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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
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.”
tweet
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
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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)
tweet
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
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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
tweet
RT @JeremiahDJohns: George W Bush appears to have started a Substck, and he currently has only 12 subscribers. https://t.co/zX6PV2H5CU
tweet
God of Prompt
probably the only article you need to stop feeling behind in AI
tweet
probably the only article you need to stop feeling behind in AI
https://t.co/TJ7XYPSLka tweet
X (formerly Twitter)
Robert Youssef (@rryssf_) on X
the tool isn't the work
The Transcript
RT @TheTranscript_: $GOOG Google DeepMind CEO: Compute scarcity forced consolidation of Google Brain and DeepMind into one
"It was getting complicated having two groups, especially given the amount of compute needed in this scaling era… even someone like Google didn't have enough compute to have two frontier projects under one house, so we needed to combine all of our resources together.”
tweet
RT @TheTranscript_: $GOOG Google DeepMind CEO: Compute scarcity forced consolidation of Google Brain and DeepMind into one
"It was getting complicated having two groups, especially given the amount of compute needed in this scaling era… even someone like Google didn't have enough compute to have two frontier projects under one house, so we needed to combine all of our resources together.”
tweet
Offshore
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God of Prompt
RT @alex_prompter: I turned Naval Ravikant's mental models into AI prompts.
It's like having the AngelList founder rip apart your career and rebuild it from leverage and specific knowledge.
Here are the 13 prompts that transformed how I build wealth: https://t.co/ijM4WvtQNP
tweet
RT @alex_prompter: I turned Naval Ravikant's mental models into AI prompts.
It's like having the AngelList founder rip apart your career and rebuild it from leverage and specific knowledge.
Here are the 13 prompts that transformed how I build wealth: https://t.co/ijM4WvtQNP
tweet
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Jukan
Bernstein remains very bearish on Kioxia.
They’ve effectively issued a sell call and are recommending investors take profits. (Target price: ¥7,000 vs. current share price: ¥22,845.) https://t.co/b9MEVySUmM
tweet
Bernstein remains very bearish on Kioxia.
They’ve effectively issued a sell call and are recommending investors take profits. (Target price: ¥7,000 vs. current share price: ¥22,845.) https://t.co/b9MEVySUmM
tweet
Michael Fritzell (Asian Century Stocks)
RT @MikeFritzell: What are the highest quality software within the Japanese SaaS industry? Not convinced that CYND and Property Data Bank are up there. Freee, Hennge?
tweet
RT @MikeFritzell: What are the highest quality software within the Japanese SaaS industry? Not convinced that CYND and Property Data Bank are up there. Freee, Hennge?
tweet
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Michael Fritzell (Asian Century Stocks)
RT @_SeanDavid: @zarazhangrui Meanwhile Anthropic looking for humans to administer their salesforce https://t.co/NB7UYETWm2
tweet
RT @_SeanDavid: @zarazhangrui Meanwhile Anthropic looking for humans to administer their salesforce https://t.co/NB7UYETWm2
tweet
Michael Fritzell (Asian Century Stocks)
RT @compound248: @JohnHuber72 I knew a guy who worked for Buffett in the immediate pre-Todd and Ted era.
His summary of the Buffett buy algorithm was, “7x avg last 5 year EBIT.”
tweet
RT @compound248: @JohnHuber72 I knew a guy who worked for Buffett in the immediate pre-Todd and Ted era.
His summary of the Buffett buy algorithm was, “7x avg last 5 year EBIT.”
tweet