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392 points lairv | 1 comments | | HN request time: 0.2s | source
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ACCount37 ◴[] No.45527927[source]
Building the robot itself is hard enough - but it was never the hardest part.

The thing to watch out for is: deployments. How many units are they pushing and to who. What kind of tasks can those robots accomplish well enough to warrant actually using them. How hard is it to adapt those robots to deployments. How that changes over time.

The hardest problem of creating a universal robot is, and always has been, AI. If Figure can deliver sharp, highly adaptive, easy to use AI? High generalization, good performance on a diverse range of tasks and in many environments out of the box? Then they have a killer product.

And a proxy to track that is reports of how many robots they deploy and to who. If they start shipping to small companies and deploying to high uncertainty spatially complex fields like construction or maintenance? If you start seeing robots unloading trucks and restocking shelves at a small town Walmart, unannounced? Big.

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sho_hn ◴[] No.45528034[source]
I think the glossy 7min trailer has it about right: The first realistic deployments will be champagne holders at parties and robots at hotel receptions. Novelty toys for the wealthy.

Commoditization and Walmart-level deployments at scale are still a few gens off.

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1. ACCount37 ◴[] No.45528198[source]
I'm willing to believe that. But we could do those "novelty deployments" with 80s tech - and we did. See: tech expos back then.

The key difference between now and then isn't smaller actuators, cheaper sensors or denser power electronics. It's the AI breakthroughs.

Doesn't need to be "at scale". Scale is a useful proxy though. But if you see two robots deployed to your average Walmart, and doing a good enough job there to cut the staff in half?

Doesn't matter that it's just two robots at a few Walmarts. Making more robots isn't that hard. The scale would inevitably follow.