OpenAI utilized approximately 10,000 AI agents to address the Navier-Stokes problem, with the system running for 88 hours. This approach highlights a shift from evaluating individual AI performance to managing large-scale agent swarms.
Scaling human intelligence is constrained by coordination costs, as adding more specialists increases the complexity of management, communication, and task distribution. In contrast, scaling AI agents is primarily a technical challenge involving compute resources and orchestration systems.
The economic impact of AI goes beyond replacing individual workers. As the cost of intellectual labor decreases, tasks previously deemed too expensive to solve become economically viable, expanding the total volume of work rather than just reducing the headcount.
Future professional value may shift toward individuals capable of orchestrating large agent swarms to solve complex problems, rather than simply using AI to perform the work of multiple people. This evolution suggests a broader expansion of the task space.
Additionally, the Qwen3.8-Flash-Next model, a 125B parameter open-source model, has been released. It reportedly competes with closed frontier models in programming and agent-based tasks. The full checkpoint in bf16 format was successfully run on a single RTX 4090 with 5.95 GB of peak VRAM usage.
