I suspect the market won't split into "AI companies" and "non-AI companies". It will split into companies that measure outcomes versus companies that mandate tools.
I've worked at places that mandated a particular editor, programming language, or methodology. Those mandates were rarely the reason people joined. The healthier organizations tended to define the outcome they wanted and let engineers choose the tools, provided the results justified the choice.
I wouldn't be surprised if AI ends up following the same pattern.
It depends on what you mean by "privacy."There are at least three separate concerns:
Does the client send telemetry? Does it sync your collections or API definitions to a cloud service? Do your API requests transit through a third-party server, or are they sent directly from your machine?
Those matter much more than whether the client is Postman, Thunder Client, or something else.
I wonder if "traffic" is becoming the wrong metric. A human visit has always implied someone consuming the content. An AI crawler may never generate a pageview from a human, yet it can still become the mechanism by which someone discovers your work later through an assistant.
In that world, machine visits aren't necessarily noise, they're another distribution channel.
I wonder if the signal people actually want is "low information density" rather than "AI-generated."
A lot of the frustration seems to come from content that takes 2,000 words to say something that could have been said in 200, regardless of whether a human or a model wrote it.
If a post is original, useful, and teaches me something, I don't care much how it was produced. What I notice is when a lot of words are used to communicate very little.
Usually it’s not a different model, it’s the same model with different inference-time settings.
“Thinking effort” typically changes the compute budget and decoding behavior (how many steps, how much exploration, sometimes internal planning loops).
Some stacks also tie it to orchestration layers or system/prompt signals, which is why it can look inconsistent across products
It's possible your competitors aren't actually solving the same problem you are. Founders tend to compare products on technical performance, while users often choose based on distribution, habit, onboarding friction, or simply which app they heard about first.
I've worked at places that mandated a particular editor, programming language, or methodology. Those mandates were rarely the reason people joined. The healthier organizations tended to define the outcome they wanted and let engineers choose the tools, provided the results justified the choice.
I wouldn't be surprised if AI ends up following the same pattern.