Dependents of an AI-megacorp for our "facts"? Our software? Our work?
It's possible these companies will become everyone's boss, and will dictate to everyone what everyone is allowed to work on, think, say, do, believe, etc.
Before Big Tech springs that trap, we must support and divert resources to open models.
We already have personalized, algorithmic advertising and what I would call “control” all over the place: things like consolidated oligarch-owned media.
AI isn’t going to change how we are advertised to or controlled all that much, at least compared to the prospect of being put out of work or taking a huge salary cut similar to the mid-century worker who used to have a $40/hour union factory job and now works at Walmart below health insurance threshold for $15/hour.
What I’m saying is that the general public is most obviously and personally impacted by their economic situation and job prospects.
Joe Citizen who lives by the rules might not even notice that new Flock camera on his street, but he will notice if he’s laid off from his job.
Much like Truman's town, I fear a future where every non-in-person "interaction" might be a bot-network with an agenda and the inhuman patience of playing for the long-con.
Or capital a comparable sum to pay an AI to approximate the skills of humans I guess is the proposed future?
Would be nice if someone figured out how to properly debug a model. Without that? OK, so you have your own open source base model trained on your preferred document set that excluded whatever you think is propaganda, and your own open source RLHF training set based on the judgement of whoever you think is a good egg, and so on.
Last I checked, nobody yet knows how to define a precise rule for automatically checking which of two models made this way is aligned better with whatever your standards are.
The metaphor would be like if we knew what a CPU was but had no idea how to do either chip design or formal verification, and instead randomly mutated the connections between transistors until our test set of 2^16 randomly selected pairs of 32-bit numbers only had one error under addition and two under multiplication.
Worse, because we're making them this way, you have to be a fairly big corporation even when you take shortcuts like DeepSeek did.
And note that I'm not disagreeing about the systemic risk that comes if these models become dictators: people are currently demonstrating they're very eager to outsource their own thinking to these models even when they ought to know better, and corporations are currently demonstrating they're very eager to force workers to use them even when they're mediocre and workers spend half the time they might save from a more competent model just fixing the damage done by their current meh-ness: https://www.theregister.com/ai-and-ml/2026/06/10/brit-worker...
The mechanism will become like taxes, you don't have to use public services thus pay those taxes, unless most people comply as it's easy to oppress those who don't.
The parallel isn't about legitimacy, but Mechanism. Some companies already oblige employees to use AI to deliver their work. In a near future we may see jobs seekers registering their AI ID for companies to decide which humans qualify to be plugged into the compensation system, at what rate, and usage conditions to avoid terminations.
Food delivery systems already show a glimpse of how it could look like.
Sure you can. But you're going to have a bad time.
2. The Amish are not a good example because AI will confer an advantage to those that control access to it that has never existed.
It's worse than this, it's more like our thinking. There's already plummetting math grades [1], handing over our thinking to AI megacorps where there's likely to be a monopoly or duopoly is an incredibly dangerous thing for humanity as a whole.
[1] https://www.dailycal.org/news/campus/academics/failing-grade...
The conundrum which tricks me though - is this a net negative or a positive? If humans are less intelligent, but their output is 2-3 times more intelligent (with AI), what's the result? At what point do we, as humans, stop comprehending anything and give all intelligent work to the neural nets?
And if that does happen, could we live in a society where no work, or at least a significantly less amount of work, is needed? To me, it seems like a dystopian net positive.
It might seem far-fetched to ask these, but I think these questions are getting more prevalent by the day.
Just listen to what the SV ownership class says out loud. They openly discuss how China cannot "win the AI arms race" and how China's development is existential. Existential to who? It's impossible to fully subjugate people with agency.
A friend of mine asked me if I was optimistic about AI. I told him, it depends on who owns it. If the people own it, I'm optimistic. If the oligarchs own it, I'm pessimistic.
What will happen? Massive. Deflation. What will you pay for an oil change? Corn? Meals? Everything is about to be free. But tokens will be expensive!! Sure but, you wont do white collar work anymore so it wont matter what tokens cost.
