u/yvthousands

Tokenized communities are the post-AI investment thesis.

They're a bet on culture, trust, belief and belonging - basically the human assets AI cannot replicate or inflate.

Crypto isn't just about price, it's about giving people a reason to build together again.

Persist forever!

AI is already reshaping how businesses are valued today.

More and more investors are beginning to price in a potential "zero terminal value" era. As AI erodes traditional business models and competitive moats, the key question becomes: Will this business still exist in two years? Will its cash flows still be defensible?

That uncertainty will inevitably be reflected in valuation multiples and the cost of capital.

But not all moats are created equal.

AI can replicate products, write code, and automate labor, but it cannot manufacture communities, inflate networks, or fake liquidity. Networks, community, liquidity may in fact prove to be some of the most durable moats in a post-AI economy.

I discussed about post-AI economics at the spx6900 conference in Amsterdam. You can watch the full presentation here https://www.youtube.com/watch?v=-c-TMzIIQzE

AI is a concentrating force: it concentrates capital investment into fewer and fewer firms. It also concentrates outcomes. If you look at the companies succeeding in the AI era, they are achieving more with fewer and fewer people, meaning the benefits are accruing to a smaller and smaller group.

Finally, physical AI and robotics are concentrating as well. These industries require economies of scale and vertical integration to drive down costs, which naturally leads to even greater concentration.

Technology and inflation are both headwinds to labor: technology makes labor less necessary and inflation rises inequality and affects the purchasing power. This made the gap between capital and labor widening over the past 50 years non stop.

To the point where people describe this as a form of "capital deepening" or "capital intensification", which is essentially going from a place where both capital and labor equally matter, to a place - where where we are today - where capital matters a lot more than labor. And post-AI, capital may be the only thing that actually matters.

There's no doubt that AI will create new wealth and new productivity gains, the question is how this new wealth will be distributed.

Technology is deflationary in nature. Between the time a technology is introduced to the time it's mature you typically get -99% in in the unit cost, and we saw this in many cases in the past: computer memories, solar energy, DNA sequencing. We are seeing this already in AI as well, where the cost per unit of intelligence is already going down 90% every year.

So how comes the cost of living has just increased massively over the past 20-30 years?

It's because we live in a "fiat system", so between new money creation and new credit creation, we get an inflationary pressure that especially manifests itself on the less scalable sectors. We end up having higher cost of food higher cost of housing, higher cost of medical services and so on.

The mission of the AI Labs is not to give you a better chatbot, but to absorb all economically valuable work.

And it doesn't matter whether doing that you destroy millions of jobs, because ultimately AI is a giant prisoner dilemma: if you don't do it the competitor will do it.

For this reason, the private investments are accelerating and you see it also in the "great power conflict" between the US and China. Everyone understands that whoever is gonna get to AGI first will enjoy massive economic, scientific and military advantages, which will be potentially long lasting.

For years we've been talking about having a human in the loop, but over the next thousand days people will be increasingly talking about how can we remove the human from the loop.

We now have AI on FPGAs running a 15,000 token a second, so now the conversation will be how can we get rid of the human.

All this is going very fast and it's already manifesting on society - so AI anxiety AI psychosis are a thing.

The complexity that AI can handle it doubles every 7 months, but when it comes to coding it doubles every 70 days.

Why is that? Why everyone is investing so much in AI for coding?

It's because everyone is optimizing to get to this point called Recursive Self-Improvement, which is some people call "Hard Takeoff". It's basically when Openai goes from having 5,000 engineers doing AI research, to having 5 million agents doing AI research. And at that point you're gonna get this "intelligence explosion" and that's how you get to AGI and Superintelligence.

This segment was taken from the SPX6900 conference in Amsterdam on May 9th, 2026.

Persist forever!

AI capabilities are expanding very fast and models are improving across every cognitive benchmark you can imagine.

We keep hearing more and more stories of crazy things that people do with AI whether it's curing the dog cancer, or building some very complex software applications.

AI is also better and better at doing scientific research: it's estimated that this year, all the scientific breakthroughs will be AI assisted, and from next year most of them will be completely AI driven.

However, when it comes to creativity the human still has an advantage!

#SPX6900

Hi everyone,

As per the title, I built a script that watches live poker videos and annotates each hand. I then ran my script on the Triton series videos and built a database with players stats.

I posted here about a week ago as I was toying with the idea to try and evaluate systematically a player EV by examining how they played in their online video history. I was originally thinking to calculate the "delta vs. GTO" tutn-by-turn and to use that as a measure of EV, but I came to realize the pratical limitations/ obstacles to do this, so I pivoted to something slightly different.

Basically I built a system that analyzes poker videos, annotate each actions and then use the data to calculate typical players' metrics. Things like VPIP% (voluntarily put in pot), PFR% (preflop raise), 3-BET% (aggression-vs-aggression) etc. Given that the data come from publicly available videos I thought it could be cool to build a "database" with the characteristics of many players, at least as a data science exercise. Here is the full data dump.

Do you think this could have use cases for the industry? Initially when I was thinking to the EV project my intent was to inform a "more scientific" staking. But in this case, I see the metrics potentially more useful when one wants to study other players and have insights on how their opponents typically play. If you have any idea, feedback, or other metrics you'd like to see I'd love to hear!