Summer is always a slow time for the tech industry. OpenAI seems fully in line with this, with their open model “[taking] a little more time” and GPT-5 seemingly always delayed a bit more. These will obviously be major news items, but I’m not sure we see them until August. I’m going to take this brief reprieve in the bombardment of AI releases to reflect on where we’ve been and where we’re going. Here’s what you should know.

1. o3 as a technical breakthrough beyond scaling
   The default story around OpenAI’s o3 model is that they “scaled compute for reinforcement learning training,” which caused some weird, entirely new over-optimization issues. This is true, and the plot from the livestream of the release still represents a certain type of breakthrough — namely scaling up data and training infrastructure for reinforcement learning with verifiable rewards (RLVR). The part of o3 that isn’t talked about enough is how different its search feels. For a normal query, o3 can look at 10s of websites. The best description I’ve heard of its relentlessness en route to finding a niche piece of information is akin to a “trained hunting dog on the scent.” o3 just feels like a model that can find information in a totally different way than anything out there.

Full analysis : A look at 2025’s AI models and what’s next: OpenAI’s o3 is a technical breakthrough, agents will improve randomly and in leaps, but scaling parameters will slow.