The A/B testing is by far the most objectionable thing from them so far in my opinion, if only because of how terrible it would be for something like that to be standard for subscriptions. I'd argue that it's not even A/B testing of pricing but silently giving a subset of users an entirely different product than they signed up for; it would be like if 2% of Netflix customers had full-screen ads pop up and cover the videos randomly throughout a show. Historically the only thing stopping companies from extraordinarily user-hostile decisions has been public outcry, but limiting it to a small subset of users seems like it's intentionally designed to try to limit the PR consequences.

Bruce here from the Twitter team.

I got finally fired.

People come at this with all kinds of life experience. The above notion of trust to me is quaint and simplistic. I suggest another way to frame trust as a more open ended question:

    To what degree do I predict another person/org will give me what I need and why?

This shifts "trust" away from all or nothing and it gets me thinking about things like "what are the moving parts?" and "what are the incentives" and "what is my plan B?".

In my life experience, looking back, when I've found myself swinging from "high trust" to "low trust" the change was usually rooted in my expectations; it was usually rooted in me having a naive understanding of the world that was rudely shattered.

Will you force trust to be a bit? Or can you admit a probability distribution? Bits (true/false or yes/no or trust/don't trust) thrash wildly. Bayesians update incrementally: this is (a) more pleasant; (b) more correct; (c) more curious; (d) easier to compare notes with others.

so who do you trust and go to? (NotClearlySo)OpenAI?