the world of ai.

For the past decade, progress in artificial intelligence has largely followed one principle: make models bigger. More parameters, more data, more compute. Larger neural networks memorize more patterns and perform better across tasks. But this strategy is reaching practical limits. Training costs are skyrocketing, inference is expensive, and even the largest models still hallucinate facts…

read more

For most of the last decade, progress in machine learning has been driven by models. Larger architectures, deeper networks, and more parameters consistently produced better results, and improvements in benchmarks largely followed increases in scale. As a result, much of the field optimized around model design. That approach is reaching diminishing returns. Across production systems…

read more

Artificial intelligence is often discussed in terms of models: larger architectures, higher benchmark scores, and incremental improvements in accuracy. In practice, however, model performance is rarely the limiting factor. Most failures in real-world AI systems occur outside the model itself, in the surrounding infrastructure that handles data, deployment, and reliability. The difference between a research…

read more