10 ms·

Access to frontier AI will soon be limited by economic and security constraints

• zelon88 5mo ago
  > And it doesn’t stop with the security questions: the Trump administration’s signature style of international engagement is to wield American leverage as a bundle. Deadlocks in trade negotiations are broken by threatening to withhold intelligence, tech deals are stalled by reference to food safety standards. And so I don’t know when a U.S. administration would choose to leverage its seemingly inevitable predeployment authority over frontier models to secure its broader interests, but I’m sure it would in due time. That means that even if we do everything ‘right’ on the security and economic side, frontier access is still fundamentally contingent as long as there’ll be divergences between governments’ strategic interests. The Trump Administration telling the very neo-fascist oligarchs who bought him an election and bought him a ballroom to play nice with their toys? At the expense of rampant capitalism? Lol. He already showed us the limit of his comprehension of the topic when he made EO 14179 limiting states from regulating AI. Trump doesn't swing for perfect pitches. He is a madman, a lunatic, and a true moron. Do not give this man any credit. I would be shocked if he could tell you the time on an analog clock.
  • thesmtsolver2 5mo ago
    [flagged]
    • partloyaldemon 5mo ago
      You can be a greedy pig and be an idiot simultaneously. You can see how those two things might even be correlated, no?
    • hjkl0 5mo ago
      Trump's second presidency is the best possible evidence that no one is driving the world in secret from behind the scenes.
    • mmasu 5mo ago
      I think “bought” here is to be read as “financed from”, not bought in the literal sense.
    • viking123 5mo ago
      I think we all know by now who he is really owned by.
    • Terr_ 5mo ago
      > to question That's a weird way to characterize months of incessant "we have incontrovertible hard evidence but you can't see it yet" claims, which--when finally forced into the light--were laughed out of every court in the nation. If it was just pure and innocent "questioning", things would be very different. We probably wouldn't have had the January 6th mob attack on Congress, for example.
• eth0up 5mo ago
  Damn. I predicted this last year and got thrashed for it. Glad to see others catching on.
• terrib1e 5mo ago
  No mention of open weights anywhere in the piece, which is weird. Qwen, Llama, DeepSeek are months behind frontier, not years. If you're a European startup worried about getting cut off from Anthropic's API in 2027, the real question is what the open-weight frontier looks like then. Probably pretty capable. That undercuts most of the doom scenario. Also, he concedes Mythos-level capabilities will be cheap next year, then handwaves it with "you need the best AI, not good-enough AI." For most use cases, frontier minus six months is fine.
  • wahnfrieden 5mo ago
    Llama is not months behind GPT 5.5 Pro. I don't think Qwen or DeepSeek are either. edit: I'm specifically referring to the "5.5 Pro" model, not regular 5.5 with Pro tier subscription. Claude has no model available that's comparable to 5.5 Pro either.
    • vasachi 5mo ago
      I’ve used DeepSeek 4 Pro through Claude. It’s fine. Plans are similar to what sonnet/opus make. Same massage-the-plan -> massage-the-code loop. Maybe the code is a bit worse, but that’s the “months behind” thing. The thing is, vast majority of code tasks aren’t a venture into the unknown. We as an industry for the most part build CRUD interfaces and dashboards. That can be achieved, with supervision, with frontier open-weights models quite well.
      • fwipsy 5mo ago
        I think maybe you are both right. Perhaps AI coding assistants just don't need to be all that smart in many cases, so open weights models are fine. At the same time, frontier models are advancing in other domains, like mathematics, where raw intelligence is a more important factor.
        • vasachi 5mo ago
          I can’t compare raw intelligence of these models, and I certainly can’t say anything about their advances in mathematics (without repeating press releases). But, erm, does it really matter? It’s not like some engineer somewhere will vibe-calculate how much weight a bridge can hold. Well, yes, someone probably will do that. But I’m pretty sure there will be consequences for the engineer errors in this vibe-calculations.
