March 14th, 2023 was the day ThursdAI was born, it was also the day OpenAI released GPT-4, and I jumped into a Twitter space and started chaotically reacting together with other folks about what a new release of a paradigm shifting model from OpenAI means, what are the details, the new capabilities. Today, it happened again! Hey, it's Alex, I'm back from my mini vacation (pic after the signature) and boy am I glad I decided to not miss September 12th! The long rumored 🍓 thinking model from OpenAI, dropped as breaking news in the middle of ThursdAI live show, giving us plenty of time to react live! But before this, we already had an amazing show with some great guests! Devendra Chaplot from Mistral came on and talked about their newly torrented (yeah they did that again) Pixtral VLM, their first multi modal! , and then I had the honor to host Steven Johnson and Raiza Martin from NotebookLM team at Google Labs which shipped something so uncannily good, that I legit said "holy fu*k" on X in a reaction! So let's get into it (TL;DR and links will be at the end of this newsletter) OpenAI o1, o1 preview and o1-mini, a series of new "reasoning" models This is it folks, the strawberries have bloomed, and we finally get to taste them. OpenAI has released (without a waitlist, 100% rollout!) o1-preview and o1-mini models to chatGPT and API (tho only for tier-5 customers) 👏 and are working on releasing 01 as well. These are models that think before they speak, and have been trained to imitate "system 2" thinking, and integrate chain-of-thought reasoning internally, using Reinforcement Learning and special thinking tokens, which allows them to actually review what they are about to say before they are saying it, achieving remarkable results on logic based questions. Specifically you can see the jumps in the very very hard things like competition math and competition code, because those usually require a lot of reasoning, which is what these models were trained to do well. New scaling paradigm Noam Brown from OpenAI calls this a "new scaling paradigm" and Dr Jim Fan explains why, with this new way of "reasoning", the longer the model thinks - the better it does on reasoning tasks, they call this "test-time compute" or "inference-time compute" as opposed to compute that was used to train the model. This shifting of computation down to inference time is the essence of the paradigm shift, as in, pre-training can be very limiting computationally as the models scale in size of parameters, they can only go so big until you have to start building out a huge new supercluster of GPUs to host the next training run (Remember Elon's Colossus from last week?). The interesting thing to consider here is, while current "thinking" times are ranging between a few seconds to a minute, imagine giving this model hours, days, weeks to think about new drug problems, physics problems 🤯. Prompting o1 Interestingly, a new prompting paradigm has also been introduced. These models now have CoT (think "step by step") built-in, so you no longer have to include it in your prompts. By simply switching to o1-mini, most users will see better results right off the bat. OpenAI has worked with the Devin team to test drive these models, and these folks found that asking the new models to just give the final answer often works better and avoids redundancy in instructions. The community of course will learn what works and doesn't in the next few hours, days, weeks, which is why we got 01-preview and not the actual (much better) o1. Safety implications and future plans According to Greg Brokman, this inference time compute also greatly helps with aligning the model to policies, giving it time to think about policies at length, and improving security and jailbreak preventions, not only logic. The folks at OpenAI are so proud of all of the above that they have decided to restart the count and call this series o1, but they did mention that they are going