Metaphors are good as a pedagogical tool for explaining topics where you're an expert and are (within reason) certain the parallel conclusion is valid. This can bridge the gap for students.
In other situations, they're a terrible argument strategy or manipulative rhetorical tactic, and the reader or listener should question every single detail if they bother to entertain it at all.
Are LLMs really like drugs that might hospitalize you? Is the government actually concerned about the well being of the people? Can you regulate LLMs as the FDA does pharmaceuticals given that they're trivially copied as files from other countries?
Another neutral party might not believe it's really a doomsday device and that what currently looks like exponential growth in capability could be an s-curve that plateaus in a year or two. After that, it will be diminishing returns to invest heavily into a tech that won't get much better.
So what are the current leaders in the field supposed to do to stave off competition? They should convince the public that they do have a doomsday device, claim it must be regulated, and then they can profit from their duopoly because it's exceedingly expensive to break into the high end of the market. The government has its own nefarious incentives, not limited to collecting fees and using the unrestricted versions for surveillance or black hat stuff.
Bah, I think this debate was already old when I first saw people arguing it on comp.lang.lisp in the 90s. I don't have a dog in this fight other than to reject the notion that Common Lisp is "coherent" and not "organically grown".
The original Scheme belongs in the category of languages like Standard ML and SmallTalk, where a small, careful, and talented group designed them with focus. Common Lisp seems like a bunch of smart people with competing interest and legacy baselines tried to meet in the middle. To the extent CL is more pragmatic, it's another example of "Worse is Better".
Scheme is (or at least was) coherent. You don't need to look any further than set/setf/setq to see that Common Lisp is "organically grown" from the fertilizer of a committee. CL does its best to make every other lisp more attractive.
I believe the idea is that you don't care what language is being used if you aren't going to look at it anyway. Given that premise, the AI can write JavaScript instead of something you need to compile separately.
I think about that kind of thing a lot. For Special Relativity, maybe? I don't know the historical interactions between Einstein, Minkowski, and Lorentz, but my gut tells me the idea was ripe. For General Relativity, I'm less optimistic the current flavor of LLMs could make that leap.
There's so much I don't know though, and I'm certain we aren't the only two who think about it :-)
I predict it will get regulated in the US, and that it will lead to regulatory capture. Solving absolutely NONE of the problems people complain about while providing NONE of the benefits AI could bring to society.
Yes, and I keep copies of the ones I like[0]. I can't run the huge ones, but the ones I can run aren't as good the "frontier" models. Regardless, I expect they will be considered contraband someday.
[0] - I've been using llama.ccp and Ollama. I should checkout vLLM.
I agree with your logic, but you should replace 2 with "AI used by governments only". The haters would have more luck getting rid of nuclear weapons than putting the AI cat back in the bag. Governments will use it for surveillance. Think "sentiment analysis" to make sure you're not a terrorist.
All that's going to happen is people will "voluntarily" take it away from themselves.
The fearmongers will tell stories about biological or chemical weapons. It'll be things you could learn from a textbook - something like mercury molecules or cultivating rabies. People will vote to ban AI.
The puritans will clutch their pearls because it can be used to make porn they don't like. They'll vote to ban AI.
People who are afraid of losing their jobs will make tangential arguments about copyright violations. They'll vote to ban AI.
So citizens won't be allowed to use AI directly.
Instead, there will be regulatory capture. Microsoft and Apple will pay fees for compliance testing (bribes). Then they'll serve you a dumbed down version you can't escape. "I see you're trying to analyze numbers. Click here for a free signup to Office 365!".
The social media sites will make sure you still have access to create rage bait slop. That improves engagement.
Big software companies will pay for bug finding services. Small open source projects won't have the money.
If you're upset by AI, you should ask yourself if that's part of the plan. Because there's a lot of money to be made and power to be stripped from citizens if everything above comes true.
