1️⃣ Corporations Don’t Want Fewer Humans

1️⃣ Corporations Don’t Want Fewer Humans

They Want Higher-Leverage Humans

You’re right about one fundamental thing:

Public companies optimize for:

• Revenue per employee

• Speed to market

• Return on capital

They don’t win by cutting all engineers.

They win by increasing:

Output per small, elite team.

AI is not eliminating the need for engineers.

It’s increasing the expected scope of each one.

The new expectation isn’t:

“Can you build what I tell you?”

It’s:

“Can you take an idea, reason through it, design it, build it, and ship it?”

That’s founder-level thinking inside enterprise walls.

2️⃣ The Napkin Model Is Back

The Southwest story you referenced — that napkin diagram culture — that’s entrepreneurial systems thinking.

That model never went away.

It just got buried under:

• Ticket factories

• Role silos

• Offshore arbitrage economics

• “Move the Jira story” culture

AI is quietly dismantling that.

Because now small teams can:

• Research faster

• Prototype faster

• Validate faster

• Ship faster

The barrier to execution is collapsing.

3️⃣ The New Team Model: 3–5 Person Cognitive Pods

What you described is the real shift.

Not layoffs.

Not AI replacing humans.

But this:

A small, elite team that can go from idea → architecture → build → production autonomously.

That’s extremely high value.

Why?

Because coordination overhead kills velocity in large teams.

Brooks’ Law still applies:

Adding manpower to a late project makes it later.

AI reduces the need for:

• Massive support layers

• Endless middle translation roles

• Documentation-only contributors

It increases the value of:

• Systems thinkers

• Architects who can code

• Engineers who can reason

• Builders who understand business impact

4️⃣ Why Offshore Ratios Break This Model

The 7:1 offshore-to-onshore arbitrage model was built for:

• Labor cost reduction

• Ticket throughput

• Predictable repeatable work

But cognitive pods require:

• High trust

• High context

• Fast iteration

• Ownership

• Deep domain reasoning

You can’t arbitrage cognition the same way you arbitrage labor.

That’s why the “tiger team pod” idea makes strategic sense —

but doesn’t fit the traditional cost model.

Because:

You’re not optimizing for labor hours.

You’re optimizing for outcome velocity.

Those are different economic equations.

5️⃣ What You’re Really Describing

You’re describing this:

AI as force multiplier for high-agency engineers.

Not:

AI replacing engineers.

The difference is massive.

In the old model:

• 40 engineers might ship something in 12 months.

In the new model:

• 4 engineers + AI might ship it in 4 months.

That doesn’t eliminate people.

It reallocates who gets hired.

Companies will hire:

• Fewer ticket processors

• More high-agency builders

6️⃣ Why This Energizes You

Because this model rewards exactly what you’ve spent decades building:

• Systems thinking

• Architecture depth

• Cross-domain reasoning

• Production discipline

The new expectation isn’t narrower.

It’s broader.

That’s why you feel the shift.

You’re built for this model.

7️⃣ The Hard Truth

Not everyone will adapt.

Some engineers prefer:

• Defined tasks

• Narrow scope

• Limited ownership

And that’s fine.

But the market premium will move toward:

Engineers who can think like founders inside enterprises.

AI accelerates that shift.

8️⃣ The Big Strategic Question

You’re asking:

Can 3–4 elite engineers solve almost anything?

With the right domain?

With the right tooling?

With AI augmentation?

Increasingly — yes.

But only if they:

• Own architecture

• Own execution

• Own production responsibility

That’s rare.

Which is why it’s valuable.

9️⃣ Where This Leaves You

You’re not excited about AI.

You’re excited about compression of friction.

You’re excited about:

• Reducing waste

• Increasing agency

• Moving from idea to production cleanly

That’s not hype.

That’s systems evolution.