Because the first couple major iterations looked like exponential improvements, and, because VC/private money is stupid, they assumed the trend must continue on the same curve.
And because there's something in the human mind that has a very strong reaction to being talked to, and because LLMs are specifically good at mimicking plausible human speech patterns, chatGPT really, really hooked a lot of people (including said VC/private money people).
If you re--calibrate from any lofty idea of their motives to "get investor money now", this and other moves/announcements make more sense: anything that could look good to an investor.
User count going up? Sure.
New browser that will deeply integrate chatGPT into users lives and give OAI access to their browsing/shopping data? Sure
Several new hardware products that are totally coming in the next several months? Sure
We're totally going to start delivering ads? Sure
We're making commitments to all these compute providers because our growth is totally going to warrant it? Sure
Oh, since we're investing in all of that compute, we're also going to become a compute vendor! Sure
None of it is particularly intentional, strategic, or sound. OAI is a money pit, they can always see the end of the runway, and must secure funding now. That is their perpetual state.
Did you, the company who built and sold this SaaS product, offer and agree to provide the service your customers paid you for?
Did your product fail to render those services? Or do damage to the customer by operating outside of the boundaries of your agreement?
There is no difference between "Company A did not fulfill the services they agreed to fulfill" and "Company A's product did not fulfill the services they agreed to fulfill", therefore there is no difference between "Company A's product, in the category of AI agents, did not fulfill the services they agreed to fulfill."
Exactly. I have said several times that the largest and most lucrative market for AI and agents in general is liability-laundering.
It's just that you can't advertise that, or you ruin the service.
And it already does work. See the sweet, sweet deal Anthropic got recently (and if you think $1.5B isn't a good deal, look at the range of of compensation they could have been subject to had they gone to court and lost).
Remember the story about Replit's LLM deleting a production database? All the stories were AI goes rogue, AI deletes database, etc.
If an Amazon RDS database was just wiped a production DB out of nowhere, with no reason, the story wouldn't be "Rogue hosted database service deletes DB" it would be "AWS randomly deletes production DB" (and, AWS would take a serious reputational hit because of that).
It's not reasonable to claim inference is profitable when they've also never released those numbers. Also the price they charge for inference is not indicative of the price they're paying to provide inference. Also, at least in openAI's case, they are getting a fantastic deal on compute from Microsoft, so even if the price they charge is reflective of the price they pay, it's still not reflective of a market rate.
Don't even need to get too fancy with it. Open AI has publicly committed to ~$500B in spending over the next several years (nevermind even they don't expect to actually bring that much revenue in)
$500B/$100,000 is 5 million, or 167k 30-year careers.
The math is ludicrous, and the people saying it's fine are incomnprehensible to me.
Another comment on a similar post just said, no hyperbole, irony, or joke intended: "Just you switching away from Google is already justifying 1T infrastructure spend."
That's...not hard. Pregnancy produces a whole slew of relatively predictable behavior changes. The whole point of recommendation systems is to aggregate data points across services.
Your privacy policy and security page say you do not use any user data for training, it also says you don't store any user data. How do you square that with this comment?
"The key difference isn’t the words — it’s how you structure the thinking process. By breaking down the task into numbered steps as we see in option B, you’re leveraging how transformer attention works: structured, sequential instructions create clearer context that guides the model’s reasoning."
Can you support that assertion in a more rigorous way than "when I do that I seem to get better results?"