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For most of the world, open-source AI is the only way forward
- pmontra 4mo agoAgreed but I want to see how it plays out. Historically a good Windows computer cost $1000 and it was all it took to start programming. How much does it cost a computer with enough resources to run a good enough AI model for agentic workflows and a reasonable time to first token? Can "most of the world" afford buying one?
- bensyverson 4mo agoYes, between Moore's Law and more efficient model architectures, we just have to let time do its work.
- Danox 4mo agoSoftware models and hardware are getting better all the time—and that’s where some big companies spending billions might stumble! In fact, Microsoft recently announced that they’re scaling back a bit on their AI investments.
- ktallett 4mo agoHence why brute force needs to be replaced with examples such as neuromorphic methods. It could realistically could be combined with mesh networking as well to utilise the capabilities of all computers locally.
- mbgerring 4mo agoAbout $2k in 2026 dollars and falling.
- simonw 4mo ago... or rising, at least as long as there's a RAM shortage.
- mbgerring 4mo agoI’d bet that there won’t be a RAM shortage for very long.
- simonw 4mo agoThe best article I've seen about that is this one by David Oks (ignore the headline, the content is much better): https://davidoks.blog/p/ai-is-killing-the-cheap-smartphone https://davidoks.blog/p/ai-is-killing-the-cheap-smartphone > It was only in 2025, as memory prices began an unprecedented surge, that the memory makers started to build new fabs targeted at HBM, all slated to start producing chips in 2027 or 2028.
- fellowmartian 4mo agoIt still won’t help unless the AI bubble pops. Even old fabs will continue pumping out HBM instead of DRAM as long as hyperscalers gobble it up.
- Avicebron 4mo agoThis seems wildly optimistic, do you have anything to support it?
- AnimalMuppet 4mo agoHistory? This isn't the first RAM shortage. When one happens, producers build more fabs. The fabs come online, the availability of memory shoots up, and the shortage goes away, usually replaced by a glut. If you want to argue that this is different from all previous RAM shortages, you can, but the burden of proof is on you to show the difference.
- nok22kon 4mo agothere is a glut if demand stops. this time demand doesn't stop. there is an exponential demand for tokens.
- Kim_Bruning 4mo agoRoughly about Eur 3-4K right this minute I think? The graphics card, ram and storage are punishing. Under more normal circumstances (hopefully late 2027) it'd be 1500-2500 depending on what you think is realistically useful. Possibly it's the same price range, allowing for inflation.
- wizee 4mo agoQwen 3.6 27B is quite good for agentic coding, and practical to run on consumer hardware. You need a system with either 32+ GB VRAM, or a unified memory system with 48+ GB VRAM and a decent integrated GPU. While not cheap, such a setup is still attainable for much of the world, and will eventually get cheaper over time. Open models hosted on non-American clouds also remain an option with a much lower barrier to entry, for cases where privacy is less critical.
- jochem9 4mo agoThere was an article on HN a few weeks ago where someone detailed how they managed to get an old datacenter GPU to run in their consumer PC, getting decent performance with qwen. He spent something like $200 on the GPU (second hand of course). So yeah, I think models on local hardware will be quite common soon among the tech savvy (such as people creating software).
- wrs 4mo agoEspecially considering the millions of 2026-class data center GPUs that massively overinvested companies are currently buying, which will be obsolete in a few years.
- robwwilliams 4mo agoI think you are right when you factor in the much more efficient newer high end GPU. That is what may make the current GPu investments obsolete in 2-4 years.
- treis 4mo agoI think those are going to be run until they die. The capex vs opex is too high to obsolete them in a few years. They'll keep serving current gen LLMs for as long as they keep running.
- Chu4eeno 4mo agoThey can also be used for other things than running the main frontier whatever model as well. E.g. grok isn't truly multi-modal, it has a callable tool that is a separate VLM it invokes on image URLs or files (for a long time it was grok-1.5v, but I think they have upgraded now, it was pretty bad). And then you have the small summarizer models for the CoT/thought traces, the guidable summarizer models for the standard browse tools, etc. There's a ton of stuff that can use an aging GPU.
