[thomascountz]

I'm not sure I follow how this metaphor plays out. Many stand to gain by harming and threatening Europe and many stand to suffer if Europe is harmed or threatened. Far be it from me to claim Europe a perfected system, but there are globalized interdependencies which exist here. I think that I'm not seeing the indicators of hard benefits which correlate to the hypotheses put forward by the financial markets. In which ways are the trillions of dollars of value meant to manifest and how do we know when they begin to do so?

[cyanydeez]

These AI models are powerful enough to be used as cyber threats, as a minimum. They may not prove out the other GDP or ROI claims, but the ability to both improve security and dismantle is means if you're not regularly building and improving these models on the software vulnerabilities your organization has, you are increasingly vulnerable to attacks.

Since it's impractical to airgap your military and readinessness capabilities, this means you need incountry capabilities because even if America weren't a fascist, you could easily be disconnected from the support from a cyberattack.

I think you're just following too much AI hype to see any clear use case.

[thomascountz]

Ok, now I understand better the metaphor. I don't understand your conclusion about AI hype, but at at rate, your earlier point is clearer.

The story I'm not readily conceiting to is that trillion-dollar frontier model training capex is the only thing that results in the defense capabilities countries need in relation to information security. From my understanding, the cyber wins being celebrated of late have come just as much (if not more) from how models are wielded than how they were trained.

Maybe the more honest framing of my question is: at what level of capability does sovereign training become the only viable path, and is that level the same as the trillion-dollar pretraining capacity TFA is actually asking about, or is it rather something more tactical/accessible? That is the part I'm genuinely more interested in, since the closer you are to benefiting from the commercial aspects of model training, the more likely it is that the answers will simply follow the money, so to speak.

[foobar10000]

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.