I feel extremely strongly that a future in which most companies depend on one or two large AI-megacorps is going to lead to excessive rent seeking sooner or later.
I remain positive that the long term steady state will consist of proprietary models, -but- with open source AI models statistically close.
If compute keeps growing the relative cost of training current frontier models will decrease. An open source Fable/Mythos model simply seems inevitable.
You have either VC funded models looking for a return on investment, or CCP funded models looking to solidify authoritarian "model Chinese society".
Maybe there are some university 4B models, but I doubt those will carry far.
I am astonished on a daily basis that my Linux computer is so close to the same experience as two operating systems put out by trillion dollar companies. It even does things that those commercial alternatives don’t do.
Also, if DeepSeek is truly putting out models with 1/10th the cost of Western competitors, and a fraction of the employee headcount, I think it implies that there will be a market for someone else to be in the space offering an alternative.
I think about how companies like IBM are so willing to contribute to Linux and give away those contributions for free because they are part of group of corporate sponsors that need an alternative to more dominant commercial players in the market.
Meta “gives away” React for similar reasons: it’s more beneficial for them to have it be a standard and be able to hire people who already know it.
It’s definitely harder to imagine the same ecosystem benefits of an AI model, but maybe it’s out there somewhere.
I could imagine some data center/VPS providers trying to sponsor something like that so that the big AI companies have less leverage over them.
Or maybe all this optimism is a pipe dream?
I feel like they aren't comparable. Open source software just requires human labor, and lots of people are willing and able to share that with the world for free.
Training AI requires capital, to build and power giant datacenters. People don't donate capital at that level.
However, Once real costs are involved, participation tanks. Open source hardware, because it actually requires money to realize, has 1/10,000 the depth of open source software, if that.
Obviously everyone wants an open source AI, but virtually no one wants to fork over money, especially when the end result is others getting it free. A proper training run would require millions of people donating hundreds of dollars. Its not something one guy over a weekend can do...
With a lot of OSS it’s just free volunteer hours.
Compute isn’t free.
The closest thing I can think of is the idea that some group of businesses who can benefit from open models being around might fund that sort of thing. It’s just hard to imagine who they might be.
We live in a world where you can "port" open source software to a new language (Rust) and close it up.
Linux will be ported to Rust and closed. It'll probably also be put under MIT/BSD because nobody cares anymore, but the companies will have their own internal private variants. And these will be the ones that see corporate development.
The value in open source is that it was a lot of concentrated value that was hard to copy, clone, or rip off. Now you can one shot a replacement with a few hundred bucks in tokens.
The economic value of Linux used to be billions of dollars. Soon it'll probably be closer to $0.
It's over.
> Meta “gives away” React for similar reasons: it’s more beneficial for them to have it be a standard and be able to hire people who already know it.
Nah, now you just one shot your thing. And you do it fast enough and with distribution and you win. Eventually human devs can't afford to keep competing and launching startups slower than a hyperscaler's own massively funded efforts.
This is the end of open source and the end of solo developers.
And when the ruthlessly effective models that can one shot entire business functions cost $1,000,000 per invocation. Oracle can afford to press the button to create, say, a new smartphone. But you cannot.
Just wait until devices start requiring trusted computing attestation. The ladder is going to be pulled up.
The scenario you describe is basically that software is free as in beer now. We as a corporation don’t really need to bother using GPL/Apache licensed software because we can one-shot something of our own and not deal with with giving back contributions to the open source community.
But that highway goes both directions. That means that the open source community can also one-shot their software, build more with fewer resources, or it might even just devalue proprietary software even further.
If software is so easy to make, what’s the point of keeping it proprietary? I can’t charge you $100/year for Microsoft Word if I can tell Claude Opus 9.0 to clone it with $100 worth of tokens.
Thinking of a open weight/source AI as gcc/perl was in the 1990s is more helpful line of approach to take here.
