OpenAI just explained how o3 got so good at coding and the answer works for all of business! AI isn’t just coding anymore—it’s optimizing entire businesses. OpenAI’s latest research shows that reinforcement learning (RL) can supercharge large language models (LLMs), making them better at solving complex, multi-step problems. While it started with competitive programming, the real breakthrough is that this method works anywhere rewards are verifiable. That means AI can now learn business strategy, finance, marketing, sales, and even hiring decisions—all by continuously improving through feedback. Think about it: 🚀 Sales teams can use RL-driven AI to optimize outreach, boosting conversions. 📈 Finance teams can have AI forecast risks, detect fraud, and optimize portfolios. 🤖 Customer service? AI chatbots are learning to handle disputes and even negotiate refunds! 📦 Supply chains? AI is dynamically adjusting routes, inventory, and warehouse logistics. And the best part? RL doesn’t need human-crafted rules—it learns dynamically from past successes and failures. That means instead of companies relying on fixed strategies, AI can adapt in real time, constantly improving. This is the future of business—AI not just assisting, but making key decisions in sales, marketing, operations, and finance. If you’re still thinking of AI as just chatbots and coding assistants, you’re already behind. Is your industry ready for this shift? Let me know in the comments! 👇 #product #productmanager #productmanagement #startup #business #openai #llm #ai #microsoft #google #gemini #anthropic #claude #llama #meta #nvidia #career #careeradvice #mentor #mentorship #mentortiktok #mentortok #careertok #job #jobadvice #future #2024 #story #news #dev #coding #code #engineering #engineer #coder #sales #cs #marketing #agent #work #workflow #smart #thinking #strategy #cool #real #jobtips #hack #hacks #tip #tips #tech #techtok #techtiktok #openaidevday #aiupdates #techtrends #voiceAI #developerlife #cursor #replit #pythagora #bolt #learning #2025 #o3 #chatgpt

OpenAI released a really important paper, and I want to tell you why it matters if you're thinking about where AI is going in business. So the paper basically talked about the technique that they used to help the O3 model become one of the most effective programmers in the world. I think it's ranked in the top 50 now. It's called reinforcement learning. The technique itself is not particularly new, but the way they laid it out helps you to think about where these models are going in the next 6 to 12 to 18 months. And the thing to take away, the key insight is that reinforcement learning works to teach a model anything where you can have a correct or an incorrect answer. And you would be surprised how many things in business fall into that category that would allow a model to do useful work eventually. Let me give you a few examples. Marketing campaign optimization. You can do that correctly. You can do that incorrectly. Customer service and AI agents. You can either answer the question and satisfy the customer or not. Legal contracts. You either write the legal word incorrectly or you write it incorrectly. Even if you look at stuff that you might potentially think was more of a continuum than binary, I think you can still get reasonable reinforcement learning results. Lead scoring. Yes, lead scoring is not either on or off. Sophisticated lead scoring has a sort of continual sort of scoring matrix, but the lead is either scored correctly or not. And you can discern that when the deal closes. Financial forecasting and risk management. You either forecast the risk at the correct probability or you do not. Business strategy and decision making, which you might think is too hard to do. I would argue that there's some things that you can actually call out as usefully model learnable. So investment portfolios fall into that strategic piece. You can have an incorrect investment portfolio given a particular macro environment. That is learnable. So essentially, reinforcement learning helps us see where models are going to do useful work in the next 6, 12, 18 months. And so I think it's a really significant paper. I'll link it in the video. Cheers.