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eicker, eicker@lemmy.world
Instance: lemmy.world
Joined: 3 weeks ago
Posts: 18
Comments: 161
Hi! š Iām Gerrit and in love with the Internet since 1994. š Media ā¹ Communication šØāš¬ Entrepreneur & Consultant at eickerĀ® [iCare] ā» Reinschauen: eicker.TV š² Get in touch! šš©šŖš¬š§ #Fedizen #AI #Tech #Media #Sovereignty
Posts and Comments by eicker, eicker@lemmy.world
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Comments by eicker, eicker@lemmy.world
About 4+ maxed M4 Studios, I guess. But thatās not the point: in 80%+ of cases, people wonāt need that kind of AI model to solve their problems.
Raw capability is only one metric: A local model probably will not beat the best cloud model any time soon, but it does not need to. If it handles 80 to 90% of everyday tasks instantly, privately and at near zero marginal cost, that is a huge win. Reserve the cloud for the genuinely hard requests, not every prompt.
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Absolutely. Looking forward to seeing the next generation of Macs.
The decentralised operation of LLMs would also be significantly simpler and cheaper for the use of decentralised renewable energy sources.
Iām waiting for the next generation of Mac Mini and Mac Studio.
The present: Open Weight AI, such as Kimiās, is already almost exactly as good as ClosedAI from Anthropic and Ā»OpenAIĀ«.
It would seem so. On the other hand, it is puzzling that they did not also allocate the necessary resources to the development of LLMs. š¤·
The interesting part is not whether Apple wins the biggest model race, but whether it changes the economics: If enough AI runs locally, every token avoided is cloud capacity nobody has to build. That is a very different business model from selling ever more cloud compute.
Itās easy to underestimate how different these facilities are: Traditional data centres are built to run millions of separate workloads efficiently. AI data centres are engineered to make tens of thousands of GPUs act like one giant supercomputer. The building may look similar, but the power, cooling, networking and economics are almost an entirely different industry.
The market is fragmented across the Member States, each of which conducts its own research. That is precisely what needs to change. Hopefully, this approach will work.
This is a significant step for Europeās AI ambitions. Access to large scale compute has become one of the biggest barriers to building competitive AI models. If executed well, these gigafactories could give European startups, researchers, and companies the infrastructure they need to innovate at home instead of relying almost entirely on US or Chinese providers.
This is exactly how the DSA is supposed to work: Once a platform reaches sufficient scale and societal impact, it faces higher transparency and accountability requirements regardless of whether itās social media, gaming, or AI. If anything, it would be more surprising if major AI platforms werenāt eventually included.
These are purely paper profits.
Unfortunately, it is highly likely to take decades before Meta loses its relevance: the network effects are enormous, and the vast majority of people over 35 will probably only leave Instagram and (from the age of 60) Facebook completely in exceptional circumstances. And I donāt even want to get started on WhatsAppā¦
Meta can absolutely pivot to renting compute for whatever model wins, but calling that an automatic death spiral feels premature: Companies with billions of users can survive technical setbacks for years. Betting against distribution has humbled plenty of investors before.
What strikes me as particularly risky at Meta is the sheer amount of power Zuckerberg wields: ultimately, itās the same problem as with Musk, even though Zuck has behaved much more moderately so far.
Interesting that Microsoft is effectively running a portfolio strategy instead of betting on a single lab. Anthropic is already paying off on paper while OpenAI looks much more volatile. That alone suggests the AI race is far less settled than the headlines usually imply, even for the company funding both sides. And now the Chinese Open Weight AI models are coming onto the scene too, offering almost the same quality.
Pragmatic, but revealing. If Meta truly believed Llama would stay ahead, it would not be preparing for a future where competitors power its products. It also makes business sense for Meta because it already rents out compute capacity instead of using it for its own models: supporting multiple leading models could attract more customers to its infrastructure.
Thatās a more realistic ask than hoping everyone slows down: If alignment methods can be made accessible to engineers without deep math, more people can stress test, critique and improve them. Knowledge spreads far more easily than voluntary restraint, especially when geopolitical incentives point the other way.
Interestingly, China doesnāt need this computing power, yet Kimi has now unveiled a competitive model all the same. I wonder how this will pan out?
Microsoft logs $3.2B from Anthropic investment, but OpenAI was a mixed bag (techcrunch.com)
Pragmatic, but revealing. If Meta truly believed Llama would stay ahead, it would not be preparing for a future where competitors power its products. It also makes business sense for Meta because it already rents out compute capacity instead of using it for its own models: supporting multiple leading models could attract more customers to its infrastructure.
Mark Zuckerberg Says US Should Not Ban Chinese AI Models, Warns of Regulatory Capture. The Trump administration is expected to announce a voluntary testing framework. (implicator.ai)
Thatās a more realistic ask than hoping everyone slows down: If alignment methods can be made accessible to engineers without deep math, more people can stress test, critique and improve them. Knowledge spreads far more easily than voluntary restraint, especially when geopolitical incentives point the other way.
Interestingly, China doesnāt need this computing power, yet Kimi has now unveiled a competitive model all the same. I wonder how this will pan out?
So have they been getting better and better?
How much more effective have the large models become for you over the last three years? Do you think development might come to a halt in China, for example, if US providers were indeed to stop developing or releasing them?
Absolutely. At the very least, everything of relevance, that is, anything that has been catalogued in libraries, for example, should be almost entirely digitised. We may well find out soon whether AI companies can still uncover anything of relevance amongst the other books, i.e. those that have not been widely published or catalogued. https://en.wikipedia.org/wiki/Book_scanning
True. I suppose Iām being a bit too hopeful there.
Universities drop AI detection tools over fears about accuracy: Some institutions are overhauling assessment and trying to move away from the emphasis on surveillance (ft.com)
To be honest, what surprises me most of all is that, after three decades of intensive digitisation of analogue media, there are still purely analogue copies in existence at all!? In that respect, I see this more as a benefit than a threat, as some people here are suggesting.
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Unfortunately, even with very long texts, this is no longer reliably possible, which is why some universities have recently stopped using such tests.
Reposted in the correct threadā¦
Itās funny that you still believe some tool could tell, with such short texts, whether they were written by an AI or a human. (And unfortunately, even with very long texts, this is no longer reliably possible, which is why some universities have recently stopped using such tests.)
Itās not about who deserves to win, itās about incentives. If one side unilaterally slows development while others donāt, the likely outcome is a shift in technological leadership, not a slower pace overall. Whether thatās the US, China, or anyone else is a separate political question from whether voluntary restraint is an effective strategy.
Ā»Slow down AIĀ« sounds noble until you ask who actually slows down: Democracies and companies that comply simply hand the advantage to rivals that donāt. History suggests voluntary technological restraint rarely wins against geopolitical competition. The race doesnāt stop: it just changes leaders.
Pacing the Frontier: A statement from 1,132 employees of frontier AI companies (pacingthefrontier.com)
This is why media ownership matters: When the people deciding what gets amplified are also celebrating political power brokers, it becomes harder to separate journalism from influence. Even if Politicoās newsroom stays independent, the optics alone are enough to make readers question where the lines really are.
Exactly. Platform loyalty is a bad archiving strategy. If one platform disappears, changes the rules, or buries old content, your work and links still survive instead of vanishing with someone elseās business decisions.