- Chu4eeno 4mo agoOpen weights/source doesn't necessarily mean running on local hardware, though. I imagine having multiple providers competing will drive down hosted versions of open weight models drastically.
- Tuna-Fish 4mo agoYou are describing OpenRouter. And yes, it does.
- Chu4eeno 4mo agoWell, open router is a bit more general, since it just proxies requests to both proprietary and open weights models. I was thinking more of the providers of inference on open weight models that openrouter proxies to.
- mncharity 4mo ago> Open weights/source doesn't necessarily mean running on local hardware, though. And we've barely started to scratch the surface on helping open-weight models "be the best they can be", with cloud burst parallel sampling and prompt mutation. Looking for best probabilistic results for a prompt, and looking for best prompt variants for a task. Adaptively scaling computes at generation, not just training. And speculatively, if agentic coding is naturally a multiplicity, what UX might enable human devs to dance with that quantum superposition? Rather than quickly collapsing to one monkey and its keyboard.
- giancarlostoro 4mo agoBefore the AI "crisis" it used to take about $3500 to get a prebuilt with a 5090 which can run good enough LLMs. I run reasonable LLMs on just 16GB of VRAM on my Mac, and the 5090 has double that.
- dgellow 4mo agoWhat model (and quant) do you run with 16GB? I assume you have a 24GB model, and dedicate 16GB to the model?
- giancarlostoro 4mo agoQuite the opposite, you want something below your max VRAM and you use up to your max VRAM for the context window, so it can run longer. https://huggingface.co/Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled https://huggingface.co/Jackrong/Qwen3.5-27B-Claude-4.6-Opus-...
- dgellow 4mo agoSorry, I meant a 24GB MacBook Pro model, to run a 16GB LLM :) My use of the term model here is confusing
- majormajor 4mo ago> Historically a good Windows computer cost $1000 and it was all it took to start programming. Gotta remember inflation here. $1K in 1995 was roughly equivalent to $2K now and wouldn't have been a particularly "good" machine then. In 1982 the Commodore 64 started at about $600 bucks, also roughly around $2K today. If you outgrew that, beefier machines back then were A LOT. It was easy to find $2k+ towers and (especially) laptops even into the 2000s, and a lot of those would be $5K+ equivalent today.
- SoftTalker 4mo agoAnd a unix workstation in those days could be high 4 or even 5 figures, depending on configuration.
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- rayiner 4mo agoIsn’t this just a bet that I’ll have an AI data center in my iPhone within 10 years? Why is that a bad bet?
- abetusk 4mo agoMoore's law or one of its generalizations still holds, so it will only be a short matter of time before a $1k computer will be able to train and run a powerful enough model.
- Windchaser 4mo agoI thought Moore's Law came to an end in the last decade? Certainly the transistors/chip or transistors/$ or flops/$ have not been progressing at the same exponential rate as during 1970-2010. There is still progress, but it's rather slower.
- abetusk 4mo agoI was careful to say "Moore's law-like". Moore's law is stated as "transistor count per area doubles about every 2 years." [0] As stated, then yes, this might be true but, while important, that's not really the quantity we care about. As you point out it's really cost per transistor or cost per flop that we mostly care about. I'm finding it hard to find a succinct and clear plot, but I believe one is provided by Our World In Data on "GPU computational performance per dollar" [1] which, to my eyes, clearly shows exponential growth in computational power per dollar. The picture for storage is a little more muddied but if you squint just right you might still be able to recover an uninterrupted exponential growth [2]. In my view, it's pretty clear that advances in AI have progressed so quickly because GPUs have been keeping up with the exponential growth of computational power (per unit cost). Exponential growth in this area is usually characterized by "S-curves", where one technology gets saturated but the exponential increase in power or decrease in cost is picked up by another, adjacent, technology, that allows the growth to continue. For compute it's CPUs to GPUs. For storage it's platter drives that are now being overtaken by SSDs. The more general phenomena is called Wright's law, or experience curve effects [3]. [0] https://en.wikipedia.org/wiki/Moore%27s_law https://en.wikipedia.org/wiki/Moore%27s_law [1] https://ourworldindata.org/grapher/gpu-price-performance?yScale=log https://ourworldindata.org/grapher/gpu-price-performance?ySc... [2] https://ourworldindata.org/grapher/historical-cost-of-computer-memory-and-storage https://ourworldindata.org/grapher/historical-cost-of-comput... [3] https://en.wikipedia.org/wiki/Experience_curve_effect https://en.wikipedia.org/wiki/Experience_curve_effect
- crazycracker 4mo agoHistorically the cost of compute has also gone down. Like just look at it as compared to a year ago. We have amazing open source models that can run on consumer hardware and if we go away from our obsession of using opus 4.8 or mythos for everything then it actually is super amazing to see what these open source models could do. I use qwen3.6:27b as a daily driver and I am heavily impressed with it.
