Yes, but that doesn't explain how OpenAI able to walk and chew bubble gum at the same time, unless you think there is some extra spark that Google is lacking.
The chat service bit is them being chill, but the real spark was the model. I say they were lucky, because AFAIK back then no one expected LLMs to show so many and so advanced general capabilities. This took everyone by surprise, and since people could already play with it, ChatGPT took off on its own - it had so much real, transformative value, that it spread out with zero marketing. That's a rare, bona fide case of "word of mouth", it was just that useful. But that wasn't a strategy, that was luck.
To their credit though, OpenAI turned this early win into an opportunity and is excellent at exploiting it. Being small helps.
IMHO, in retrospect, the failure gradient of early LLMs is underappreciated in driving adoption.
Windows 95 failure: blue screen with inscrutable error code. Everyone noticed that.
LLM failure: run-around non-answer (user shrugs and tries again) or confident and plausible incorrect answer (user doesn't recognize this without research).
Essentially, the ways in which LLMs didn't work were the most hidden and hardest to discover failure mode.
Which was perfectly tuned for the "I'm going to try this thing for 5 minutes and be amazed" first impression.
Which allowed subsequent generations to backfill the capability gaps.
Tl;dr - We shouldn't underappreciate quiet-failing as a product adoption driver.
I'm certain it drives a lot of early user retention in the short term, but I feel strongly that this is ultimately a very myopic view which will prove catastrophic in the long term in much the same way that swallowing exceptions at runtime builds compounding technical debt you'll have to reckon with sooner or later
more broadly, there is just so much handwaving away all the black box parts of deep neural networks that are completely opaque and there seems to be very little interest in building the tooling to properly visualize, explore, and DEBUG latent space; until those priorities change this whole thing is a huge time bomb.
imagine if instead of coming with full memory dumps and diagnostic codes, BSODs just said "sorry, your computer had an oopsie!", and not a single engineer at Microsoft had a complete understanding of why the BSOD happened in the first place; sometimes it just does that! whoops!
So, MacOS? ;)
In all seriousness, I wasn't opining on the usefulness of opaque/hidden errors, but rather the effectiveness of them.
In an alternate reality where the first LLMs instead spit back an error reference instead of English, I don't think we would have seen nearly as rapid mass market adoption.
And, not to put too fine a point on it, early conversational LLMs and image diffusion models were literally trained so their junk output is as plausible as possible.