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Comment by a-priori | Hacker News Reader

It kind of confirms a hypothesis I have that the next phase of AI development will be about getting smaller (in terms of model size and compute), because smaller is more capital efficient for training (allowing faster iteration and more iteration cycles for a given amount of capital), allows for denser inference (more inference for a given amount of compute hardware), and allows for more edge inference applications.

The goal will be to develop smaller models with more efficient architectures, that have similar or even better performance than larger models.

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Yes, continual learning is currently a hot topic since this is what will allow an "AI intern or new employee" to learn on the job rather than be stuck on groundhog day, or pre-trained for every eventuality - needing to anticipate all the quirks and proprietary knowledge of every customer!

Continual learning tends to imply individualized models, else there is no data privacy (the secrets learned on the job at your company now being available to your competition), which really turns the current AI business model of a single centralized model served to everyone on it's head. If every customer has a different model that essentially means the end of batch processing with the same weights loaded into the GPU.

The direction this suggests is a move away from centrally served common models to locally served individual ones, which generally requires them to be smaller, even if some larger companies may be willing to invest in beefier hardware.

I think this is at least in part why the AI companies are trying NOT to implement true continual learning and see if they can instead finesse it by implementing continual compacted(?) memorization instead, since then it's "just" additional context that needs to be recalled and fed into every request, not weights that need feeding into the GPU. I don't think memorization is any substitute for learning, especially learning of practiced skills, but since it's far easier to implement, and non-disruptive to the cloud-based API business model, this is what we will see first.

The recent news of NVIDIA' investment in Sutskever's SSI has a tiny hint of this also, talking about SSI advising on NVIDIA's future architectural direction - apparently pushing it in a different direction than current (cloud-based, pre-trained) models. NVIDIA may be quite happy to see a move towards local models.

The middle ground would be continual learning for a group of users. For a company with a couple thousand employees, one model learning on all your employees might be viable. With some filtering for information that is supposed to stay compartmentalized (HR data, stuff under NDA, core technolgy). But this only really works if you bring enough volume to make it economical to effectively server you a whole other model

It begs an interesting question as well: are frontier labs stuck in the incumbent phase of the "innovator's dilemma," where there's such intense pressure to maintain the flavor/character of their widely-loved existing models, that they dare not invest their resources into radical reimagination of their approach towards architectures for smaller models? A company like Moonshot does not have this cultural limitation.

The frontier labs would be well served in carving out 20k sub-clusters and giving research teams carte blanche in building things with radically different architectures - with full permission to distill whatever they want from the flagship models. We'd expect to see more product lines that feel "different" from the flagship models if this were already being done.

Eventually, multi-modality might even get offloaded as workflows, which might allow models to do this at a fraction of the data and compute required.

I am adding multi-modality to https://github.com/guilt/TinyToT, and I see that dis-aggregating capabilities, very similar to how our own sensory organs work, seems to be paying off quite well.

It feels like we're not really bottlenecked by model performance now anyway. Most cases I find where a model is being an idiot can be remedied by rethinking the context and tooling available to that model.

If they got a lot cheaper and only a little dumber, and we got a little smarter about how we use them, they could appear to the bystander as much more useful than they are.

This is likely what Google has been doing, as it suites them best to have small fast models rather than hulking slow giants. Lower intelligence but way more ability to serve. Anthropic would probably need a datacenter the size of a small country to serve Fable on a Google search/services scale.

> It kind of confirms a hypothesis I have that the next phase of AI development will be about getting smaller

isn't that, in the end, the case with all/most technologies?

i can get a petaflop of compute capacity in a dgx spark for relatively cheap nowadays. that used to be a whole supercomputer like 20 years ago.

Getting smaller is how the last revolution in computing happened.

A VAX 11/780 was good, but an 80386 was a lot better, since the latter could run on 3 AA batteries and the former needed 6,000 watts of 3 phase.

Yes but that's because of the scaling laws for transistors. ML models seem to get better the bigger they are. If you want to compress the world's information, you need to look at all the information in the world.

Qwen 3.6 27b is way ahead of Llama 70b.

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