The "Bitter Lesson" is not just about using more compute, but leveraging it scalably. Current LLMs are inefficient because they only learn during a discrete training phase, not during deployment where most computation occurs. This reliance on a special, data-intensive training period is not a scalable use of computational resources.

The boom from LLMs was a 'shortcut' that mined intelligence from existing human data. This has limits. To achieve novel breakthroughs beyond that corpus, the field now re-integrates the original DeepMind philosophy of agents learning through interaction (like reinforcement learning) to generate truly new knowledge.

Computer scientist Rich Sutton's "bitter lesson" is evolving. The new frontier for AI performance isn't just more pre-training data; it's vast amounts of "experiential data" from real-world user interactions. Models post-trained on this experience data are beginning to outperform those trained only on static, human-knowledge datasets.

Rich Sutton argues that LLM-based assistants are not "experiential learners." Despite in-context learning, their fundamental weights never change after deployment. This prevents them from truly adapting, forming new concepts, or updating their core understanding based on user interactions.

Richard Sutton, author of "The Bitter Lesson," argues that today's LLMs are not truly "bitter lesson-pilled." Their reliance on finite, human-generated data introduces inherent biases and limitations, contrasting with systems that learn from scratch purely through computational scaling and environmental interaction.

The essence of Sutton's "Bitter Lesson" is a warning against the temptation to embed human knowledge into AI systems. Instead, AI progress is driven by general methods like search and learning that scale with computation, a lesson learned over decades of research.

Richard Sutton's "Bitter Lesson" suggests general compute always wins. Applied to LLMs, building complex workflows or fine-tuning yields only temporary gains that the next-generation general model will erase. Always bet on the more general model.

The rapid, step-change improvements in LLMs are likely slowing down. This is because models have already been trained on most of the available internet, and the compute budget required for each incremental improvement is increasing exponentially to an unsustainable degree. A new architectural breakthrough, not just more data and compute, is needed for the next leap.

Richard Sutton, whose "Bitter Lesson" essay was a foundational argument for scaling compute in AI, has publicly aligned with critiques from LLM skeptic Gary Marcus. This surprising shift suggests that the original simplistic interpretation of "more compute is all you need" is being re-evaluated by its own progenitor.