- ssivark 4mo agoI don't understand the justification for local hardware with cost as the motivation. The same (or bigger/better) open weights models can served by third parties at much higher resource utilisation, and will therefore be much cheaper!? Especially because the world is likely to persist, at least for a while, in state where computing hardware demand drastically exceeds supply resulting in high prices for hardware. So why wouldn't you want to max out utilisation and amortize costs, at least for typical (non sensitive) use cases.
- layer8 4mo agoIMO the more useful distinction is in analogy to VPS versus SaaS/PaaS. Open models allow you to use any inference provider you like, including local ones, similar to running open-source software using VPS providers. You’re not bound to a particular SaaS/PaaS as you are with closed model providers. That same freedom also allows you to self-host when you care about that.
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- skydhash 4mo ago> Historically a good Windows computer cost $1000 and it was all it took to start programming Started with computers around 2009 and later bought an oldish computer (a pentium 4 PC) for the equivalent of 50 usd. Codeblocks and Python Idle were free at the time (C and Python were the first languages I learned). The barrier to programming has always been low as the only thing you needed was books (the internet made things easier) and access to a PC (I had friends with laptop and my school lab).
- Supermancho 4mo agoIn 1998 I had a second PC for the first time in my life. It was an old i486DX2-66 that my dad gave to me. He said it cost him $50 secondhand and was bought so I could put Linux on it.
- epolanski 4mo agoIf we had 256-512 GB ram unified memories at 2022 prices, we'd be talking 1500 computers.
- nicoburns 4mo agoI think AI is still in it's "mainframe era". Once upon a time, computers that ran useful workloads filled a whole room. And now that's true of computers that run (state of the art) AI models. My guess would be that this won't always be the case, although I certainly don't claim to have a clear idea of how things will play out.
- Thraway198 4mo agoWith the free tiers available now you can make your own document processors and other such office software, in one pass.
- orbital-decay 4mo agoHow far back are we talking? Historically there were just a few computers in the world, personal computing revolution happened much later. And a private inference rig costs roughly the same in 2026 as a personal computer did in the early 80's, if you want the performance comparable with a personal workstation of that time (vs a mainframe/supercomputer).
- onel 4mo agoEven if you go with an open source AI model, it will still make a lot more financial sense to pay a provider then actually invest in the hardware to self-host it. It's definitely worth investing in self-hosting the agent infrastructure around the model though: all the documents, knowledge base, all the connectors, the agent itself to run on your hardware
- echelon 4mo agoWe don't need rinky-dink RTX models that budget VRAM. We need large scale open weights models just as capable as what's at the frontier. And we need the ability to rent compute and spin up the weights easily. One-click, easy enough for anyone. Easier than nerd tools like ComfyUI, Claw, and node graph garbage. Freedom is owning very large scale weights. Anything less is subsistence.
- ktallett 4mo agoWe need to improve the waster and energy usage and this method doesn't. Most are not reinventing the wheel, a shared AI repository, communicated between online local computers would save a lot of need for these large models.
