The Bitter Lesson

The Bitter Lesson

Every AI builder knows the anxiety: you spend months engineering prompts, tuning pipelines, and chaining calls together — then a new model drops and half your work evaporates overnight. It turns out researchers have been wrestling with this exact dynamic for 30 years, and they keep arriving at the same uncomfortable answer. That answer is called the Bitter Lesson — and understanding it might be the most important thing you can do for whatever you're building right now. From Deep Blue to AlexNet to modern LLMs, scale keeps beating sophistication, and knowing which side of that line your work falls on makes all the difference. Links - Richard Sutton, "The Bitter Lesson" - Alon Halevy, Peter Norvig, and Fernando Pereira, "The Unreasonable Effectiveness of Data" - Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton, "ImageNet Classification with Deep Convolutional Neural Networks"

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Episoder(324)

Better Know A Benchmark: ExploitGym

Better Know A Benchmark: ExploitGym

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A Data-Driven Reality Check on AI in Business (Interview with Tom Davenport, Babson College)

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A Scientific Deep Dive into Overconfident LLMs: Interview with Kaitlyn Zhou (Cornell)

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10 Aug 33min

Reasoning Models: When LLMs Went Beyond Fancy Autocomplete

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