Today was a massive win. I had my meeting with the Head of AI for our global group, and it went beyond anything I had imagined. The Pitch: 132 Orders and a "Broken" System I had the chance to present a real-world challenge: Manually processing 132 sales orders in April. The workflow is a nightmare: Open each order, find the amount, cross-check it with an Excel sheet, invoice it, and repeat. To make it worse, there is a known bug in our D365 environment where the amount column simply shows "0" in the grid, meaning I can’t just export a list. It requires manual clicks. In a busy finance department, this takes days because of constant interruptions. I presented my workflow and explained how this concept isn't just for one task—it’s a framework for almost every repetitive monthly task we have. I knew from my previous Rebill Project that if I can automate the "friction," I can win back my time. The Result: Skipping the Queue When I told the Head of AI that this could turn a 3-4 day job into about 1 hour, his eyes lit up. Even though Claude Code is still stuck in corporate governance (it's currently with our CEO to decide on a global rollout), he didn't want me to wait. He immediately assigned me a Microsoft Copilot Studio license. These are highly restricted—usually, there’s a long waiting list, and if you don't use it for 30 days, you lose it. He bypassed the entire queue to get me started right away. Moving the Needle with IT To get "Copilot Cowork" talking to D365, I had to submit a technical IT ticket to enable the Model Context Protocol (MCP). I made sure to CC both the Head of AI and my own manager. The Head of AI jumped straight into the ticket with this comment: "I talked to Allan today. He has an idea to speed up a process in finance and save days of work... The use of the MCP server for this would help him very much. Open for a call if needed or any other help for the team."

Whatever model you're using right now is good enough. The question isn't capability anymore. It's taste. Capability has been commoditizing for eighteen months. The benchmarks plateaued in the territory where the difference stops mattering for most work. The model is no longer the lever. Watch what the labs are shipping right now and notice the same thing from two directions. Anthropic shipped Claude design. Identity, typography, layout, voice, the editorial spine the whole product runs on. The brand has a point of view and they're letting it carry the surface. OpenAI shipped pets. Floating overlay. /pet command. Custom personality presets. The brand is leaning into character, presence, attachment. Don't read these against each other. Read them together. Both labs are reaching for the same lever at the same time, in different registers. Both are admitting taste is now load-bearing. Two flavors of the same lever Editorial taste fits a power-user surface. Rigorous. Stable. A design system signals reliability. Character taste fits a wider surface. Warm. Present. Pets signal companionship. Neither is "better." They're aimed at different rooms. Picking which room you're in, and refusing to be a generic version of all rooms, is the work. What this means for the rest of us If the labs are now competing on taste, the same thing is happening one layer down. To everyone using them. When AI gives you all the tools, your taste is the differentiator. To some extent. Craft, distribution, relationships still matter. But the lever that just rotated for the labs is rotating for the rest of us too. The model can't tell you what to make. Your judgment about what to do with all of it can. The takeaway The model is good enough now. The next leverage point isn't more capability. It's the judgment to use it well. Taste is the lever. For them. For us. Full breakdown. The good-enough plateau, the two registers of taste, and what it means for makers, all live here: https://aris-space.com/documents/thoughts-and-scribbles/the-taste-transition

After watching the Flagler workshop and reading the companion: Vote below, then tell us in the comments: where did you land on the PhD thought experiment?

After reading the full history of thinking machines: Vote below, then tell us in the comments: which part of the timeline surprised you the most?

This course set me up with the knowledge. One of my clients is picking up 10 seats of Claude enterprise and I’ll be leading the team on how to get set up correctly for the long term. Cheers. Let’s hope this is just the beginning!