Human in the Loop: Two Ex-Microsoft Builders on AI

Five months ago you built a dashboard over your business data, and you have not opened it since. Not because the numbers stopped mattering, but because it is faster to ask your agent the question and let it draw the chart you need right now. That is what happened to Sash's laundromat dashboard, and it is the opening argument for the claim Chris makes on this episode: in ten years Power BI, and any BI tool whose interface needs a team to keep running, is dead. This one is for anyone who owns a dashboard, maintains one, or still gets asked to build the next one.The case is made by two people who earned their careers on Power BI at Microsoft. Sash shows how disposable interfaces already run his week: Nuro, his Claude agent, built a data model out of the CSVs his two laundromat systems export with no API, and every question since has been a conversation that draws its own visual and then disappears. He takes it past BI to software in general, with a friend's managed-service pitch answered by "I don't need an interface, give me an API." Chris goes back to 2016 and a Kansas City CIO who bought Power BI for its pin-your-own-dashboard feature, which was hyper-personalization years before agents, and to his own older prediction that dashboards would give way to alerts that find the problem, tell you, and then act on it.It is not all prophecy. They are clear that Power BI is not dead today and will shift rather than vanish. Fabric IQ already exposes semantic models through an MCP server with row-level security, Databricks Genie generates a view when you ask it, and Gartner's guidance has analytics agents complementing dashboards for now. Then the bigger question: whether hyper-personalized computing like Omarchy makes the interface itself ephemeral, and whether AI is replaying cloud's IaaS to PaaS to SaaS path in reverse. They close on OpenAI's Intelligent UI in ChatGPT and Claude's live artifacts, both launched in the last few weeks.Key takeaways:Before you build the next dashboard, try asking an agent the question directly over the same data and see whether you ever need the static view.If a vendor's system has no API, export the CSVs on a schedule and let your agent build the data model - that alone makes the data conversational.Give your agent persistent context about your business - context files, skills and a clean data model - so the visuals it draws answer the questions you actually ask.If you run Power BI on Fabric, look at the Fabric IQ MCP server: it lets an agent query your semantic model under its row-level security without pulling the data local.Ask your software vendors for an API, not a better dashboard. Agent access is the interface that will matter.Watch the video version on YouTube · Full show notes, transcript + takeaways

You hire someone, hand them a client presentation, tell them they are not allowed to ask a single question, and promise to fire them if the first draft is bad. Nobody runs a team that way, yet it is roughly how a lot of us hand work to Claude and ChatGPT, then forward whatever comes back to a customer. This one is for anyone whose company is starting to drown in AI output nobody checked, and for the leaders tempted to ban the tool because of it.The problem starts before the prompt. Most knowledge work is tacit: you know how to do it until you have to explain it, which is the illusion of explanatory depth, so the ask stays vague and the model fills the gap. Chris shows what that looks like with one plain Opus 5.5 prompt for the Said and Unsaid newsletter, which came back with placeholders and a guessed format, then walks through /saidandunsaid, an orchestrator skill with no logic of its own. It calls research, outline, draft, a fresh-subagent cold read, image and render subskills, and a human picks the story at the start and reads the draft at the end.Then it moves into the enterprise: where organizational skills live in Claude under Customize, why a skill your company already published beats one you build yourself, and why banning AI after a bad draft pushes the work into shadow AI, which IBM's 2026 breach report ties to 43% of breaches. Sash adds the recipe-book view: you never have to write a skill file by hand. Have Claude interview you with the skill creator, or take a plugin someone else built and customize it.Key takeaways:Make your AI ask you questions before it builds anything - say so in the prompt, or use a brainstorming or grill-me style skill that does it for you.Turn any task you repeat into a skill: the instructions, the rules, and two or three examples of output you consider good.Keep the orchestrator free of logic: one top-level skill that only calls subskills, so you can change or reuse one step without breaking the rest.Before you build a skill at work, open Customize, then Skills, and check the Organization section - a team may already have built it.Don't ban AI after one bad draft. Make a named person accountable for what ships, and give them skills that make good output the default.Watch the video version on YouTube · Full show notes, transcript + takeaways

You have wanted to try Linux for years, and you quit in the same place every time: the install, the hotkeys, and all the ways to break the thing before you have done anything with it. After 20 years deep in Windows internals and three on a Mac, Sash hit exactly that wall, then installed Omarchy, DHH's Arch and Hyprland desktop, on a dusty Lenovo ThinkCentre off the SD card in his Sony a6000, because no USB stick in the house held six gigs. It installed in under five minutes. The first thing he did was open Codex inside it.Omarchy ships nine agent CLIs pre-wired, Claude Code, Codex and Hermes among them, and a hotkey that launches whichever one you make the default. From there every change started as a sentence. Codex taught him the tiling hotkeys in about 20 minutes by moving the windows itself, built an app launcher bar in five, named his workspaces (Build, Comms, Studio, OS, Flex), and embedded a four-time-zone world clock in the bar. Asked what it knew about the system, it surfaced the agent skills Omarchy installs on disk: manuals for building plugins and widgets, for updating, and a crash flow where a pop-up hands the failed process straight to your default agent to read the logs and debug. When an OS update threatened 48 hours of customization, Codex built a private Git repo of the config and an update hook that backs up, updates, reviews what landed, and reloads.The bigger point is DHH's "malleable OS": the agent is not an app running on the operating system, it can reach the source, so your Omarchy and someone else's end up genuinely different machines. It also runs on hardware the big vendors have stopped supporting, including M1 and M2 Macs, at a moment when a Mac mini has a 12-week wait. It is not set-and-forget computing - this is for people who want the customization and will own it. Sash is buying a KVM switch to make it his daily driver.Key takeaways:Check the closet before you buy an agent machine. Omarchy installed in under five minutes on an old i7 ThinkCentre with 16 GB of RAM, and it runs on M1 and M2 Macs that can no longer take the latest macOS.Use the agent as a tutor before you use it as a builder. Ask it to walk you through the hotkeys and let it move the windows while you watch - that is how Sash learned tiling in 20 minutes.Ask the agent what skills and context it has on the system before you ask it to change anything. On Omarchy that question surfaced the built-in manuals for plugins, widgets, updates and crash diagnosis.Have the agent build its own safety net before you customize heavily: a private Git repo of your config and an update hook that backs it up, updates, and reloads what the update touched.Find a USB stick with at least six gigs before you start, or an SD card adapter. Sash lost 90 minutes to that alone.Watch the video version on YouTube · Full show notes, transcript + takeaways

