Reuven Cohen’s Post

🤔 How do reasoning models like OpenAi o3 actually work and why are they so expensive? The biggest AI news this week is OpenAI’s O3 model smashing ARC AGI benchmarks. But what makes this model so remarkable? At its core, o3 isn’t just a larger or faster language model—it’s reflective. It reasons. It thinks the way we do when solving problems, especially in complex tasks like coding. If you’ve ever tackled a coding issue with ChatGPT, you know the process: draft a spec, implement (copy and paste) it, test it, debug it, and repeat—sometimes dozens or even hundreds of times until it works. Each iteration (hop) refines the solution, building on what you’ve learned, adding anything missing or fixing things that doesn’t work. That’s exactly what o3 does. It embodies this multi-step, iterative approach natively, rather than relying on us to guide it with repeated prompts or external logic. But this capability comes at a cost. Solving the ARC benchmarks required billions of tokens (a token is word or space) and over a million dollars in compute. Why? Because the model doesn’t cut corners. It exhaustively reasons through problems, internally iterating over potential solutions just as we would, but at a scale that’s orders of magnitude greater. As an example, the autopilot bots I’ve been building for coding operate on a “set it and forget it” model—perfect for running overnight. They generally cost around $100-$200 to produce 30,000 to 40,000 lines of functional code. Completing this process takes several hours and around 3-4 million tokens (the equivalent of 1 million lines of code) before reaching a successful result. They build pretty much anything, the most important part is a decent specification and testing framework to guide it. What o3 really achieves is a baked-in multi-hop reasoning process—what we might call a “private chain of thought.” Instead of multiple independent requests (human or api) to refine a task, the model reasons through the full solution internally, reducing the need for external guidance but demanding massive compute power to pull it off. The o3 model excels by combining deductive, inductive, and abductive reasoning in a unified framework. Deductive reasoning allows it to apply general principles to specific problems, ensuring precision. Inductive reasoning enables it to learn patterns from data, forming broader generalizations. Abductive reasoning fills in gaps, offering plausible explanations when information is incomplete. Combined, these approaches mimic human problem-solving, allowing the model to iterate, adapt, and reason through complex tasks effectively. The result? A model that mirrors human problem-solving but reminds us that reasoning—while transformative—still comes with a hefty price tag.

🤔 How do reasoning models like OpenAi o3 actually work and why are they so expensive? The biggest AI news this week is OpenAI’s O3 model smashing ARC AGI benchmarks. But what makes this model so remarkable? At its core, o3 isn’t just a larger or faster language model—it’s reflective. It reasons. It thinks the way we do when solving problems, especially in complex tasks like coding. If you’ve ever tackled a coding issue with ChatGPT, you know the process: draft a spec, implement (copy and paste) it, test it, debug it, and repeat—sometimes dozens or even hundreds of times until it works. Each iteration (hop) refines the solution, building on what you’ve learned, adding anything missing or fixing things that doesn’t work. That’s exactly what o3 does. It embodies this multi-step, iterative approach natively, rather than relying on us to guide it with repeated prompts or external logic. But this capability comes at a cost. Solving the ARC benchmarks required billions of tokens (a token is word or space) and over a million dollars in compute. Why? Because the model doesn’t cut corners. It exhaustively reasons through problems, internally iterating over potential solutions just as we would, but at a scale that’s orders of magnitude greater. As an example, the autopilot bots I’ve been building for coding operate on a “set it and forget it” model—perfect for running overnight. They generally cost around $100-$200 to produce 30,000 to 40,000 lines of functional code. Completing this process takes several hours and around 3-4 million tokens (the equivalent of 1 million lines of code) before reaching a successful result. They build pretty much anything, the most important part is a decent specification and testing framework to guide it. What o3 really achieves is a baked-in multi-hop reasoning process—what we might call a “private chain of thought.” Instead of multiple independent requests (human or api) to refine a task, the model reasons through the full solution internally, reducing the need for external guidance but demanding massive compute power to pull it off. The o3 model excels by combining deductive, inductive, and abductive reasoning in a unified framework. Deductive reasoning allows it to apply general principles to specific problems, ensuring precision. Inductive reasoning enables it to learn patterns from data, forming broader generalizations. Abductive reasoning fills in gaps, offering plausible explanations when information is incomplete. Combined, these approaches mimic human problem-solving, allowing the model to iterate, adapt, and reason through complex tasks effectively. The result? A model that mirrors human problem-solving but reminds us that reasoning—while transformative—still comes with a hefty price tag.

