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Repeatedly violating rules
- Jun 30, 2018
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(https://simonwillison.net/2023/May/4/no-moat/)
The premise of the paper is that while OpenAI and Google continue to race to build the most powerful language models, their efforts are rapidly being eclipsed by the work happening in the open-source community.
They go on to explain quite how much innovation happened in the open-source community following the release of Meta’s LLaMA model in March:
The paper concludes with some fascinating thoughts on strategy. Google has already found it difficult to keep its advantages protected from competitors such as OpenAI, and now that the wider research community is collaborating in the open they are going to find it even harder:
As for OpenAI itself?
The premise of the paper is that while OpenAI and Google continue to race to build the most powerful language models, their efforts are rapidly being eclipsed by the work happening in the open-source community.
While our models still hold a slight edge in terms of quality, the gap is closing astonishingly quickly. Open-source models are faster, more customizable, more private, and pound-for-pound more capable. They are doing things with $100 and 13B params that we struggle with at $10M and 540B. And they are doing so in weeks, not months.
They go on to explain quite how much innovation happened in the open-source community following the release of Meta’s LLaMA model in March:
A tremendous outpouring of innovation followed, with just days between major developments (see The Timeline for the full breakdown). Here we are, barely a month later, and there are variants with instruction tuning, quantization, quality improvements, human evals, multimodality, RLHF, etc. etc. many of which build on each other.
Most importantly, they have solved the scaling problem to the extent that anyone can tinker. Many of the new ideas are from ordinary people. The barrier to entry for training and experimentation has dropped from the total output of a major research organization to one person, an evening, and a beefy laptop.
Why We Could Have Seen It Coming?
In many ways, this shouldn’t be a surprise to anyone. The current renaissance in open-source LLMs comes hot on the heels of a renaissance in image generation. The similarities are not lost on the community, with many calling this the "Stable Diffusion moment" for LLMs.
The paper concludes with some fascinating thoughts on strategy. Google has already found it difficult to keep its advantages protected from competitors such as OpenAI, and now that the wider research community is collaborating in the open they are going to find it even harder:
Keeping our technology secret was always a tenuous proposition. Google researchers are leaving for other companies on a regular cadence, so we can assume they know everything we know, and will continue to for as long as that pipeline is open.
But holding on to a competitive advantage in technology becomes even harder now that cutting edge research in LLMs is affordable. Research institutions all over the world are building on each other’s work, exploring the solution space in a breadth-first way that far outstrips our own capacity. We can try to hold tightly to our secrets while outside innovation dilutes their value, or we can try to learn from each other.
As for OpenAI itself?
And in the end, OpenAI doesn’t matter. They are making the same mistakes we are in their posture relative to open source, and their ability to maintain an edge is necessarily in question. Open-source alternatives can and will eventually eclipse them unless they change their stance. In this respect, at least, we can make the first move.