alt Hacker NewsOpen-source AI can, by definition, never "win". AI is just hillclimbing today, and closed labs can always absorb everything the open world does and build upon it.
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.
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Those closed labs need to justify the investment still, and as we approach stagnation in model capabilities that’s harder and harder. Right now Fable and Mythos are cutting edge, but soon enough they’ll be commodities. And for every company like OpenAI/Anthropic that wants to get ahead with a SOTA model, there’ll be a hundred companies aiming to commoditize their complements.
AllegroLisp is very far behind SBCL.
Open source models don't need to be anywhere near as good as Claude Mythos or even Claude Sonnet to 'win'.
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.
>>AI is just hillclimbing today
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.
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They win when they hit saturation for a common task, which is already happening. The second big win will be when the average person can run it on their own hardware.