- echelon 4mo agoNO! This is the wrong approach that will turn us into serfs. We need big honking models that do what the leading foundation hyperscaler models do to within a few percentage points of measured performance. The small-scale models are not productive, and the duct tape solutions built on top of them are hobbyist-tier "year of Linux on desktop" toys. I imagine fedora-wearing, crypto-shilling, coupon-cutting boffins every time I see small weights thing lauded as the future. This is the Pine Phone F-Droid of AI. "SMS works most of the time on my phone, I swear! I don't really need my banking app!" That is not big model energy. Nothing outside of the top ten is worth spending any time on, and we need to focus on models that bridge the gap. You're talking about impractical toys for highly technical people wasting their own time. That doesn't move the needle or have any economic impact on the competitive landscape. We need sharp teeth that bite at the legs of the top-tier foundation labs and hold them back from running away with the prize. We've been through this time and time again over the last thirty years. It's the same shaped problem as before. We don't need toys - we need real infra for real people paying money to do work. Not freeware for freeloaders who don't spend and invest in the problem space. Large models fit that precisely, because it forces investment into a wide variety of open infra, routers, inference engines, etc. Not to mention the weights ecosystem itself.
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- blakesterz 4mo agoThere's a video of the entire session here: https://webtv.un.org/en/asset/k14/k14ej1ucqu?kalturaStartTime=749 https://webtv.un.org/en/asset/k14/k14ej1ucqu?kalturaStartTim... (if that link doesn't work, it starts about 12 minutes into the start)
- verdverm 4mo agohad to click the play button, but it keyed to the 12m mark
- mbgerring 4mo agoThere is no reason we should accept the enclosure of the digital commons represented by AI. The data these models are trained on amounts to the total intellectual and artistic output of human kind through recorded history. It belongs to all of us, and accordingly, so should the models and weights produced by it.
- logancbrown 4mo agoThe effort of humans who had to toil through training models belongs to everyone? Do they no longer have any ownership over their hard work?
- petcat 4mo agoNot too mention the unbelievable cost of actually doing all that training.
- SoftTalker 4mo agoPeople used to make the similar arguments about programming languages and compilers. Now you'd need extaordinary requirements to justify paying licensing or usage fees for a language runtime or compiler.
- riddlemethat 4mo agoThey got paid. That’s what the money was for. It’s the investors who backed these foundational model companies who will hold the bag as more open source models come along and consume more market share.
- thewebguyd 4mo agoI agree, but > the investors who backed these foundational model companies who will hold the bag Is awfully bold to assume that private credit is who will be holding the bag here. The IPOs are coming to shift the risk to the index funds & retail. Once insider lock up periods expire, I suspect a massive sell off.
- robwwilliams 4mo agoYann is on the mark. Almost amusing to see the EU along with its many former “subjects” realize they are at great risk of joint Chinese-American hegemony in AI. We should all be more terrified of a few nation states defining the agendas and policies of AI use than current Ai variants that a inherently without purpose or autonomy. Great analogy to the fear of the printing press being really bad news in that it enabled the rabble to get aroused.
- oceanplexian 4mo agoAI is the canary in the coal mine. They don't have an AI problem they have an everything problem. Inability to maintain energy security, declines in manufacturing, their social programs are no longer sustainable (Pension age rises and reforms), German car industry is in decline, increased spending demands for defense, and so on. All that's needed is another sovereign debt crisis to spark what is essentially dry tinder and I think the EU is a lot closer to collapsing than anyone even remotely realizes.
- blitzar 4mo ago> EU is a lot closer to collapsing than anyone even remotely realizes I have been hearing that will happen next year for the last 20 years. No this time it isn't different.
- robwwilliams 4mo agoNot how I see it. And I have seen Europe close up for 60 years. It is wealthier and happier than it was even 15 years ago. Visit Spain, Portugal, Greece, and of course the other larger northern states; no comparison. Immigration has served them well in wealth and health. Compare Europe to the hollowed out core of the USA and you will shudder. Visit rural America. The European “problem” is they live to have a life whereas in the USA we seem to live to work.
- oceanplexian 4mo agoI was in Italy 6 months ago, more than one person told me they are at the point of desperation due to the incredibly low wages (<3000 EUR/month).
- guesswho_ 4mo ago[dead]
- paxys 4mo agoWe aren’t going to have Open Source AI without Open Source hardware specs and Open Source manufacturing. Software has been solo driving open computing for far too long, and with AI now the bottlenecks are finally moving up the stack.
- OsrsNeedsf2P 4mo agoWhat..? We have open weights AI already, and even if we didn't, I don't see how Open Source hardware is a pre-requisite
- paxys 4mo agoWeights are meaningless if you can’t run the model from a computer under your desk.
