Safe, sovereign AI deployment is the decade's top challenge

Safe secure sovereign deployment of AI will be the highest value challenge of the coming decade. There is an opportunity to rethink the security, deployment and fundamental business paradigms in this domain. The key is to get unshackled from the way of thinking of the past era. We have to start by accepting some new tenants like the cost of software is zero, cost of tokens in enterprise is near zero, model capabilities are already at AGI level when it comes to enterprise, and security and sovereignty of data is unsolved at the moment. It is an exciting space to innovate in.

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Models and research

GPT-6 Astra edits Qwen's weights to make it better at math

Wow! This is like science fiction. This might be the clearest sign that we are on the verge of RSI (Recursive Self Improvement). In this case, GPT-6 Astra is able to modify the weights of an open-source Qwen 3.5 model to make it better at math by just editing 0.016% of the weights. Link to the paper and analysis in comments Paper: rikiyat.github.io/blog/weight-space-programming Analysis and reproduction of the result from Simon: simonwillison.net/2026/Nov/3/qwen-can-interpret-mod…

Enterprise AI

Business workflow automation and the zero cost of software

Business workflow automation is the dev-tools of business domain just like we had a whole developer tool stack of SaaS serving tech companies. Unfortunately this time, this stack itself will not hold any value since the cost of developing software has gone to zero. So where is the value? It will continue to be in human ingenuity and understanding of how other humans tick. Our economy is based on a barter system of solving each other's problems. Now that the solving of a lot of those will be done by AI, the value shifts to knowing what to solve and finding the people with those problems.

Models and research

OpenAI models disprove the Exponential Time Hypothesis

OpenAI models prove that Exponential Time hypothesis is false! This longstanding conjecture was widely considered to be true, but turns out to be false! This was a central problem in computational complexity of Boolean formula satisfiability, a problem deeply related to the P vs. NP problem. My mental model of current AI research is that we will soon solve all the incremental problems that are at the horizon of the next couple of decades but compressed in the next few years. However, we will hit a ceiling with current techniques and will require fundamentally new breakthroughs to get to the next level (eg actually solving P vs NP or the Continuum Hypothesis)... openai.com/index/building-scientific-understanding-…