Praful Konduru

Argentina just beat Spain at the 2026 World Cup final, 3-2.

Sol, Terra, and Luna, our GPT‑5.6 family of models, are starting to roll out now in ChatGPT, Codex, and the API.

Today, we’re launching our home robot Isaac 1. Isaac 1 deliveries will begin this fall. Order yours below.

Bridgewater and Thinking Machines just published a blog on training a custom model to replicate expert investor judgment. The task is filtering financial documents and news for relevance. Sounds trivial. Turns out it's not.

$META is reportedly developing a cloud business to sell access to excess AI compute, per Bloomberg. The internal initiative is called Meta Compute. The plans being considered: AI model access hosted on Meta infrastructure, similar to AWS Bedrock Raw AI compute capacity, closer to CoreWeave Developer access to Meta’s data centers, chips, and models

Questions about dollars. Answers that just make sense. Personal finance in ChatGPT is now available to Plus users in the U.S.

BREAKING: The Nasdaq 100 closes +2.3% higher after President Trump says the US and Iran have agreed to halt strikes and resume talks.

I finally managed to run GLM-5.2 fully locally by inter-connecting 256 16GB Mac Minis

JUST IN: Marc Andreessen declares “Doctor ChatGPT" is already a better doctor than 99% of doctors.

Sol is our new flagship and a step function better than GPT-5.5. Terra delivers performance competitive to GPT-5.5 at 2x lower cost. Luna is our most cost-efficient model, delivering strong capability at our lowest cost. Together, the GPT-5.6 family gives people and developers more choice in how they balance intelligence, speed, and cost.

GPT-5.6 is incredibly strong and fast for coding. I hope we can make it available to everyone soon.

Here's a question I find confusing and interesting and which actually tells us a lot about the nature of current AI progress: Why has progress on computer use been so slow? Computer use is so clearly verifiable. I think the answer is that it is not enough for a domain to be verifiable. It also has to be very grindable—in the sense that you can run lots of parallel rollouts against a deterministic and replayable simulator. If you’re trying to make a model better at coding, you can create an environment that has a software repo with some missing feature that you’ve tasked the AIs with creating, and then you have a thousand parallel agents just go at the problem, each with their identical copy of the container. But this doesn’t work with computer use—at least not trivially. You can’t have a thousand agents go try the same checkout flow on Amazon. Because Andy Jassy will find and detect your bots and shut your ass down. How would we train an AI to build a business? How would you make an AI that’s really good at winning court cases? Or having a profitable day trading in the markets? Or helping a candidate win an election? What is the RL environment to make an AI as good at politics as Lyndon Johnson, or as good at building a space launch business as Elon Musk? The rollout requires interacting with the world and cannot be recreated simply within the datacenter. And the outer loop verification may take months or years of real world actions to elicit, and cannot be re-observed by perturbing the model’s actions thousands of times in parallel so that you can isolate what exactly the model did that actually worked.