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This is what the inside of a modern AI data center looks like.

Every section in this image represents an entire industry. Companies like Schneider Electric, Vertiv, Eaton, and ABB handle power. Cisco and Arista build the networks. Dell and HP produce the servers that keep it running.

What really powers this isn’t just code or chips. It’s copper. Miles of copper wiring connect every rack, generator, and cooling system, carrying electricity around the clock. A single large data center can use tens of thousands of miles of copper, and hundreds more are being built each year.

Every generator, switchgear, and cooling tower depends on metal and wiring. AI’s growth is creating a massive wave of demand for copper, steel, and energy. This is physical infrastructure, not just software. The AI boom is being built by miners, engineers, and electricians just as much as by coders.

i interviewed the claude code team @AnthropicAI yesterday and here are 3 of their principles for building great AI products:

build for yourself
AI has reset the gameboard for software—if you're using AI all day and you build something for yourself, there's a good chance no one's ever done it before. and if you build for yourself, you can just follow your taste and intuition for what to make. this is how @bcherny and @_catwu build Claude Code and it's also how we build products @every.

simple to start, insanely powerful
great AI products are extremely simple to start—Claude Code is just a text input in a terminal—but are flexible and extensible enough to be insanely powerful. this lets users use it for things that the Claude Code team never expected. this unlocks the last principle...

use latent demand to guide your roadmap
when you build a flexible, extendable AI product your users are going to hack it to meet their needs. figure out all of the hacks, and build them as a features or new products. this way your power users are your collaborators on your roadmap.

RIP fine-tuning ☠️

This new Stanford paper just killed it.

It’s called 'Agentic Context Engineering (ACE)' and it proves you can make models smarter without touching a single weight.

Instead of retraining, ACE evolves the context itself.

The model writes, reflects, and edits its own prompt over and over until it becomes a self-improving system.

Think of it like the model keeping a growing notebook of what works.
Each failure becomes a strategy. Each success becomes a rule.

The results are absurd:

+10.6% better than GPT-4–powered agents on AppWorld.
+8.6% on finance reasoning.
86.9% lower cost and latency.
No labels. Just feedback.

Everyone’s been obsessed with “short, clean” prompts.

ACE flips that. It builds long, detailed evolving playbooks that never forget. And it works because LLMs don’t want simplicity, they want *context density.

If this scales, the next generation of AI won’t be “fine-tuned.”
It’ll be self-tuned.

We’re entering the era of living prompts.

This blew my mind 🤯

McKinsey just reported that 80% of companies now use AI but only 1% are doing it well.

That single number explains why every “AI initiative” you’ve heard about sounds impressive but delivers nothing.

Most companies didn’t integrate AI. They outsourced curiosity.
They bought licenses, ran pilots, and called it innovation.

The few that did transform they look nothing like the rest.
They rebuilt workflows around models, retrained staff to think in prompts not PowerPoints, and put AI governance right next to the CFO.

McKinsey’s numbers are brutal:

• Only 21% redesigned core workflows for gen-AI.
• Just 28% have their CEO personally leading governance.
• Those who did are already reporting higher EBIT from gen-AI.

Everyone else is still “experimenting.” Translation: burning budget on proofs of concept with no system design, no feedback loops, and no metrics.

The lesson’s obvious: AI ROI doesn’t come from the model.
It comes from the org that’s willing to break itself to use it.

The future isn’t AI-native companies.

It’s AI-rewired ones.

This is one of THE BRILLIANT papers with a BIG claim. 👏

Giving an LLM just 78 carefully chosen, full workflow examples makes it perform better at real agent tasks than training it with 10,000 synthetic samples.

"Dramatically outperforms SOTA models: Kimi-K2-Instruct, DeepSeek-V3.1, Qwen3-235B-A22B-Instruct and GLM-4.5. " on AgencyBench (LIMI at 73.5%)

The big deal is that quality and completeness of examples matter way more than raw data scale when teaching models how to act like agents instead of just talk.

They name the Agency Efficiency Principle, which says useful autonomy comes from a few high quality demonstrations of full workflows, not from raw scale.

The core message is strategic curation over scale for agents that plan, use tools, and finish work.

🧵 Read on 👇

🧵 This research basically says we should do the opposite of what every AI company is building right now.

Instead of AI that gives you answers, we need AI that gives you better questions. And the reason why will change how you think about intelligence itself.

1/11

Think about how you normally interact with AI: You ask → It answers → You accept → Move on.

But have you ever noticed what happens to your thinking muscles during this process? They're quietly atrophying.

Here's where it gets weird...

2/11

Researcher Philipp Koralus discovered something unsettling: AI "helpers" are creating two equally bad outcomes:

Path A: Get overwhelmed by complexity → Give up → Lose agency

Path B: Get perfectly crafted answers → Stop thinking → Lose autonomy

Both roads lead to the same destination: a smaller you.

3/11

But wait... wasn't AI supposed to augment human intelligence?

The problem isn't the technology. It's the philosophy behind it.

We've been building AI like it's a really smart encyclopedia when we should be building it like Socrates.