• coderenegade 5mo ago
  The distillation risk has been brewing for a while now. In a very real sense, the model is the data, so if the data is locked down because of how valuable it is, it was only a matter of time before fully open access to the models would be revoked. There's also an additional economic concern that rarely gets mentioned: because no one has cracked continual learning, keeping models up-to-date and filling in gaps in performance requires retraining on an ever growing dataset. Granted, you aren't starting from scratch each time, but the scaling required just to stay relevant looks daunting. I don't know where any this goes on a societal level, but I've believed since the release of deepseek r1 that access to frontier models would eventually be locked up behind contracts, since the only moats protecting the models themselves are purely artificial. It remains to be seen how effective China is at pushing the envelope, and whether they are interested in providing unfettered access. And on top of that, it remains to be seen how well these models actually turn out to scale in the long run.
  • BrtByte 5mo ago
    This is a good point, especially the "model is the data" framing
  • ehnto 5mo ago
    They are also not getting the same quantity or quality of data as was possible in the first years of "ingest". Compared to the beginning, from here on it is more like a drip feed of new training data. Still immense volumes of data, but we are talking 1 year of data production from society versus centuries of text and data ingested in a short time frame.
    • nayroclade 5mo ago
      For pre-training, yes. But for post-training you need high-quality labelled datasets for reinforcement learning. So far AI has been most successful in coding, because you can translate the usage into such datasets, and thus produce a virtuous cycle: More usage produces more data, which produces better models, which drives more usage. The question is whether this same model can successfully be applied in disciplines like medicine, law, engineering, etc.
• evdubs 5mo ago
  What's the likelihood that universities eventually become open model providers?
  • baq 5mo ago
    Zero, but update priors when you see campus football stadiums replaced with datacenters and gas turbines
    • kridsdale1 5mo ago
      When my dad got his Computer science degree at Queens University (Ontario) in the late 1970s, there was an entire faculty-sized building on campus with no windows and few doors. This wasn’t just the CompSci department, the building WAS the computer.
  • digitaltrees 5mo ago
    Oh. Thats an interesting concept. Expand
  • root_axis 5mo ago
    Way too expensive.
  • pjc50 5mo ago
    Universities are struggling to prevent their students using AI, because it makes both learning and evaluation extremely difficult.
    • davrosthedalek 5mo ago
      This. It's extremely frustrating, AI can be a 24/7 tutor, but too many students use it to do their work instead. We have to really rethink how and what we teach, and how we evaluate. Scoring (non-handwritten) homework is pointless, even contra-productive (because it incentivizes cheating, even for the students who don't want to, just to not be outscored by the cheaters). Hand-written homework means the students at least have to have read the work once... And soon, with AI glasses, even in-classroom tests will be difficult.
• sho 5mo ago
  I am no-where near as concerned by this as I was a year ago, when I was expecting the axe to fall at any moment before the Chinese labs achieved some sort of escape velocity. I now think it's too late, all the cats are out of all the bags, there's no moat except maybe a temporal one of a few months, the genie is out of the bottle. There is no secret sauce the US labs have that the Chinese ones don't, or won't have soon enough. Deepseek 4 and Kimi 2.5 are not quite Claude 4.5/GPT5.5 but there's no fundamental principle missing - they are strong evidence that there's no real advantage the "frontier" labs possess that isn't related to scale, which they will gain in time (if they even need to). The RL post-training techniques that work are widely known and easily copied. All Deepseek is really lacking is data, which they're getting - and the harder Anthropic/the USG makes it to access claude in china, the more of that precious data they'll get! I used to sort of entertain the "fast take-off breakaway" scenario as being plausible but not really anymore. The only genuine moat the frontier labs have is their product take-up, which isn't nothing, far from it, but it's not some unbreakable technological wall. Too late guys - it might have been too late for quite some time.
  • BrtByte 5mo ago
    I agree the genie is out of the bottle technologically. I'm less convinced that means access stops being politically and economically important. The bottle may be gone but the best lamps are still expensive
    • jorvi 5mo ago
      Virtually no one is going to pay for the best performing lamp if the next best lamp does 90% as good for an order of magnitude cheaper. I will say, as pointed out by others, DeepSeek and other Chinese providers still lack a bit in the tooling that Claude has, but they'll get there.
      • BrtByte 5mo ago
        If the second-best lamp is 90% as good and 10x cheaper, most people will use the second-best lamp...
