Everyone thinks Apple stopped innovating when Tim Cook became the CEO.

But 90% of Apple's value was created after Jobs died & if you understand Larry Greiner's Organizational Growth Model, you'll understand why Jobs asked Cook to replace him... This isn't about Cook being lucky. There's a 1972 business theory that predicted exactly why Apple needed him to survive.

Larry Greiner noticed that every successful company goes through 5 predictable growth stages. And each stage requires completely different leadership skills.

Here's how it works:

Stage 1: Creativity - Founders build from zero with vision
Stage 2: Direction - Need for organized systems
Stage 3: Delegation - Scaling through managers
Stage 4: Coordination - Complex operations
Stage 5: Collaboration - Mature optimization

However, the problem is that each stage creates its own crisis that kills companies if not handled correctly.

Let me explain why...

Be like Amazon.

> secretly build an AI coding assistant
> save $260M in costs and eliminate 4,500 years of developer work
> now quietly sell it to all the Fortune 500s

I got curious about their entire business model and dug deeper.

What I found: First, let me give you some context:

Amazon had 30,000+ Java applications stuck on ancient versions. Technical debt from hell.

Manual migration would've taken 50+ developer-years just to plan.

They decided to bet everything on AI instead.

Intel spends 31% revenue on R&D (competitors spend 7-11%), outsources 1/3rd of production to TSMC, and still posted a $18.8 Billion loss in 2024.

Everyone thinks Intel lost the semiconductor war due to the competition.

But the truth is something else... For 40 years, Intel ruled semiconductors with one simple philosophy.

"Real Men Have Fabs."

While other companies just designed chips, Intel did everything - design, manufacturing, the whole stack.

This made them unstoppable during the PC era.

But then the world changed...

Microsoft has spent $140B buying companies since 2014 and everyone missed what they were really doing.

Minecraft → LinkedIn → GitHub → OpenAI partnership → Nuance → Activision.

They weren't buying products. They are creating an ecosystem better than Apple.

A thread🧵 February 4, 2014: Satya Nadella becomes CEO of a dying company.

Windows Phone: Dead
Surface: Failing
Stock: Flat for a decade
Market cap: $300 billion

His first big move? Spend $2.5 billion on... Minecraft.
Everyone laughed.

How would investing in gaming help??

Three guys tracking spam from a dorm room, built a $40B company that can delete any website from the internet.

Today it protects 1 in 5 websites and stops 44 billion attacks daily.

Because they give away for free what competitors charge $100K+ for.
Here's the insane story🧵 2009: Matthew Prince (lawyer teaching cyberlaw), Michelle Zatlyn (chemistry grad rejected by Google), and Lee Holloway (programmer who codes to death metal) have a problem.

They're tracking spam across 185 countries but can't stop it.
Homeland Security calls them.

Google's Street View cars collected 600GB of your personal emails, passwords, and private messages while taking pictures of your neighborhood.

60 million people across 30+ countries were surveilled without their consent for years.

Thread. First, look at the list of data Google was stealing via your WiFi:

• Complete emails with full content
• Login passwords for your accounts
• Every website you visited
• Medical records and personal documents
• Private messages and chat logs

The question is how?

This is Tom Zhu

Elon Musk’s right-hand man.

This is the man who made it possible to cut electric car production time to 2 minutes.

Here’s how he made Tesla the most valuable company in the world: 🧵
Tom Zhu is Tesla’s most influential executive after Elon.

• Built Gigafactory Shanghai in 9 months
• Delivered 54% of Tesla's 1.37M cars in 2022
• Acquired $70 M in Tesla stock in 2023

And yet... he still shares rides to work.

Google, Meta, and OpenAI brag about their AI ‘scientists’ publishing revolutionary research.

But a landmark study just found their ‘groundbreaking’ research fails 100% of real-world experiments & scores 3/10 on scientific rigor.

Here’s what’s buried beneath the AI hype cycle: Researchers analyzed 28 AI-generated papers from leading AI scientist systems.

Every single paper had a 100% failure rate on basic scientific standards.

If human scientists had this track record, we'd shut down every university on Earth.

Let's dive in 👇

BREAKING: Zuckerberg offered an AI researcher $1 BILLION to join Meta & got rejected.

Then the next 12 researchers did the same thing.
Here's what's really happening inside Meta's AI division:

(hint: they're spending $72B to fail) 🧵⬇️ First, look at what Zuck posted about Meta's AI vision yesterday:

"Superintelligence will help humanity accelerate progress"
"Directed centrally towards addressing valuable problems"

Sounds inspiring, right?

Until you realize what top AI researchers think of this plan.

How to write prompts for Claude using XML tags to get 10x better results:

I tested 1000+ prompts. XML beats everything else.

Here's the exact format (with copy-paste templates, plus screenshots and examples you can use today) 🧵 By tweet 20, you'll learn:

✓ Why your prompts fail
✓ Core XML structures
✓ <thinking> debugging method
✓ Multi-doc comparison
✓ Workflow automation
✓ Attributes for precision
✓ Common mistakes to avoid
✓ 48-hour implementation plan
✓ Copy-paste XML prompt for any task

AI transformed how SDRs work.

Not by replacing them, but by pairing them with GTM Engineers.

We've guided 110+ companies through this evolution:
Same pipeline. 71% less cost.

