Offshore
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Benjamin Hernandez😎
$BROS: Caffeinated Growth is Scaling
Dutch Bros ($BROS) pops +18% pre-market. Revenue surged 29% in Q4 and the 2026 guidance of $2B+ suggests a massive retail expansion. While peers struggle, $BROS is scaling without friction.
My watchlist is live.
$IREN $OPEN $RKLB $ASTS $F https://t.co/1Rtr44s8wt
tweet
$BROS: Caffeinated Growth is Scaling
Dutch Bros ($BROS) pops +18% pre-market. Revenue surged 29% in Q4 and the 2026 guidance of $2B+ suggests a massive retail expansion. While peers struggle, $BROS is scaling without friction.
My watchlist is live.
$IREN $OPEN $RKLB $ASTS $F https://t.co/1Rtr44s8wt
tweet
Offshore
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Lumida Wealth Management
Nick Leeson was 28 years old.
Worked in the basement of Barings Bank's Singapore office.
Lost $1.4 billion in unauthorized trades.
Destroyed a 233-year-old bank in 3 years.
Britain's oldest merchant bank. Financed the Napoleonic Wars. Survived two World Wars.
Couldn't survive one trader in Singapore.
tweet
Nick Leeson was 28 years old.
Worked in the basement of Barings Bank's Singapore office.
Lost $1.4 billion in unauthorized trades.
Destroyed a 233-year-old bank in 3 years.
Britain's oldest merchant bank. Financed the Napoleonic Wars. Survived two World Wars.
Couldn't survive one trader in Singapore.
tweet
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
tweet
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
Photo
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
tweet
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
Video
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