The tool used to achieve a thing must be open.
What matters is physical infrastructure (datacenters), the lead on competitors / open source models, and distribution/mindshare.
People questioned whether there could ever be a viable open source operating system, yet Linux has been a viable option for a desktop environment for decades now, and that's not to mention its ubiquitous use as a server or phone OS.
You have to start some where. Im guessing, making progress also brings in new ideas how to move further.
It's the most logical solution for AI anyway, considering that it's training on humanities collective knowledge. It should be more of a public-funded and public-access resource, rather than something greedy tech companies distribute like crumbs while they use unlocked powers internally to clone all of our businesses and swallow the economy.
It highlights the difference between companies like Nvidia and Anthropic to me, where one is clearly all about the money and power, and the other is doing it because they genuinely want to accelerate progress and make cool stuff as the driving factor. It's no surprise therefore, that Nvidia is the worlds largest open-source contributor to AI, with over 800 open-weight models.
Of course, these models run on Nvidia hardware, so they benefit from it as a company. But with that healthy mindset, they found a way to contribute that not only benefits everyone, but also benefits themselves.
Contrast to Anthropic, who has gone the complete opposite direction. Closed off everything, restricting everything, fearmongering progress, regulatory capture attempts, the list goes on. I mean, they won't even agree on using AGENTS.md as a standard because CLAUDE.md is free marketing for them. That's the level of disgusting greed we are dealing with...
From a game theory perspective, the cooperative strategies tend to win. As a result, Nvidia has set themselves up for a lifetime. Anthropic however, is playing a strategy of winner takes all, and they're happy to see the world and the entire AI industry collapse in the process.
But yeah they are good shovel seller and competitor to actually evil companies that literally wants to eat all the world chips and energy supply.
Compared to bizes like Oracle, Microsoft, or Facebook, I felt that Anthropic was more interested in progress (not to the neglect of business―AI training is expensive at the end of the day), but maybe I've just not seen what you've seen.
And, there is the issue of data poisoning from untrusted nodes. I've almost cracked that last issue with a self-healing checkpointed rollback system that doesn't have to throw out anything that follows the corrupt datum.
But, I'm just one person with an idea and I don't have infinite funds to make this happen. This isn't a small project.
Maybe there would be interest in something like this, now that entire frontier labs are being banned from making further progress.
The total power of all GPUs on the planet dwarf their capabilities, if we had a way to harness them in a distributed way efficiently. We wouldn't be able to train a Fable as fast as them, but eventually having access is better than never having access.
That just isn't true. It misunderstands exactly how much silicon has gone directly to those companies, and exactly how much more powerful said silicon is compared to consumer grade gear.
And there's already people working on this, I think the people associated with Hermes agent.
Because of this, I think it might not be possible to have AI *only* open-weight; major players like OpenAI, Anthropic, Google will likely stay for good, with better models than open-source versions.
I think it might look something like Photoshop & GIMP, with Photoshop being a frontier lab, and GIMP being the open-weight model. GIMP is decent for many different image editing workflows, but Photoshop is just better.
I would definitely prefer to have an open-weight model better than frontier labs'. Though I don't think it's possible.
That is, of course, unless they develop their own hardware specifically to run this open model. But, that does ruin the point of open models.
Even if the GIMP of LLMs is only 80% as good as the VC-funded stuff, that will still be plenty useful for lots of people.
And I think just having the option to use open source models is a win, even if it turns out to be true they'll never be quite as good as the proprietary ones.
(Yet; I do worry about future required hardware attestation for basic things, but that's another issue.)
I learn it hard from prusa 3d printer open model
It should be clear by now that there’s a whole universe of work to do with the models we have today, from studying to securing to ‘harness’ing. There are tons of economic benefits to be reaped already, if applied carefully. Doesn’t that sound nicer than rolling the dice with the lives of trillions?
It doesn't really matter for most use cases, because the way AI is working is capability saturation. https://www.delanceyukschoolschesschallenge.com/the-rising-t...
