| We are there already pretty much - if I understand your point (“How the models are wielded”) refers to the harness - which is part of model training already. Fable was trained to use Claude code harnesses effectively to keep plugging at a problem with a lot of working memory and world knowledge and reasoning capability - and to keep hitting that search space intelligently. And it is not just cybersecurity. I’ll give several examples from my own recent experience : 1. Cyber - already discussed - has an issue that the bigger models can actually do a full end-to-end exploration of an exploit - go from theory to an actual deployable payload. 2. CAD/CAM/Mechanics - CalculiX (ccx) - an open-source FEA and similar mechanical solver - think Siemens or ANSYS, but open-source. A team I was helping was trying to do a design mount of a physical object that would need to reduce vibrations in a frequency band - think microphone mount basically. Usual loop would be design, analyze with Siemens, go to beginning. AI loop is have AI design, then analyze using ANSYS, then analyze result, change design, iterate. That loop did not produce anything useful for elastic materials because ANSYS would take 12 hrs to do acoustic analysis using a GPU. 1 week of autonomous work by a frontier model resulted in a modification and custom solver added to ccx that could simulate the acoustics (vibrations) _in that particular problem_ in about 20 seconds - mainly because it could try new mathematical ideas, then compare them against ANSYS reference for quality of solution, and iterate. And 1 week _after that_ the frontier model - iterating on one design per minute - came up with multiple 3-d printable ground-breaking mounts - including sending one off to xeometry for printing and getting it shipped back. Existing designs had a 20 db drop in the frequency ranges needed - this one had 60. For reference 40 DB is basically infinity :) While this was for microphones - you can imagine that vibration reduction is a big thing in engine, suspension, and weapon mounting, and well, in general things that move. 3 person team btw - unthinkable even 1 year ago. 3. Pharma. Different company - but given a known Density Function Theory or Kappa Cluster molecule simulator, one can run nice agentic loops over frontier models to do chemical or pharmaceutical research - there’s a reason Anthropic is launching Claude Chemistry. Note that then limiting factor is the multi-week runtime of Kappa Cluster and similarly molecular dynamics simulators. If one _could_ speed that up for a particular problem space or molecule type, one could very very quickly have a high-end reasoning model iterate to a good molecular design - and frontier models are getting very good at precisely automating the ML research needed to do that autonomously - after all, there’s a reference there. 5 ppl - 2 years ago would be a research institute. 4. Physical AI - robotics - same principle. 5. This is basically the bet Bezos is doing with his new company. Please do not underestimate the effect these models will have on our ability to improve our ability to effect the world - this is just starting to hit now. I think we can all extrapolate the GDP and defense impact of this - or at least that there will be a very significant one. |