Offshore
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Wasteland Capital
Inflation ice cold at +2.4%, 🥶 like the winter storm, even in the BLS numbers (Truflation is at +0.7%).
Shelter, healthcare, electricity & restaurants still big drivers annually, while energy saw a big drop (except piped gas!). Transports jumped m/m. Whataboutthem tariffs, huh? https://t.co/Pq8N1tTlVE
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Inflation ice cold at +2.4%, 🥶 like the winter storm, even in the BLS numbers (Truflation is at +0.7%).
Shelter, healthcare, electricity & restaurants still big drivers annually, while energy saw a big drop (except piped gas!). Transports jumped m/m. Whataboutthem tariffs, huh? https://t.co/Pq8N1tTlVE
tweet
Offshore
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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
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God of Prompt
RT @rryssf_: new paper argues LLMs fundamentally cannot replicate human motivated reasoning because they have no motivation
sounds obvious once you hear it. but the implications are bigger than most people realize
this quietly undermines an entire category of AI political simulation research
tweet
RT @rryssf_: new paper argues LLMs fundamentally cannot replicate human motivated reasoning because they have no motivation
sounds obvious once you hear it. but the implications are bigger than most people realize
this quietly undermines an entire category of AI political simulation research
tweet
Offshore
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Clark Square Capital
RT @Reignots: Small GungHo buyback is the right direction today through needs more pushing from Strategic to get the old board moving. Meanwhile $GRVY now sits on a ballooning $62/share of cash with a $66.50 stock price. There is no downside, only (lots of) upside. https://t.co/zt92YLpLfV
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RT @Reignots: Small GungHo buyback is the right direction today through needs more pushing from Strategic to get the old board moving. Meanwhile $GRVY now sits on a ballooning $62/share of cash with a $66.50 stock price. There is no downside, only (lots of) upside. https://t.co/zt92YLpLfV
Fun little pullback on what appears to be algo selling into a thin, thin stock - will you let <$2MM of volume (so likely <$400K of actual position change) convince you this company is worth 5% lower today, and <1x P/E ex-cash? Added ~25% to long today. Will explode higher. $GRVY tweet
Offshore
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Brady Long
AI just built me a full marketing deck while I slept.
The gap between people asking AI questions vs delegating entire projects is getting massive and most people don't even know autonomous agents exist yet.
I tested SkyBot for 3 days. Here's what actually happened 🧵 https://t.co/MRgTubOVV3
tweet
AI just built me a full marketing deck while I slept.
The gap between people asking AI questions vs delegating entire projects is getting massive and most people don't even know autonomous agents exist yet.
I tested SkyBot for 3 days. Here's what actually happened 🧵 https://t.co/MRgTubOVV3
tweet
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The Few Bets That Matter
In hindsight, $NBIS quarter’s highlight comes from its CapEx. Not just CapEx, but how it is financed.
We’re talking about a ~$23B company planning ~$18B CapEx while publicly stating they expect to finance 60% organically via FCF, cash & commitments.
They also have clients financially committing before delivery to enable buildouts; a strong proof of trust especially when those are $META and $MSFT.
Last but not least, the remaining 40% could be financed through equity-backed debt, one of the cheapest funding sources - dilution being even cheaper but paid in later.
I'd remain cautious on the sector and wouldn’t expect $NBIS to outperform if the leaders don’t but the company has a very impressive financing roadmap, healthier than many hyperscalers at this stage.
This has always been a core part of the bull case.
But remaining that healthy through end of 2026 is pretty impressive. The market probably loves this, although I still believe there are other valid concerns short term.
Still, getting out ahead CapEx wise is impressive.
tweet
In hindsight, $NBIS quarter’s highlight comes from its CapEx. Not just CapEx, but how it is financed.
We’re talking about a ~$23B company planning ~$18B CapEx while publicly stating they expect to finance 60% organically via FCF, cash & commitments.
They also have clients financially committing before delivery to enable buildouts; a strong proof of trust especially when those are $META and $MSFT.
Last but not least, the remaining 40% could be financed through equity-backed debt, one of the cheapest funding sources - dilution being even cheaper but paid in later.
I'd remain cautious on the sector and wouldn’t expect $NBIS to outperform if the leaders don’t but the company has a very impressive financing roadmap, healthier than many hyperscalers at this stage.
This has always been a core part of the bull case.
But remaining that healthy through end of 2026 is pretty impressive. The market probably loves this, although I still believe there are other valid concerns short term.
Still, getting out ahead CapEx wise is impressive.
Few $NBIS notes after this quarter.
I'll be the bear, once more.
I continue to believe the market will punish the stock - or not reward it as much as many expect.
Not because the company isn’t excellent, but because it did not reward $GOOG, so why would it reward $NBIS for the same behavior?
Fundamentally, everyone will be bullish. Demand is through the roof, compute was sold out, management is planning to build more sites, etc...
Everything FinX wants to see.
From a market perspective, Q4 CapEx slowed down, guidance talks about ~20% increase of contracted power for FY26 without news on connected power, except for the upgrade from 7 sites to 16 sites.
This means FY26 CapEx will accelerate - just like for everyone else, and won't slow down FY27 as contracted power continues to climb.
More spending. Which was punished across all hyperscalers.
Also note that ARR guidance wasn’t increased, meaning no beat expected hence nothing above expectations and no buildouts closing faster than expected.
Some will say "why would you want more? It doesn't matter, they are executing at their pace"
I disagree. Acceleration is everything, otherwise you'll miss on expectations just like they did.
That revenue miss is due to real-world constraints, as I’ve shared yesterday and for months: you cannot build faster than physics and logistics allow you to.
The issue is that growth factually slows/doesn't accelerate. Growth stocks work on acceleration not stable growth.
The why doesn’t matter, even if you’re supply constrained.
Growth slows, CapEx increases, cash generation decreases, and there are no certainties that demand won’t be fulfilled by other hyperscalers by the time infrastructure is built.
Like many of you, I believe there will be demand and everything will be fine. But today, you cannot know. You can bet on it, but you cannot know.
That is the issue. And that is why the market might react like it did for $GOOG.
I continue to believe the company is excellent and its future is bright. And that the stock won’t be rewarded as much as many expect in the short term.
I’d love to be wrong. tweet
Benjamin Hernandez😎
The market is giving us setups!
$NX $RIG $CSCO $BROS $INTU $CNCK $UL $DBGI $AAPG $XXII $CMPS $CRM
$NX is quietly up 35% in a month. Building products are working. $RIG merger creates a deepwater powerhouse. $CSCO is the safe haven in tech.
Hit the link for analysis!
tweet
The market is giving us setups!
$NX $RIG $CSCO $BROS $INTU $CNCK $UL $DBGI $AAPG $XXII $CMPS $CRM
$NX is quietly up 35% in a month. Building products are working. $RIG merger creates a deepwater powerhouse. $CSCO is the safe haven in tech.
Hit the link for analysis!
tweet
Offshore
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Quiver Quantitative
BREAKING: Representative Byron Donalds just filed new trades.
One of them was a purchase of Bitcoin, $BTC.
Donalds sits on the House Subcommittee on Digital Assets.
Full trade list up on Quiver. https://t.co/VH45vqkoC1
tweet
BREAKING: Representative Byron Donalds just filed new trades.
One of them was a purchase of Bitcoin, $BTC.
Donalds sits on the House Subcommittee on Digital Assets.
Full trade list up on Quiver. https://t.co/VH45vqkoC1
tweet