SDRs focus on relationships. Engineers handle the systems.

How this hybrid model actually works: The hybrid model splits work by what humans and machines do best:

GTM Engineers (1 person):
- Build automated systems
- Manage AI tools
- Create prospecting campaigns
- Handle technical operations

SDRs (focused specialists):
- Own the phone
- Build relationships
- Close deals

OpenAI just admitted ChatGPT Agent can help create bio weapons.

Their fix? Disable memory. Deploy TWO AIs to watch everything. Ban users and call law enforcement.

OpenAI released a full 40-page safety report on this. I read every word.

Here are the wildest revelations:🧵
OpenAI hired 16 PhD bioweapon experts to attack ChatGPT Agent. Here's exactly what they found:

ChatGPT's Agent provided the "clearest pathway to harm" they'd ever seen from any AI.

Not theory. Not speculation. Clear, proven threat.

I spent 100+ hours studying why some AI agents work flawlessly while others hallucinate constantly.

The difference? "Context Engineering."

Not prompt engineering.
Not fine-tuning.
Not RAG.

A Dummies Guide to Context Engineering (with code & examples): 🧵 Before we dive into the details, here's some interesting info:

You see we've all been obsessed with the WRONG thing - Prompt Engineering.

That's just 10% of what the AI sees.

The other 90%? Enter "context engineering"

I use 40+ tools to run our $6M/year funnel at ColdIQ.

Everyone said I'm crazy. Tool sprawl would kill us.

Then we added $760,000 in revenue last month alone.

Here's our exact tech stack that makes $500k/month 🧵⬇️ Let's look at our previous month's performance:

- 35,000+ unique site visitors
- $760,000 in new annualized revenue
- From 4 traffic sources working together

How? Our GTM tech stack is how
(full funnel at the end of the thread)

The 4 stages of our stack & how it all works:

Elon Musk: "Grok 4 is PhD level in every subject."

Bold claim.

So I created 4 ACTUAL impossible tests and tested Grok against ALL major models.

Grok didn't just FAIL. It HALLUCINATED an entire PhD thesis.

Only ONE model scored 4/4

The results will surprise you🧵⬇️ I created 4 twisted tests no AI had seen, not textbook problems like "Explain the Trolley Problem."

Test 1: Real revenue + Hardy-Ramanujan theory
Test 2: €20k bribe + utility functions
Test 3: Memristor chips + thermal physics
Test 4: NPV calc + ethical paradoxes

Ran it across Grok 4, Opus 4, GPT 4o, and Gemini 2.5 pro.

lemlist (@lemlist) makes $30M a year. Their CEO showed me exactly how and walked me through their entire playbook.

Not the basic stuff. The actual tools. All 50+ of them. Including the sketchy competitor databases and the intent signals nobody talks about.

Here's the complete breakdown:🧵⬇️ First, the wildest part:

They use their competitors' enrichment tools. Against them.

Apollo, People Data Labs, Crunchbase - they pull from ALL of them simultaneously through Clay. Then cross-reference to find the best data.

4,000 cold emails. 1 lead. 0.025% conversion.

Fast forward: $6M+ ARR, Top 1% Agency Award, 30+ person team.

All built on cold outreach.

Here's exactly how I went from the worst cold emailer alive to building a multi-million dollar outreach machine:

Thread 👇 Lesson 1/6: The List IS the Strategy

I spent months perfecting my copy. Still got ignored.

Then I flipped my approach:

- 90% effort on WHO to email.
- 10% on what to say.

Everything changed.

Devs spend hours crafting prompt chains. Marketers write novels to ChatGPT.

They're all doing it wrong.

One example > Ten instructions. I'll prove it.

The prompting technique that sounds too simple to work (but dominates everything): I tested 50+ prompting methods across 10,000 queries last month.

Tree-of-thought.
Complex chains.
Mega prompts.
Role-playing.

One method delivered 90% of the results with 10% of the effort.

It's called one-shot prompting. And it's stupidly simple.

You've been evaluating AI productivity completely WRONG.

While everyone debates chat quality, METR just tested every major model & found the ONE metric that predicts real-world utility:

"50% Task Completion Time Horizon."

What this metric means (& how each model did):🧵 Here's what "50% Task Completion Time Horizon" means:

"The length of human tasks that AI can complete with 50% success rate."

If humans take 30 minutes on a task, & AI succeeds 50% of the time on 30-minute tasks, the AI's time horizon is 30 minutes.

But how did METR arrive at this metric?

Most cold emails get 1-3% replies and land straight in spam.

You're competing with outdated methods while others use frameworks that actually work.

These 12 proven frameworks increase reply rates by 10x in 2025:🧵 These frameworks come from @BowTiedSalesGuy, @WillAllred117, Josh Braun, and a few other copywriting masters.

Each one has been battle-tested across millions of emails.

Here's every framework:

Everyone's building AI agents. Nobody knows what that means.

Your "agent" probably just follows if-then rules.

Real agents achieve 41% better performance because they think, not follow.

Time to learn the difference (then build a real one in 10 minutes):🧵 Here's the simplest test to know what you built:

Ask: "Can it handle something I didn't plan for?"

If no → You built a workflow
If yes → You might have an agent

Let me show you exactly what this means...