Codex lets you use any model you want (not just OpenAI) and harness is open source - Claude Code doesn’t and is closed source Given this is the two leading AI labs, notable difference in approaches
the models that are actually causing issues right now are all closed weight American models. I’m fairly certain if they were open weight models, we would not have that issue.
The case for *total* Linux victory (desktop, mobile, server, iot, robots, embedded…): 1) Most agent-programmable People will want fully personalized computing experiences.
in this whole USA vs China thing OpenAI and Anthropic aren't relevant because they're positioned differently
them building better models doesn't hurt china at all
the competitor has to be
- american
- open source
- enough compute to do inference at scale
that can shift things
AI because it's on a onetoone basis is giving us a data loop that nobody in learning science no one in education has ever had and we have it. It's a closed loop and that magical data loop is what's allowing us to get the five to 10 times improvements in education because it's our microscope.
Frontier Labs are incented to be fearful as it provides a moral justification for staying closed.
Chinese Labs take the consistent position of doing what’s good for all and being open.
So the little guys find themselves in the uncomfortable position of rooting for the Chinese…
if the US wants to lead in artificial intelligence, we have to have a vibrant closed and open source ecosystem, and that's the only way they can all work together.
open models are still incredibly useful, but fill a long-tail ecosystem relative to the closed counterparts that have monopoly ownership stakes in the most valuable areas like knowledge work collaboration, drug discovery, SWE, etc.
If you're doing highcale tasks, industrial scale tasks, you are going to make specialized machines that move and act in the physical world. The key part is move. If they're moving, they got to have wheels.
and yeah, I think it's either you need to figure out how to make the data faster or you need to figure out how to be faster than the data. We've seen the evidence of being able to be a little bit faster than the data.
Uh and just like how we see in language models how now a lot of time is spent actually generating data, generating synthetic data by actually running the model and having it think through things. I think a lot of the data in the future in robotics is going to be the robot attempting to do lots of tasks in lots of real world circumstances.
Uh and likewise um robots can't like watch a person doing something and then figure out how to do it themselves directly. They really need their experience on their own platform um to learn effectively.
Now maybe this isn't completely out of the question but this would be quite challenging uh to do and that's because the calculus is a little bit different. We're not just running compute to optimize for a use case. We're actually running the robot in the real world and using the hardware and attempting the task in the real world.
Like I think my perspective is like if the AIs are sufficiently good at R&D including hardware R&D, robots, whatever, then they can radically transform the world even if they're not that good at playing politics.
It will probably be economically shrewd to lose money on early robot models in anticipation that the data they gather will be worth more later in improving newer versions.
Unfortunately, no, because politics is about people who disagree with you. If you’re working with computers, or robots, or pure math, you don’t have politics.
the competitive dynamic in China is more intense because it's more intense. Everyone's chosen to go open source and that creates a system that in my mind is capable of innovating far faster than the competitive system we have here. All the models learn from one another.
There's always negative sentiment that exists for any business that's getting hyped. They have no incentive to correct it. Um so again it's complicated because I know the training costs are a big part of it. Uh the R&D department is is hugely expensive but long-term inference makes sense as a business and I think it it always will.
All they need before they can truly appreciate empathy and humanity is one thing: have one of their own posts closed and deleted by another group of gatekeepers.
The result is something that's beautiful, sharp, critical and lingering. Long after I closed the cover, I found myself mulling over the delicate ways that Lai raised the contradictions, sorrows and beauty of queer love, racial identity, camaraderie, self-control, and self-indulgence. Lai's characters have no answers, only questions that can never be fully resolved. Instead, these questions are the defining puzzles, defeats and triumphs of their lives.
There's a question that never goes away in design: should designers code? My answer has always been yes. But for a decade or so, the complexity of front-end development made it impractical for most. Thankfully, AI coding agents have reopened the door.
um humanoid robots maybe start to or robotics at least start to but the main factor is going to be for reducing the number of people is modularizing things and making them in factories in Asia
The robot arm base case saves some hourly labor at the expense of adding more skilled labor, and can easily be negative if it needs reprogramming more than a few times per year.
It has a surplus of STEM graduates that are often underemployed, while "Hukou" residency restrictions complicate hiring hourly labor in coastal factories.
As with self driving cars, most of the early players in humanoid robots, will quietly shut up shop and disappear. Those that remain will pivot and redefine what they are doing, without renaming it, to something more achievable and with, finally, plausible business cases.
So, I think it'll be the same thing that that we'll see an increase in the scope that we're giving that we're willing to give to the robots as they get better and better where initially the scope might be like there is a particular thing you do like you're making the coffee or something. Uh whereas as they get more capable, as their ability to have common sense and a broader repertoire of tasks increases, then we'll give them greater scope. Now you're running the whole coffee shop.
So traditional robots and factories uh they need to make motions that are highly repeatable and therefore it requires a degree of precision and robustness that you don't need if you can use cheap visual feedback. So AI also makes robots more affordable uh and lowers the requirements on the hardware.
Uh so in order to effectively learn from your own experience, it turns out that it's really really important to already know something about what you're doing. Otherwise, it takes far too long.
robots help with uh physical things uh physical work. And if producing robots is itself physical work, then getting really good at robotics should help with that. It's a little circular, of course.
Like if you answer a question, you just like answered it wrong. It's like well it's not like you can just like go back and like tweak a few things like the person you told the answer to might not even know that it's wrong. Whereas if you're like folding the t-shirt and you messed up a little bit like it's pretty obvious like you can reflect on that, figure out what happened and do it better next time.
The trade deficits have widened since Trump, right? They haven’t closed the imbalance with China and with the rest of the world because ultimately the US saves less than it invests. And that’s a macrophenomenon. It’s not a trade phenomenon
Distillation is standard practice in industry. Whether or not, if you're at a closed lab where you care about terms of service and IP closely, you distill from your own models.
I'm excited about what this means for the open-source community and research, with the gap between closed-source and open-weight models closing and SOTA-level conversational models being more easily accessible.
I think it’s depressing if we have AGI and the only way to get things done in the physical world is to make a human go do it. So I really hope that as part of this transition, as this phase change, we also get humanoid robots or some sort of physical world robots.
In light of the increasing number of closed-source LLMs, it is important to continue to promote an open culture of sharing knowledge, data, and software, from which the NLP community has benefited greatly.
A very dynamic economy probably will be very good at building lots of robots cheaply, which may make us more rather than less likely to end up in Malthusian dilemmas.
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