- wrs 4mo agoHow does that follow? Plenty of open source software runs in a commodity datacenter. This is about the API bottleneck, not the physical location of the GPU.
- AnimalMuppet 4mo agoFine, but I have a computer under my desk. I didn't have to wait for open hardware in order to get it.
- Windchaser 4mo agoI don't follow. If weights are open, can't competing providers pop up? Including, e.g., coalitions of anarchists who collectively share compute and collaborate on modifications to the weights. Even if it's too expensive to run the models on your own personal hardware, open weights may still make it possible to take power back from the big private corporations.
- wmf 4mo agoI don't see any evidence of this. Open source software thrived on proprietary hardware for decades.
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- randomuser558 4mo ago[flagged]
- dippogriff 4mo agoEdge models will get much better after the current insane capex and organic data for pre-training is dried out. But hard to see how the best open source models will ever come close to the best closed ones.
- dbolgheroni 4mo agoIt's already happening. GLM-5.2 ranks quite close to SOTA models. Some might argue that benchmarks can't measure the real effectiveness for day-to-day usage but that's another discussion.
- peterlk 4mo agoOver the long term, it seems like open models must win out. This feels like it rhymes with the story of operating systems. Despite the enormous financial contributions of Microsoft and Apple, linux still won because control matters over the long term. I predict that mech interp and things like Neuronpedia will matter more and more over time, and the frontier providers are disincentivized from providing those tools
- petcat 4mo ago> linux still won because control matters over the long term. what has Linux won? Servers? sure
- peterlk 4mo agoYeah. I would expect that Samsung or Apple’s bundled AI things (whatever those end up being) will be good enough for consumers, and open models will be the things that actually get used in the products of the future because they will allow technical people to alter models in ways that specialize them for whatever use case they’re building for. They will also be the onramp for engineers/data scientists of the future, and thus will have more available, specialized knowledge over time. The bitter lesson would claim that we only need one model with more data thrown at it, but I’m a bit skeptical that we’ll end up with “the one true way” of building a model, and I think there will be model tradeoffs that we can pick from as the industry matures.
- seanclayton 4mo agoLaptops with Chromebooks. Mobile with Android phones. Game consoles in Steam decks and now Steam Machines. The things linux appears on are growing. The things proprietary OSes appear on are shrinking. Losers and victors in a war are often defined by who loses and who gains the most.
- sademo 4mo agodepends on what you mean by winning out most of the profits will be hold by closed proprietary models and open sourced models will be barely able to fund their own research and development cost just like how apple has barely 10-15% of market share of smartphones but takes away 80-85% of all profits. I kinda see a similarity of this may turn out for AI as well.
- prmoustache 4mo agoWhat is Open-Source AI? Has it been defined? By all accounts, all AI companies starting with open are doing proprietary stuff. All models delivered for free as "open-models" are just freeware as no source is really provided.
- layer8 4mo agoThe talk is about this: https://thealliance.ai/projects/tapestry https://thealliance.ai/projects/tapestry Collaboratively trained open-weight models, is my understanding.
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- mossTechnician 4mo ago> LeCun framed AI as an infrastructure-level platform that will soon mediate "all of our interaction with the digital world with information more generally" This doesn't read as revolutionary, it reads as accepting a premise without explaining how and why it will and must be the case. Some of my favorite parts of the internet - wikis and forums and blogs - are special to me because there is no procedurally generated mediator between me and the information. I don't want a summary that was never intended by the author. I don't want the rough edges shaved off every blog until they all sound like they were written with an identical voice. Maybe some people would like that some of the time, but all?
- cesarvarela 4mo agoAs AI amplifies everyone’s output, you’ll need AI to amplify your input capacity—or you’ll fall behind.
- grim_io 4mo agoThis is already the reality for a large portion of the population. They(we) don't consume directly from the firehose of the internet, but from whatever "The Algorithm" has curated for them(us).
- wbl 4mo agoEver since stumble upon.
- Thraway198 4mo ago
- snootypoot 4mo agonot only is he correct, but this path will result in openai going the way of enron. i hope in the case of openai and their collaborators in nvidia we see more people going to prison.
- dgellow 4mo agoWould need changes in the US government before that can happen