Dimitry Nakhla | Babylon Capital®
What helped make Amazon so successful — persistent, large-scale reinvestment — now appears to be the very source of discomfort for some investors.
___
I’d argue it’s largely a function of fear around the unknown / uncertainty.
On the surface, the concern is understandable. But historically, wouldn’t you want a business with a long track record of disciplined, high-return reinvestment to continue investing heavily?
Over time, Amazon has demonstrated itself to be a strong steward of capital. Patient shareholders who looked through near-term noise have been rewarded accordingly.
$AMZN
tweet
What helped make Amazon so successful — persistent, large-scale reinvestment — now appears to be the very source of discomfort for some investors.
___
I’d argue it’s largely a function of fear around the unknown / uncertainty.
On the surface, the concern is understandable. But historically, wouldn’t you want a business with a long track record of disciplined, high-return reinvestment to continue investing heavily?
Over time, Amazon has demonstrated itself to be a strong steward of capital. Patient shareholders who looked through near-term noise have been rewarded accordingly.
$AMZN
It will always amaze me how armchair experts will claim big tech is wasting money on all this capex with “unknown” ROI
Meanwhile $AMZN straight up says they WILL be capacity constrained for years… tweet
X (formerly Twitter)
Aria Radnia 🇮🇷 (@QualityInvest5) on X
It will always amaze me how armchair experts will claim big tech is wasting money on all this capex with “unknown” ROI
Meanwhile $AMZN straight up says they WILL be capacity constrained for years…
Meanwhile $AMZN straight up says they WILL be capacity constrained for years…
Offshore
Video
Moon Dev
I Analyzed 5,000 Whale Wallets: The $200,000 “Human Tax” You Are Paying to Hyperliquid
tracked five thousand of the wealthiest traders on hyperliquid and what i found proves that your biggest fear about the market is actually a lie. most of us think the big guys have some secret edge that we could never access
they have the math degrees and the hundred million dollar bankrolls so we assume they must be winning while we struggle. but when i ran a script to see how many of them actually survived the results were so catastrophic that it changes everything you know about trading
i spent years thinking i was the problem because i kept getting liquidated and losing money to over trading. i even spent hundreds of thousands of dollars on developers to build apps because i thought i was not smart enough to code the solutions myself
then i decided to learn live on youtube and started building my own bots to solve the problems i was facing. i wanted to know if the whales were actually better than us or if they were just better at hiding their losses
the script i wrote scanned the top five thousand depositors on hyperliquid who each put in at least a million dollars. these are the elite players the institutions and the guys we are supposed to be afraid of in the order book
as the progress bar ticked up i watched something unbelievable happen right in front of my eyes. out of the first thirteen hundred wallets checked only four hundred had more than ten thousand dollars left in their accounts
that means over seventy percent of the biggest traders on the planet have been completely obliterated. they started with millions and ended up with almost nothing which means they are trading just like the average person in a casino
this leads to a massive question about why these people with unlimited resources are failing so spectacularly. the answer is not just bad luck or market manipulation but something much more subtle that is draining your account right now
one of the biggest killers of any trading account is the hidden cost of being a human being. when you trade by hand you are prone to emotions and those emotions force you to use market orders because you feel like you have to get in right now
market orders are basically an emotional tax that you pay to the exchange for the privilege of being impatient. the fees for a market order are usually three times higher than a limit order and that difference is the line between profit and bankruptcy
i saw one trader who had spent over six hundred thousand dollars just on transaction fees. if that person had used a simple bot to enter and exit their positions they would have saved nearly two hundred thousand dollars in cash
that is money that could have stayed in their account but instead it went straight to the exchange. this is exactly why the exchange wants to speed you up and keep you staring at those flashing lights all day long
the more emotional you get the more you trade and the more you pay in market fees. it is a game designed to make you fail and even the smartest guys with a hundred million dollars are falling for it
most people tell me they want to keep their intuition when they trade because they think they have a special feeling for the market. but i have to ask you if your intuition is worth the three hundred percent premium you are paying in fees every single time you click a button
there is no intuition in paying three thousand dollars for an iphone that costs one thousand just because you could not wait two seconds. yet that is exactly what hand traders do every single day when they refuse to use automation
the math is actually terrifying when you look at how fast fees can kill a healthy account. if you have a twenty five thousand dollar account using high leverage and trading five times a day you will blow up in about a month just on fees alone
that is not even counting the money you lose on bad trades which makes the situation even worse. by simply switching to a[...]
