Posted by rellem 10 hours ago
First sentence: In New Zealand's far north, a Māori broadcaster...
...oh boy, that's all you need to read to know what kind of media diet the writer is on.
They tell us about how the farmers and native people and whateve are all happy with their chatbots and models. The major effects are a massive and ever-increasing energy use - in a time where we must cut back and economize to avoid further global warming; a massive diversion of investment capital - especially in the US; fantastic stock valuations for a few tech giants (gee, I wonder whether any of them is related to Mozilla somehow); and other effects one could survey, all more significant by far than the examples they bring.
There's nothing practical about open-source models yet that makes them even remotely comparable to closed frontier models.
All the hype around GLM, Qwen, now Kimi.... Are people really this naive that they believe these reports or, more worringly, are people NOT using these models and seeing the HUGE gap that still exists?
Take a task, any medium-sized task, decently scoped that you'd trust to give to Sonnet to finish without a hitch. Now give it to ANY open-source frontier model and watch them struggle and go in circles while failing tool calls and randomly assuming things.
Open-source is and has been amazing but its so hard to deploy reliably and at scale and there's still big problems in the underlying models with instruction following and tool calling that makes it basically unusable for production workloads at a decent price point...
Claude used to be much worse than it is now, just as bad the open weights models are. And the open weights were worse. The labs will also try to keep the lead, but at some point people start seeing real value from open models. Maybe you say they're not ready yet for medium tasks, but everyone sees the writing on the wall.
The biggest moat of these giant labs and models is increasingly shifting towards deployment capabilities and (debatably) having better (proprietary) harnesses.
The models themselves can be impressive on benchmarks, but unless they can be served reliably to customers either at scale, hosted somewhere, or even on edge with predictable latency and memory usage, then frontier will always be leading.
But at this point we do expect that open weights _hosted_ options become feasible for the tasks they're using the frontier models for. And because of the lack of "legal monopoly" (intellectual property of whatever kind), they're way cheaper, not mention more flexible.
The launch of the tinker platform from Thinking Machines is an example of the "more flexibility" part that people want (and they chose to make their model open weights, maybe because this is the angle they want to push).
At this point I think it's realistic enough that the ball is in OpenAI / Anthropic's court to figure out how to respond to this threat to their business model.
That said, I think it's concerning that there are apparently only a couple of providers of hosted open weights inference, due to the complexities of doing so (per Dax from OpenCode's tweets).
i'm... not sure? This assumes ~stagnation in task-possibility. We've had ~exponential progress for like 3+ years now; I'd have never dreamed the tooling I hammer daily would exist in my lifetime just.. 3? years ago. And it's improving daily.
Maybe Open will win, maybe Closed will keep pushing the envelope. The world here is raw enough i don't think anyone can make any significant claim other than 'holy shit this is useful and moving Fast'.