I think it mostly shows that there is no moat
And no one would say that about TSMC.
So there is clearly a moat there somewhere.
Comparing the R&D costs of creating GPT-4o vs. DeepSeek V3 (the latest gen for which we already have good accurate numbers) it looks like the latter cost 1/20th as much to create.
If Samsung could catch up with TSMC for 1/20th of the cost, people definitely would say that TSMC has no moat.
Which numbers you believe is of course going to affect whether you think there's a moat or not. That's largely orthogonal to your TSMC/Samsung analogy I responded to. If you think the "moatists" are wrong because they believe the wrong numbers, that's fine, but then there's no need for the analogy.
[1] https://galileo.ai/blog/llm-model-training-cost
[2] https://medium.com/@theiand/how-can-deepseek-a-5-6-million-l...
Clever architecture --> Ok but OpenAI/Anthropic can use these as well and they also have very smart people with their secret clever architectures
Distilling --> Ok but distilling means you will never be smarter than the original. Furthermore, reasoning is now hidden by private labs and they have poison pill answers for distilling if they can detect it. They will be able to detect distilling better and better.
Cheaper electricity --> Ok this is cancelled out by their chips being much less efficient due to not having ASML EUV machine access.
So I don't see why fundamentally their training costs are cheaper over the long term.
I'm looking for a no-moatist to convince me.
There's not 100 frontier labs, it's not like airline companies
Actually not a bad comparison. Both burn massive amounts of up front capital to protect an oligopoly in the hopes their commodity product eventually pays off.
For most people, the app IS the AI.
And even for its wonkiness, ChatGPT has had the best UX/UI of them all.
The way to win the AI wars in the eyes of the common folk is through the frontend, to be the Apple of AI, as it were.
Chinese counterpart like CXMT and Huawei is begin producing their own chip
You cant block an entire nation level effort with tariff
And consumers use 4x as much per capita. Industrial generation per capita China comes out ~2x
> industrial electricity prices in China are roughly 34% higher than in the US
For which industrial customer and where? Chinese compute hubs are on par to slightly cheaper on pure electricity costs.
Conversely the US makes it more expensive with interconnect and upgrade fees as well as hefty take or pay contracts.
A 1GW datacenter in VA for example would add 5-10c kWh and a 12 year take or pay deal
They exist to keep people using and advancing the tools on their hardware.
2. Any open models created from this will not necessarily need their silicon, see apple mlx.
Nvidia could just the same rent those GPUs out for inference and actually have way better margins than they do right now. Antitrust and putting all your eggs in one basket are why they don’t, similar to TSMC.
2. Neither do AI labs. See Anthropic buying TPUs, deploying with AMD. OpenAI on Maia, Cerebras, their own wafers.
The only moat they appear to have is by hoarding compute, and the current trajectory of hardware shows that isn't permanent either for very long
I'm genuinely so confused when people say this with a straight face. Are you talking about coding? Desktop use? Prose? Or something else?
Sol is a much smaller models and it shows. It often misses the forest for the trees.
Same. It makes me wonder what types of things the person must be working on.
People will see/think that I'm doing very well with my LLM use, and ask me what I'm doing. I tell them, they try it, then later they come back to me saying they just couldn't get it to work.
I work on very low level stuff (think RTL/FPGA, firmware, software where optimising for nanoseconds is just normal).
For me Sol is the only cost effective model available. Fable 5.1 is indeed good and vastly better than original Fable (which refused to work on most of my stuff for 'safety' reasons).
It's very good at this sort of low level stuff to the point that I really can't understand/relate to people having a good time with Opus (which comparatively performs extremely poorly on my particular workload).
I also just don't like how lazy Anthropic models are. They will do 10% of what is asked and then summarily declare victory.
Sol on the other hand is more like "one of us", slight touch of the 'tism, extremely pedantic, will go to the edge of the known universe if that is what it takes to prove/fix/build what you asked for or run out out of credits trying.
It's a personal and workload dependent thing. For me right now Sol for 99% of stuff because Fable 5.1 still burns through $5k in credits a day.
Sol will leave a mess of excessive redundant tests and isn't so great at abstraction ; but it produces more reliable working systems.
It's kind of nice to have access to both, but I don't have the $$ for that right now, so I just keep the Codex sub
It's still incredibly important to have a human in the loop correcting design decisions and having good taste.
Swapping mssql to SQLite would also work perfectly
Is a very sane tech stack, you're just biased against Microsoft.
Half the world's enterprise apps run on that combination, or a minor variation of it.
Like Java it is full featured ("batteries included") but unlike Java it is relatively terse and actually pleasant to work with.
Oh, and unlike Python, it is very fast, within spitting distance of compiled Rust and C++ web apps.
People with high IQ often do this IRL. There's training tension in this area. Intelligence and overcomplication correlate and are hard to extricate.
In some cases, there are also nuances that we don't pick up on. Here it's our preference for simplification that's showing up. We set the lossy compression factor higher than it does.
I just dont get how its good for some, and bad for others. It makes me suspect that the models performance is not even against problem sets and it really is just a probabilistic prediction machine. Which then makes me very skeptical of GPT-6 Astra, because if their big claim is Computer Use then it is probably bad in a bunch of other areas.
> I just dont get how its good for some, and bad for others.
If I were to listen to my hunch, it would tell me that it's all up to the prompts that ends up going over the wire (including all the bloat some people have), what workflow/process you use and what the existing state of the project is.
I've had Claude do the same thing where it goes off and spends 100% of my tokens on 3 functions and an ungodly amount of tests / scaffolding that do almost nothing when I gave it an underdeveloped idea.
Claude has the exact opposite problem, especially opus-5, where I literally can’t trust it to print hello world without taking a shortcut, or just simply lying and saying it printed it when it didn’t, behind a giant wall of inscrutable text. I find it very ironic that Anthropic is the vendor of the lazy lying cheating model that does almost everything you tell it to it do.
I’d really kill for something that balances instruction following and loop escaping behavior better. Fable 5.1 does seem a lot better, feeling more like 4.6 behavior, and honestly Sol has improved as well. I’m pretty psyched for the next generation, as I think the competition has heated up so much that things will improve really fast to the point of marginal utility opportunity being increasingly close to epsilon.
> model_context_window = 1000000
> model_auto_compact_token_limit = 900000
I believe it does consume your usage a bit faster though.
... looks around ...