OpenAI declares 'code red' as Google catches up in AI race

>I've never seen an organization take the actual proper steps (fire mid-management and PMs) to prevent the same thing from happening again.

One time, in my entire career have I seen this done, and it is as successful as you imagine it to be. Lots of weird problems coming out from having done it, but those are being treated as "Wow we are so glad we know about this problem" rather than "I hope those idiots come back to keep pulling the wool over my eyes".

Anecdotally yes. Since launch I’ve observed probably 50% of the folks that were “ChatGPT those that” all the time suddenly talking about Gemini non-stop. The more that gets rolled into Google’s platform the more there’s point to using separate tooling from OpenAI. There’s a reason Sam is calling this “code red.”

My go-to example is The Truman Show [0], where the victi--er, customer is under an invisible and omnipresent influence towards a certain set of beliefs and spending habits.

[0] https://www.youtube.com/watch?v=MzKSQrhX7BM

> Google will do just what Microsoft did with Internet Explorer and bundle Gemini in for 'Free' with their already other profitable products and established ad-funded revenue streams.

Just some numbers to show what OpenAI is against:

    GMail users: nearing 2 billion
    Youtube MAU: 2.5 billion
    active Android devices: 4 billion (!)
    Market cap: 3.8 trillion (at a P/E of 31)

So on one side you've got this behemoth with, compared to OpenAI's size, unlimited funding. The $25 bn per year OpenAI is after is basically a parking ticket for Google (only slightly exaggerating). Behemoth who came with Gemini 3 Pro "thinking" and Nano Banana (that name though) who are SOTA.

And on the other side you've got the open-source weights you mentioned.

When OpenAI had its big moment HN was full of comments about how it was game over for Google for search was done for. Three years later and the best (arguably the best) model gives the best answer when you search... Using Google search.

Funny how these things turns out.

Google is atm the 3rd biggest cap in the world: only Apple and NVidia are slightly ahead. If Google is serious about its AI chips (and it looks like they are) and see the fuck-ups over fuck-ups by Apple, I wouldn't be surprised at all if Alphabet was to regain the number one spot.

That's the company OpenAI is fighting: a company that's already been the biggest cap in the entire world and that's probably going to regain that spot rather sooner than later and that happens to have crushed every single AI benchmark when Gemini 3 Pro came out.

I had a ChatGPT subscription. Now I'm using Gemini 3 Pro.

Pre-training is just training, it got the name because most models have a post-training stage so to differentiate people call it pre-training.

Pre-training: You train on a vast amount of data, as varied and high quality as possible, this will determine the distribution the model can operate with, so LLMs are usually trained on a curated dataset of the whole internet, the output of the pre-training is usually called the base model.

Post-training: You narrow down the task by training on the specific model needs you want. You can do this through several ways:

- Supervised Finetuning (SFT): Training on a strict high quality dataset of the task you want. For example if you wanted a summarization model, you'd finetune the model on high quality text->summary pairs and the model would be able to summarize much better than the base model.

- Reinforcement Learning (RL): You train a separate model that ranks outputs, then use it to rate the output of the model, then use that data to train the model.

- Direct Preference Optimizaton (DPO): You have pairs of good/bad generations and use them to align the model towards/away the kinds of responses you want.

Post-training is what makes the models able to be easily used, the most common is instruction tuning that teaches to model to talk in turns, but post-training can be used for anything. E.g. if you want a translation model that always translates a certain way, or a model that knows how to use tools, etc. you'd achieve all that through post-training. Post-training is where most of the secret sauce in current models is nowadays.