You've (Likely) Been Playing The Game Of Life Wrong

Your success eV is opportunities x success-rate. Out of the gate, hard work improves success rates, but not the count of opportunities. Connections improve opportunities, it doesn't do shit for success-rate. Luck does for both sides of the mix. As a startup founder in my 40s, the entire VP/SVP level in silicon valley is people one or two hops away for an intro, though admittedly a lot of the first hop there is through the other founders. The original paper about importance of weak ties is under appreciated, because people you work with tend to go on to better & bigger things - especially in the beginning of your career, if you are a nice person. Veritasium put out a very nice video[1] about knowing which sort of game we're playing. It is a power law game. There are only two takeaways I had, "show up" and "get lucky". There is no winning without showing up. [1] - [this video]

> Won't scaling laws apply to it anyway? Yes, of course. Scaling Laws will always apply, but that's not really the point[0] The fight was never "Scale is all you need" (SIAYN) vs "scale is irrelevant" it was "SIAYN" vs "Scaling is not enough". I'm not aware of any halfway serious researcher that did not think scaling was going to result in massive improvements. Being a researcher from the SINE camp myself... Here's the thing: The SIAYN camp argued that the transformer architecture was essentially good enough. They didn't think scale was all you needed, but that the rest would me minor tweaks and increasing model size and data size would get us there. That those were the major hurdles. In this sense they argued that we should move our efforts away from research and into engineering. That AGI was now essentially a money problem rather than a research problem. They pointed to Sutton's Bitter Lesson narrowly, concentrating on his point about compute. The SINE (or SINAYN) camp wasn't sold. We read the Bitter Lesson differently. That yes, compute is a key element to modern success, but just as important was the rise of our flexible algorithms. In the past we couldn't work with such algorithms because of lack of computational power, but that the real power was the algorithms. We're definitely a more diverse camp too, with vary arguments. Many of us look at animals and see that we can do so much more with so much less[2]. Clearly even if SIAYN were sufficient, it does not appear to b