• nl 5mo ago
  Quote: > “The two AI superpowers are going to start talking. We’re going to set up a protocol in terms of how do we go forward with best practices for AI to make sure nonstate actors don’t get a hold of these models,” Bessent told Joe Kernen on Thursday, on the sidelines of President Donald Trump’s two-day meeting in Beijing with Chinese President Xi Jinping. https://www.cnbc.com/2026/05/14/us-china-ai-rules-bessent-us-lead.html https://www.cnbc.com/2026/05/14/us-china-ai-rules-bessent-us... OpenAI is already talking openly about gated access to their models (see this OpenAI podcast episode for example: https://openai.com/podcast/#oai-podcast-episode-16 https://openai.com/podcast/#oai-podcast-episode-16) Separately there's also a very active effort to stop open weight releases. It's dangerous to those who think access to frontier intelligence is important.
• BrtByte 5mo ago
  The uncomfortable implication is that "AI sovereignty" may end up being less about training your own GPT-class model and more about securing compute, energy, datacenter security and contractual access
  • chii 5mo ago
    It's the same as energy sovereignty.
  • AndroTux 5mo ago
    Yeah, so it's just business as usual: If you have ungodly amounts of money, you can essentially do anything, and if you don't, you can't. It's always been this way, and it'll always be this way. I don't see this as a world-ending issue.
  • Zigurd 5mo ago
    The sovereignty part of that may have more to do with access to users' data and interactions by law-enforcement and intelligence surveillance.
  • kridsdale1 5mo ago
    Au contraire, I think we’ll soon see $10,000 self contained boxes (512 GB Mac Studio, basically) that you plug in to a LAN and have infinite inference. People pay for cars and trucks (same cost scale) globally as they’re profit-generators for every user (as far as giving them access to jobs). This is the same.
• shevy-java 5mo ago
  So now AI is about apartheid. I am not liking this at all.
  • 9dev 5mo ago
    Stop using this word so lightheartedly. You have no clue what you are talking about, and ridicule millions of people who have suffered through decades of oppression.
• ares623 5mo ago
  I wonder if the countries that don't have "AI Sovereignty" end up being like what Japan is now, technologically. It's stuck in 90's/early 2000's tech and norms (i.e. left behind) but its infrastructure and society chugs along (the demographic problem is a separate issue). Would that make those countries more attractive to young people perhaps? As a place to grow and learn skills where the opportunities are non-existent in the AI Sovereign countries.
• digitaltrees 5mo ago
  The thing is, the open source models are are smart enough to do most work if the harness and orchestration is right. So even if the next gen model get locked behind monopoly pay walls build Real things in the real world and fight for a humane world
  • margorczynski 5mo ago
    The availability of open models with such capabilities are based on the goodwill of the Chinese. And that might end eventually, especially that the matter is one decision of Xi and the party.
    • digitaltrees 5mo ago
      True but I’d argue they are good enough now to do 10 years of continued workflow automation. It’s like the internal combustion engine or personal computer, at some point they were good enough for broad categories of work. I think that’s where the current models are.
• partloyaldemon 5mo ago
  All the downsides of your cliched agi nightmares but with the “intelligence” of your bog standard national security functionary
• adrithmetiqa 5mo ago
  Considering the economic angle, one possible long term future is that access to frontier models is only realistic for the wealthiest 1% They will use this access to the ultra intelligent models to increase their wealth further. Inequality will continue to be negatively impacted
• wewewedxfgdf 5mo ago
  Its worse than that - all AI features will get broken down into even finer slices and you will have to pay for everything based on the finest level of slice they can make and still make money.
• viking123 5mo ago
  If Amodei and the co. were in charge the models would alert the police if someone said "boob" and the goys would only get GPT 2 level models, hell, even that might be too dangerous.
  • petesergeant 5mo ago
    > goys I suspect this was just a throwaway word usage, but its usage here ends up being pretty anti-Semitic, so probably worth reconsidering its use if that wasn’t the intention of your post.
    • nubg 5mo ago
      What part of what he said was false? Dario Amodei and especially Sam Altman have been treating the general public like cattle. And goy simply means non-Jew, how can not talking about Jews be anti-Semitic?!
