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Comment by rsalus | Hacker News Reader

> The TAM for robots is much, much larger than for coding or services

how do you figure?

The physical work in the world far outstrips the information work. Most information work is simply organizing physical work, attempting to make physical work more efficient.

The promise of intelligence might be larger still. By scaling and using superintelligent LLMs to write code for itself, it's possible that the whole field of robotics is just another problem you can point LLM agents at and expect to be solved by afternoon, just like one of those math puzzles. "Traditional" robotics R&D (or any R&D really) would be worthless due to abundance.

You're just confirming GP's point. If AI agents make those software problems trivial, the physical tasks are all that's left.

Regard it as market segments. It's not hard to envision eg. agriculture & food processing robotized to the point where no human ever touches your food. A few generations in, and people would see potatoes as "nutrient-containing object that comes from a factory" and forgot how to grow potatoes.

I'm rooting for the 'market segment' where AGI (or ASI) finds solutions to long-standing science questions, that are hard to obtain but easy to verify. Or makes new discoveries. Stuff like cancer research, protein folding, synthetic biology, new materials, battery tech, number theory, particle physics, etc etc.

I'm with you. As these models grow in ability and commoditize across the TAM of basically every business in the world, it's going to get cheaper and cheaper to solve everything.

How many people have homes that require chores to be done? Laundry, cleaning, setting/clearing tables, yard work, some consider cooking a chore.

If I could get an affordable robot to do a subset of them, I'm in the market for one.

Makes complete sense! In fact, it seems to me that the most value sits in those messy, unstructured environments like cluttered homes.

I wonder... How can these foundational models actually learn to deal with that without being deployed in those scenarios? It feels like a chicken-and-egg problem: you need a ton of real-world data from chaotic homes to train robust models, but you also need robust models before you can safely deploy robots into those exact homes at scale

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