Learning not to trust the All-In podcast

All-In is one of the few podcasts I listen to where I don’t exactly like the hosts and disagree with a high percentage of what they say. But I find them interesting, and their recent shilling for Trump gave me a bit more of a nuanced insight into what they see as Trump’s strengths.

I take everything they say with a huge grain of salt. It is incredible how confidently they talk about certain topics where it’s clear even to an uneducated listener that they only have a surface level understanding.

Their flip-flopping on AI - from it being the best thing ever to being completely overhyped and underperforming - and then back again - has been amusing.

I enjoy their insights on slightly less hyperbolic topics like SaaS business models and other more mundane things. There can be some genuine nuggets of wisdom there.

Jason sometimes pushes back on the political stuff and attempts to be a voice of reason (relatively speaking - though I’m revealing my bias there) and that can sometimes prompt some actual interesting debate. I probably wouldn’t be able to bear listening at all without him on it.

Mainly though I think it can be good to listen to people you don’t agree with every so often.

That's the best description of the All-In Podcast I've read.

It's often infuriating to listen to someone being confidently wrong, but occasionally there are some good insights.

Agreed - I find it useful to get unfiltered insight into how ultra high net worth people think about the world and view/approach things and what sources they use to form opinions.

I also find it useful to compare/calibrate how much about finance that's not VC specific (i.e. macro economics, interest rates, commodities, etc.) I know relative to ultra high net worth people.

It does require active listening to spot the subtle/not subtle bias, errors in logic etc.

> I think it can be good to listen to people you don’t agree with every so often

I 100% agree. However I don't think it's valuable to get information from people who misrepresent data like All-In. In fact it can be counterproductive to listen to people who are misinforming you. If I can't trust my sources then it hurts more than it helps. This goes the other way too - you should fact check the people who are on your side. In my experience though, when I try sampling new content from people who are biased towards Trump, it's easy to find hypocrisies and misinformation.

They make me feel like becoming super rich is achievable — even they could do it!

There's even government infrastructure for it in most states and countries! They're called lotteries.

The part i find most fascinating is that when JCal does push back, the YouTube comments are so disproportionately telling him his opinion is wrong (in a venomous way), he is ruining the podcast, Sacks is running circles around him, etc.

> Their flip-flopping on AI - from it being the best thing ever to being completely overhyped and underperforming - and then back again - has been amusing.

One could tell they had no idea what they were discussing on many occasions, specifically on AI.

Jason and Chamath said AI prompted them to start "coding" again while entertaining the notion that AI will eventually replace all programmers in a matter of months. One day, AI will help the best to become "10 X" engineers. Another day, AI is a dud.

Friedberg said multiple times that everybody would create their Hollywood movie thanks to AI when there is little to no indication people would ever do this, leaving aside the production capability of LLMs to do so.

He has no problem with large language models trained on copyright data but didn't even consider the ethical implications, conflating how humans and machines learn, which is rather simplistic for such an intelligent person to say. He then retro-pedaled in a later episode, not on that specific point exactly, but when he realized he would prefer his businesses and investments to keep their proprietary licenses and hard-earned know-how.

It is! Just let yourself be evil and get your MBA