      • petesergeant 5mo ago
        Using “goy” as a stand-in for — say — “plebs” is the anti-Semitic piece, because it implies that Jews treat non-Jews as lesser, discriminate against them, and that’s it’s common enough as to be standard usage. I am willing to believe op doesn’t know Amodei is Jewish, hadn’t thought through the implications (even if Amodei wasn’t), and didn’t mean anything by it, but it’s not just a harmless phrase for “commoners”
        • krautburglar 5mo ago
          Altman and Amodei are both a) nasty pieces of work, b) bona fide jews of matrilineal descent, & c) treat damn near everyone as "outsiders" (i.e. goyim), so use of the word goyim is a component of the truth. You are criticizing and policing the truth. In the short-run, truth can be buried. In the long-run, it always wins. So keep shoveling. We can wait.
      • djeastm 5mo ago
        What does their being Jewish have anything to do with anything?
• chvid 5mo ago
  DeepSeek is not a distillation of Claude or ChatGPT - stating this is just idiotic politics at this point. The Chinese labs have reached "escape velocity" long ago - they will continue development regardless of API access to US models or the willingness of US labs to share their research.
• phantomathkg 5mo ago
  Instead of soon, how about just "now"? I would imagine not single everyone on HN have enough disposable income that allow us to subscribe Claude Max or other similar max plan of other models without thinking. Some people mentioned open weight model, but there are two hurdles. One the current economic mean securing the best hardware is already stupidly expensive compare to a year or two ago. And the open weight model lack the magic that Claude/Gemini/OpenAI put in the proprietary one, meaning one will have to create their own agent that is clever enough to search the internet when it knows its training data is stale.
  • kouteiheika 5mo ago
    > I would imagine not single everyone on HN have enough disposable income that allow us to subscribe Claude Max or other similar max plan of other models without thinking. You don't need the Max plan with other models if you're not going completely crazy. Other providers have much more generous limits than Anthropic.
• dobreandl 5mo ago
  [flagged]
• pu_pe 5mo ago
  The more fundamental bottleneck is not even the frontier models, it's the datacenters. Let's say Europe breaks apart from the US completely tomorrow. It does not have enough datacenters (or GPUs in general) to sustain its inference needs even if it would resort to Chinese open models. And to build new datacenters, it would need to source parts from the US and China. In other words, if AI does have continued significant economic impact, only the US and China would be able to leverage it completely. The rest of the world is implicitly betting that AI won't be good enough, or that eventually the compute curve flattens out so using a model that is 10x larger only leads to marginal benefits.
  • deleted 5mo ago
    [deleted]
  • davesque 5mo ago
    > The more fundamental bottleneck is not even the frontier models, it's the datacenters. Is it even though? Quantization and speculative decoding are improving the local AI story by leaps and bounds every month.
    • zozbot234 5mo ago
      Speculative decoding is not that useful at scale, it's mostly about making local single-user inference faster. When you're batching multiple inferences together, that's already as fast as the verification you have to perform w/ speculative decoding.
      • peheje 5mo ago
        The future will have LLMs running local at your laptop/devices. If not almost exclusively then at least for 90-95% of the tasks. Speculative decoding is just one technique out of many existing and more to come that will make this even more viable. The gap is closing on both fronts. Software gets faster/more clever. Hardware gets faster and smaller. The single user story is the story. I'm obviously speculating myself, but that's how I see it.
    • 5mo ago
• Animats 5mo ago
  Over on the image generation side, "frontier AI" seems to be coming along rather well. Watch this video, which was released eight days ago.[1] Can you find any flaws? Two years ago, just getting hands with the right number of fingers was tough. Last year, there were jarring errors in every scene. Now, very little is wrong. How much longer will anyone need Hollywood studios? [1] https://www.youtube.com/watch?v=4zTCLIhScCM https://www.youtube.com/watch?v=4zTCLIhScCM
  • _diyar 5mo ago
    > Can you find any flaws Physics.
  • piker 5mo ago
    Still in the uncanny valley for me. Like watching AI the film. That said, it’s 3-minutes long and maintains the setting across many different angles, zoom levels, etc. pretty impressive.
  • quink 5mo ago
    And even if there weren’t any jarring errors, and rest assured there’s about a billion of them, there’s no appeal to this. It’s all context free short unassociated clips of pretty faces dancing on a beach. And? There’s no narrative, there’s now sense of reality, it’s just a sense of here’s a million pixels of colours that have proven to go well with each other, it’s _slop_. It’s been years and the only place AI has conquered in visual entertainment is as a subpar Photoshop replacement to fill in the B-roll gaps for those that don’t have the patience or money to do it the proper way.
