AI on top, but the value is in the data underneath

What we're tracking

• •When AI agents still need real software There is a tempting argument going around: once you have capable AI agents doing the work, you no longer need purpose-built business software. Give the agents a database and let them run. It is worth understanding why that argument is seductive and where it breaks down, because it changes how you spend money this year.
• •Winning a dispute is an evidence problem If you take payments online, you will eventually face a "product not received" dispute — a customer, or their bank, claiming an order never arrived. What determines whether you keep the money is rarely persuasion. It is whether you can produce the right evidence quickly and in the right form.
• •Choosing which AI, not just whether Google released three new Gemini models today. Notably, none of them was the next flagship: TechCrunch reported that "Google released Gemini 3.6 Flash, 3.5 Flash-Lite, and Flash Cyber, but the continued absence of Gemini 3.5 Pro raises fresh questions about its AI strategy."[3]
• •AI that shows its work GitHub described a feature it calls canvases, aimed at making AI output something you can work with rather than just read. In its words, "Canvases turn AI into interactive workspaces where you can visualize information, explore workflows, and take action across complex tasks."[4]
• •Meeting resources in one place Google also revamped the Google Meet homepage on the web. The stated purpose is organisation: "We're introducing a revamped Google Meet homepage on the web to help you stay organized and prepared throughout your entire meeting workflow."[5]
• •The common lesson Four of today's five items are, on the surface, about AI. Read together, they point somewhere unglamorous. The dispute analysis wins on records captured at the time. The agents story ends at the need for real, structured software. The interactive workspace matters only because it lets a person check the work. Even the meeting redesign is really about putting the right material where you can find it. The model on top gets the attention; the data and structure underneath decide whether any of it is worth paying for. When you choose a tool this quarter, spend most of your judgement below the AI l