[Stevvo]

"With o3 now public knowledge, imagine how long it’s been churning out new thinking at expert level across every field."

I highly doubt that. o3 is many orders of magnitude more expensive than paying subject matter experts to create new data. It just doesn't make sense to pay six figures in compute to get o3 to make data a human could make for a few hundred dollars.

[bookaway]

Yes, I think they had to push this reveal forward because their investors were getting antsy with the lack of visible progress to justify continuing rising valuations. There is no other reason a confident company making continuous rapid progress would feel the need to reveal a product that 99% of companies worldwide couldn't use at the time of the reveal.

That being said, if OpenAI is burning cash at lightspeed and doesn't have to publicly reveal the revenue they receive from certain government entities, it wouldn't come as a surprise if they let the government play with it early on in exchange for some much needed cash to set on fire.

EDIT: The fact that multiple sites seem to be publishing GPT-5 stories similar to this one leads one to conclude that the o3 benchmark story was meant to counter the negativity from this and other similar articles that are just coming out.

[mrshadowgoose]

Can SMEs deliver that data in a meaningful amount of time? Training data now is worth significantly more than data a year from now.

[GolfPopper]

>churning out new thinking at expert level across every field

I suspect this is really, "churning out text that impresses management".

[tshadley]

Seems to me o3 prices would be what the consumer pays, not what OpenAI pays. That would mean o3 could be more efficient in-house than paying subject-matter experts.

[mrbungie]

For every consumer there will be a period where they need both the SME and the o3 model for initial calibration and eventual handoff for actually getting those efficiencies in whichever processes they want to automate.

In other words if you are diligent enough, you should at least validate your o3 solution with an actual expert for some time. You wouldn't just blindly trust OpenAI your business critical processes, would you? I would expect at least 3 month - 6 months for large corps and even more considering change management, re-upskilling, etc.

With all those considerations I really don't see the value prop at those prices and in those situations right now. Maybe if costs decrease ~1-3 orders of magnitude more for o3-low, depending on the the processes being automated.

[lalalali]

What is open ai margin on that product?

[dartos]

That’s an interesting idea. What if OpenAI funded medical research initiatives in exchange for exclusive training rights on the research.

[onlyrealcuzzo]

It would be orders of magnitude cheaper to outsource to humans.

[dartos]

Not as sexy to investors though

[aswegs8]

Wait didn't they just recently request researchers to pair up with them in exchange for the data?

[DougN7]

Someone needs to dress up Mechanical Turk and repackage it as an AI company…..

[jitl]

That’s basically every AI company that existed before GPT3

[rtsil]

Unless the quality of the human data are extraordinary, it seems according to the TFA that it's not that easy:

> The process is painfully slow. GPT-4 was trained on an estimated 13 trillion tokens. A thousand people writing 5,000 words a day would take months to produce a billion tokens.

And if the human-generated data was so qualitatively good that it is smaller by three order of magnitudes, than I can assume it would be at least as expensive as o3.

[az226]

Only a matter of time. The costs are aggressively going down. And with specialized inference hardware it will go further down.

Cost of coordination is also large. Immediate answers are an advantage/selling point.