Brady Long
Job market is in shambles rn https://t.co/sBZ0LuCozC
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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]
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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

Moon Dev
Openclaw Use Cases That Actually Print Money

While everyone else is just producing ai slop https://t.co/AAO2FT9M8C
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God of Prompt
RT @godofprompt: How to use LLMs for competitive intelligence (scraping, analysis, reporting): https://t.co/xlGOSpRQPy
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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.”
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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.
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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.
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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.
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Video: Stripe | Dan Sundheim of D1 Capital | (10/22/2025)
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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
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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
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Brady Long
“Bro I can’t believe we used to order food to the house with our fingers”

Nothing like spending 2 hours to get/train openclaw to automate something that takes me 5 minutes to do once a week…

🤷 - Joe Speiser ⚡️
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Dimitry Nakhla | Babylon Capital®
When you kept buying this $AMZN dip all the way down to $200… and now you’re out of dry powder below $200 https://t.co/6gAqHaIeoU
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Michael Fritzell (Asian Century Stocks)
RT @StefanFSchubert: Despite the housing crisis, London housebuilding has collapsed to a historic low not seen anywhere else in the developed world. By @jburnmurdoch. https://t.co/G8EfjPlRvS
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