在
机器人前沿与50个朋友交谈后的10个辛辣镜头🤖🔥
1/ Astra IS机器人的GPT-3时刻。具有讽刺意味的是，现在它终于来了，但机器人领域的一些人似乎不愿意承认这一点。也许是因为（遗憾的是）它不是来自机器人公司？
2/机器人技术正在成为前沿实验室的另一个垂直领域。在某些方面和编程没有太大区别。为什么要为每个行业重新发明通用情报？
3/ 直到现在，机器人技术一直很难承保。没有令人信服的预训练缩放法，模型、数据或硬件上没有一致性。难怪一些批评者将其斥为一个分散的行业，玩着昂贵的“模拟游戏”。阿斯特拉或许终于能提供一条可信的扩张之路。
4/机器人基础模型初创企业有其案例。但它到底有多强呢？论点是:Astra处理系统2（推理），而专业模型保留系统1（技能）和系统0（运动控制）。公平。但这不就已经是一个更狭隘的价值主张了吗？谁说边界就到此为止了？
5/跟随人才。据说OpenAI围绕核心的Astra模型重组了其30-40人的机器人团队。在@Tesla_Optimus，听到有人对通用车型采取类似行动。组织图可以告诉我们比任何演示更多的
信息。
6/机器人数据热潮是真的。百万小时的采购项目，有些可能达到数千万小时。关于每个前沿实验室的预算接近10亿美元的讨论。
7/看好机器人硬件的销量。随着智能变得更容易获取，制造有用机器人的障碍可能会显著减少。护城河转向
可靠性、灵活性、集成性和部署性。
8/机器人技术可能推动下一个半导体和存储器超级周期。边缘运行的100B参数模型仅4位权重就需要约50GB。现在将这个数字乘以数百万机器人。你觉得@JensenHuang为什么这么痴迷于机器人技术？@elonmusk看到了它的到来:x.com/elonmusk/statu...
9/自动驾驶对一般模型的威胁免疫吗？在编码和机器人之后，为什么认为自动驾驶仍然是专有的垂直模型的专属领域？对我来说，机器人出租车越来越像是解决了问题。但如果前沿实验室进入自动驾驶领域，我们可能会看到苹果与安卓的竞争。想想这对$TSLA、Waymo和其他OEM厂商的影响。
10/下一个瓶颈可能不是智力。这是部署。模型的改进速度快于我们在混乱的现实世界中部署机器人的能力。集成、硬件方差、故障恢复和经济性仍然非常困难。真正的护城河或许是将智力转化为可靠、有利可图的体力工作。
--
我意识到其中一些做法可能会误导人们，尤其是那些花了多年时间努力推进机器人技术的人。那不是我的本意。
我分享它们是为了引发辩论，而不是宣布赢家和输家。阿斯特拉出局才一个月左右，现在下定论还为时尚早。更重要的是，当TAM如此庞大——可能是科技领域最大的——时，应该有足够的空间来支持不同的方法和许多赢家。
10 Spicy Takes After Talking to 50 Friends at the Frontier of Robotics 🤖🔥
1/ Astra IS robotics' GPT-3 moment. Ironically, now that it's finally here, some in robotics seem reluctant to acknowledge it. Perhaps because it (sadly) didn't come from a robotics company?
2/ Robotics is becoming just another vertical for frontier labs. Not so different from coding in some respects. Why reinvent general intelligence for every industry?
3/ Robotics was too hard to underwrite until now. No convincing pretraining scaling law, no convergence on models, data, or hardware. No wonder some critics dismissed it as a fragmented industry playing an expensive "demo game". Astra may finally offer a credible path to scale.
4/ Robotics foundation-model startups have a case. But how strong is it? The argument: Astra handles System 2 (reasoning), while specialized models retain System 1 (skills) and System 0 (motor control). Fair. But isn't that already a narrower value proposition? And who says the boundary stops there?
5/ Follow the talent. Word is OpenAI reorganized its 30–40-person robotics team around the core Astra model. Hearing similar moves toward general models at @Tesla_Optimus. Org charts may tell us more than any demo.
6/ The robotics data gold rush is real. Million-hour procurement programs, some potentially reaching tens of millions of hours. Talk of budgets approaching $1B per frontier lab.
7/ Bullish on robotics hardware volumes. As intelligence becomes more accessible, the barriers to building useful robots could fall dramatically. The moat shifts toward reliability, dexterity, integration, and deployment.
8/ Robotics could fuel the next semiconductor and memory supercycle. A 100B-parameter model running on the edge requires ~50GB just for 4-bit weights. Now multiply that by millions of robots. Why do you think @JensenHuang is so obsessed with robotics? @elonmusk saw it coming: x.com/elonmusk/statu…
9/ Is self-driving immune to the general-model threat? After coding and robotics, why assume self-driving remains the exclusive domain of proprietary, vertical models? Robotaxi increasingly feels like a solved problem to me. But if and when frontier labs move into self-driving, we could see an Apple vs. Android dynamic. Think about the implications for $TSLA, Waymo, and other OEMs.
10/ The next bottleneck may not be intelligence. It's deployment. Models are improving faster than our ability to deploy robots in the messy real world. Integration, hardware variance, failure recovery, and economics remain brutally hard. The real moat may be turning intelligence into reliable, profitable physical work.
--
I realize some of these takes may rub people the wrong way, especially those who have spent years doing the hard work of advancing robotics. That's not my intention.
I'm sharing them to spark debate, not to declare winners and losers. Astra has only been out for a month or so, and it's far too early to draw definitive conclusions. More importantly, when the TAM is this enormous - potentially the largest in tech - there should be plenty of room for different approaches and many winners.