I Analyzed 5,000 Whale Wallets: The $200,000 “Human Tax” You Are Paying to Hyperliquid
tracked five thousand of the wealthiest traders on hyperliquid and what i found proves that your biggest fear about the market is actually a lie. most of us think the big guys have some secret edge that we could never access
they have the math degrees and the hundred million dollar bankrolls so we assume they must be winning while we struggle. but when i ran a script to see how many of them actually survived the results were so catastrophic that it changes everything you know about trading
i spent years thinking i was the problem because i kept getting liquidated and losing money to over trading. i even spent hundreds of thousands of dollars on developers to build apps because i thought i was not smart enough to code the solutions myself
then i decided to learn live on youtube and started building my own bots to solve the problems i was facing. i wanted to know if the whales were actually better than us or if they were just better at hiding their losses
the script i wrote scanned the top five thousand depositors on hyperliquid who each put in at least a million dollars. these are the elite players the institutions and the guys we are supposed to be afraid of in the order book
as the progress bar ticked up i watched something unbelievable happen right in front of my eyes. out of the first thirteen hundred wallets checked only four hundred had more than ten thousand dollars left in their accounts
that means over seventy percent of the biggest traders on the planet have been completely obliterated. they started with millions and ended up with almost nothing which means they are trading just like the average person in a casino
this leads to a massive question about why these people with unlimited resources are failing so spectacularly. the answer is not just bad luck or market manipulation but something much more subtle that is draining your account right now
one of the biggest killers of any trading account is the hidden cost of being a human being. when you trade by hand you are prone to emotions and those emotions force you to use market orders because you feel like you have to get in right now
market orders are basically an emotional tax that you pay to the exchange for the privilege of being impatient. the fees for a market order are usually three times higher than a limit order and that difference is the line between profit and bankruptcy
i saw one trader who had spent over six hundred thousand dollars just on transaction fees. if that person had used a simple bot to enter and exit their positions they would have saved nearly two hundred thousand dollars in cash
that is money that could have stayed in their account but instead it went straight to the exchange. this is exactly why the exchange wants to speed you up and keep you staring at those flashing lights all day long
the more emotional you get the more you trade and the more you pay in market fees. it is a game designed to make you fail and even the smartest guys with a hundred million dollars are falling for it
most people tell me they want to keep their intuition when they trade because they think they have a special feeling for the market. but i have to ask you if your intuition is worth the three hundred percent premium you are paying in fees every single time you click a button
there is no intuition in paying three thousand dollars for an iphone that costs one thousand just because you could not wait two seconds. yet that is exactly what hand traders do every single day when they refuse to use automation
the math is actually terrifying when you look at how fast fees can kill a healthy account. if you have a twenty five thousand dollar account using high leverage and trading five times a day you will blow up in about a month just on fees alone
that is not even counting the money you lose on bad trades which makes the situation even worse. by simply switching to a[...]
Offshore
Moon Dev I Analyzed 5,000 Whale Wallets: The $200,000 “Human Tax” You Are Paying to Hyperliquid tracked five thousand of the wealthiest traders on hyperliquid and what i found proves that your biggest fear about the market is actually a lie. most of us think…
bot that uses limit orders you can extend the life of that same account by hundreds of days
this realization is why i say code is the great equalizer for the retail trader. it allows us to build walls around our capital that the big institutions are too emotional to build for themselves
i used to be the guy getting liquidated and feeling like i was gambling my life away. but now i have fully automated systems that trade for me instead of getting emotional and clicking buttons in the middle of the night
you do not need a math degree to do this and you certainly do not need to spend hundreds of thousands of dollars on developers like i did. you just need to realize that the market is a system and the only way to beat a system is to build a better one
the big guys are failing because they are still humans trying to outsmart a machine. when you automate your trading you stop being the victim of the order book and you start becoming the architect of your own success
i decided to build all of this live on youtube so everyone could see that it is possible to iterate your way to success. it is not about being right on every trade but about having a system that is robust enough to survive the madness
the whales are getting obliterated because they think their money makes them smarter than the code. but the data shows that seventy five percent of them are gone and they are never coming back to the market
you have a choice to keep trading like a whale who is headed for a zero balance or to start building the bots that will protect your future. the tools are all there for you to take and the code does not care how much money you started with
it only cares about the logic you give it and the discipline you have to let the system work. if you want to stop being part of the seventy five percent who blow up then it is time to stop clicking buttons and start writing lines of code
this journey started for me because i was tired of losing and i knew there had to be a better way. now that i have seen the data i know for a fact that the only way to win long term is to remove yourself from the equation entirely
the exchange is counting on you to be emotional and to keep paying those massive fees. but once you automate your entry and exit you take that power away from them and put it back in your own pocket
the real secret to trading is that there is no secret other than staying alive long enough to let the math work in your favor. if the biggest traders in the world cannot survive with their intuition then you should probably reconsider yours
take a look at your own fees and your own trade history and ask yourself if you are building a legacy or just funding someone else’s exchange. the answer is usually written in the code and it is waiting for you to find it
