China is gaining an efficiency edge in AI by using "distillation"—training smaller, cheaper models from larger ones. This "train the trainer" approach is much faster and challenges the capital-intensive US strategy, highlighting how inefficient and "bloated" current Western foundational models are.

Classifying a model as "reasoning" based on a chain-of-thought step is no longer useful. With massive differences in token efficiency, a so-called "reasoning" model can be faster and cheaper than a "non-reasoning" one for a given task. The focus is shifting to a continuous spectrum of capability versus overall cost.

Newer LLMs exhibit a more homogenized writing style than earlier versions like GPT-3. This is due to "style burn-in," where training on outputs from previous generations reinforces a specific, often less creative, tone. The model’s style becomes path-dependent, losing the raw variety of its original training data.

Model architecture decisions directly impact inference performance. AI company Zyphra pre-selects target hardware and then chooses model parameters—such as a hidden dimension with many powers of two—to align with how GPUs split up workloads, maximizing efficiency from day one.

Performance on knowledge-intensive benchmarks correlates strongly with an MoE model's total parameter count, not its active parameter count. With leading models like Kimi K2 reportedly using only ~3% active parameters, this suggests there is significant room to increase sparsity and efficiency without degrading factual recall.

Companies like OpenAI and Anthropic are intentionally shrinking their flagship models (e.g., GPT-4.0 is smaller than GPT-4). The biggest constraint isn't creating more powerful models, but serving them at a speed users will tolerate. Slow models kill adoption, regardless of their intelligence.

Autoencoding models (e.g., BERT) are "readers" that fill in blanks, while autoregressive models (e.g., GPT) are "writers." For non-generative tasks like classification, a tiny autoencoding model can match the performance of a massive autoregressive one, offering huge efficiency gains.

The binary distinction between "reasoning" and "non-reasoning" models is becoming obsolete. The more critical metric is now "token efficiency"—a model's ability to use more tokens only when a task's difficulty requires it. This dynamic token usage is a key differentiator for cost and performance.

While frontier models like Claude excel at analyzing a few complex documents, they are impractical for processing millions. Smaller, specialized, fine-tuned models offer orders of magnitude better cost and throughput, making them the superior choice for large-scale, repetitive extraction tasks.