🤔 How do reasoning models like OpenAi o3 actually work and why are they so expensive? The biggest AI news this week is OpenAI’s O3 model smashing ARC AGI benchmarks. But what makes this model so remarkable? At its core, o3 isn’t just a larger or faster language model—it’s reflective. It reasons. It thinks the way we do when solving problems, especially in complex tasks like coding. If you’ve ever tackled a coding issue with ChatGPT, you know the process: draft a spec, implement (copy and paste) it, test it, debug it, and repeat—sometimes dozens or even hundreds of times until it works. Each iteration (hop) refines the solution, building on what you’ve learned, adding anything missing or fixing things that doesn’t work. That’s exactly what o3 does. It embodies this multi-step, iterative approach natively, rather than relying on us to guide it with repeated prompts or external logic. But this capability comes at a cost. Solving the ARC benchmarks required billions of tokens (a token is word or space) and over a million dollars in compute. Why? Because the model doesn’t cut corners. It exhaustively reasons through problems, internally iterating over potential solutions just as we would, but at a scale that’s orders of magnitude greater. As an example, the autopilot bots I’ve been building for coding operate on a “set it and forget it” model—perfect for running overnight. They generally cost around $100-$200 to produce 30,000 to 40,000 lines of functional code. Completing this process takes several hours and around 3-4 million tokens (the equivalent of 1 million lines of code) before reaching a successful result. They build pretty much anything, the most important part is a decent specification and testing framework to guide it. What o3 really achieves is a baked-in multi-hop reasoning process—what we might call a “private chain of thought.” Instead of multiple independent requests (human or api) to refine a task, the model reasons through the full solution internally, reducing the need for external guidance but demanding massive compute power to pull it off. The o3 model excels by combining deductive, inductive, and abductive reasoning in a unified framework. Deductive reasoning allows it to apply general principles to specific problems, ensuring precision. Inductive reasoning enables it to learn patterns from data, forming broader generalizations. Abductive reasoning fills in gaps, offering plausible explanations when information is incomplete. Combined, these approaches mimic human problem-solving, allowing the model to iterate, adapt, and reason through complex tasks effectively. The result? A model that mirrors human problem-solving but reminds us that reasoning—while transformative—still comes with a hefty price tag.

🤔 How do reasoning models like OpenAi o3 actually work and why are they so expensive? The biggest AI news this week is OpenAI’s O3 model smashing ARC AGI benchmarks. But what makes this model so remarkable? At its core, o3 isn’t just a larger or faster language model—it’s reflective. It reasons. It thinks the way we do when solving problems, especially in complex tasks like coding. If you’ve ever tackled a coding issue with ChatGPT, you know the process: draft a spec, implement (copy and paste) it, test it, debug it, and repeat—sometimes dozens or even hundreds of times until it works. Each iteration (hop) refines the solution, building on what you’ve learned, adding anything missing or fixing things that doesn’t work. That’s exactly what o3 does. It embodies this multi-step, iterative approach natively, rather than relying on us to guide it with repeated prompts or external logic. But this capability comes at a cost. Solving the ARC benchmarks required billions of tokens (a token is word or space) and over a million dollars in compute. Why? Because the model doesn’t cut corners. It exhaustively reasons through problems, internally iterating over potential solutions just as we would, but at a scale that’s orders of magnitude greater. As an example, the autopilot bots I’ve been building for coding operate on a “set it and forget it” model—perfect for running overnight. They generally cost around $100-$200 to produce 30,000 to 40,000 lines of functional code. Completing this process takes several hours and around 3-4 million tokens (the equivalent of 1 million lines of code) before reaching a successful result. They build pretty much anything, the most important part is a decent specification and testing framework to guide it. What o3 really achieves is a baked-in multi-hop reasoning process—what we might call a “private chain of thought.” Instead of multiple independent requests (human or api) to refine a task, the model reasons through the full solution internally, reducing the need for external guidance but demanding massive compute power to pull it off. The o3 model excels by combining deductive, inductive, and abductive reasoning in a unified framework. Deductive reasoning allows it to apply general principles to specific problems, ensuring precision. Inductive reasoning enables it to learn patterns from data, forming broader generalizations. Abductive reasoning fills in gaps, offering plausible explanations when information is incomplete. Combined, these approaches mimic human problem-solving, allowing the model to iterate, adapt, and reason through complex tasks effectively. The result? A model that mirrors human problem-solving but reminds us that reasoning—while transformative—still comes with a hefty price tag.