The only exception to this is fields that are inherently adversarial (to nature or others) and an edge relative to competition matters.
Open source 'winning' just means that there exists at least one open source alternative to closed models which is as good as, say, GPT 4... I mean, we're essentially there already with Google Gemma models.
As a software engineer, I didn't notice any difference in my productivity since Sonnet. Of course Opus is better and I'm sure Fable is better yet, but we're already hitting diminishing returns in terms of economic value.
I went from Cursor running one of the earlier GPT models to Claude Code on Sonnet and that was essentially a 5x productivity boost for me. Before Claude Code, I only used AI for small snippets. With Claude Code + Sonnet, I could trust it for entire sub-tasks... But I still don't trust Opus with full end-to-end features. I'm not sure it will ever get there. It probably doesn't need to.
Companies need software engineers to have a certain moderately high level of talent but above that level, they really don't care AT ALL. They don't even notice the difference, even if the gap is significant.
Is this really true? We just don't know what the maximum capability of AI is. If it turns out AI can be as intelligent and capable as something like Data from Star Trek, no one is going to be thinking GPT 4 is good enough.
That's what the Fable harness felt like. You give it a goal and it could try to get there through the shortest path given the tree of possibilities to get there. Iteratively, or recursively.
Perhaps if we make a open coding AI, the design must be along these lines. Something that's easy to train, and serve from local machines. Albeit has loop / recursive hill climbing facilities built it. That way the model gradually keeps moving towards the solutions, in iterations/recursions.
Once this is done, other multi modal things could be pursued.
Your reflexively negative comments on anything relating to AI are as insight-free as they are numerous; it's all just vague shitting-on without even a hook or argument that could be engaged with and debated. It's pretty tiring, honestly. If you really think your point of view is valuable and others should pay attention to it, rather than just filtering it out like the trollish noise it usually is, why don't you put a little more effort in?
https://github.com/cobusgreyling/loop-engineering
Its hard to come up with new names for novel processes, you mostly reuse what is close enough and well known.
Given a problem P-
1. Provide a list(S) of solutions(S1, S2 ... SN) ordered in the most efficient(For some definition of efficiency) implementation means possible.
2. Execute S1, ... SN.
3. If P is fixed by a solution in the list, halt.
4. Else for each S1 ... SN , execute steps 1 through 4 until, all dependencies and sub problems are resolved to eventually solve P.
This obviously needs lots of tokens, which is all the more reason why we need AI to run locally on our machines.
Being Open Source (tm) will not protect you from the government/others imposing controls on your silicon or what it is allowed to do, which is already happening around the world.
Even having the models be open source won't fix the regulation or economic incentives. Which is not something you can compress into a couple of paragraphs.
AI is civilizational infrastructure and it needs civilizational solutions. Not just source.
Everybody knows AI firms pirated to train, nothing will come of it. A plain example of classist application of law.
The reason for the willy nilly application of their own laws will always be 'national security', of course, since they own infrastructure their interests are a national security.
So tech may shake things up whenever it makes great leaps, but finance capitalism quickly adapts and absorbs the waves.
to me Open Source, like Free Software, is something i can run on my own computer. any AI system that runs on a computer that i do not control is by my definition not Open Source.
so how then can Open Source AI win? it can't even compete. even if we collect enough money and create a dedicated Open Source organization to build and run a community owned AI datacenter, how does that help?
so what exactly is the demand here?
This is not true at all. It would be open source if you could download it and run it anywhere that is capable, and are free to move it and modify it as much as you want.
Just because you don't have a computer at home powerful enough doesn't mean it isn't open source.
Right now there a few people who can run a 1T model at home, even less who can run a 5T model and probably single digits who can run a 10T model.
But if an open source 10T model was available you can be sure people would find new ways to quantize it, new ways to configure hardware and and new ways to think about problems that would make it useful.
1T+ models (Deepseek v4, Kimi K2.6 etc) are available as open weights now, and for ~$5000-$10000 you can run them usefully at home. 2 years ago no on was contemplating that.