    • Paradigma11 5mo ago
      How elitist of you to belittle 95% of all creators who are no better than that.
      • kridsdale1 5mo ago
        People with no talent making claims that their output is on par with master craftsmen deserve to be belittled.
• jdw64 5mo ago
  As someone who actively monitors the Chinese internet as well, I believe we are heading toward a world split into two distinct AI spheres. Coming from South Korea—a nation outside the US-China dichotomy—the fundamental issue I see is the closed nature of the American AI ecosystem. Products like Gemini, GPT, and Claude are API and subscription-based, meaning their pricing and access terms can change at any moment. If that volatility increases, developers desperate to escape vendor lock-in will inevitably turn to local models. Chinese open-source models like Qwen and DeepSeek are already exerting massive influence over our domestic AI ecosystem. While the US still revolves around CUDA, China has built its own CANN ecosystem. Most impressively, Chinese local models are incredibly accessible, even for a foreigner like me. I believe that while the US will retain dominance over the cutting-edge frontier inside Silicon Valley, the logical ecosystem—the models that individuals can actually download, run, modify, and build upon—will increasingly be dictated by China. Closed American models may lead in absolute performance, but open Chinese models will act as the foundational anchor against price resistance. If US companies attempt excessive price hikes, these powerful open models will cap those increases. This feels remarkably parallel to the history of Linux servers. Data centers chose Linux because, at scale, avoiding licensing costs, maintaining deployment control, and escaping vendor lock-in are critical. Windows Server still plays a role where vendor accountability and specific enterprise integrations are required, but in large-scale infrastructure, open systems overwhelmingly won. We are likely to see the exact same phenomenon in AI. A n open local model doesn't have to be the absolute bset. If it is 'good enough,' cheap, easy to deploy, and free from volatile vendor pricing, it will become the core of the infrastructure layer. If that happens, the foundational 'layer of thought' embedded in our systems might no longer be based on American cognitive frameworks, but on Chinese ones I am certain that AI will be deeply integrated directly into our infrastructure. The reason is simple: spending time memorizing YAML syntax just to configure a CI/CD pipeline is a complete waste of time. Because of this, we will inevitably see a surge in services that orchestrate small, domain-specific agents tailored for these exact niches. When that happens, are we really going to integrate expensive American model APIs to run them? Or will we just rent small GPU servers and spin up local models? I strongly believe the latter is far more likely.
  • oliculipolicula 5mo ago
• mc-serious 5mo ago
  Open-Source will handle access to models, someone will find a way. Security by obfuscation has never worked.
• threepts 5mo ago
  When intelligence is a commercial commodity, it is only bound to happen that the rich gatekeep it to secure their socioeconomic status. But, I think, with every revolution, hierarchies have only historically fallen only for the former serfs to rise. The industrial revolution, the renaissance -> all were marked by an massive shift in the socioeconomic status and the rise of the middle class. I think AGI, when it happens, will only raise equality. I may be wrong.
  • tornikeo 5mo ago
    I think so too. The rich will be richer, but also more people will have more at the same time. As Civilization put it: 'Just as it has always been'
  • kklisura 5mo ago
    It took almost two centuries for broad middle-class living standards to become common for large population after Industrial revolution - and that happened after intense fight for rights and fair share of economic gains. So, sure, AGI might raise equality - but that's only if we fight for it.
  • csomar 5mo ago
    I mean what if this is an inverse revolution?
• schnitzelstoat 5mo ago
  [dead]
• xer 5mo ago
  [dead]
• Havoc 5mo ago
  Think this somewhat underestimates economic pressures the US labs are under. OpenAI etc need to make crazy revenue to get their investment math to work. Perhaps you can sell some tokens to privileged partners at a premium rate but I think they’ll need global scale ultimately
• sublimefire 5mo ago
  > margins shrink and become razor-thin You need to understand that these models are provided by the corporate entities, they are expensive to maintain, iterate and run. There is still no strong correlation between the use of AI and the business outcomes so there should be a real ceiling to how much enterprises would pay for tokens. The gov is a usual choice to establish contracts and get some stability, similar to building nuclear reactors or military equipment. And posturing about limiting model access is just saying it is expensive to subsidise its use for cat image generation or call summaries. I am pretty sure we have not found the killer app (like an IDE even) for us to extract all the possible value from the models yet. I would even go as far as to say that the synthesis between a human and AI could leverage average models to achieve a lot more compared to the model/agent working on its own. edit: Just to add to this, I am going through Mythos scans and it is not perfect, very much similar to what pentesters would do with the added bloat of noise in reports about nonissues.
