Mark Anthony

[Rohit @ai_rohitt]

INSTEAD OF WATCHING AN HOUR OF NETFLIX TONIGHT.

This 1 hour Stanford lecture by Joel Peterson will teach you more about negotiation and getting what you want than most people learn in years.

Bookmark it and give it an hour, no matter what.

[Charles Harris @chasharris1025]

Check out my buddy @jimroppelroarroar...lots of good nuggets. The Trading Secrets of a $200 Million Hedge Fund Manager | TraderLion Qu... https://t.co/zlQ5cmoVds via @YouTube

[Matt Shumer @mattshumer_]

https://t.co/ivXRKXJvQg

[THE SHORT BEAR @TheShortBear]

Some thoughts on AI

The market is increasingly splitting between those who own real infrastructure and those whose primary economic contribution is their intelligence or labor.

As AI systems scale, intelligence itself becomes abundant, cheap, and reproducible, while the truly scarce assets remain the infrastructure that deploys, coordinates, and monetizes that intelligence.

This includes physical infrastructure like energy and robotics, as well as digital infrastructure such as compute, data, networks, settlement layers, and AI systems themselves.

As intelligence and robotics symbiosis evolves and becomes commoditized, labor stops being the dominant claim on economic output.

This shift extends beyond manual work into intellectual labor: analysis, design, decision-making, and even creativity increasingly resemble capital-intensive processes rather than human ones.

Income derived from labor compresses, while income derived from ownership of infrastructure expands. The economic center of gravity moves from who works to who owns. The K-Shape economy grows exponentially.

In this environment, the likely end state is not mass unemployment but a decoupling of income from labor as we know. Nevertheless the speed of the change will create stress we can not yet fully envision.

The economy bifurcates into two broad roles: those who own and allocate productive infrastructure, and the broader population whose economic relevance is no longer tied to productivity but to participation, consumption, and legitimacy.

People are no longer paid primarily for work, they are compensated because the system is productive without them and requires social stability to function.

This creates the conditions for a dividend-based economic model. As crazy as it sounds today, it feels like there is almost no way past it.

Infrastructure owners, whether private or public, redistribute a portion of AI-driven surplus to the population, not as charity or welfare, but as a dividend derived from shared participation in the system.

The framing is critical: individuals are not compensated because they are unproductive, but because the infrastructure generates excess value at scale and depends on broad social consent and demand to sustain itself.

Work does not disappear, but it changes form.

Human activity shifts toward domains that are valuable yet poorly priced by markets dominated by machines: care, education, culture, governance, mediation, creativity, and community coordination.

These roles become socially essential but economically subsidized by surplus rather than directly compensated through wages.

As a result, income becomes less central to identity, and status, reputation, trust, and influence emerge as the primary differentiators among individuals.

The structure of ownership becomes the critical variable determining outcomes. If infrastructure is narrowly owned and governed opaquely, redistribution becomes unstable and politically contested, leading to legitimacy crises and social fragility.

If infrastructure is credibly neutral, partially shared, or governed through transparent and procedural mechanisms, redistribution is perceived as legitimate, and social cohesion becomes sustainable.

The question is not whether redistribution occurs, but whether it is forced and adversarial or structural and accepted.

This transformation reshapes the role of the state. A country increasingly becomes the sum of the infrastructure it controls and the governance applied to it.

Sovereignty shifts away from borders and labor forces toward control over energy systems, compute, data, settlement networks, and AI infrastructure.

Political power follows economic power, and economic power concentrates in the ownership and coordination of critical systems rather than in population size or workforce productivity.

In this future, humans do not disappear from the economy, but they disappear from price discovery, also partially through billions of AI agents transacting at all times for us.

Markets stop answering the question of what a human is worth, and that question moves into the realm of politics, governance, and culture.

The central conflict is no longer about jobs, but about who owns the machines, who governs them, and who holds a legitimate claim on their output.

The end game is not inherently dystopian or utopian.

It is a choice between extractive infrastructure with unstable, coerced redistribution and legitimate infrastructure with broadly accepted dividends.

The societies that navigate this transition successfully will not be those that attempt to preserve labor at all costs, but could be those that redesign ownership, governance, and participation around a world where intelligence is abundant and infrastructure is everything.

Happy to hear feedback.

[HY6 @highyield6]

Must read. Framework on Warsh nomination

[Mark Anthony @MacAnthony28]

@jasonjamesbnn It saddens me that so many Canadians are actually this dumb…. Retarded posts like this don’t help. Next you’re going to tell us how career politicians should be running our country. People who have never accomplished anything or had a real job. 👍

[Mark Anthony @MacAnthony28]

@yegwave what a joke.

[Mark Anthony @MacAnthony28]

@anyatrades No way that’s your mom

[Mark Anthony @MacAnthony28]

@GadSaad @Jacob_Frey The Somalis give him a cut of the money they steal via fraud. He’s willing to sell out his country for a quick back door buck. Just like waltz..
They should be put in prison

[Lance Breitstein 🇺🇸🌎 @TheOneLanceB]

-WHY DOES TECHNICAL ANALYSIS WORK!?
-WILL IT ALWAYS WORK!?

In this video I address a series of questions that are fundamental to our craft. Important ones for all of us to reflect on (answers are IMO and subjective).

