If the ICC gets sanctioned there will probably be a big boost in EU companies leaving USA services for good. Many are already very seriously talking about it, but need to be 'pushed' a bit more.
> The EU has become ever more dependent on the US in the past few years, notably on energy and AI.
Energy yes, particularly since the Iran war started. AI, not really. Like most Europeans are using US models, but the switching costs are so low here that they could easily swap these out for Chinese models.
It's the cloud/hyperscaler dependencies that are the real problems here.
Yes, they run US models on US hardware and there is no alternative. In principle there are indeed Chinese models, but in practice there are also political considerations.
Europe pretty much does not exist in AI and the political considerations at this point push it towards overwhelming dependency on the US.
Chips developed and controlled by the US. Taiwan is controlled by the US. Europe has no leverage.
Europe does not exist in AI. That is the sad truth, no need to try to pick hole in reality... It's quite healthy to listen to EU noise less because otherwise people tend to drink the kool-aid and believe that the EU (and Europe) is anywhere in most strategic developments (AI and related chips are particularly dire but also missed the boat on EV, struggling on renewables, nowhere in smartphones, etc). Yes, there is Arm so that's that (not in the EU).
See this video for how Alexander Nix (former CEO) presented the data he had on every adult in the united states https://youtu.be/APZUrj8SN0M?t=297 (start at 4:55 min)
But your video doesn't show that? It's just shows high level aggregations. It doesn't say "homer simpson living at 123 evergreen terrace, springfield" is 47 years old, votes republican, earns $50.1k, and rates 3/5 on agreeableness.
"We could see where these people are on the map. if we wanted to drill down further, we could resolve the data to an individual level where we have close to 4000-5000 data points on every adult in the United States"
That seems more of a case of someone trying to hype up their product, than something that's actually real. What's likely actually happening is that they got a vaguely complete list of adults somehow (eg. voter rolls or DMV registrations), then the rest of the attributes are filled in using guesswork (eg. the name "Homer Simpson" probably implies he's white and male, the zip code he lives in implies he's middle class, etc.)
I am not a "no-moatist" per se but one can argue their might be a plateau to how good a inference llm can become. If this is the case the playing field shifts to context, tools and harness, which are much cheaper to build an compete on.
Good marketing and definitive proof that local (read: on-prem & air-gapped) models with correct context and tools are good enough to perform on par and above SOTA cloud hosted solutions.
We have seen this point many times before with different technologies. The first computers at university were big and expensive, same as this machine. Give it a few years and this functionality will be a commodity.
I think what will happen is what happened to something like 4K video decoding before where it ends up in silicon costing almost nothing to run extremely fast on device.
"Good enough" LLM functionality (for the use case) will be on-die or on-chip for cars, appliances, etc. This will provide speeds of chatjimmy at a battery-level power consumption.
Probably this will also happen for software engineering. Some usb-powered AI accelerator with Kimi K3 (and in future even better) performance running at 10K+ tokens/sec under 50W of power purchasable for almost no cost. Need a better model? Buy the new hardware. Old hardware is probably still fine for a lot of other use-cases. I expect China to be a big player here, it fits their open-model and hardware-manufacturing strategy.
Scaling a model on a chip is quite hard.
ChatJimmy is based on Llama 3.1 8 billion.
Kimi K3 has 2.8 trillion parameters.
That are 350x more parameters.
I would expect that Gemma 4 E2B (approx 5.1 billion parameters) or maybe even Gemma 4 26 billion A4B at some point is running on a chip.
Scaling is definetly hard - but there is no absolute requirement to put huge flagship models into this technology (although it might be possible over time).
A fairly dumb but FAST model has it's own totally distinct use-cases even if it can't be scaled in size. Think about a LLM-infused-Alexa where the response time is instant. Where you can request it looks at hotel options in Montreal, and it starts answering in half a second rather than a few minutes.
Plus some sort of slow smart + fast dumb combo architectures might also work really well for different classes of problems.
You can change the prompt, and give the agent tools, and that keeps the agent up to date.
