AI and National Security - Something Doesn't Add Up
Paul Brown
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AI and National Security - Something Doesn't Add Up<br>Banning AI for export, massive AI valuations, strange things afoot (or is it just me)?
Paul Brown<br>Jun 15, 2026
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I think the whole thing about Anthropic’s latest and most powerful AI model ‘Fable’ is a bit strange - it was banned from export from the US for national security concerns - “so powerful it’s dangerous”. I suspect there’s something a bit more involved going on, and while this is just a hunch I don’t know how I am wrong: I don’t think the AI moat is anywhere near as deep as the AI company valuations imply. I think this has been a big driver in the national security issue, but to release the full decision process would result in a significant loss in AI company valuations - a real market whammy. There are various other reasons to suspect that the current valuations are over-hyped - power availability, GPU lifespan, circular accounting - but here I’ll cover something that I see in my work as a consultant implementing AI in heavy industry:<br>The big prize for these AI companies is taking a share of the knowledge worker economy - work that can be done remotely via zoom calls could conceivably be taken over by AI and, given the size of the prize, the valuation of these companies is correspondingly gigantic.<br>Thanks for reading! Subscribe for free to receive new posts and support my work.
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There are lots of ‘small language model’ companies springing up, lots of people and companies talking about training their own domain-specific models. It’s an advantage because these models - while less ‘general purpose’ than the big frontier models, are much faster to run, much cheaper, more private and secure, and add value as intellectual property of the company. So how do you make these smaller, or domain specific, models?<br>To train any sort of AI you need a source of truth - a set of questions with accurate answers, in the same way that to teach a kid you ask them questions and get them to answer. You need maybe 2000 sets of these question/answer pairs to ‘fine-tune’ a model (where you take a general-purpose large language model and refine it to perform better in a particular domain), and you need even more question/answer pairs to improve the performance of a model such that you can release a new and refined model from it (i.e. train your own model). The issue is where do you get these question/answer pairs, the source of truth?<br>It’s a huge amount of work to extract information from books / industry documents (not to mention the serious legal and copyright infringements), and you need some sort of human expert verification to make sure the answer is the correct answer to the question (particularly if in a niche domain) - that’s a lot of verification for thousands of questions. (btw I haven’t seen any of this collation or human verification going on in industry).<br>So what happens is... the big AI companies spend their billions on training their models, making the cutting-edge frontier models. They release them for public use, charging for them as required to make their training investment back. But the smaller AI companies don’t want to pay the big money for accessing the frontier models (I’ve paid a couple of dollars per question, they’re expensive), and they don’t want to go to the effort of the acquiring and distillation and verification of massive datasets for getting these question-answer pairs. So instead they send their questions to the frontier models and get the answers from that more powerful AI. They use these answers to train or fine-tune their own models.<br>So instead of spending $millions on developing the data for their own models they’ve just let the big boys do the work, paid them for answers (and only the answers - no ongoing use), and used them to refine their own models that aren’t as powerful as the big models, but are powerful enough.<br>If that’s the case, then why would the market value the big AI companies based on an idea that they’re going to capture significant market share of the knowledge economy? No company would want to develop the most powerful models as they’d be the ones spending money, the ones basically getting ripped off.<br>In terms of the national security concerns - I really don’t doubt that newest OpenAI / Anthropic models are the most powerful, but my guess is that the concern is more that rival states will immediately suck the data and use them to increase the power of their own models (and they would also avoid the big costs). To lend this more credibility, given that these big frontier models are run on the big-company (American) infrastructure, surely it would be more beneficial from a national security perspective to just monitor the LLMs to see what these geopolitical rivals are working on? A clear window into the Taliban’s mind, so to speak.<br>I’ve heard that maybe these models will be the last which are publicly accessible,...