@mhdempsey Sure,
The claim is that this is a ridiculous thing to say. But it implies that companies aren’t doing this.
Are they?
(In Robotics it’s … bad)
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: https://t.co/oKQi0XDhiL
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.
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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.
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Full diligence report: https://t.co/qlaR5qUzpr
This is a clever joke,
But in case it isn’t, if the over thinker can’t immediately see where they are over thinking, they are juggling more similar sized parameters in their head.
Nothin wrong with that. The “under” thinker thinks they have a cleaner smaller model.
"Don't overthink it" ok but what is thinking? Is it disengaged mental reflection or a highly circumstance-detached rehearsal of possible actions? What is "le penseur" doing? Pretty unclear advice tbh
@hsu_steve The issue here is that there are two tiers. For the bulk of papers, an AI review will trivially produce a better outcome. But for genuine out of the box work an AI reviewer cannot bring the taste to discern a genius from a crockpot.
@shafu0x 😓 SW arch is kinda harder
Milennia math problems are solved by smart people and then they are done.
SW arch gets put out into a world by teams of bright engineers and architected and testers, and then crazy users and services try to destroy it.
@facontidavide 😓🙄
It could be argued that Gazebo is one of the fattest, most inefficient sw projects that has a reasonably sized user base.
Having Claude go at it is like scraping 2 tons of barnacles off a 1 ton boat.
Good luck!
@cyvanessali Robotics deployment expert.
Focus on what makes product stick with non-technical users. What can be done technically to close the gap.
PhD in Robotics with significant experience. (Old)
Not sure about employment, but would love to introduce/talk about what you’re doing.