> The McDonald's touch-screen self-order kiosk takes 27 clicks to get a meal. They try to up-sell you 3 times. Just let me pay for my fucking burger, Jesus Christ. The product manager, the programmer, the executives. None of these people care.
I was working in this space! And I got fired for refusing to work on more upsell features for clients like Coca Cola and such.
I don't want to work on adding fucking ADS into checkout. That is fucked up.
I have an interesting anecdote about that. I was consulting for a very large tech company on their advertising product. They essentially wanted an upsell product to sell to advertisers, like a premium offering to increase their reach. My first step is always to establish a baseline by backtesting their algorithm against simple zeroth and first-order estimators. Measuring this is a little bit complicated, but it seemed their targeting was worse than naive-bayes by a large factor, especially with respect to customer conversion. I was a pretty good data scientist, but this company paid their DS people an awful lot of money, so I couldn’t have been the first to actually discover this. The short story is that they didn’t want a better algorithm. They wanted an upsell feature. I started getting a lot of work in advertising, and it took me a number of clients to see a general trend that the advertising business is not interested in delivering ads to the people that want the product. Their real interest is in creating a stratification of product offerings that are all roughly as valuable to the advertiser as the price paid for them. They have to find ways to split up the tranches of conversion probability and sell them all separately, without revealing that this is only possible by selling ad placements that are intentionally not as good as they could be. Note that this is not insider knowledge of actual policy, just common observations from analyzing data at different places.
2. What are they doing? AGI/ASI is a neat trick, but then what? I’m not asking because I don’t think there is an answer; I’m asking because I want the REAL answer. Larry Ellison was talking about RNA cancer vaccines. Well, I was the one that made the neural network model for the company with the US patent on this technique, and that pitch makes little sense. As the problem is understood today, the computational problems are 99% solved with laptop-class hardware. There are some remaining problems that are not solved by neural networks, but by molecular dynamics, which are done in FP64. Even if FP8 neural structure approximation speeds it up 100x, FP64 will be 99% of the computation. So what we today call “AI infrastructure” is not appropriate for the task they talk about. What is it appropriate for? Well, I know that Sam is a bit uncreative, so I assume he’s just going to keep following the “HER” timeline and make a massive playground for LLMs to talk to each other and leave humanity behind. I don’t think that is necessarily unworthy of our Apollo-scale commitment, but there are serious questions about the honest of the project, and what we should demand for transparency. We’re obviously headed toward a symbiotic merger where LLMs and GenAI are completely in control of our understanding of the world. There is a difference between watching a high-production movie for two hours, and then going back to reality, versus a never-ending stream of false sensory information engineered individually to specifically control your behavior. The only question is whether we will be able to see behind the curtain of the great Oz. That’s what I mean by transparency. Not financial or organizational, but actual code, data, model, and prompt transparency. Is this a fundamental right worth fighting for?