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Comment by dalbasal | Hacker News Reader

Very true.

The over-exuberant business version of this is money-men types who see companies as a collection of investments and cash flows. This leads them to think of business problems in "resource allocation" terms. If company X's is bad at UI, customer service or whatnot than this will be fixed by "investing" more in it.

Here they see a big juicy prize: first to market with self driving taxis. They understand that it's risky but they still assume a very strong correlation between the amount of money going in, and the probability of their big juicy prize coming out.

The whole approach is a bad idea in new technology. Instead of thinking in expiremental, creative-discovery terms, they have a very precise destination and they try to brute force a way to that destination with money.

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Yes. I suppose in the scope of industrial process there are basically two types of action: shoveling (a scalable technique that is understood and can be trained) and invention (creating new scalable techniques). If it's just shoveling, capital investment may very well yield higher output.

Invention on the other hand happens on a constrained pipeline.

The shoveling that looks like invention is when an existing invention is adapted (and sometimes called innovation). I presume it's hard for outsiders to say when a subject on which expertise is not yet commoditized is ready for shoveling or not.

I think this is what happened here - since everyone were hyping the AI revolution, they presumed the technique was ready for industrial adaption and only lacked in dedicated subject experts and existing processes which can be adapted. They presumed there was some core invention that could be adapted and monetized.

Given how poorly understood subject AI is, and how lucrative a self driving car would probably be, some risk taking in this area is certainly understandable. I have no idea if the investments were in line with the risks and rewards or not.

It worked pretty well for the Manhattan Project and the Apollo missions. I think that you're romanticizing technological progress a bit if you don't believe that more money (often) leads to more progress.

Both of your examples where government-run "monopolies" - that is, if you were a physics or rocketry expert, you didn't have much of a choice of which organization to join in order to build an A-bomb or rocket.

And it's likely you got kicked out of the competition's country for being Jewish. America got super lucky.

I dunno if I'm romaticizing. A massive mission/goal driven exercise can be quite romatic. Tesla did that, and I'd call that more romantic than (for eg) FB's path.

Good points though. Tons of resources, people working in secret, and a total absence of "market feedback" have yielded big, impressive results before.

I think their point is that you can't just buy progress on demand.

And I think the counterpoint offered was that the Mamhattan Project and Apollo programs did just that. Note that in both cases you had government-deep pockets and global sociopolitical ramifications as team motivators-a potent combination.

No, they set up a multi-year governmental project. It's rather different from throwing money at a c-suite executive and telling them to hire developers.

those where quite different projects, having to surmount lot of known unknowns. self driving is still fraught with unknowns unknowns. once the problem gets mapped out money will enable company to power trough.

Smells like hindsight bias.

not really. both those indicated projects had all the required component worked out already, plus the physics at their basis where understood. there was a working reactor and the governing phenomena was well understood. it was mostly a refinement and tuning process to get it right. same with apollo, at that point nasa had been launching people in space for a decade, working out the docking procedures and capsule operations.

contrast with deep neural network, there are no models to predict overfitting or undertraining beyond what training cross validation say. hardly the same situation. there's no model that can predict a network confidence and network output as a classifier is still very rough even before it gets converted into car commands (i.e. see Uber report)

those cited project had a theoretical framework on which they were built upon. neural network are still rooted in 'tune them if they don't work' stage.

It only works when you're talking massive, government-backed support for basic scientific and technological research. The tech industry likes to lean on this narrative, but the reality is they just exploit the gains of basic research and are probably reaching the limit of what can be drawn from the last time this happened (i.e. when governments created the internet). This is why you see tech shifting into so much rent-seeking nonsense and bizarre financialization strategies that mirror Wall St/the banking industry. They're scraping the bottom of the government-financed research well and don't have much actual ability to create fundamental technological innovation themselves.

The good news is all we have to do is shift back to massive government investments in basic research to course correct and compete with entities like China(who seem to have a clearer picture of how Capitalism actually works in practice).

Part of the reason why companies aren't simple functions of cash in -> results out is because the ability to produce results is 100% dependent on hiring the right people (who may not even exist for your problem domain). Cash only helps in so much as possibly being enough money to hire those people, but it says nothing about whether management will effectively exploit the human resources it hires, i.e. whether management will shut up and get out of the way or whether the "right people" will be hobbled by infighting, corporate politics, and "policy".

Until investors can come up with a way of measuring those confounding human factors, just plowing money into companies and praying for a miracle will continue to be a debased and ineffective investment strategy.

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