$250K to run a 10T model might be possible now. There are many companies that will pay that, and that will push the tools and techniques downwards for the rest of us.
But I am going to need a much beefier machine to get it to the point where it can do any but very trivial dev tasks acceptably fast, and I'm going to need a much beefier model, perhaps one not so aggressively quantized, to keep it on task without the wheels completely falling off. Already we're talking serious money outlay, perhaps still within my programmer salary to accommodate, but just barely. And we're not even where near the performance characteristics a frontier model can support.
Qwen 2.5 72B is surprisingly capable, almost on par with GPT-4o if not a little better. You can run it on a 128GB Mac Studio with 8-bit quantization. You need about 77GB for the weights and ~15GB for your context window & cache.
Pricing remains to be seen, but there's also those new nvidia laptops coming out the surface laptop ultra should have 128GB RAM w/ Blackwell GPU, they're saying 1 petaflop of AI compute, if you can tolerate Windows (no idea if it'll boot Linux until the hardware is out).
These models are roughly ~1 year or less behind the frontier models. We really just need hardware to catch up and alleviate the price pressure on RAM.
To make any agent "good", there are two components: the model and the harness. Very few companies can train models, but anyone can build a harness. How much does the harness matter? Can I build a harness that's good enough that I can use open source models with opus level performance? That's the question I've been trying to answer by building better harnesses. None of the existing frameworks have the functionality I need to build a good harness. The features I need are language-level... and so I started building a language called Agency[0].
It's been six months and its going well. Some of the things Agency can do are wild:
- It can pause and serialize execution at any point, making HITL easy
- It has some neat safety capabilities such as handlers[1] and PFA[2]
- You can bundle up any agent as an HTTP or MCP server[3]
- I'm now working on a built-in optimizer to optimize agents (think DSPy).
Obviously, it's a huge undertaking, but having worked with the Agency for six months, I can't imagine going back to another framework. It makes things so easy. I'm working on its built-in agent now [4]. My goal it to get it to be as good as Claude Code, but using open source models. It's still early days, lots of rough edges, but if this sort of thing interests you, I'd love to have a few more people test it out.
[1] https://agency-lang.com/guide/handlers.html
[2] https://agency-lang.com/guide/partial-application.html
[3] https://agency-lang.com/cli/serve.html
[4] https://github.com/egonSchiele/agency-lang/blob/main/package...
It doesn't seem to be showing any signs of stopping. Have you used Fable 5? It's a fantastically capable model and trumps anything that came before it. Seedance 2.0 is categorically the best video model, and it's only a few months old.
> the entire business is run by a few old men
Startups tend to skew young, and in this case it's no different. Most of the leaders of AI companies are decades younger than the CEOs in other types of industries.
> who think AI is everything and invest huge sums of money to show other AI companies they need to improve to get more funding from old people.
They're spending capital to win market share and to try to build a moat. One of the most important things in business is building a durable way to keep competitors from taking your market. You spend enormous capital to win customers, and it would suck if other businesses could watch what you did, spend less money, and come in and take everything away. The money being spent is an attempt to have a durable lead.
It's working. Enterprise contracts are deep and sticky tendrils that work through governments and large companies. Both OpenAI and Anthropic have massive partnerships with Fortune 500s, the DoD, you name it - and these contracts will last and print enormous amounts of money. This makes it incredibly hard for other players to enter the market and build a cash flow with which to compete and thrive.
> find something new and innovative
This is easier said than done. It's an incredibly hard problem. It took decades to find the last big technological waves: the PC, the internet, broadband, smartphones. Now AI. These are generational step function increases. The groundwork can be decades old, but it takes time to proliferate before it can become a big business.
Other possibilities include fusion, green tech, quantum computing (useful for crypto breaking, etc.), AI drug discovery, etc. If you go into research one day, try to find an interesting field with potential for commercialization - that could make you very wealthy if you find something you enjoy working on, with lots of greenfield opportunity, that is ripe for turning into products.
