China’s ByteDance discovers new scaling law that could sustain AI boom

Researchers at the Chinese tech giant have been analysing how fast AI agents can improve by performing real-world tasks In a research paper published on Thursday, ByteDance’s Seed AI team revealed that AI agents – autonomous software that executes tasks on a human’s behalf – can double their learning speed every three months by interacting with real-world environments over extended periods. The finding comes as the global AI industry searches for new ways to improve models. For years, developers relied on feeding systems more data and computing power during initial training, but prominent industry figures – including OpenAI co-founder Andrej Karpathy – have warned that this brute-force approach cannot last forever. However, despite the fact that tech firms are pivoting towards agentic AI, ByteDance researchers noted in the paper that how these autonomous systems “learn from real-world environments after deployment remains far less understood”. To address the problem, the team developed EdgeBench, a benchmarking suite featuring 134 ultra-long-horizon tasks spanning a wide range of areas from software engineering and scientific discovery to formal mathematics and professional knowledge work.

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