Author: Carter

For years, the artificial intelligence industry has followed a simple, brutal rule: bigger is better. We trained models on massive datasets, increased the number of parameters, and threw immense computational power at the problem. This formula worked for most of the time. From GPT-3 to GPT-4, and from crude chatbots to reasoning engines, the “scaling law” suggested that if we just kept feeding the machine more text, it would eventually become intelligent.But we are now hitting a wall. The internet is finite. High-quality public data is becoming exhausted, and the returns on simply making models larger are diminishing. The leading…