The agent doesn't know the date, or know what hotels there are in Montreal, it sees:
> System: You are an AI agent. The date is 11th August 2026. Your knowledge cut-off is March 2023. User is based in <date>. If you need to search for something to support the user say {search:<term>} and a list of options will be provided along with instructions on how to access. Or say {help} for a full list of commands.
> User: Can you help me find hotels in Montreal for next weekend?
The AI then interacts with the tools given in the base prompt, which can obviously be updated. So it then goes:
> AI: Of course, let me search for that. {search: hotels in montreal for 16th August}
> System: [Provides list of websites]. Say {read[n]} to read option or say {start subagent:<goal>) to register subagent.
> AI: {start subagent: List hotels on booking.com available on 16th August}.
[etc etc, then eventually]
> AI: Yes I have searched for you and I found a few options!
While you can't change embedded knowledge, a good model knowing that the date is 5th January 2040 can infer certain things (e.g. while it might not have been trained on certain deaths, it can probably guess that it should search before answering if it means a person would be 102 and their last information is from 2024)
Yes, to update the blueprint for new models two layers will be updated. That is the NN.
To instead update the data on which to operate you could use a RAG to query.
(As in "the Pathfinder 2.0 NN is on the chip; the geodata is in the OpenGeoMaps dump-DB-nightly" - not really overlapping with LLM+RAG but may give an idea in a different scenario.)
I mean, it's pretty damn reliable now. Has been for simple high contrast Q&A for a while now.
I've just been doing research and experiments for work related stuff.
Typically we've used plain embeddings for a lot of high contrast documents aka discrete facts.
However I've been working with a >1000 page document of complex procedures with incredibly low contrast where embedding falls flat.
There's top down/graph searching, bottom up/embedded; alts like colbert, reranking, reasoning, search agents and now (though seemingly quite new) specific search agent models.
Ultimately I found that a reasoning enabled search agent doing a hybrid of bottom up (with reranking) followed by top down, gave the absolute best results. Paired with Luna for cheaper and faster tokens it benchmarks pretty well even for vague references to procedures.
I would imagine that search specific models just coming out are even better and I'll have to evaluate using these but for now the above works well for us.
Having an agent get vector search results to use as anchors and then being able to explore the sections and subsections above that, then eventually digesting as much as is relevant (big context, cheap tokens) is amazing.
Very interesting and very good (though, let us say, much more recent that ancient): only, it paradoxically does suggest that the computational cost (when compared either to a simple "search by vectors distance" or to the task failures that we can have witnessed by chief implementations*) strongly suggest the benefit of 1000x speed boosting, energy conservative Taalas board.
*(Of course it has "always" worked well for «simple high contrast Q&A», ever since the base embeddings technology worked properly: that is almost by definition; it is on real world use cases, where the nuances of reality are present, that it failed miserably.)
And outside of idiotic demos, who exactly is going to ask an LLM to look at hotels in Montreal for them? This usecase has never made sense to me in the slightest
In the slightest? The user goes to their computer and types in "Montreal hotels" and Google comes up with no shortage of results, including a bit from their LLM. So that's already happening, but how do you narrow down the results from that initial search? Click around on Expedia for an hour? You probably know what you care about, just tell the LLM that you have dogs or are a vegan or whatever instead of wasting a bunch of time doing it by hand yourself.
> who exactly is going to ask an LLM to look at hotels in Montreal for them
Anybody who has a specific informal query ("SELECT ... FROM ... WHERE has_carpark AND ... ORDER BY score(has_jacuzzi , walk_distance(...) ...) DESC") but does not want to research and cross the different scattered info himself (does not want to build the virtual DB himself).
I mean it was just an example, but I get AI to do things like this all the time... ChatGPT planned my latest work trip by looking through flights, hotels and pulled together 3 options of itineraries for me, and wrote an email I could send to my client with estimated costs etc.
