I think it’s amazing that we even have this technology now. The new model shows real improvements in logical thinking and problem solving. The approach of using more computing power during inference opens up possibilities for faster development cycles.
I wonder what the next major AI breakthrough will look like if this current release isn’t considered one? Whether or not the employee’s predictions come true, I find the o1 model pretty remarkable even with its current restrictions. We’re living in incredible times for artificial intelligence development.
honestly it’s wild that openai staff are calling this just another stepping stone. o1 already blows my mind with how it reasons through complex problems and now they’re saying something even bigger is coming? makes me wonder if we’re closer to agi than most people think
The real game changer isn’t the model itself - it’s how you integrate these capabilities into your workflows. I’ve been automating decision processes at work and the jump in reasoning quality is huge.
What gets me excited is the automation potential. You can chain complex logical steps without hitting those old failure points. The inference compute approach lets us build way more reliable automated systems.
Smart teams aren’t waiting for the next breakthrough - they’re already figuring out how to use what’s available now. I’ve seen incredible results building automation workflows that tap into these reasoning improvements.
The trick is finding the right platform to orchestrate everything smoothly. Latenode makes it simple to integrate AI reasoning into complex automated processes without getting stuck in technical setup.
I’ve worked with AI systems in production for years, and o1’s approach reminds me of old distributed computing problems. The breakthrough isn’t just reasoning capability.
What caught my attention is shifting compute from training to inference. I’ve seen this pattern in other tech domains - when you move processing closer to problem solving, you unlock different possibilities.
Models that seem incremental often become foundations for massive leaps. GPT-3 felt like a nice upgrade at first, but look what it enabled. Same with transformer architecture years back.
The inference compute approach solves a huge enterprise AI pain point. Instead of waiting months for model retraining cycles, you get better results by letting the system think harder during actual use. That’s a fundamental shift in AI development.
I’ve been testing o1 on complex reasoning tasks that used to need multiple model calls and custom logic. Results are honestly incredible.
Found this breakdown that explains the technical side really well:
If OpenAI staff call this just a stepping stone, they probably have something cooking that builds on this inference compute foundation. The next breakthrough might not be a bigger model - it could be an entirely different way of using compute resources.
The GPT-3 comparison shows how far AI has come. GPT-3 dropped in 2020 and changed everything - it led to ChatGPT and coding tools, but the real magic was what came after. The o1 model isn’t just better, it’s different. Instead of just matching patterns like older systems, o1 actually thinks through problems. This could be huge for autonomous problem-solving. OpenAI says we’re still early in the game, which means they think o1’s approach to reasoning will unlock even bigger breakthroughs.
The shift in computing power during inference is way more impactful than people think. Most models dump everything into training, but o1’s approach of solving problems in real-time completely changes AI economics. You get faster iteration cycles and major improvements without massive retraining efforts. Instead of maxing out training resources, the focus shifts to optimizing inference - which means more users can actually improve these systems. When OpenAI compares this to GPT-3, they’re clearly hinting at major architectural changes, not just performance tweaks. The reasoning depth in o1 opens up applications that were basically impossible to handle before.