i am going to keep building and showing every step of the process because i want everyone to have access to the great equalizer. the whales are falling but you do not have to fall with them if you are willing to learn and adapt
your financial freedom is not going to come from a lucky trade but from a persistent bot that never sleeps and never gets tilted. start small and keep iterating until you have built the system that sets you free from the screen forever
tweet
this realization is why i say code is the great equalizer for the retail trader. it allows us to build walls around our capital that the big institutions are too emotional to build for themselves
i used to be the guy getting liquidated and feeling like i was gambling my life away. but now i have fully automated systems that trade for me instead of getting emotional and clicking buttons in the middle of the night
you do not need a math degree to do this and you certainly do not need to spend hundreds of thousands of dollars on developers like i did. you just need to realize that the market is a system and the only way to beat a system is to build a better one
the big guys are failing because they are still humans trying to outsmart a machine. when you automate your trading you stop being the victim of the order book and you start becoming the architect of your own success
i decided to build all of this live on youtube so everyone could see that it is possible to iterate your way to success. it is not about being right on every trade but about having a system that is robust enough to survive the madness
the whales are getting obliterated because they think their money makes them smarter than the code. but the data shows that seventy five percent of them are gone and they are never coming back to the market
you have a choice to keep trading like a whale who is headed for a zero balance or to start building the bots that will protect your future. the tools are all there for you to take and the code does not care how much money you started with
it only cares about the logic you give it and the discipline you have to let the system work. if you want to stop being part of the seventy five percent who blow up then it is time to stop clicking buttons and start writing lines of code
this journey started for me because i was tired of losing and i knew there had to be a better way. now that i have seen the data i know for a fact that the only way to win long term is to remove yourself from the equation entirely
the exchange is counting on you to be emotional and to keep paying those massive fees. but once you automate your entry and exit you take that power away from them and put it back in your own pocket
the real secret to trading is that there is no secret other than staying alive long enough to let the math work in your favor. if the biggest traders in the world cannot survive with their intuition then you should probably reconsider yours
take a look at your own fees and your own trade history and ask yourself if you are building a legacy or just funding someone else’s exchange. the answer is usually written in the code and it is waiting for you to find it
i am going to keep building and showing every step of the process because i want everyone to have access to the great equalizer. the whales are falling but you do not have to fall with them if you are willing to learn and adapt
your financial freedom is not going to come from a lucky trade but from a persistent bot that never sleeps and never gets tilted. start small and keep iterating until you have built the system that sets you free from the screen forever
tweet
Offshore
Video
God of Prompt
RT @godofprompt: Sora, Runway,... they all do the same damn thing.
You prompt. You wait. You get a clip. You start over.
That's not creation. That's a glorified vending machine with a $20/month subscription.
PixVerse R1 just made all of it look ancient. Real-time 1080P video that listens to you while it's generating. No render bar. No fixed clips. No "try again."
Here's why nobody's ready for this: 👇
tweet
RT @godofprompt: Sora, Runway,... they all do the same damn thing.
You prompt. You wait. You get a clip. You start over.
That's not creation. That's a glorified vending machine with a $20/month subscription.
PixVerse R1 just made all of it look ancient. Real-time 1080P video that listens to you while it's generating. No render bar. No fixed clips. No "try again."
Here's why nobody's ready for this: 👇
tweet
The Transcript
Microsoft commits to building frontier in-house foundationreducing OpenAI dependence
"We have to develop our own foundation models, which are at the absolute frontier, with gigawatt-scale compute and some of the very best AI training teams in the world" - $MSFT AI chief
[FT]
tweet
Microsoft commits to building frontier in-house foundationreducing OpenAI dependence
"We have to develop our own foundation models, which are at the absolute frontier, with gigawatt-scale compute and some of the very best AI training teams in the world" - $MSFT AI chief
[FT]
tweet
Offshore
Photo
God of Prompt
RT @godofprompt: 🚨 I just read Google DeepMind’s new paper called "Intelligent AI Delegation."
And it quietly exposes why 99% of AI agents will fail in the real world.
Here’s the paper:
Most “AI agents” today aren’t agents.
They’re glorified task runners.
You give them a goal.
They break it into steps.
They call tools.
They return an output.
That’s not delegation.
That’s automation with better marketing.
Google’s paper makes a brutal point:
Delegation isn’t just splitting tasks.
It’s transferring authority, responsibility, accountability, and trust across agents dynamically.
And almost no current system does this.
Here’s what they argue real delegation actually requires:
1. Dynamic assessment
Before assigning a task, an agent must evaluate:
- Capability
- Resource availability
- Risk
- Cost
- Verifiability
- Reversibility
Not just “who has the tool?”
But: “Who should be trusted with this specific task under these constraints?”
That’s a massive shift.
2. Adaptive execution
If the delegatee underperforms…
You don’t wait for failure.
You reassign mid-execution.
Switch agents.
Escalate to a human.
Restructure the task graph.
Current agents are brittle.
Real agents need recovery logic.
3. Structural transparency
Today’s AI-to-AI delegation is opaque.
If something fails, you don’t know:
- Was it incompetence?
- Misalignment?
- Bad decomposition?
- Malicious behavior?
- Tool failure?
The paper proposes enforced auditability and verifiable completion.
In other words:
Agents must prove what they did.
Not just say they did it.
4. Trust calibration
This is huge.
Humans routinely over-trust AI.
AI agents may over-trust other agents.
Both are dangerous.
Delegation must align trust with actual capability.
Too much trust = catastrophe.
Too little trust = wasted potential.
5. Systemic resilience
This is the part nobody is talking about.
If every agent delegates to the same high-performing model…
You create a monoculture.
One failure.
System-wide collapse.
Efficiency without redundancy = fragility.