It's quite telling that the 8B Taalas chip was already reticle-sized on TSMC N6. I mean, we're talking about a process that does ~100 MTr/mm², ROM needs about one transistor per bit, but can probably be packed more densely than general logic. Something like, say, 150 megabit/mm² is not a lot. N6 has a 850 mm² reticle limit. This roughly tracks, the article says the chip has 8B parameters and apparently spends about half the area on ROM. There's a reason AI accelerators just use a ton of silicon area (each HBM3 die is >1000mm² of silicon). I imagine this is not terribly viable unless they make it a lot more space efficient e.g. using MLC ROM if they don't already, or use stacked dies with a ROM-optimized process. And then we're back to not cheap, though reticle chips were never in the cheap area to begin with.
Taalas exploits the low cardinality to store one 4-bit weight with one transistor. (They are using metal layer traces for the ROM, and connecting an access transistor to light up one of 16 options.)
Their system is honestly very efficient for the weights, the problem is the KV-cache. That's why HC1 only supports such short context, they use SRAM for that and spend most of what's left of the die for it. The recent advancements that made attention more efficient are probably going to be very useful for them.
A full wafer like Cerebras is about 60x that, and N2P has about 3x the transistor density. So right now it's technically feasible to etch a 1.4 trillion parameter model. So roughly DeepSeek-V4-Pro class. Imagine that running a factory, for example.
Cerebras have special techniques to work around etching errors / bad cores on their wafers. This is possible since their wafers are effectively hundreds of identical copies of redundant cores. Can't do that for a globally unique model.
Etching failure in that situation would be like brain-damage in a human, all sorts of weird effects would start appearing.
There's several ways to engineer around that as the errors are detectable. There's a big literature on how to trade off speed or transistors for error correction. [1]
(Is Cerebras doing something novel? CPUs and memory blocks have been doing those things for a long time too, since the error rate is otherwise too high for normal size chips as well)
A few hundred bad bits/transistors in a trillion+ parameter model would compromise its abilities not one iota...the models are inherently lossy and resistant to "brain damage"...
Did you mean that each HBM3 stack is that large? Because it only takes one glance to see that the memory chips are much smaller than reticle-sized GPUs they sit next to.
I have found that for some personal prose-related projects, QWEN 3.6 35B A3B is an amazing model even quantized down to 4 bits. I actually find it's "writing" style as a GM for an LLM-powered solo text adventure game, better than even some of the faster/dummer frontier models like GPT-5.6-Luna or Haiku 4.5, and it runs (slowly) on a 3090 with a 80k context.
So I have faith in these embedded LLM chips when it comes to fun projects like that. I have not personally found my quantized QWEN good at agentic tasks, though, and it LOVES to make shit up when asking questions about documents in the prompt.
Now imagine you have a chip which is just that model, but can do it at absolutely insane speed. Like tens of thousands of documents a second.
Same for things like text-to-speech or speech-to-text. Think of the accessibility wins if subtitling becomes insanely accurate and fast and omnipresent.
There are all sorts of domains like that, and the trend has been such that smaller models are getting smarter and smarter. If you can stick them in parking meters, traffic lights / street crossings, mobility aids, etc etc I just see so much potential win.
Yes, it also opens up a faster recurring revenue model for hardware companies, faster model obsolescence than how often you change a computer, a server, or a GPU card. I hope they can figure out trillion-parameter models rapidly.
Nvidia happened to be the best option for AI after building machines for graphics, so it makes sense they weren't the best idea from scratch for this specific use case. Especially given the scale of the demand and the possibility of recurring revenue, I hope a lot of smart people will try to solve it, compete with each other, and deliver us extremely fast and cheap intelligence.
And one can say LLMs are not as smart as a human, but a lot of the reasoning humans do for product and service generation isn't smart at all—it's just a bit of fuzzy input/output plus some reasoning rules. And then if you hook a robot up to the LLM, you can get results in atoms instead of bits.
I'm very excited about the future. I also hope it will stop money from flowing to bureaucrats who are incentived to keep the problems open to keep the money flowing, and instead facilitate sharing directly with the people (for example, no money to the state to solve homelessness—instead, spay instead with intelligence output to build a house and provide food as part of taxes.)
