One field that isn’t slowing down is AI-powered software engineering. This week saw Cursor’s Composer, Cognition’s (Devin and Windsurf) SWE-1.5, and China’s MiniMax M2 all launching within days of each other. Each promises blazing speeds: Composer at 250 tokens per second, SWE-1.5 at 950 tokens per second via Cerebras, and MiniMax costing just 8% of Claude Sonnet’s price.
These mixture-of-experts architectures activate only a fraction of their parameters when running. MiniMax activates just 10 billion of its 200 billion parameters per request. Using RL (reinforcement learning) on the code AI-powered developers are generating, rather than historic datasets, these models achieve near-frontier intelligence at very high efficiency.
This mini-explosion of small, fast models from vendors outside the major labs could reshape the specialised model business. If companies can train competitive models for specific domains, the dominance of GPT-5, Claude and Gemini becomes less certain.
But speed creates new challenges. Testing Cursor’s Composer at ExoBrain we found the model works so quickly it’s hard to follow its reasoning. With Claude Opus or GPT-5 Codex High, developers can oversee the process step-by-step, spot issues, and intervene. Composer can restructure entire codebases in seconds, for better or worse, before you realise what’s happening.
As these models get faster, they move well beyond human supervision speed. The philosophical divide between Cursor’s and Windsurf’s hybrid approach and Devin’s async model becomes critical: do we want AI that augments our real-time engineering, or autonomous agents we dispatch and hope return with working solutions?
The new Cursor 2.0 tackles this with a redesign that abandons the traditional file-explorer-first layout for an agent-centric workspace, where developers manage parallel AI processes rather than individual files. You can now run up to eight agents simultaneously, each in isolated git “worktrees”, comparing different approaches to the same problem before selecting the best solution. Is this the future UI for the hybrid human-agent manager?
Takeaways: The simultaneous arrival of multiple fast coding models suggests we may be reaching a point where training competitive AI has become accessible to specialised vendors, not just tech giants. But as speed increases, so does the requirement for trust, and that trust must be earned through reliability that will take some time to demonstrate.