I believe almost nobody thinks original thoughts. I never have. At best I applied an idea from one area to another, which is something AI can do.
Moreover, most novel advancements seem like they come when society is ready for them - the nearly simultaneous discovery of calculus for example.
Pick any thought of yours you truly believe is novel and do a serious literature search on the topic and adjacent fields. Ask an AI to help you with the search if necessary :-)
For me, I'm very enthusiastic about it's use for programming, mathematics, and as a teaching assistant[0]. I'm very worried about it being used for automated surveillance, terrible customer service, and deceptive targeted advertisements. I'm unconcerned about slop and alignment issues. I'm very much in favor of local models (democratization), just like I'm a fan of Wikipedia for making so many topics available to everyone for free.
[0] I don't see a lot of people using LLMs to learn a new topic, but I had a really great experience by walking through some math I wanted to know, forcing it to go slowly, and writing code and test cases for each concept to make sure it wasn't hallucinating. There are no "choose your own adventure" textbooks like this, and there are no professors who would be that patient with me in office hours. I don't think it will work well for unmotivated learners.
There was also a decent amount of enthusiasm for the "long tails" because with unlimited virtual shelf space, you could find products that would not have enough mass appeal to the average consumer to justify their space on physical shelves. For instance, Netflix would loan you a DVD of almost any movie but Blockbuster only stocked the middle of the bell curve.
There are so many flags to llama.ccp that I won't try to say anything too strong, but I believe things related to `--kv-offload` mean you can have the KV cache in GPU VRAM, regular GPU RAM, paged to disk, etc...
I'm on a Mac with unified memory, so I can't easily benchmark it for you, but I think a PC with 64GB of regular RAM and a 24GB gaming card could swap between multiple sessions without too much pain. The weights could stay resident on the GPU.
On the other hand, I did just dump some Project Gutenberg texts into a prompt, and building that cache in the first place was slower than I though it would be.
The names for the pieces are confusing, so it's easy to talk past each other. For instance, you're saying "Codex the agent", which isn't a thing now. It's currently GPT-5.5, and at one point it was GPT-5.3-Codex, so when I say "Codex", I meant the MacOS "harness". Similar for Claude Code vs Claude Opus/Sonnet.
Anyways, I don't know specifics well enough to argue with you on anything, but there is a cost for input tokens, and you see/pay it when you use the API directly or through OpenRouter. Maybe you looked at the leaked source for the Claude Code and can tell me definitively otherwise, but Anthropics and OpenAI's incentives for when to compact are not always aligned with the users depending on pricing plans.
I think you're right about the cost/benefit trade-off in general, but I do wonder how much "compaction" Codex and Claude do is to keep context fresh and how much is to save (them) runtime costs.
If you've got a 1M token context, but they constantly summarize it down to something much smaller, is it really 1M tokens of benefit? With a local model, you can use all 256k tokens on your own terms. However, I don't have any benchmarks to know.
Your point about caliber/quality is fair, but I have been pretty astonished by some of the newer/better models (Gemma 4 variants, GPT-OSS before that).
However, there's not a lot of memory increase to have multiple sessions in parallel with one model. It's an HTTP server, and other than some caching, basically stateless.
Lol, I totally agree about anyone using the non-computable angle.
However, I've got a 20GB GGUF file on my disk that can write code better than 99% of the people I ever worked with in the last 25 years, and ravens seem pretty clever with about 2 billion neurons... I have no idea what the lower bound is.
Metaphors are good as a pedagogical tool for explaining topics where you're an expert and are (within reason) certain the parallel conclusion is valid. This can bridge the gap for students.
In other situations, they're a terrible argument strategy or manipulative rhetorical tactic, and the reader or listener should question every single detail if they bother to entertain it at all.
Are LLMs really like drugs that might hospitalize you? Is the government actually concerned about the well being of the people? Can you regulate LLMs as the FDA does pharmaceuticals given that they're trivially copied as files from other countries?