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.

Significant opportunity exists in re-architecting how AI models work. Instead of building ever-larger single models, the focus is shifting to creating networks of smaller, specialized models that collaborate, which can drastically reduce the cost per token produced.

Performance gains increasingly come from the "harness"—the surrounding system of tools, data connections, and agentic workflows—not the underlying model. Stanford's "meta-harness" concept shows a 6x performance gap on the same model, suggesting the product layer is where real innovation and competitive advantage now lie.

The relationship between computing power and AI model capability is not linear. According to established 'scaling laws,' a tenfold increase in the compute used for training large language models (LLMs) results in roughly a doubling of the model's capabilities, highlighting the immense resources required for incremental progress.

According to Liquid AI's CEO, the primary application of architectural research has become enabling efficiency—reducing cost, latency, and memory without sacrificing quality. The next major breakthroughs in AI *capability* are more likely to stem from new learning algorithms and data paradigms rather than architecture alone.

Today's AI boom is fueled by scaling computation, which is a known engineering challenge. The alternative, embedding nuanced, human-like inductive biases, is far harder as it requires a deep understanding of the problem space. This difficulty gap explains why massive models dominate AI development over more targeted, efficient ones—scaling is simply the more straightforward path.

Unlike text-based LLMs where simply increasing parameter count works, Verge Labs found the biggest AI performance gains in biology come from scaling data modalities—adding new types of data like proteomics and imaging. Fusing different data sources is more critical than just making the model bigger.

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.

Rethinking and rewriting core systems, like DeepMind's distillation infrastructure, is a prerequisite for advancing research. These large software engineering investments unlock new capabilities, leading to dramatic improvements in model performance and understanding of scaling laws.