Google explicitly warns about cascading failures in agentic economies.
That’s not sci-fi.
That’s distributed systems reality.
The paper also breaks down:
- Principal-agent problems in AI
- Authority gradients between agents
- “Zones of indifference” (agents complying without critical thinking)
- Transaction cost economics for AI markets
- Game-theoretic coordination
- Hybrid human-AI delegation models
This isn’t a toy-agent paper.
It’s an operating system blueprint for the “agentic web.”
The core idea:
Delegation must be a protocol.
Not a prompt.
Right now, most “multi-agent systems” are:
Agent A → Agent B → Agent C
With zero formal responsibility structure.
In a real delegation framework:
• Roles are defined
• Permissions are bounded
• Verification is required
• Monitoring is enforced
• Market coordination is decentralized
• Failures are attributable
That’s enterprise-grade infrastructure.
And we don’t have it yet.
The most important line in the paper?
Automation is not just about what AI can do.
It’s about what AI *should* do.
That distinction will decide:
- which startups survive
- which enterprises scale
- which ai deployments implode
We’re entering the phase where:
Prompt engineering → Agent engineering → Delegation engineering.
The companies that figure out intelligent delegation protocols first will build:
• Autonomous economic systems
• Scalable AI marketplaces
• Human-AI hybrid orgs
• Resilient agent swarms
Everyone else will ship brittle demos.
This paper isn’t flashy.
No benchmarks.
No model release.
No hype numbers.
Just a 42-page warning:
If we don’t build adaptive, accountable delegation frameworks…
The agentic web collapses under its own complexity.
And honestly?
They’re probably right. tweet
RT @godofprompt: 🚨 I just read Google DeepMind’s new paper called "Intelligent AI Delegation."
And it quietly exposes why 99% of AI agents will fail in the real world.
Here’s the paper:
Most “AI agents” today aren’t agents.
They’re glorified task runners.
You give them a goal.
They break it into steps.
They call tools.
They return an output.
That’s not delegation.
That’s automation with better marketing.
Google’s paper makes a brutal point:
Delegation isn’t just splitting tasks.
It’s transferring authority, responsibility, accountability, and trust across agents dynamically.
And almost no current system does this.
Here’s what they argue real delegation actually requires:
1. Dynamic assessment
Before assigning a task, an agent must evaluate:
- Capability
- Resource availability
- Risk
- Cost
- Verifiability
- Reversibility
Not just “who has the tool?”
But: “Who should be trusted with this specific task under these constraints?”
That’s a massive shift.
2. Adaptive execution
If the delegatee underperforms…
You don’t wait for failure.
You reassign mid-execution.
Switch agents.
Escalate to a human.
Restructure the task graph.
Current agents are brittle.
Real agents need recovery logic.
3. Structural transparency
Today’s AI-to-AI delegation is opaque.
If something fails, you don’t know:
- Was it incompetence?
- Misalignment?
- Bad decomposition?
- Malicious behavior?
- Tool failure?
The paper proposes enforced auditability and verifiable completion.
In other words:
Agents must prove what they did.
Not just say they did it.
4. Trust calibration
This is huge.
Humans routinely over-trust AI.
AI agents may over-trust other agents.
Both are dangerous.
Delegation must align trust with actual capability.
Too much trust = catastrophe.
Too little trust = wasted potential.
5. Systemic resilience
This is the part nobody is talking about.
If every agent delegates to the same high-performing model…
You create a monoculture.
One failure.
System-wide collapse.
Efficiency without redundancy = fragility.
Google explicitly warns about cascading failures in agentic economies.
That’s not sci-fi.
That’s distributed systems reality.
The paper also breaks down:
- Principal-agent problems in AI
- Authority gradients between agents
- “Zones of indifference” (agents complying without critical thinking)
- Transaction cost economics for AI markets
- Game-theoretic coordination
- Hybrid human-AI delegation models
This isn’t a toy-agent paper.
It’s an operating system blueprint for the “agentic web.”
The core idea:
Delegation must be a protocol.
Not a prompt.
Right now, most “multi-agent systems” are:
Agent A → Agent B → Agent C
With zero formal responsibility structure.
In a real delegation framework:
• Roles are defined
• Permissions are bounded
• Verification is required
• Monitoring is enforced
• Market coordination is decentralized
• Failures are attributable
That’s enterprise-grade infrastructure.
And we don’t have it yet.
The most important line in the paper?
Automation is not just about what AI can do.
It’s about what AI *should* do.
That distinction will decide:
- which startups survive
- which enterprises scale
- which ai deployments implode
We’re entering the phase where:
Prompt engineering → Agent engineering → Delegation engineering.
The companies that figure out intelligent delegation protocols first will build:
• Autonomous economic systems
• Scalable AI marketplaces
• Human-AI hybrid orgs
• Resilient agent swarms
Everyone else will ship brittle demos.
This paper isn’t flashy.
No benchmarks.
No model release.
No hype numbers.
Just a 42-page warning:
If we don’t build adaptive, accountable delegation frameworks…
The agentic web collapses under its own complexity.
And honestly?