• nikhilpareek13 5mo ago
  [flagged]
• tactlesscamel 5mo ago
  How much money are you all paying to use this tech? Last I even tried, it would cost my entire salary. Yet, everyone and their newborns are using it every day for everything. How is this possible?
  • deleted 5mo ago
    [deleted]
  • duskdozer 5mo ago
    Probably a combination of: - people living in places with higher cost of living and corresponding salaries - people whose employers are paying for it - people who aren't actually using it but are being paid to hype it up online - bots
  • kouteiheika 5mo ago
    Depends on which exact model we're talking about, and on your salary. For example, with the $40/month Kimi Code subscription the limits are so generous that you can use it every day all the time for everything (basically just have an agent constantly running doing something) and never run out of tokens/hit the limits.
• dmantis 5mo ago
  I hope regular people will stop using "national security" and "national interests" as euphemisms and framing, and will call these things a psychopathic fight for power. Assuming that some humans are worse than others because of their flag picture and that they deserve less access to resources is barbarism. There is no security in limiting access to NSA-style entities; it's an absolute insecurity for everyone but them throughout the whole world. How is that in anyone's "interests"? We see every day now how suspicious bugs that look exactly like backdoors (i.e., Microsoft BitLocker) get exposed. That's in humanity's interests (and those of particular nations as a subset) — not being subjugated by small rings of professional outlaws. We need these instruments to defend people, everywhere. We don't need to give a leverage to any state psycho. Let's make everyone of them weaker.
• Garlef 5mo ago
  I'm not so sure about "soon" - the big labs are profiting from the discovery and experimentation efforts by independent contributors (openclaw, etc) and reducing their capabilities also reduces input from this side.
• Ozzie-D 5mo ago
  [flagged]
• rsolva 5mo ago
  In our company of 24 employees, we get by with two DGX Sparks. We don't use AI heavily, but each Spark can serve about 6-8 concurrent requests with a full context lenght of 256k, which is decent. We get about ~35 t/s depending on the model we use (currently Qwen3.5 122B A10B and Qwen3 Coder Next), but we might set up a smaller model too for simpler tasks. This works for us and will work for years to come. It is not SOTA, but it works darn well for our purposes, and we control the compute and data flowing through it, so totally worth it.
  • zozbot234 5mo ago
    That's pretty nice actually, how much KV cache does that model require at full context? That tends to be the main limit to running concurrent requests locally, there's KV quantization but it has outsized negative impact on model quality.
    • rsolva 5mo ago
      I have experimented with both q8 and q4 for KV cache. I can't find any difference between q8 and fp16, but q4 suffers more when the context grows. q8 seems like a good compromise and gives us enough ctx for about 6-8 concurrent, full context sessions. But we have not fully tested those limits yet, as the context windows rarely reach the limit.
  • uejfiweun 5mo ago
    This is pretty cool. How would you say that these open models compare to SOTA on coding tasks? I pay $200/mo for Claude Max but honestly this sounds way more fun.
