Why is this guy so popular of late? Is it because he manages to book interesting podcast guests? He doesn't seem to have any particularly interesting or impressive original thoughts on display...
He's pretty good at curating very smart people and interviewing them. There's no shortage of interviews with double talking politicians and polemicists so podcasts like his are a breath of fresh air. I'm not watching his podcasts to learn about Dwarkesh, but to learn what his guests have to say.
The bar for wide-audience podcasters and interviewers is low. They're expected to be generalists who can ask good questions, not experts driving the conversation.
Now, expertise is valuable for focused Podcasters like Peter Attia for fitness or Razib for population genomics. But, Dwarkesh is trying to cover all tech and wider HN-friendly content. For that purpose, his CS education and knowledge of the tech industry workings are solid.
He does a better job than Lex, Rogan, or other tech-adjacent Podcasters. Nowhere near as personable as some of the more mainstream successes. But, when those podcasters get tech guests, we're stuck with tired questions like:"Will AI kill us all?".
Tend to agree. His interview with Leopold Aschenbrenner and subsequent interview with François Chollet revealed a pretty obvious LLM maximalist position. Kind of off-putting.
He (along with Jack Ma) is certainly among the luckiest people on Earth... I'm not sure if it's just consistently poor translation / English skills, but every time I hear either of those guys speak it's a visceral reminder that you don't need to be super smart to get super rich.
That seems like a very strange opinion... "the professor's time was wasted"?? The pursuit of education (and often degrees) is meaningful on a personal level. A productive research career is simply one of infinitely many paths that a person could choose to take.
Most likely for the same reason that so many people seem to think they need a vector-specific database and a framework like langchain to build any type of GenAI-enabled application... the content marketing is working.
Not all (or most) people-problems can be solved with software. Recruiting/hiring is a pure people-problem. No two people are the same. No two groups of people (i.e., companies) are the same. It's a "problem" that everyone has felt, but it honestly seems like an unsolvable problem... and "problem" mostly likely isn't even the right word - more of an uncomfortable reality.
It seems YC is trending younger with this AI wave (I believe average age of the most recent batch was ~26) - kids dropping out of college to build startups without ever having worked at a real software engineering job... I imagine this type of story is not at all uncommon at the moment.
I listened to a talk to that Jim Simons gave at San Francisco State University about a decade ago in which he mostly recounts his life and career. Towards the end when he was describing the state of the fully realized RenTech firm, he mentioned that they collect approximately 7TB of data per day, and that the #1 investment rule is that a human never, under any circumstances, intervenes with what the model (he said "the computer") decides.
Which in those days was absolutely staggering. But I think they were looking for similar patterns in historical data to current data, not trying to fit current data into a set of predefined patterns or algos from historical data.
From a high level engineering perspective (and not being an expert in blockchain systems), the amount of complexity in the Ethereum ecosystem seems... excessive? And it seems likely the amount of complexity will only continue to increase over time. We tend to prefer 'elegant solutions' and simple systems in engineering, and this seems quite the opposite. Is it truly necessary to solve the problems that it's trying to solve?
I'm not sure how much more 'actually complex' it is. I think we can often underestimate the complexity of incumbent systems because most of the complexity is not in the open.
I'm sure that market makers and exchanges in traditional finance have a lot of complexity behind them.