They’re probably right. tweet
Offshore
Video
Moon Dev
Openclaw Use Cases That Actually Print Money
While everyone else is just producing ai slop https://t.co/AAO2FT9M8C
tweet
Openclaw Use Cases That Actually Print Money
While everyone else is just producing ai slop https://t.co/AAO2FT9M8C
tweet
Offshore
Photo
God of Prompt
RT @godofprompt: How to use LLMs for competitive intelligence (scraping, analysis, reporting): https://t.co/xlGOSpRQPy
tweet
RT @godofprompt: How to use LLMs for competitive intelligence (scraping, analysis, reporting): https://t.co/xlGOSpRQPy
tweet
Offshore
Video
Dimitry Nakhla | Babylon Capital®
RT @DimitryNakhla: Dan Sundheim, Founder & CIO of D1 Capital Partners, on what stock he’d buy if there was a 10-year lockup:
“There’s very few tech companies I feel comfortable saying because I think tech just changes too quickly so it wouldn’t be a tech company. It would have to be a company with a moat that’s incredibly difficult to penetrate, with a growth rate well above GDP for a long time.
I like 𝐒𝐢𝐞𝐦𝐞𝐧𝐬 𝐄𝐧𝐞𝐫𝐠𝐲 quite a bit… a company called 𝐂𝐥𝐞𝐚𝐧 𝐇𝐚𝐫𝐛𝐨𝐫𝐬 which I like a lot… they own the majority of the incinerators in the United States. You can’t really build more incinerators because of NIMBY.”
___
𝐓𝐡𝐞 𝐥𝐞𝐬𝐬𝐨𝐧:
Investing ultimately comes back to analyzing a company’s moat. Not just whether a business has competitive advantages — but: how easily can those advantages be replicated?
How many layers of barriers to entry protect the business?
How durable are those advantages over long periods of time?
𝘛𝘩𝘦 𝘭𝘰𝘯𝘨𝘦𝘳 𝘺𝘰𝘶𝘳 𝘪𝘯𝘷𝘦𝘴𝘵𝘮𝘦𝘯𝘵 𝘩𝘰𝘳𝘪𝘻𝘰𝘯, 𝘵𝘩𝘦 𝘮𝘰𝘳𝘦 𝘤𝘳𝘪𝘵𝘪𝘤𝘢𝘭 𝘵𝘩𝘦𝘴𝘦 𝘲𝘶𝘦𝘴𝘵𝘪𝘰𝘯𝘴 𝘣𝘦𝘤𝘰𝘮𝘦.
𝘈𝘴 𝘵𝘦𝘤𝘩𝘯𝘰𝘭𝘰𝘨𝘺 𝘢𝘥𝘷𝘢𝘯𝘤𝘦𝘴, 𝘥𝘪𝘴𝘳𝘶𝘱𝘵𝘪𝘰𝘯 𝘳𝘪𝘴𝘬 𝘯𝘢𝘵𝘶𝘳𝘢𝘭𝘭𝘺 𝘳𝘪𝘴𝘦𝘴. 𝘞𝘩𝘢𝘵 𝘭𝘰𝘰𝘬𝘴 𝘥𝘰𝘮𝘪𝘯𝘢𝘯𝘵 𝘵𝘰𝘥𝘢𝘺 𝘮𝘢𝘺 𝘯𝘰𝘵 𝘳𝘦𝘮𝘢𝘪𝘯 𝘴𝘰 𝘸𝘪𝘵𝘩𝘰𝘶𝘵 𝘳𝘦𝘢𝘭 𝘴𝘵𝘳𝘶𝘤𝘵𝘶𝘳𝘢𝘭 𝘱𝘳𝘰𝘵𝘦𝘤𝘵𝘪𝘰𝘯𝘴 — 𝘪𝘯𝘵𝘦𝘭𝘭𝘦𝘤𝘵𝘶𝘢𝘭 𝘱𝘳𝘰𝘱𝘦𝘳𝘵𝘺, 𝘳𝘦𝘨𝘶𝘭𝘢𝘵𝘰𝘳𝘺 𝘣𝘢𝘳𝘳𝘪𝘦𝘳𝘴, 𝘪𝘯𝘴𝘵𝘢𝘭𝘭𝘦𝘥 𝘣𝘢𝘴𝘦, 𝘴𝘸𝘪𝘵𝘤𝘩𝘪𝘯𝘨 𝘤𝘰𝘴𝘵𝘴, 𝘯𝘦𝘵𝘸𝘰𝘳𝘬 𝘦𝘧𝘧𝘦𝘤𝘵𝘴, 𝘩𝘢𝘳𝘥 𝘢𝘴𝘴𝘦𝘵𝘴, 𝘦𝘵𝘤.
