Koike completed his machine last year, and it works—to some degree. It sorts cucumbers with an accuracy of seventy per cent, which is low enough that they must subsequently be checked by hand. What’s more, the vegetables still need to be placed on the photo stand one by one. Koike’s mother, in other words, is in no immediate danger of being replaced, and thus far, she and her husband are none too impressed. “They are quite severe,” Koike said. “ ‘Oh, it’s not useful yet,’ ” they tell him.
The first interesting thing about this article was that it was written in 2017.
That feels like light years away in the world of AI. Back in that time, I was also using TensorFlow as part of a class at Hope College. In those days, "AI" was little more than linear regression. It could handle more variables than a human, but it was basically doing complicated calculus. Every percentage increase in predictive power took incredible amounts of tuning. The hardware wasn't there yet either. I remember kicking off a neural network training set (the new hotness that now powers LLMs) and waiting hours for terrible results.
Even so, it was a heady world back then. TensorFlow from Google was released in 2015 and it's AI Go Player beat the first human around the same time. People soon calmed down. Partly because Google sat on the technology which would go on to produce LLMs as we know them now, knowing it could cannibalize their Search Dominance^1. Partly because it is hard to commercialize these technologies.
Most of the above is an aside, though an interesting one. The real key here is what I think the true potential of AI is. More and more, I think the LLMs are a distraction. They are cool and useful for some specific tasks (like coding and marketing and image generation). But they are going to be a footnote at the end of the day.
Agents are not going to be the real use case here either. Probabilistic models have a clear upper bound in their usefulness for many business applications. No the real winner here is robotics. We are going to see a new industrial age spurred by these models. They are finally allowing us to generalize robotic movement, sensor interpretation, and memory.
Back in college, I almost worked at a manufacturing company that built the robots which built Teslas. Each machine was a huge undertaking. Why? Because they all required bespoke programming, bespoke sensors, bespoke designs. What if you could create a general purpose machine at scale that could flexibly manufacture anything?
I think the real power of LLMs is their potential to bring the promise of 3D printing to life for large scale industrial production. Well, actually not quite. That would probably require more precision than these can handle. Maybe I mean "automation" here. I'm talking autonomous cars, drone delivery, warehouse management, and more. That shit is going to get taken over by these general purpose robots.
Nvidia and many other companies are hard at work on this problem. This is where the real money is. The only way LLMs survive as anything other than a commodity is if they nail Agents. I'm doubtful. These things are incredibly useful, but the reality is there is no differentiation (outside of the artificial friend angle, which is sticky). The models are only getting smaller and faster and it's a matter of time before we can run them all locally. Between hardware and software improvements, that is inevitable.
That means the only real meat and margin here is robots. Hardware is hard and the training data for robots is much harder to come by. There are real moats in robotics. All right that's all I've got for this evening, rant over.
^1 - I forgot where I heard this argument, but there's another argument to be made that Google was just retooling everything from the ground up to be AI first. That's interesting and Sundar Pichai is on record saying just this in 2016. If you look at their bets after that, this actually grows more compelling as laid out here.