From o1 to o3: Path to AGI? Just a few months ago, we were amazed by the capabilities of OpenAI’s o1 model, a "reasoner" that brought a new level of thoughtfulness to AI performance. Now, with the showcase of o3, we’re witnessing a leap that feels nothing short of revolutionary. Here’s what makes o3 so remarkable: - 2700+ Codeforces Rating: o3 operates at a level equivalent to the top 0.2% of competitive programmers in the world—Grandmaster territory. To put this into perspective, this is akin to an IQ level of over 150 which is typically considered as "genius". - 96.7% on AIME: It absolutely crushed the AIME math benchmark, solving nearly all problems correctly. - 25% on FrontierMATH: On the PhD-level math benchmark, it scored 25%, a staggering improvement from o1’s mere 2%. These aren’t just incremental improvements—they’re quantum leaps in capability. The Secret to o-series Models Success: Scaling Test-Time Compute The o3 model proves how scaling test-time compute can dramatically boost performance without changing the underlying architecture or parameter count. This shows that the same model, given more time and resources to reason, can achieve far greater results. For example: - Tasks that required hundreds of retries with o1 can now be solved in just a few attempts by o3. - Complex problems that were almost unreachable for o1 are now solved with confidence. This isn’t just a technical upgrade; it’s a glimpse into how far we can push existing AI technologies. The Wild Cycle of Iterative Improvement Here’s where things get crazy 🤪 We might not need entirely new architectures to reach AGI. Instead, OpenAI has unlocked a potential self-improvement loop: 1. Start with a "base" model like GPT-4. 2. Develop a "reasoner" model (o1, o3) that scales test-time compute for higher quality results. 3. Use the reasoner to generate a massive, high-quality synthetic dataset, including data far more complex than what exists publicly. 4. Train a new base model (e.g., GPT-5) on this enriched dataset, making it significantly smarter than the previous one. 5. Build an even stronger reasoner model using the new base—and repeat the cycle. With enough resources, this approach could lead to AGI much sooner than anyone anticipated. Imagine: every iteration of this cycle produces an AI that’s smarter, more capable, and better at generating even more complex data for the next round. It’s a compounding effect, and there’s no obvious limit to how far it can go. What’s Next? This isn’t just about AI setting new records in competitive programming or math. The implications are staggering. We’re seeing proof that AGI might be achievable with the architectures we already have today. AI has already "solved" chess, Go, and other games once thought to be the ultimate tests of intelligence. Now, it’s poised to "solve" fields like mathematics. By leveraging iterative reasoning and scaling, AI could soon provide solutions and insights that were previously beyond human reach. Much like how DeepBlue's victory in chess marked a turning point for AI in strategy games, o3’s success could be the beginning of a new era where AI dominates abstract, technical domains. My Prediction for 2025 By 2025, I believe AI will surpass humans in an incredibly diverse range of technical fields, much like how DeepBlue surpassed humans in chess. We’ll also witness a surge of scientific discoveries made entirely by AI, pushing the boundaries of human knowledge in ways we can’t yet imagine. It’s astonishing how quickly we’ve progressed from o1 to o3. The question now is: how far can this iterative improvement cycle take us? Could this be the final sprint to AGI?