___
𝙒𝙝𝙖𝙩’𝙨 𝙗𝙚𝙖𝙪𝙩𝙞𝙛𝙪𝙡 𝙖𝙗𝙤𝙪𝙩 𝙎𝙪𝙣𝙙𝙝𝙚𝙞𝙢’𝙨 𝙛𝙧𝙖𝙢𝙞𝙣𝙜 𝙞𝙨 𝙩𝙝𝙚 𝙚𝙢𝙥𝙝𝙖𝙨𝙞𝙨 𝙤𝙣 𝙙𝙪𝙧𝙖𝙗𝙞𝙡𝙞𝙩𝙮 𝙤𝙫𝙚𝙧 𝙚𝙭𝙘𝙞𝙩𝙚𝙢𝙚𝙣𝙩.
Particularly what he mentioned about 𝐂𝐥𝐞𝐚𝐧 𝐇𝐚𝐫𝐛𝐨𝐫𝐬:
“You 𝘤𝘢𝘯’𝘵 𝘳𝘦𝘢𝘭𝘭𝘺 𝘣𝘶𝘪𝘭𝘥 𝘮𝘰𝘳𝘦 𝘪𝘯𝘤𝘪𝘯𝘦𝘳𝘢𝘵𝘰𝘳𝘴 𝘣𝘦𝘤𝘢𝘶𝘴𝘦 𝘰𝘧 𝙉𝙄𝙈𝘽𝙔 and as you have 𝙢𝙤𝙧𝙚 𝙤𝙣𝙨𝙝𝙤𝙧𝙞𝙣𝙜𝘵𝘩𝘦𝘳𝘦’𝘴 𝘨𝘰𝘪𝘯 𝘵𝘰 𝘣𝘦 𝘮𝘰𝘳𝘦 𝘩𝘢𝘻𝘢𝘳𝘥𝘰𝘶𝘴 𝘸𝘢𝘴𝘵𝘦 and they have both the incinerators and 𝘵𝘩𝘦𝘺 𝘩𝘢𝘷𝘦 𝘵𝘩𝘦 𝙣𝙚𝙩𝙬𝙤𝙧𝙠 𝘵𝘰 𝘨𝘰 𝘤𝘰𝘭𝘭𝘦𝘤𝘵. And so it’s just a very very good business and the starting multiple is very reasonable.”
𝐓𝐡𝐞 𝐛𝐚𝐫𝐫𝐢𝐞𝐫𝐬 𝐭𝐨 𝐞𝐧𝐭𝐫𝐲 𝐞𝐦𝐛𝐞𝐝𝐝𝐞𝐝 𝐢𝐧 𝐭𝐡𝐚𝐭 𝐬𝐭𝐚𝐭𝐞𝐦𝐞𝐧𝐭 𝐚𝐫𝐞 𝐢𝐧𝐜𝐫𝐞𝐝𝐢𝐛𝐥𝐲 𝐩𝐨𝐰𝐞𝐫𝐟𝐮𝐥:
𝐑𝐞𝐠𝐮𝐥𝐚𝐭𝐨𝐫𝐲 𝐁𝐚𝐫𝐫𝐢𝐞𝐫𝐬 → Hazardous waste facilities face extreme permitting hurdles
𝐍𝐈𝐌𝐁𝐘 𝐄𝐟𝐟𝐞𝐜𝐭 t → Even if permitted, communities resist new incinerators
𝐒𝐜𝐚𝐫𝐜𝐢𝐭𝐲 𝐨𝐟 𝐀𝐬𝐬𝐞𝐭𝐬 → Very few licensed hazardous waste incinerators exist
𝐇𝐚𝐫𝐝 𝐀𝐬𝐬𝐞𝐭 𝐀𝐝𝐯𝐚𝐧𝐭𝐚𝐠𝐞 → These are not easily replicated digital products
𝐍𝐞𝐭𝐰𝐨𝐫𝐤 𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 → Collection, transportation, disposal ecosystem
𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐞𝐝 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦 → Owning both disposal + logistics compounds the moat
This is what a real moat looks like.
___
Interestingly:
$SIE.DE: +13% YTD
$CLH: +13% YTD
𝐀 𝐫𝐞𝐦𝐢𝐧𝐝𝐞𝐫 𝐭𝐡𝐚𝐭 𝐥𝐨𝐧𝐠-𝐭𝐞𝐫𝐦 𝐢𝐧𝐯𝐞𝐬𝐭𝐢𝐧𝐠 𝐢𝐬𝐧’𝐭 𝐚𝐛𝐨𝐮𝐭 𝐜𝐡𝐚𝐬𝐢𝐧𝐠 𝐭𝐡𝐞 𝐟𝐚𝐬𝐭𝐞𝐬𝐭 𝐬𝐭𝐨𝐫𝐲 — 𝐢𝐭’𝐬 𝐚𝐛𝐨𝐮𝐭 𝐨𝐰𝐧𝐢𝐧𝐠 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬𝐞𝐬 𝐭𝐡𝐚𝐭 𝐚𝐫𝐞 𝐡𝐚𝐫𝐝𝐞𝐬𝐭 𝐭𝐨 𝐝𝐢𝐬𝐫𝐮𝐩𝐭.
