Interesting, I thought it was because so few of them have any idea how their organizations actually function, because so much of their work is performative.

(I have been a developer, sysadmin, director (x2), and president).

I live in a purple state (also a Canadian citizen but that's beside the point).

I hate to say it, but Gemini lies less frequently than paid models from OPenAI and Anthropic (Open AI is worst in my use cases).

My guess is that Google has better training data (and uses less synthetic data which might be creating training feedback loops in other models), has more of a "be calibrated" model than a "be helpful" model, but it could just be that they leverage more RAG than leveraging weights more.

But, I really shouldn't speculate the "why" as I'm out of my domain. Just curious if others use all the models they can and compare outputs as much as I do.

I'm not sure I understand this, it doesn't feel like what I have "lived" for the least 30 years.

Median real income might not be down statistically, but the purchasing power of professional incomes relative to housing, education, and major life costs clearly feels lower than it did in the mid 90s. An inflation-adjusted six-figure salary today does not deliver the same lifestyle position it once did.

Man... healthcare costs, too. Hell, even computers! Raw computing power per dollar is cheaper than ever, but the minimum spec required to function professionally has risen so much that the real cost of staying technologically current feels higher.

Sure, it might cure cancer, but only for the wealthiest.

Sure, we'll go to space, but only after the planet is irreversibly trashed and poisoned and the only "poors" that will be in space will be the modern equivalent of non-unionized coal miners.

Or both.

Historically though, the data suggests that mass layoffs have a huge impact (negatively) on productivity after a short term "boost" by the survivors.