Other countries need to invest collectively in open alternatives, and AI must be considered critical infrastructure rather than a commercial venture. Building small firms to compete against behemoths will not accomplish that.
And by open I mean open weights AND open training pipelines.
The obvious path is the Grok path. That is, anybody with a big pile of money can read some papers and hire some people and make a model which is at or near the frontier. Beating current models by a hair at current benchmarks is not as hard as it looks because you will be building the system to beat those benchmarks from the beginning. [1]
Six months or a year later people will start to realize that you're not really improving or making progress though because that's something entirely different.
Now real advances in the long term are going to come out of smaller companies working on things like
Like the frontier models are just too expensive to do the experimental work which will lead to advances in the science, the science is going to advance through work on little models whereas companies like OpenAI and Anthropic are very committed to maximize the performance of their existing systems in the short term and it is the intense competition that will keep them in an "Innovator's Dilemma" situation where their customers are going to reject anything really new which doesn't perform the same. [3]
[1] ... and even if you don't cheat the model will cheat for you
[2] ... not necessarily that one in particular, but something like that
[3] companies like Microsoft that "disrupt themselves" ignoring their customers are afraid of an Innovator's Dilemma situation but are paradoxically not stuck in it because they are monopolies who can force their customers to do something they don't like.
FTA: “Elements of the Trump administration recognize this logic: The State Department’s Pax Silica initiative is an early attempt to draw countries in the semiconductor supply chain into AI-for-resources agreements. Middle powers should take the opening, but use it to extract real commitments.”
So, how does one extract real commitments from the Trump administration? AFAICT, keeping promises for a week already is hard for them.
So far as I know the two places where people have successfully launched social networks are Silicon Valley and China, and I'd say other parts of the US are excluded -- like Cambridge VCs just didn't want to have any part of Facebook at all and the trauma of being rejected by them is part of Zuckerberg's origin story and why he won't listen to anybody else about anything.
In Europe you could point to various forms of fecklessness of which the GDPR popups are probably the worst contemporary example but Europe saw that similar fecklessness was going to shut it out of the aviation industry and after the false start of Concorde created the very successful Airbus. So if you are imagining Europe being successful at anything today you have to imagine they are creating an "Airbus of ..."
Europe invested heavily in the semantic web because they were worried about the "tower of babel" problem that affects commerce, that could be a goal for LLM development even though LLMs are already pretty good at that. One thing I found out in my market research was that people in the rest of EMEA (namely Africa and the Middle East) feel "too many languages" are a barrier to development and wanted that semantic revolution even more than the Europeans but didn't have the resources to spend on R&D.
And by open I mean open weights AND open training pipelines.
The obvious path is the Grok path. That is, anybody with a big pile of money can read some papers and hire some people and make a model which is at or near the frontier. Beating current models by a hair at current benchmarks is not as hard as it looks because you will be building the system to beat those benchmarks from the beginning. [1]
Six months or a year later people will start to realize that you're not really improving or making progress though because that's something entirely different.
Now real advances in the long term are going to come out of smaller companies working on things like
https://en.wikipedia.org/wiki/Mamba_(deep_learning_architect... [2]
Like the frontier models are just too expensive to do the experimental work which will lead to advances in the science, the science is going to advance through work on little models whereas companies like OpenAI and Anthropic are very committed to maximize the performance of their existing systems in the short term and it is the intense competition that will keep them in an "Innovator's Dilemma" situation where their customers are going to reject anything really new which doesn't perform the same. [3]
[1] ... and even if you don't cheat the model will cheat for you
[2] ... not necessarily that one in particular, but something like that
[3] companies like Microsoft that "disrupt themselves" ignoring their customers are afraid of an Innovator's Dilemma situation but are paradoxically not stuck in it because they are monopolies who can force their customers to do something they don't like.
So, how does one extract real commitments from the Trump administration? AFAICT, keeping promises for a week already is hard for them.
In Europe you could point to various forms of fecklessness of which the GDPR popups are probably the worst contemporary example but Europe saw that similar fecklessness was going to shut it out of the aviation industry and after the false start of Concorde created the very successful Airbus. So if you are imagining Europe being successful at anything today you have to imagine they are creating an "Airbus of ..."
Europe invested heavily in the semantic web because they were worried about the "tower of babel" problem that affects commerce, that could be a goal for LLM development even though LLMs are already pretty good at that. One thing I found out in my market research was that people in the rest of EMEA (namely Africa and the Middle East) feel "too many languages" are a barrier to development and wanted that semantic revolution even more than the Europeans but didn't have the resources to spend on R&D.