___
Video: Stripe | Dan Sundheim of D1 Capital | (10/22/2025)
tweet
RT @DimitryNakhla: Dan Sundheim, Founder & CIO of D1 Capital Partners, on what stock he’d buy if there was a 10-year lockup:
“There’s very few tech companies I feel comfortable saying because I think tech just changes too quickly so it wouldn’t be a tech company. It would have to be a company with a moat that’s incredibly difficult to penetrate, with a growth rate well above GDP for a long time.
I like 𝐒𝐢𝐞𝐦𝐞𝐧𝐬 𝐄𝐧𝐞𝐫𝐠𝐲 quite a bit… a company called 𝐂𝐥𝐞𝐚𝐧 𝐇𝐚𝐫𝐛𝐨𝐫𝐬 which I like a lot… they own the majority of the incinerators in the United States. You can’t really build more incinerators because of NIMBY.”
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𝐓𝐡𝐞 𝐥𝐞𝐬𝐬𝐨𝐧:
Investing ultimately comes back to analyzing a company’s moat. Not just whether a business has competitive advantages — but: how easily can those advantages be replicated?
How many layers of barriers to entry protect the business?
How durable are those advantages over long periods of time?
𝘛𝘩𝘦 𝘭𝘰𝘯𝘨𝘦𝘳 𝘺𝘰𝘶𝘳 𝘪𝘯𝘷𝘦𝘴𝘵𝘮𝘦𝘯𝘵 𝘩𝘰𝘳𝘪𝘻𝘰𝘯, 𝘵𝘩𝘦 𝘮𝘰𝘳𝘦 𝘤𝘳𝘪𝘵𝘪𝘤𝘢𝘭 𝘵𝘩𝘦𝘴𝘦 𝘲𝘶𝘦𝘴𝘵𝘪𝘰𝘯𝘴 𝘣𝘦𝘤𝘰𝘮𝘦.
𝘈𝘴 𝘵𝘦𝘤𝘩𝘯𝘰𝘭𝘰𝘨𝘺 𝘢𝘥𝘷𝘢𝘯𝘤𝘦𝘴, 𝘥𝘪𝘴𝘳𝘶𝘱𝘵𝘪𝘰𝘯 𝘳𝘪𝘴𝘬 𝘯𝘢𝘵𝘶𝘳𝘢𝘭𝘭𝘺 𝘳𝘪𝘴𝘦𝘴. 𝘞𝘩𝘢𝘵 𝘭𝘰𝘰𝘬𝘴 𝘥𝘰𝘮𝘪𝘯𝘢𝘯𝘵 𝘵𝘰𝘥𝘢𝘺 𝘮𝘢𝘺 𝘯𝘰𝘵 𝘳𝘦𝘮𝘢𝘪𝘯 𝘴𝘰 𝘸𝘪𝘵𝘩𝘰𝘶𝘵 𝘳𝘦𝘢𝘭 𝘴𝘵𝘳𝘶𝘤𝘵𝘶𝘳𝘢𝘭 𝘱𝘳𝘰𝘵𝘦𝘤𝘵𝘪𝘰𝘯𝘴 — 𝘪𝘯𝘵𝘦𝘭𝘭𝘦𝘤𝘵𝘶𝘢𝘭 𝘱𝘳𝘰𝘱𝘦𝘳𝘵𝘺, 𝘳𝘦𝘨𝘶𝘭𝘢𝘵𝘰𝘳𝘺 𝘣𝘢𝘳𝘳𝘪𝘦𝘳𝘴, 𝘪𝘯𝘴𝘵𝘢𝘭𝘭𝘦𝘥 𝘣𝘢𝘴𝘦, 𝘴𝘸𝘪𝘵𝘤𝘩𝘪𝘯𝘨 𝘤𝘰𝘴𝘵𝘴, 𝘯𝘦𝘵𝘸𝘰𝘳𝘬 𝘦𝘧𝘧𝘦𝘤𝘵𝘴, 𝘩𝘢𝘳𝘥 𝘢𝘴𝘴𝘦𝘵𝘴, 𝘦𝘵𝘤.
___
𝙒𝙝𝙖𝙩’𝙨 𝙗𝙚𝙖𝙪𝙩𝙞𝙛𝙪𝙡 𝙖𝙗𝙤𝙪𝙩 𝙎𝙪𝙣𝙙𝙝𝙚𝙞𝙢’𝙨 𝙛𝙧𝙖𝙢𝙞𝙣𝙜 𝙞𝙨 𝙩𝙝𝙚 𝙚𝙢𝙥𝙝𝙖𝙨𝙞𝙨 𝙤𝙣 𝙙𝙪𝙧𝙖𝙗𝙞𝙡𝙞𝙩𝙮 𝙤𝙫𝙚𝙧 𝙚𝙭𝙘𝙞𝙩𝙚𝙢𝙚𝙣𝙩.