(Stay with me - this gets practical)

4/11

Imagine if your AI assistant never gave you direct answers. Instead, it asked:

"What assumptions are you making here?"
"How might someone disagree with that?"
"What would change your mind?"

You'd probably be annoyed at first. Then something interesting would happen...

5/11

Your brain would start doing what brains do best: making connections, questioning assumptions, building understanding from the ground up.

This is what Koralus calls "decentralized truth-seeking" - and it's the opposite of how current AI works.

Here's why this matters for you:

6/11

Next time you catch yourself asking AI for "the answer," try this experiment:

Ask it to help you think through the problem instead.

"What questions should I be asking about X?"
"What perspectives am I missing?"
"Help me examine my assumptions."

Watch how your thinking changes.

7/11

The current AI model treats you like a task-completion machine:

Problem → Solution → Done

The Socratic model treats you like a sense-making human:

Problem → Inquiry → Understanding → Wisdom

One optimizes for efficiency. The other optimizes for growth.

8/11

Now you might think: "But I want fast answers! I don't have time for philosophy!"

And I get it. But here's the thing - this approach might actually make you faster at complex decisions over time, not slower.

Because you'll be building judgment, not just consuming answers.

9/11

This research suggests we're at a critical fork:

Option 1: AI that thinks FOR us → dependency → diminished capacity

Option 2: AI that thinks WITH us → partnership → enhanced judgment

The choice we make in the next few years shapes the next few decades.

10/11

Here's what you can test today:

When facing a tough decision, don't ask "What should I do?"

Instead ask "What questions haven't I considered?"

Use AI as your thinking partner, not your decision outsourcer.

Notice how it feels different.

Makes you wonder what else we're optimizing for the wrong thing...

Some useful prompting tips here.

In-context-learning is always the best.

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Examples beat instructions Wasted weeks writing perfect instructions. Then tried 3-4 examples and got instant results. Models pattern-match better than they follow rules (except reasoning models like o1)

Version control your prompts like code One word change broke our entire system. Now I git commit prompts, run regression tests, track performance metrics. Treat prompts as production code

Test coverage matters more than prompt quality Built a test suite with 100+ edge cases. Found my "perfect" prompt failed 30% of the time. Now use automated evaluation with human-in-the-loop validation

Domain expertise > prompt tricks Your medical AI needs doctors writing prompts, not engineers. Subject matter experts catch nuances that destroy generic prompts

Temperature tuning is underrated Everyone obsesses over prompts. Meanwhile adjusting temperature from 0.7 to 0.3 fixed our consistency issues instantly

Model-specific optimization required GPT-4o prompt ≠ Claude prompt ≠ Llama prompt. Each model has quirks. What makes GPT sing makes Claude hallucinate

Chain-of-thought isn't always better Complex reasoning chains often perform worse than direct instructions. Start simple, add complexity only when metrics improve

Use AI to write prompts for AI Meta but effective: Claude writes better Claude prompts than I do. Let models optimize their own instructions

System prompts are your foundation 90% of issues come from weak system prompts. Nail this before touching user prompts

Prompt injection defense from day one Every production prompt needs injection testing. One clever user input shouldn't break your entire system

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From r/PromptEngineering

New @GoogleResearch paper builds a personal health assistant that reads a user’s data, answers health questions, and coaches daily habits.

It evaluates the system on 10 tasks with 7,000+ human annotations and 1,100 hours from experts and users.

The assistant covers 4 needs, interpreting a user’s data, giving clear facts, offering wellness advice, and handling symptom questions.

It uses 3 specialist agents, a data science agent, a domain expert agent, and a health coach agent.

The data science agent plans analyses and writes code to turn wearable streams and medical records into clear numbers.

The domain expert agent joins those numbers with medical knowledge and user context to explain findings and reason about likely causes.

The health coach agent runs multi turn chats that set goals, surface barriers, and track progress using motivational interviewing.

An orchestrator decides which agent to call, coordinates their work, and returns one joined answer.

In studies the agents and the full system improved diagnosis quality and increased user rated coaching helpfulness versus a strong baseline.

Overall, this is a practical template for turning a general chatbot into a personal health assistant grounded in personal data.

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Paper – arxiv. org/abs/2508.20148

Paper Title: "The Anatomy of a Personal Health Agent"

Can LLM agents can trade stocks profitably in a realistic, clean setup.

The paper tests that and builds StockBench, a clean setup where an agent reads daily prices, fundamentals, and news, then chooses buy, sell, or hold.

They show that these agents can sometimes make profits, but their performance is inconsistent and often no better than a basic equal-weight buy-and-hold portfolio.

The window uses fresh 2025 data to block training leakage.

The stock pool is 20 large Dow Jones names for stable inputs.

Each morning the agent reviews positions, pulls extra fundamentals on chosen stocks, then sets dollar targets that turn into shares.

Results use final return, maximum drawdown, and the Sortino ratio for downside risk.

Across 82 days some agents beat equal weight buy and hold and reduce losses, but gains are small.

Reasoning tuned models do not consistently win, larger portfolios add noise, and removing news or fundamentals hurts results.

So LLM agents can trade and manage risk to a degree, yet strong and reliable outperformance is not shown.