0:00 - What is TA? Why does it work?
15:52 - Is it getting less effective and will TA one day not work?
22:35 - Why won't it stop working like many other edges?
28:35 - Why doesn't Citadel utilize it or have a TA desk?
33:01 - Does TA work better near-term or long-term?
38:35 - What about market interconnectedness?
39:35 - How long to know if you have edge?
42:00 - Why can't quant backtests match the edge of TA traders?
44:05 - Druck made 25% CAGR. Why are TA traders returns so high? Capital needed for skilled "league" trader to make $50k?
47:35 - Do TA traders need to adapt and evolve?
48:50 - Are all TA patterns created equal?
49:35 - Do rise of passive flows make TA harder?

[Shake Pryzby @ShakePryzby1]

In December '23 I decided it was time to begin taking steps 'out of the shadows' in order to get my name out there to start my own fund, which has been my dream since I first achieved profitability in 2015.

I saw @traderlion, who is the premiere educational space in the financial world, would interview anybody who won the US Investing Championship.

So I made a little plan, win the '24 USIC. Earn my TL Interview, join twitter Jan '25. Launch Fund '26.

I did not win so I had to resort to plan B which was bothering the shit out of my guy @RichardMoglen all year 😂. I will tell you, there is nothing sweeter in life than delayed gratification. This is a big full circle moment for me.

I do things a little different than most, but at the end of the day it's what simply works for ME, and that's all I can hope to accomplish.

Check this out. 3 hours of gems. I hope you learn something. 13 years of blood, sweat & keyboards lead me to this moment!

Thank you again to my friends over at @traderlion!

[Mark Anthony @MacAnthony28]

@bruce_barrett So sad....

[BuccoCapital Bloke @buccocapital]

Few thoughts on retail investing since it’s popping up again on FinTwit:

- As I’ve said before, I think it’s probably the lowest ROI activity you can do if you’re trying to get rich. Focus on your career
- You should index 90% of your money
- You have zero edge day to day. Your advantages are that you have duration, no mandate, and nobody forcing you to sell
- It follows that the fewer trades you do, the better. The more trades you do, the worse you will do
- Process is the only thing that matters. How rigorous is your process? How do you reflect on what worked and what didn’t? Do you even know why it worked? Otherwise it’s just luck. For most people is still probably just luck.
- Your biggest enemy is yourself. You have no institutional guardrails to stop you from doing stupid things
- If you are thinking of it YTD you have already lost. That’s not your game, and is a recipe for failure. Trying to beat the market every single year will cause you to overtrade, where you have a structural and insurmountable disadvantage
- It’s OK to just do it for the love of the game. Because it’s the greatest game on earth!

[Tim Ferriss @tferriss]

Watch my brand-new conversation with Bill Gurley: https://t.co/7Acjd6OPIu

[Hans C Nelson 🗽 @HansCNelson]

Or watch on YouTube here:
https://t.co/HwntwZESNd

[Hans C Nelson 🗽 @HansCNelson]

Google's recent Gemini 3 release has shocked the world with undeniable proof that the TPU is a powerhouse AI chip, leaving many to wonder what lays in store for companies like @NVIDIA & @xAI.

Listen to @GavinSBaker lay out the exact strategy that Jensen Huang & @ElonMusk are going to roll out over the next 12 months to beat @Google at AI.

The world is in for a HUGE surprise.

----------

 We'll see the first models trained on Blackwell in early 2026.

I think the first Blackwell model will come from XAI. And the reason for that is just, according to Jensen, no one builds data centers faster than Elon.

Jensen has said this on the record.

...

So if you're Jensen or Nvidia, you need to get as many GPUs deployed in one data center as fast as possible in a coherent cluster so you can work out the bugs.

And so this is what X AI effectively does for Nvidia because they build the data centers the fastest.

They can deploy Blackwells that scale the fastest, and they can help work with Nvidia to work out the bugs for everyone else.

So because they're the fastest, they will, they'll have the first Blackwell model.

...

We know that these Blackwell models are gonna be really good.

...

Then something even more important happens.

So the GB 200 was really, really, it was really hard to get it going.

The GB 300 is a great chip. It is drop in compatible in every way with those GB 200 racks. Now you're not gonna replace the GB 200s there.

Just any data center that can handle those, you can slot in the GB 300s, and now everybody's good at making those racks and you know how to get the heat out. You know how to cool them.

You're gonna put those GB 300s in and then the companies that use the GB 300s, they're going to be the low cost producer of tokens.

Particularly if you're vertically integrated.

If you're paying a margin to someone else to make those tokens, you're probably not gonna be.

I think this has pretty profound implications because I think it has to change Google's strategic calculus.

----------

Listen to the full conversation between Gavin & @patrick_oshag below 👇

[Mark Anthony @MacAnthony28]

@GavinSBaker What an awesome guy. Great story btw

[Mark Anthony @MacAnthony28]

@elonmusk That’s scary

[Connor Bates @ConnorJBates_]

Humanoid Tech

Brain ~ AI/Compute $BIDU $BABA $CDNS $TXN $AMD $NVDA $SNPS $ARM

AI Vision ~ Image Sensor $MCHP $AMBA $ON

Sensing ~ Embedded Control & Power Mgmt $NXPI $STM $IFNNY $ALGM $ROHCY

Tactile Sensor ~ $HSAI

Source: Morgan Stanley

[Mark Anthony @MacAnthony28]

@elonmusk It’s the only way..