A childhood friend of mine sells fruit back in my hometown. A while ago he asked me how to use AI. He’s already using a basic AI assistant now — which tells you something: even someone with no tech background knows the AI era has arrived. (He’s now asking me how to cook crayfish, but that’s a different story.)

The AI era is here. That’s not up for debate. What’s worth talking about is how it’s changing the way companies actually operate.

What Every Boss Actually Cares About

Efficiency. Cost reduction.

Put plainly: 10 acres of land used to require 10 oxen. Can 1 ox handle it now? Push further — do you need any oxen at all?

This question sounds simple, but for organizations, it maps onto three fundamentally different paths.

A: One Person, One Role — Vertical Tooling

The most conservative and stable path: roles stay intact, but every person upgrades through specialized AI tools.

Product managers use ChatGPT to write PRDs. Designers use Figma AI to generate layouts. Frontend devs use Cursor for code. Backend devs use Claude CLI for full project work. Each role runs faster in their own lane — no structural disruption required.

The underlying logic: encode domain expertise into AI prompt engineering, pair it with good UX, and let each person become a stronger version of themselves.

Tool / Context Product Manager Designer Frontend Dev Backend Dev
Requirement/Code Gen ChatGPT / Claude (PRDs) Midjourney / DALL·E 3 (visuals) Cursor (code completion) Cursor / Claude CLI (full project)
Flow / Prototyping Notion AI (docs, meeting notes) Figma AI + Magician (auto layout) v0 by Vercel (UI code gen) GitHub Copilot (completion)
Analysis / Collaboration Gamma (AI-generated decks) Framer AI (site generation) Bolt.new (full-stack prototyping) Devin / SWE-agent (auto debug)

Real advantages:

  • Fast, stageable output — no waiting until launch to see results
  • Narrow context means lighter AI load and better quality
  • Clean knowledge accumulation — less information chaos

Real limitations:

  • Handoff between roles is still painful — downstream AI may not respect upstream output
  • Still one human per lane, so cost savings have a ceiling

B: One Person, All Roles — The Super Agent All-In

The aggressive path: one person, one super agent, a world of only documents and code.

Requirements doc → interaction design → frontend code → API spec → backend logic → database schema — all in one shot, end to end.

The appeal is real: no handoff between people or AI systems means near-zero information loss. Labor costs drop dramatically.

But the requirements are steep:

  • The AI needs to be genuinely smart enough to handle real business complexity
  • Context management must be precise — one slip and the whole thing collapses
  • The human driver needs cross-domain baseline knowledge: not expert in everything, but able to tell good from bad
  • Any logic-heavy work needs a self-consistent testing loop, or the risk is uncontrollable

C: One Person, Multiple Roles — The Middle Ground

PMs and designers are naturally close — one person with AI can cover both. Same for frontend and backend. This creates a “two-person, two-role” setup.

The tradeoffs sit between A and B: fewer handoff steps, less friction — but the cognitive gap between PM and designer, and between frontend and backend, doesn’t disappear. Compromise always comes with some loss.

How It Actually Played Out

These aren’t theoretical categories. This is roughly what happened.

Phase 1: AI’s arrival was stunning. Every role upgraded independently. No more overtime. Excitement everywhere.

Phase 2: Ambition expanded. Everyone wanted to go end-to-end alone. “One-person companies” flooded the discourse. Crayfish could save the world, apparently.

Phase 3: Reality hit. Facing real consumer products — high UX expectations, reliability requirements — teams started hiring again:

  • Zhang San: you own design through PRD
  • Li Si: you own all of engineering
  • Lao Wang: you own infra and reliability
  • The boss: I’ll handle operations

Why “No Oxen” Is Still Hard

Four real walls stand in the way:

1. Organizational inertia — the inductor effect
Organizations resist sudden change like an inductor resists sudden current shifts. Old role structures, reporting chains, and performance systems don’t vanish because AI arrived.

2. Consumer habit as historical debt
Users’ expectations were shaped by 15 years of internet products. AI capability saying “I’m ready” doesn’t mean users are.

3. Infrastructure gap — steam engine pulling a carriage
Toolchains, deployment pipelines, monitoring systems are still catching up. Often the AI capability is there; the engineering foundation isn’t.

4. AI still hits a ceiling
For complex business logic, long-horizon decisions, and high-consistency requirements — current AI isn’t quite there. Not impossible, just not reliable enough yet.

The revolution has arrived. The progress bar is still loading.

My fruit-selling friend may never understand what we’re debating. But he’s already using AI — and in its own way, that’s the revolution completing itself.

If you’re thinking through how to push AI efficiency forward in your own team, I’d love to hear your take.