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 bottleneck for AI is not raw intelligence but understanding new context. This requires models that continuously learn from new data and interactions, moving beyond the static pre-train/fine-tune paradigm and deeply baking new information into the model weights.

General LLMs are optimized for short, stateless interactions. For complex, multi-step learning, they quickly lose context and deviate from the user's original goal. A true learning platform must provide persistent "scaffolding" that always brings the user back to their objective, which LLMs lack.

The original playbook of simply scaling parameters and data is now obsolete. Top AI labs have pivoted to heavily designed post-training pipelines, retrieval, tool use, and agent training, acknowledging that raw scaling is insufficient to solve real-world problems.

The current limitation of LLMs is their stateless nature; they reset with each new chat. The next major advancement will be models that can learn from interactions and accumulate skills over time, evolving from a static tool into a continuously improving digital colleague.

Instead of viewing LLM development as discrete layers (pre-training, SFT, RL), it's more accurate to see it as a "marble cake" where these processes are swirled together. This explains why complex behaviors like introspection emerge even in models without sophisticated "character training," suggesting they are more fundamental.

Google is disaggregating its model creation process to insert a "mid-training" stage. This allows models to become specialized for functions like coding before the final behavioral tuning phase, a technical shift aimed at closing the capability gap with rivals like Anthropic and OpenAI in enterprise applications.

To minimize the total cost for a certain level of performance, the compute budgets for a model's lifecycle stages should be balanced. A powerful heuristic is to equalize the costs: the compute spent on pre-training should roughly equal the compute for RL/fine-tuning, and also equal the total compute for user inference.

When asked to analyze 100 papers, LLMs often admit they didn't complete the task. This failure stems from outcome-based training, which prioritizes a plausible-looking final output over correctly following the required process, revealing a fundamental flaw in current training paradigms.