Good luck with your game! You should post it here on HN when you finish. You'll get lots of great reviews, comments, and early players. :)
Right now, and likely forever, because biological threats can be sanctioned at a supply-chain level, the risk of AI is all digital. Fraud, phishing scams, spam, hacks, etc.
The only way we harden the worlds infrastructure to the point that it can withstand attack from bad AI is if we have an abundance of access to frontier intelligence to develop countermeasures.
Otherwise, bad actors will develop these capabilities behind closed doors and use them to hold the world hostage and cause irreparable harm. There's no putting the genie back in the bottle. Good and open-access AI and the people using it are the digital immune system.
If there's an asymmetry where bleeding edge is gated off to only a small group, and allowed to gain exponential power over the immune systems defense grid, the slightest infection will lead to death of the host.
Ever since a Chinese firm released DeepSeek I immediately came to the realization that any US tech firm "owning" AI is simply not going to happen. China will make sure of it. It's in their national security interest not to let that happen.
From the POV of geopolitics, IMHO the US shot itself in the foot by banning the export of the best chips to China. The US also somehow has the power to prevent a Dutch company (ASML) from selling to China too. That makes a little more sense to ban but the combination of banning EUV exports AND banning the best chips sowed the seeds for the destruction of all of this.
By banning chip sales, the US inadvertently created a captive market for Chinese chips with Chinese companies. If there were no chip ban, Chinese companies probably would've bought US chips. But they can't. So they can only buy from Huawei and SMEE (indirectly). The US forced China to realize it was in their national security interest to copy the best lithography and, by extension, the best AI chips.
So DeepSeek was reportedly developed on either older NVidia hardware or smuggled newer NVidia hardware but that won't last either. At some point it'll be completely Chinese made chips that are doing this.
And what's the biggest cost for a model? Training. But you do that once and the model like any software is infinitely copyable so China can under OpenAI, Anthropic and SpaceX (xAI) and that's what they're doing.
But it gets worse for the AI moat. Local models are going to get cheaper and cheaper to run. You can already run 31B models on sub-$5000 hardware. What do you think it'll cost in 5 years? Will larager parameter models keep getting better or will there be a law of diminishing returns? What is a B100 workload now, will be a Macbook Pro workload in as little as 5 years.
What if all these AI data centers are ultimately just going to be commoditized cloud hardware like AWS in the not too distant future? We already see Google renting big from SpaceX. I think the writedown on all these data center investments and the companies that are doing them is going to be extreme in the next 5 years.
You can one-shot a port of Linux to Rust and stop contributing to open source.
The value of software is going to tend towards zero. The value of the software developer the same.
Anthropic is now a kingmaker. It gets to decide which businesses get the expensive private model that can generate entire business functions at the drop of a hat. If you can't afford the price tag, then competition in the market is not for you.
Computing is no longer "personal". It's for big biz only.
Touch grass brother. Seriously.
Who's gonna pay to power an open source AI? Will it perform well enough to make Chat-GPT and Claude obsolete?
These things can't even center a div correctly half the time.
Not everything is code. Just because it generates a shitty SaaS clone for you and that seemed magical, it does not mean we are approaching "AGI".
An AGI could design an Oil tanker, manage the project from start to finish, handle all contract negotiations and purchasables, payroll, scheduling. Then it could do that 50x over and start a leading logistics firms.
In reality an LLM can't even complete upwork projects that are worth $20 an hour more than 4% or the time.
Source:
https://labs.scale.com/leaderboard/rli
4% guys, 4%. It cannot complete entry level work on fucking Upwork 96% of the time. Stop falling for the marketing and sorry but an LLM will never be AGI.
Its literally just text autocomplete with some RLHF post training, holy shit im losing my mind. I want this hype to end so badly holy shit I need this to end.
Anthropic just kneecapped themselves, and possibly OpenAI and Google as well, with their FUD strategy that got fable shutdown by the government.
