Testing before you trust, and pricing before you assume

What we're tracking

• •Test an AI model on your own data, not on its reputation There is a quiet assumption in a great deal of software buying: the dearest option must be the safest one. For ordinary tools that assumption is often wrong, and for AI models it can be costly. A model's price reflects how it was built and how much the vendor has chosen to charge, not how well it will do the particular job you have in mind. The only honest way to know how a model performs on your work is to run your work through it and score the results.
• •Price is a hypothesis, and you test it on the next deal Most prices are set once and then left alone, because changing them feels risky. The risk people picture is upsetting the customers they already have. The advice from SaaStr today separates those two worries cleanly. If you want to move upmarket, the suggestion is to double your pricing on your next deal, and pointedly not on your existing customers [2].
• •A calendar setting that is really a governance decision The third item looks small and is not. Google is adding the ability to block the senders of unsolicited event invitations in Google Calendar [3]. On the surface it removes a nuisance. Underneath, it is about who gets to put something on your team's time without being asked.
• •The thread, and what to do with it Put the three together and a single working rule emerges. Decide on what you can see. Test the AI model against your own inputs rather than its billing tier. Test your price on a new deal rather than guessing at your worth. And give each person real control over what lands on their calendar, instead of leaving the door open because it came that way. None of these is a grand strategy. Each is a small, checkable step that replaces an assumption with a fact, and the value of a fact is that it holds up when someone pushes back on it.