[pookieinc]

Can't wait to try this. What's amazing to me is that when this was revealed just one short month ago, the AI landscape looked very different than it does today with more AI companies jumping into the fray with very compelling models. I wonder how the AI shift has affected this release internally, future releases and their mindset moving forward... How does the efficiency change, the scope of their models, etc.

[patrickhogan1]

I thought it was o3 that was released one month ago and received high scores on ARC Prize - https://arcprize.org/blog/oai-o3-pub-breakthrough

If they were the same, I would have expected explicit references to o3 in the system card and how o3-mini is distilled or built from o3 - https://cdn.openai.com/o3-mini-system-card.pdf - but there are no references.

Excited at the pace all the same. Excited to dig in. The model naming all around is so confusing. Very difficult to tell what breakthrough innovations occurred.

[nycdatasci]

Yeah - the naming is confusing. We're seeing o3-mini. o3 yields marginally better performance given exponentially more compute. Unlike OpenAI, customers will not have an option to throw an endless amount of money at specific tasks/prompts.

[echelon]

There's no moat, and they have to work even harder.

Competition is good.

[lesuorac]

I really don't think this is true. OpenAI has no moat because they have nothing unique; they're using mostly other people's (like Transformers) architectures and other companies hardware.

Their value-prop (moat) is that they've burnt more money than everybody else. That moat is trivially circumvented by lighting a larger pile of money and less trivially by lighting the pile more efficently.

OpenAI isn't the only company. The Tech companies being beaten massively by Microsoft in #of H100s purchases are the ones with a moat. Google / Amazon with their custom AI chips are going to have a better performance per cost than others and that will be a moat. If you want to get the same performance per cost then you need to spend the time making your own chips which is years of effort (=moat).

[sumedh]

> That moat is trivially circumvented by lighting a larger pile of money and less trivially by lighting the pile more efficently.

Google with all its money and smart engineers was not able to build a simple chat application.

[mianos]

But with their internal progression structure they can build and cancel eight mediocre chat apps.

[malaya_zemlya]

What do you mean? Gemini app is available on IOS, Android and on the web (as AI Studio https://aistudio.google.com/).

[robrenaud]

It's a joke about how Google has released/cancelled/renamed many messenging apps.

[tmnvdb]

It is not very good though.

[aprilthird2021]

Gemini is pretty good, And it does one thing way better than most other AI models, when I hold down my phone's home button it's available right away

[lukan]

"OpenAI has no moat because they have nothing unique"

It seems they have high quality trainingsdata. And the knowledge to work with it.

[aprilthird2021]

They buy most of their data from Scale AI types. It's not any higher quality than is available to any other model farm

[sangnoir]

> That moat is trivially circumvented by lighting a larger pile of money and less trivially by lighting the pile more efficently.

DeepSeek has proven that the latter is possible, which drops a couple of River crossing rocks into the moat.

[withinboredom]

The fact that I can basically run o1-mini with deepseek:8b, locally, is amazing. Even on battery power, it works acceptably.

[tmnvdb]

Those models are not comparable

[withinboredom]

hmmm... check the deepseek-r1 repo readme :) They compare them there, but it would be nice to have external benchmarks.

[sublimefire]

When you want to use AI in business you need some guarantees that the integration will not break because the ai company goes down or because of some breaking changes in a year. There is a reason why MSFT is in business. Similarly you will not buy Google because they do not like keeping products forever, you will not buy some unknown product just because it is 5% cheaper. OpenAI has a strong brand at the moment and this is their thing, until companies go to MSFT or AMZ to use their services with the ability to choose any model.

[brookst]

Brand is a moat

[cruffle_duffle]

Ask Jeeves and Altavista surely have something to say about that!

[geerlingguy]

Add Yahoo! to that list

[esafak]

Their brand is as tainted as Meta's, which was bad enough to merit a rebranding from Facebook.

[petesergeant]

> OpenAI has no moat

... is definitely something I've said before, and recently, but:

> That moat is trivially circumvented by lighting a larger pile of money

If that was true, someone would have done it.

[lumost]

Capex was the theoretical moat, same as TSMC and similar businesses. DeepSeek poked a hole in this theory. OpenAI will need to deliver massive improvements to justify a 1 billion dollar training cost relative to 5 million dollars.

[usef-]

I don't know if you are, but a lot of people are still comparing one Deepseek training run to the entire costs of OpenAI.

The deepseek paper states that the $5mil number doesn't include development costs, only the final training run. And it doesn't include the estimated $1.4billion cost of the infrastructure/chips Deepseek owns.

Most of OpenAI's billion dollar costs is in inference, not training. It takes a lot of compute to serve so many users.

Dario said recently that Claude was in the tens of millions (and that it was a year earlier, so some cost decline is expected), do we have some reason to think OpenAI was so vastly different?

[lumost]

Anthropic’s ceo was predicting billion dollar training runs for 2025. Current training runs were likely in the tens/hundreds of millions of dollars USD.

Inference capex costs are not a defensive moat as I can rent gpus and sell inference with linear scaling costs. A hypothetical 10 billion dollar training run on proprietary data was a massive moat.

https://www.itpro.com/technology/artificial-intelligence/dol...

[dutchbookmaker]

It is still curious though as far as what is actually being automated?

I find huge value in these models as an augmentation of my intelligence and as a kind of cybernetic partner.

I can't think of anything that can actually be automated though in terms of white collar jobs.

The white collar model test case I have in mind is a bank analyst under a bank operations manger. I have done both in the past but there is something really lacking with the idea of the operations manager replacing the analyst with a reasoning model even though DeepSeek annihilates every bank analyst reasoning I ever worked with right now.

If you can't even arbitrage the average bank analyst there might be these really non-intuitive no AI arbitrage conditions with white color work.

[gdhkgdhkvff]

I don’t want to pretend I know how bank analysts work, but at the very least I would assume that 4 bank analysts with reasoning models would outperform 5 bank analysts without.

[wahnfrieden]

Collaboration is even better, per open source results.

It is the closed competition model that’s being left in the dust.