But that doesn't impact Chinese providers. Then can US companies get investments for expensive model development if they can't actually sell those models-as-a-service?
In the meantime, open source will continue its march onward because while slower, it's completely open source, and the models are already good enough to improve their own work as well as build out the next gen of models.
All we can do is hope we end up in the one where things are ok.
information wants to be free
These models and the hardware they are running on will get even more efficient. We are nowhere near the physical limits of what we can achieve.
Not anymore! Well, if you're like Elon and already taking down the bottle of Cuatro Comas from the high shelf, the economies of scale will continue to work in your favor.
But one of the really neat things about AI is that there is no limit in sight to the scaling incentive. More compute will always get you more: more training, more inference, more parameters, more capacity to build more and better models, more spare capacity to run the slop your models have already built to generate the slop that will succeed it. Back in the dot-com days, or even the "big data" days, you wanted to scale up rapidly but there was a limit: there were only so many customers and they could only produce so much data you could only ingest so fast. In the late 90s, one of the world's most trafficked sites, ftp.cdrom.com, ran on a (single!) dual-processor Pentium Pro system. That was just serving files, and there was certainly room for more CPU oomph to provide more sophisticated services to a huge customer base. But once those customers were served, more compute, storage, and network capacity didn't buy you enough to justify the capex. That is emphatically not the case with AI, and so the incentives for the AI companies are to buy as much compute as they possibly can. What this means in practicing is pre-purchasing capacity at the semiconductor fabs to manufacture chips exclusively for you, and there's only so much of that capacity in the world. Trillion-dollar companies can easily outbid the entire consumer market, and so the incentives for the fabs are now to sell to AI companies at the expense of the consumer market. That's why you're seeing memory prices go through the roof. Modularized RAM for end-user PC builds will soon go the way of the CRT: it will cease to exist as a market product, it won't be manufactured anywhere by anyone. GPUs, CPUs, and storage will soon follow. The only devices end users will be permitted to purchase are all-in-one integrated devices, with CPU, RAM, GPU, storage, and networking either integrated in-chip or soldered on, and they will have just enough capacity to connect to the cloud services the user wants most to use. Most likely, you will be permitted a subscription to such a device, with automatic hardware upgrades at periodic intervals supplied by the manufacturer. If your subscription lapses the device bricks itself. Almost certainly, the OS will be locked down, with no end-user option to install a different one or even run unapproved software.
If reasonably powerful computer hardware for end users exists in this future, it will be available from a single company: Apple. Only they have the leverage to prevent ~100% of manufacturing capacity from going to high-roller, big-tech firms.
And once it leaks, it's permanently in the wild.
Interesting times.
K
That’s really the only thing stopping people from training or running these models at home:
but ok, who is going to initiate such a treaty? US? the orange man won't, and even if he did, no one would care. by the time his term is over and the next AIPAC spokesperson is elected, it will be even more late than it is now. EU? impotent and irrelevant. China? lmao.
Biden's GPU controls should give you an idea. Thank you, China. Open source AI must win.
Famously, the PowerMac G4 was briefly subject to export controls. Apple turned it into a marketing campaign.
Go ask Claude to criticize Anthropic and see how long your account stays active.
Or are we still collectively brainwashed by the strategic false equivalence established by Big AI CMOs?
I'd never though I'd have to utter the expression "open as in beer".
The blatant attempt at manipulating vocabulary here is... quite blatant.
The weights are the useful artifact here. You can modify them, fine tune them and do what you want with them.
Unlike binary software there is nothing limiting that.
It is also useful to have access to the training recipes and to some extent the data. But I'm of the opinion that learning on something is not copyright infringement, so there are many circumstances where distributing the raw training data will not be possible.
For me this is like Open Office: it is open source, and largely inspired by and learned from Microsoft Office. But they don't need to distribute MS Office for Open Office to be Open Source.
In addition there are models that meet the criteria you appear to propose. The AllenAI models are a good example.