"Cybernetics" reminds me a bit of "artificial intelligence", in this sense that it's "whatever doesn't work yet".

When Kantorovich brought linear programming to industrial production, it was clearly just a mathematical optimization. When Glushkov proposed running whole planned economies on it, that was cybernetics because the required computations were unachievable. But when businesses and militaries use it for design now, it's just operations theory.

When Jay Wright Forrester applied models of feedback and imperfect information to supply chains, that was simple economics. When he brought them to urban design and the environment, and Beer brought them to business and government management, the appropriate computing power and datasets were unavailable - so that was cybernetics. Now that environmental models are meaningfully grounded in reality, they're back to being mere models.

Of course, I have a bit less sympathy for the cyberneticists than for AI researchers. ML/AI work frequently follows a pattern in which research is explicitly speculative, then realized, then dismissed from the outside as "just algorithms". Its worst excesses are predicting major advances like NLP on very short timelines, not misrepresenting the present. Cybernetics seems more prone to over-promising what it can do now. And rather than researchers making advances which are then dismissed, the common pattern seems to be outsiders implementing techniques, at which point cyberneticists retreat to some new speculative claim where there's no competition.

FWIW, when I talk about Cybernetics I pretty much mean what's in Ashby's "Introduction to Cybernetics". I don't find much value personally in the "second-order" cybernetics and all that.

I figure that Beer believed his own shtick, but, man, you make it sound bad.