I think this combined with a bit of memory and something like the “high-bandwidth flash” they just announced (if it works out), could be an interesting thing for some resident (burned) experts + active moe streamed from HBF.
I expect one day having small lower power demand drive sized devices with proprietary burned-in models that are quite fast running on-device in robotics and such. Commoditizing LLMs via burned and locked hardware seems likely when LLMs have stabilized (when we reach a year between releases again) and the hardware is capable and “disposable” enough. “Buy a robot and upgrade it forever* (5 years) with newer models (sold separately)”, at least until the planned hardware obsolescence that the “interface has changed to support newer hardware, so you’ll need to upgrade (again) to use the latest features”.
I like "good enough" LLMs for search and quick trivia. But "cars, appliances, etc" is exactly where LLMs are between noxious and dangerous.
My food processor could use a self-cleaning feature. It could only be made worse by some system that, IDK, changes the setting based on off-hand comments about "I don't know what his beef is.."
Think bigger: A washing machine and dryer with an arm and a camera.
It can sort your dirty and clean clothes before washing and after drying, based on your preferences.
It defaults to common sense things like washing similar materials and weights, keeping delicates in their own load, grouping sensitive colors like red.
Not saying it's the superior way, but to be honest I wouldn't know how to start some washing machines without consulting manual first, having very dumb simple appliance myself.
I have seen smiling people talking to their connected car, replying with a dumb condescending bad-actor voice.
I had also seen Frankie Boyle in his parody of Knight Rider: "Michael, I am Kitt, your car, stop taking the medications, they want you to take them so you will be unable to talk to me"...
Imagine an energy efficient IC for a small multimodal model that can do voice to text, text to speech, question/answer, tool calling, and structured output.
Wire that up to a microcontroller that parses the structured output to constrain the model (rather than giving the model direct hardware access).
Now you have an assistive tech mode for supporting vision impaired users without requiring them to configure an app on their phone, pair devices, etc. And so now the user can just speak to the washing machine to tell it what to do.
And because models are getting better and better at multi-language support, you can rely on a single model to cover a wide range of spoken languages. And therefore you don't need a bunch of variants of this chip for a single product line.
TLDR this gives a path to replace "always online" and "wifi enabled" devices with fully on-device capabilities without being forced to abandon assistive technology support.
I don't think that the ability to record and transcribe people's words will encourage manufacturers to remove Wi-Fi functionality. If anything, that would encourage them to keep it. Imagine the marketing possibilities.
sure but once the capability is there it's not unlikely we'll see regulatory pressure from regions like the EU or certain US states to ban or heavily limit sale of "collect all your data" wifi enabled devices.
IMHO the only reason there's not been pushback from the EU already is because so much assistive tech is only possible with wireless connectivity currently. Once privacy conscious assistive tech is viable those crackdowns will probably be on the horizon even if only for the security risk associated with millions of internet connected devices with poor security posture.
According to aljazeera's article, "the core investor would be a New York venture capital investment firm created by Joshua Kushner, the brother of US President Donald Trump’s son-in-law, Jared Kushner."
> RDW said the EU sets stricter requirements for safety during vehicle approvals than the U.S. "This means that the FSD Supervised version in the U.S. is NOT comparable to the FSD Supervised version in the EU," it said, without providing further details.
The Netherlands has one of the highest road safety standards in the world. More info on differences between USA and NL on the site of RDW (Dutch)
My 75" Samsung The Frame (2024) uses 70w in 'art mode'. It has a motion sensor and you can configure to fully switch off after some timeout.
I see a lot of blocked requests in my OPNsense firewall (not sure what exactly) but I see that with almost all 'smart' devices (which I like to keep local).
The previous ICC sanctions started a lot of discussion in the EU. For example, people loose not only access to Microsoft services but also bank/credit card (Visa is USA). https://www.theguardian.com/law/2026/feb/18/international-cr...
EU is slow, but poke it enough and it will leave USA services (and then never come back). For example, Nextcloud can substitute MS365, ICC itself did so after the previous sanctions: https://delta.tudelft.nl/en/article/international-criminal-c...
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