#30 — o1
September 18, 2024•2 min read
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Founders, take note: OpenAI just dropped "o1" (codenamed "Strawberry"), a reasoning-focused AI model that could fundamentally change your tech stack decisions in 2024.
Why it matters
This isn't just another incremental AI update. o1 represents a paradigm shift in how machines approach complex problems—potentially offering startups the kind of reasoning capabilities previously reserved for specialized human talent.
The big picture
o1 introduces a new compute-intensive approach where the AI deliberately "thinks longer" before responding, enabling it to solve multi-step problems from complex math to coding challenges that would stump existing models.
The economics: At $15 for 1M input tokens and $60 for 1M output tokens (compared to GPT-4o's $5/$15), o1 commands premium pricing that founders will need to factor into their runway calculations.
The technology edge: Unlike traditional LLMs that follow a single thought path and compound errors, o1 explores multiple reasoning approaches simultaneously—similar to how your best engineers might tackle a complex problem.
Training innovation: OpenAI's synthetic chain-of-thought methodology represents a breakthrough in how reasoning is taught to machines, potentially opening new opportunities for specialized AI applications.
Between the lines
The "inference scaling" approach means startups can essentially pay more for better answers—a new value proposition where compute time directly correlates with solution quality.
o1-mini hints at a future where reasoning AI could run locally on devices while pulling cloud knowledge when needed—a potential game-changer for edge computing startups.
The bottom line
For founders building in AI, o1 represents both opportunity and challenge: its superior reasoning capabilities could enable entirely new products, but its higher costs demand careful consideration of unit economics.
Smart founders will start experimenting with o1 now to understand where its reasoning capabilities justify the premium pricing—and where existing models remain the more practical choice.
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