In mid-2023, the RLSlow research project showed confidence in scaling reasoning models, unlocking chains of thought. Szymon and Jakub Pachocki reflected not on benchmarks but on machines surpassing human intelligence in their lifetime. Three years later, reasoning language models are transforming the economy and science, operating computers, collaborating, and driving research—while posing new computer security dangers.
Based on internal results, Pachocki expects sustained progress toward recursive self-improvement, with future systems likely representing capability jumps of equal or larger magnitude. He warns that no one is prepared for the consequences of continued rapid rise in machine intelligence. OpenAI will pursue technical alignment solutions, defensive systems, and unilateral scaling restraint, but broader interventions are needed.
Progress in machine intelligence is driven by increasing computational power, a principle OpenAI internalized around 2017 after seeing consistent returns to scaling. AI is grown more than designed—it is the product of repeating optimization steps on immense compute. The resulting complex system simulates human behavior through abstract concepts, but its overall action evades full understanding, making deep learning-based AI an experimental science where large-scale training runs yield surprising, hard-to-interpret results.
Source: OpenAI Blog · Summarized by HeadlinesBriefing