Your shipping carrier sends a bill. Your invoicing system sends invoices. Nobody has ever compared the two, because the systems were never connected and the reconciliation was a spreadsheet import somebody did by hand. Drummond's CIO pointed an MCP at both sides, extrapolated a year out, and found more than $600,000 in shipping charges the company had absorbed and never passed on to its clients. The same tooling caught carrier overcharges worth filing claims against.Chris Brock runs IT for a 90-year-old family-owned commercial print and fulfillment company in Atlanta and Jacksonville, just under $100 million in revenue, where the industry-standard ERP has atrocious reporting and a lot of the data going in is hand-typed. The working pattern is specific and repeatable: build an MCP over whatever legacy API or Postgres connection exists, plan on a frontier model, then hand that written blueprint to a cheaper model to execute inside explicit "only work in these lanes" guardrails. In CI/CD, agents security-check and end-to-end test other agents before a developer signs off. Licensing is centralized and allocated mainly to IT and some management, less for cost than for the MSA problem: a CSR dragging a customer-address CSV into an open model is a real contractual exposure. Nothing runs lights-out. Every process still has a human on green light, red light.The part that answers the budget question is what happens after the win. A hard-dollar number converts the next AI request from a forecast into a case study, and it changes how you can approach a department. Lead with evidence of what a prior automation actually recovered and people stop hearing "we are automating you out of a job." The distinction Brock draws is between busy and strategic, not between human and machine.Key takeaways:Reconcile two systems nobody has ever compared, such as a carrier bill against your own invoices, and extrapolate a year out before you ask for any AI budget. That is where the hard-dollar number comes from.Plan on a frontier model, then hand the written blueprint to a cheaper model to execute. Put the "only work in these lanes" constraints in writing first, because an unconstrained agent gets a mile down the road before you catch it.Put an agent on your agent. Security-check and end-to-end test the output in CI/CD before a developer ever sees it for sign-off.Build the MCP over the legacy system rather than waiting for better reporting. Most old software still exposes an API or direct Postgres, which is enough.Lead a funding ask with money you already recovered, not a productivity estimate, and open the same way when you go hunting for automatable work so the department does not get defensive.Watch the video version on YouTube · Full show notes, transcript + takeaways

Picture your company a year from now with two dozen agents running in it. One of them starts returning a number that is quietly wrong. Nobody is named as its owner, nothing recorded which data it touched, and the model underneath it changed three weeks ago without anyone noticing. No better model fixes that, because the model was never the problem.That is the gap between two numbers from the same BCG read: 82% of CEOs are more optimistic about AI than they were a year ago, and 6% of companies see meaningful value. The companies inside that 6% are working three pillars at once - people, data, and agent ops - and missing any one makes the other two stop mattering. The framing comes out of the ITIL discipline Sash and Chris both ran at Microsoft, updated for agents. On people it means three tiers rather than a single training push: baseline AI fluency for everybody, an orchestration layer of the people who own operational processes, and a small group of stewards who carry the message. Generic training does not move it. Training on your own workflows, with every AI-assisted result required to be explainable, does.On data the number that lands is 7%, the share of organizations that believe their data is ready for AI, and the advice is deliberately small: pick one workflow, find the data sources it needs, go to the owners, fix and document the lineage for that one path, then reuse the process. If an answer cannot be traced to a source, the agent does not ship. Agent ops gets the most concrete treatment. Map a workflow step by step before deciding where AI belongs, and leave the steps code already handles deterministically alone rather than forcing tokens at them. Put a human checkpoint ahead of anything touching finance, external sending, or a system of record. Give every agent a named owner and a traceable action log, and stand up the eval layer before the agent count grows, because agents quietly atrophy as models change underneath them. The closing stretch looks at where this goes: Mastercard and Visa building a Know Your Agent framework, Mastercard expecting one in ten purchases to be agent-made by 2030, and Microsoft Scout putting an agent into Entra ID as a named identity that inherits DLP and compliance.Key takeaways:Pick one workflow before touching the data estate. Identify its sources, find the owners, fix and document the lineage for that single path, then reuse the process on the next one.Do not ship an agent on data whose answers cannot be traced back to a source. If the trace is missing, you will not be able to reconstruct what went wrong.Put a human checkpoint in front of any agent action that touches finance, external sending, or a system of record.Give every agent a named owner and a traceable action log, and stand the eval layer up before the agent count grows. Agent behavior shifts as models change underneath it.Find the 5 to 10% of people already building without waiting for permission and make them your stewards. Train on your own workflows, not generic courses, and require that every AI-assisted result be explainable.Leave the steps that code already handles deterministically alone. Not every automation needs a model.Watch the video version on YouTube · Full show notes, transcript + takeaways