Offshore
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Michael Fritzell (Asian Century Stocks)
RT @econcallum: Datacentres now account for 7% of US electricity demand https://t.co/6AojVY5RvB
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RT @econcallum: Datacentres now account for 7% of US electricity demand https://t.co/6AojVY5RvB
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Offshore
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Michael Fritzell (Asian Century Stocks)
New post just published: on Casio (6952 JP)
- New CEO Shin Takano has revamped the organization
- Latest quarter timepieces revenues +34.5% YoY
- EV/Sales of 1.0x https://t.co/iB9kX6Gzcb
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New post just published: on Casio (6952 JP)
- New CEO Shin Takano has revamped the organization
- Latest quarter timepieces revenues +34.5% YoY
- EV/Sales of 1.0x https://t.co/iB9kX6Gzcb
tweet
Offshore
Photo
Michael Fritzell (Asian Century Stocks)
From @firstadopter’s The Nvidia Way: “The Finns… everything they touch turns to gold”
So why do we never hear about Finnish tech companies (outside of Linux, of course)? https://t.co/ZcXRcd4H4w
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From @firstadopter’s The Nvidia Way: “The Finns… everything they touch turns to gold”
So why do we never hear about Finnish tech companies (outside of Linux, of course)? https://t.co/ZcXRcd4H4w
tweet
Offshore
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Michael Fritzell (Asian Century Stocks)
I'd love to get your thoughts on the new Asian Century Stocks site and emails. What do you like about it, and what can be improved. In other words, how can I make the service more valuable to you? https://t.co/EYsQp4X5j9
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I'd love to get your thoughts on the new Asian Century Stocks site and emails. What do you like about it, and what can be improved. In other words, how can I make the service more valuable to you? https://t.co/EYsQp4X5j9
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Offshore
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God of Prompt
RT @godofprompt: I should charge $99 for this.
But I'm giving away our Claude Mastery Guide for free.
We just updated it with a full Claude Skills section, the feature most people still don't know exists.
Inside:
→ 30 prompt engineering principles
→ 10+ mega-prompts ready to copy
→ Mini-course from beginner to advanced
→ How to build Skills that make Claude remember your workflows forever
→ Glossary + strategic use cases
This turns Claude from a chatbot into your actual work system.
Comment "Claude" and I'll DM it to you.
(Must be following me to receive it)
tweet
RT @godofprompt: I should charge $99 for this.
But I'm giving away our Claude Mastery Guide for free.
We just updated it with a full Claude Skills section, the feature most people still don't know exists.
Inside:
→ 30 prompt engineering principles
→ 10+ mega-prompts ready to copy
→ Mini-course from beginner to advanced
→ How to build Skills that make Claude remember your workflows forever
→ Glossary + strategic use cases
This turns Claude from a chatbot into your actual work system.
Comment "Claude" and I'll DM it to you.
(Must be following me to receive it)
tweet
Offshore
Photo
God of Prompt
RT @godofprompt: This site is literally a prompt library with thousands of prompts for Claude, ChatGPT & Nano Banana. https://t.co/de2Z55Vkn0
tweet
RT @godofprompt: This site is literally a prompt library with thousands of prompts for Claude, ChatGPT & Nano Banana. https://t.co/de2Z55Vkn0
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
The Transcript
RT @TheTranscript_: $KKR Head of IR: Dividend raised for 7th straight year since C-Corp conversion.
“We intend to increase our annual dividend from $0.74 to $0.78 per share...the seventh consecutive year that we've increased our dividend.”
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RT @TheTranscript_: $KKR Head of IR: Dividend raised for 7th straight year since C-Corp conversion.
“We intend to increase our annual dividend from $0.74 to $0.78 per share...the seventh consecutive year that we've increased our dividend.”
tweet
Offshore
Photo
God of Prompt
RT @rryssf_: researchers at Max Planck analyzed 280,000 transcripts of academic talks and presentations from YouTube
they found that humans are increasingly using ChatGPT's favorite words in their spoken language. not in writing. in speech.
"delve" usage up 48%. "adept" up 51%. and 58% of these usages showed no signs of reading from a script.
we talk about model collapse when AI trains on AI output. this is model collapse, except the model is us.
tweet
RT @rryssf_: researchers at Max Planck analyzed 280,000 transcripts of academic talks and presentations from YouTube
they found that humans are increasingly using ChatGPT's favorite words in their spoken language. not in writing. in speech.
"delve" usage up 48%. "adept" up 51%. and 58% of these usages showed no signs of reading from a script.
we talk about model collapse when AI trains on AI output. this is model collapse, except the model is us.
tweet
Jukan
Reports of ‘Sharp Decline’ in Memory Prices Spreading Within China… Traders Say “Extent of Correction Is Limited”
∙ As the Lunar New Year holiday gradually approaches, reports of a sharp decline in memory prices within China have been emerging one after another. In the North American market as well, share prices of major memory companies have been pulling back from their highs.
∙ Based on a recent on-site visit by a reporter to Shenzhen’s largest Huaqiangbei market, spot market memory prices had declined somewhat, but the extent of the correction was not significant. Speculative sentiment has also calmed to a degree. In the flash memory market, some products are showing a trend of ‘further price increases.’
(Securities Times)
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Reports of ‘Sharp Decline’ in Memory Prices Spreading Within China… Traders Say “Extent of Correction Is Limited”
∙ As the Lunar New Year holiday gradually approaches, reports of a sharp decline in memory prices within China have been emerging one after another. In the North American market as well, share prices of major memory companies have been pulling back from their highs.
∙ Based on a recent on-site visit by a reporter to Shenzhen’s largest Huaqiangbei market, spot market memory prices had declined somewhat, but the extent of the correction was not significant. Speculative sentiment has also calmed to a degree. In the flash memory market, some products are showing a trend of ‘further price increases.’
(Securities Times)
tweet
Offshore
Photo
Michael Fritzell (Asian Century Stocks)
RT @JeremiahDJohns: George W Bush appears to have started a Substck, and he currently has only 12 subscribers. https://t.co/zX6PV2H5CU
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RT @JeremiahDJohns: George W Bush appears to have started a Substck, and he currently has only 12 subscribers. https://t.co/zX6PV2H5CU
tweet