑infrastructure‑driven exposures are increasingly embedded in the index rather than an active, opt‑in trade. The fixed‑income leg of the AI cycle is becoming meaningfully concentrated in a small set of issuers, echoing the concentration investors worry about on the equity side.

Brian Cosgray, Co-founder & CEO at Elevate, cuts through the noise around AI. The verdict? Not everything should be automated. AI works best when the task is structured, repeatable, and tied to clean data. In benefits, this appears in areas like claims processing and helping users navigate complex workflows. See how Elevate fixes these ongoing issues: https://bit.ly/4tcCBgy 📽️ Clip from Money20/20 2025. #Money2020usa

Most people are thinking about AI the wrong way. They’re trying to layer automation on top of broken processes, and that doesn’t fix anything; it just makes problems scale faster. I enjoyed hearing Dan French talk about his Discovery Day because that’s how I built Empire years ago, and it’s living proof that taking the time to look meticulously at processes, however time-consuming and expensive, pays off. And now, with AI, it’s more important than ever because a clean process allows for correct automation. Not the other way around. If your foundation is messy, AI won’t save you; it’ll expose you. We unpacked a lot in this conversation. Listen to the full episode with Dan here: https://hubs.ly/Q04dWWrR0 or watch it here https://hubs.ly/Q04dWPTZ0

The contractors winning with AI do 2 things differently. Neither is top-down. Johnny's noticed that everyone he's spoken to at conferences this year seems to be using AI. But the smart ones aren't just adopting it. They're handling it differently: → They understand the limits of AI → They incentivize their people to explore the tools Not push them down as top-down decisions. Once you know what AI can't do, you can imagine what problems it should solve. And once your team is exploring instead of being told, the tools actually fit into the day-to-day. That's where the productivity shows up.

What if, instead of just focusing on making emails better and doing the same things more efficiently, organizations started thinking about using AI as a tool to (perhaps dramatically) improve their positive impact on the world?

“Everyone talks about AI like it just arrived.” It didn’t. In this post, Ole Winther, Professor in AI and Co-founder of Raffle, reflects on 30+ years in the field and one simple truth: The ideas aren’t new. The timing is. Which means the edge isn’t AI itself anymore. It’s knowing what to do with it. Most companies will produce more. Few will actually understand more. That’s the gap we’re focused on at Raffle: Turning anonymous website traffic into real customer insight. Because in an AI world, understanding beats output.