Particularly what he mentioned about 𝐂𝐥𝐞𝐚𝐧 𝐇𝐚𝐫𝐛𝐨𝐫𝐬:
“You 𝘤𝘢𝘯’𝘵 𝘳𝘦𝘢𝘭𝘭𝘺 𝘣𝘶𝘪𝘭𝘥 𝘮𝘰𝘳𝘦 𝘪𝘯𝘤𝘪𝘯𝘦𝘳𝘢𝘵𝘰𝘳𝘴 𝘣𝘦𝘤𝘢𝘶𝘴𝘦 𝘰𝘧 𝙉𝙄𝙈𝘽𝙔 and as you have 𝙢𝙤𝙧𝙚 𝙤𝙣𝙨𝙝𝙤𝙧𝙞𝙣𝙜𝘵𝘩𝘦𝘳𝘦’𝘴 𝘨𝘰𝘪𝘯 𝘵𝘰 𝘣𝘦 𝘮𝘰𝘳𝘦 𝘩𝘢𝘻𝘢𝘳𝘥𝘰𝘶𝘴 𝘸𝘢𝘴𝘵𝘦 and they have both the incinerators and 𝘵𝘩𝘦𝘺 𝘩𝘢𝘷𝘦 𝘵𝘩𝘦 𝙣𝙚𝙩𝙬𝙤𝙧𝙠 𝘵𝘰 𝘨𝘰 𝘤𝘰𝘭𝘭𝘦𝘤𝘵. And so it’s just a very very good business and the starting multiple is very reasonable.”
𝐓𝐡𝐞 𝐛𝐚𝐫𝐫𝐢𝐞𝐫𝐬 𝐭𝐨 𝐞𝐧𝐭𝐫𝐲 𝐞𝐦𝐛𝐞𝐝𝐝𝐞𝐝 𝐢𝐧 𝐭𝐡𝐚𝐭 𝐬𝐭𝐚𝐭𝐞𝐦𝐞𝐧𝐭 𝐚𝐫𝐞 𝐢𝐧𝐜𝐫𝐞𝐝𝐢𝐛𝐥𝐲 𝐩𝐨𝐰𝐞𝐫𝐟𝐮𝐥:
𝐑𝐞𝐠𝐮𝐥𝐚𝐭𝐨𝐫𝐲 𝐁𝐚𝐫𝐫𝐢𝐞𝐫𝐬 → Hazardous waste facilities face extreme permitting hurdles
𝐍𝐈𝐌𝐁𝐘 𝐄𝐟𝐟𝐞𝐜𝐭 t → Even if permitted, communities resist new incinerators
𝐒𝐜𝐚𝐫𝐜𝐢𝐭𝐲 𝐨𝐟 𝐀𝐬𝐬𝐞𝐭𝐬 → Very few licensed hazardous waste incinerators exist
𝐇𝐚𝐫𝐝 𝐀𝐬𝐬𝐞𝐭 𝐀𝐝𝐯𝐚𝐧𝐭𝐚𝐠𝐞 → These are not easily replicated digital products
𝐍𝐞𝐭𝐰𝐨𝐫𝐤 𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 → Collection, transportation, disposal ecosystem
𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐞𝐝 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦 → Owning both disposal + logistics compounds the moat
This is what a real moat looks like.
___
Interestingly:
$SIE.DE: +13% YTD
$CLH: +13% YTD
𝐀 𝐫𝐞𝐦𝐢𝐧𝐝𝐞𝐫 𝐭𝐡𝐚𝐭 𝐥𝐨𝐧𝐠-𝐭𝐞𝐫𝐦 𝐢𝐧𝐯𝐞𝐬𝐭𝐢𝐧𝐠 𝐢𝐬𝐧’𝐭 𝐚𝐛𝐨𝐮𝐭 𝐜𝐡𝐚𝐬𝐢𝐧𝐠 𝐭𝐡𝐞 𝐟𝐚𝐬𝐭𝐞𝐬𝐭 𝐬𝐭𝐨𝐫𝐲 — 𝐢𝐭’𝐬 𝐚𝐛𝐨𝐮𝐭 𝐨𝐰𝐧𝐢𝐧𝐠 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬𝐞𝐬 𝐭𝐡𝐚𝐭 𝐚𝐫𝐞 𝐡𝐚𝐫𝐝𝐞𝐬𝐭 𝐭𝐨 𝐝𝐢𝐬𝐫𝐮𝐩𝐭.
___
Video: Stripe | Dan Sundheim of D1 Capital | (10/22/2025)
tweet
Michael Fritzell (Asian Century Stocks)
RT @Floebertus: I met some smart guys in Singapore while going through their stock market. They are very underfollowed:
@Iqbal_yusuf1994
@illyquid
@capytalmgmt
tweet
RT @Floebertus: I met some smart guys in Singapore while going through their stock market. They are very underfollowed:
@Iqbal_yusuf1994
@illyquid
@capytalmgmt
tweet
Moon Dev
todays zoom was openclaw crazy
you missed todays private zoom where i gave openclaw 6 different claude codes
we went deep into how she can build almost anything
get a ticket for tomorrows zoom and youll unlock the full replay from today
dont miss it https://t.co/Aw7dcEw2RV
moondev
tweet
todays zoom was openclaw crazy
you missed todays private zoom where i gave openclaw 6 different claude codes
we went deep into how she can build almost anything
get a ticket for tomorrows zoom and youll unlock the full replay from today
dont miss it https://t.co/Aw7dcEw2RV
moondev
tweet