    • rsolva 5mo ago
      Nowadays I use our local setup 95% of the time, but it is not that long since that flipped for me personally. Context: I have a $20 Claude Code subscription, and have used it for a handfull of small-ish projects the last year, in parallel with local models on my AMD 9700XTX (24GB) at home. Mostly Ministral 14B and more recently Qwen3.6 27B Dense 4q. Historically, the tooling (interferens engines and harness) has been the biggest challenge when using local models, a lot of the benefits from Claude Code was a rather unified and well oiled agent system. Local setups often bring with them sutle incompatibilities between models, inference engines and agent systems that are not obvious from initial testing, but cause trouble on projects larger than a couple of files. The Spark setup at work is now at a point where I do not miss Claude, like at all. A big part of this is the harness and the tools available to the agent, most critically a good tool for searching online. I use my Kagi subscription to allow the models to fetch up-to-date information, and the Kagi MCP I use also has a summarizer which is very helpful in avoiding rapidly filling up the context window. I mostly use Zed and it's native agent, which only recently got muuuch better, and on the terminal I use Pi with a minimal selection of extensions (currently pi-kagi-search, pi-smart-fetch, pi-btw and pi-diffloop). I also have Pi in Zed via the ACP, but it does not work so well with some of the extensions, especially the lack of a built-in permission system is a problem, when YOLO-mode is the only mode :) Honestly, as long as you have a model that is decent at tool calling, your good. Having a solid and stable frame around your model makes a huge difference. The only caveat in all of this is that I spend most of my time on smaller projects and debugging on linux base systems, not huge and complex code bases, so your mileage might vary. The next phase at work is to set up a chatGPT-like webinterface, and so far LibreChat is at the top of my shortlist. We had OpenWebUI for a while, but it is so bad at using MCP tools that it is practically non-functional for us. LibreChat is a bit more work to set up, but the interface and it's MCP story is much more solid. The goal is to plug in our internal helpdesk, docs and task manager system to LibreChat via MCPs to give us a quick way to query and gather information that is currently very time consuming to do on your own.
• jillesvangurp 5mo ago
  Physics and economics will drive cost. Current token pricing is based on unsustainable investment and energy cost. However, this is more of an optimization problem than an inherent show stopper. Token cost will inevitably come down over time. But this could take a while before it catches up with demand. Manufacturing will step up to provide cheaper GPUs. Etc. There will be some consolidation but the whole thing will converge on something that should make long term economical sense. Ultimately it's a resource control issue. To power AI you need land/space (to build on), water, energy, and lots of hardware. Hardware needs to be manufactured and engineered. It needs metals, some exotic materials, machines, etc. More resources in other words. If you look at China vs US here, they are really well positioned in terms of resources and supply chains. The US has fallen behind quite a bit on energy and all the critical resources needed to produce hardware. AI is bottle necked on a lot of stuff that China has or makes in abundance. For the frontier models, there are a growing number of companies and countries that provide them. We're used to mostly talking about the US ones. But of course the Chinese have a lot of capability here and they are not that far behind. And that's judging by the models they choose to release under OSS licenses. Those models are not their frontier models. And there are a lot of other countries developing and using models that aren't necessarily talking openly about what they are doing. The irony with these frontier models is that they only generate revenue if people can use them. Why sink billions in AI infrastructure and models without a revenue model? The reality with Mythos is that you have to assume that the Chinese (and others) are not that far behind and may already be running an equivalent model that they just haven't told anyone about yet. Anthropic gate keeping Mythos and its findings is probably wise. But it's not long term sustainable to depend on that happening or working very well. Or even on them even being a leader in this space. This is becoming an arms race between countries, and economies. And it's an economical and resource control race. Developing and researching in the open has advanced things massively. But it has also empowered the rest of the world. Both Anthropic and OpenAI are staffed with people from all over the world. You have to assume that they probably aren't very good at keeping things secret.
  • zozbot234 5mo ago
    Those billions in AI datacenter infrastructure will eventually be repurposed to run smart models like Mythos, not ChatGPT or even Opus/Sonnet. That future "revenue model" is quite robust to any foreseeable competition from on-prem FLOSS inference. It's a natural fit to the actual capabilities of large datacenter-scale compute.
• seydor 5mo ago
  We should be aiming for less token usage, ideally none at all. The current AI is using LLMs to expanding horizontally but with the goal of achieving vertical progress - inventing truly new stuff and being able to eliminate our biggest problems. problems like cancer need only be solved once, and is no more tokens needed after that.
• resters 5mo ago
  I think we'll know this is true when deepseek becomes illegal in the US. I tried to sign up for deepseek API access directly from the company out of gratitude for the open source contribution (deepseek.com) but payments are blocked by US government rules.
  • arcatech 5mo ago
    The point of open weight models is not having to rely on specific providers for them.
    • resters 5mo ago
      For now open weight models are legal. Conceivably the government could claim that an open weight model "contained" forbidden information and simply ban it. We live in authoritarian times.
• voxleone 5mo ago
  [flagged]