🤔 How do reasoning models like OpenAi o3 actually work and why are they so expensive? The biggest AI news this week is OpenAI’s O3 model smashing ARC AGI benchmarks. But what makes this model so remarkable? At its core, o3 isn’t just a larger or faster language model—it’s reflective. It reasons. It thinks the way we do when solving problems, especially in complex tasks like coding. If you’ve ever tackled a coding issue with ChatGPT, you know the process: draft a spec, implement (copy and paste) it, test it, debug it, and repeat—sometimes dozens or even hundreds of times until it works. Each iteration (hop) refines the solution, building on what you’ve learned, adding anything missing or fixing things that doesn’t work. That’s exactly what o3 does. It embodies this multi-step, iterative approach natively, rather than relying on us to guide it with repeated prompts or external logic. But this capability comes at a cost. Solving the ARC benchmarks required billions of tokens (a token is word or space) and over a million dollars in compute. Why? Because the model doesn’t cut corners. It exhaustively reasons through problems, internally iterating over potential solutions just as we would, but at a scale that’s orders of magnitude greater. As an example, the autopilot bots I’ve been building for coding operate on a “set it and forget it” model—perfect for running overnight. They generally cost around $100-$200 to produce 30,000 to 40,000 lines of functional code. Completing this process takes several hours and around 3-4 million tokens (the equivalent of 1 million lines of code) before reaching a successful result. They build pretty much anything, the most important part is a decent specification and testing framework to guide it. What o3 really achieves is a baked-in multi-hop reasoning process—what we might call a “private chain of thought.” Instead of multiple independent requests (human or api) to refine a task, the model reasons through the full solution internally, reducing the need for external guidance but demanding massive compute power to pull it off. The o3 model excels by combining deductive, inductive, and abductive reasoning in a unified framework. Deductive reasoning allows it to apply general principles to specific problems, ensuring precision. Inductive reasoning enables it to learn patterns from data, forming broader generalizations. Abductive reasoning fills in gaps, offering plausible explanations when information is incomplete. Combined, these approaches mimic human problem-solving, allowing the model to iterate, adapt, and reason through complex tasks effectively. The result? A model that mirrors human problem-solving but reminds us that reasoning—while transformative—still comes with a hefty price tag.

🤔 How do reasoning models like OpenAi o3 actually work and why are they so expensive? The biggest AI news this week is OpenAI’s O3 model smashing ARC AGI benchmarks. But what makes this model so remarkable? At its core, o3 isn’t just a larger or faster language model—it’s reflective. It reasons. It thinks the way we do when solving problems, especially in complex tasks like coding. If you’ve ever tackled a coding issue with ChatGPT, you know the process: draft a spec, implement (copy and paste) it, test it, debug it, and repeat—sometimes dozens or even hundreds of times until it works. Each iteration (hop) refines the solution, building on what you’ve learned, adding anything missing or fixing things that doesn’t work. That’s exactly what o3 does. It embodies this multi-step, iterative approach natively, rather than relying on us to guide it with repeated prompts or external logic. But this capability comes at a cost. Solving the ARC benchmarks required billions of tokens (a token is word or space) and over a million dollars in compute. Why? Because the model doesn’t cut corners. It exhaustively reasons through problems, internally iterating over potential solutions just as we would, but at a scale that’s orders of magnitude greater. As an example, the autopilot bots I’ve been building for coding operate on a “set it and forget it” model—perfect for running overnight. They generally cost around $100-$200 to produce 30,000 to 40,000 lines of functional code. Completing this process takes several hours and around 3-4 million tokens (the equivalent of 1 million lines of code) before reaching a successful result. They build pretty much anything, the most important part is a decent specification and testing framework to guide it. What o3 really achieves is a baked-in multi-hop reasoning process—what we might call a “private chain of thought.” Instead of multiple independent requests (human or api) to refine a task, the model reasons through the full solution internally, reducing the need for external guidance but demanding massive compute power to pull it off. The o3 model excels by combining deductive, inductive, and abductive reasoning in a unified framework. Deductive reasoning allows it to apply general principles to specific problems, ensuring precision. Inductive reasoning enables it to learn patterns from data, forming broader generalizations. Abductive reasoning fills in gaps, offering plausible explanations when information is incomplete. Combined, these approaches mimic human problem-solving, allowing the model to iterate, adapt, and reason through complex tasks effectively. The result? A model that mirrors human problem-solving but reminds us that reasoning—while transformative—still comes with a hefty price tag.