ChatGPT was too polished and product-ready to have been a runaway low-key research preview, like Meta's Galactica was. That is the legacy you build around it after the fact of getting 1 million users in 5 days ("it was build in my garage with a modest investment from my father").

I had heard (but now have trouble sourcing) that ChatGPT was commissioned after OpenAI learned that other big players were working on a chatbot for the public (Google, Meta, Elon, Apple?) and OpenAI wanted to get ahead of that for competitive reasons.

This was not a fluke of striking gold, but a carefully planned business move, generating SV hype, much like how Quora (basically an expertsexchange clone) got to be its hype-darling for a while, helped by powerfully networked investors.

>This was not a fluke of striking gold, but a carefully planned business move

Then that execution and operationalization failure is even more profound.

You are under the impression that OpenAI "just failing to scale efficient mining of that gold", but it was one of the fastest growing B2C companies ever, failing to scale to paid demand, not failing to scale to monetization.

I admire the execution and operationalization, where you see a failure. What am I missing?

If the leadership of a hyper scaling company falls apart like what we've seen with OpenAI, is that not failure to execute and operationalize?

We'll see what comes of this over the coming weeks. Will the service see more downtime? Will the company implode completely?

If you have a building that weathers many storms and only collapses after someone takes a sledgehammer to load bearing wall, is that a failure to build a proper building?

Was the building still under construction?

I think your analogy is not a good one to stretch to fit this situation

If someone takes a sledgehammer to a load bearing wall, does it matter if the building is under construction? The problem is still not construction quality.

The point I was trying to make is that someone destroying a well executed implementation is fundamentally different from a poorly executed implementation.

Then, the solution would be to separate the research arm from a product-driven organization that handles making money.