Can you build a team of just AI tools?
Google’s Amit Rawal runs a two-person team. Output of 6-8 full-time employees.
He calls them “AI team members.”
He uses 6-7 core models at all times, testing 5-8 more in rotation. And he treats every tool like a hire: recruits, tests, trains. When they underperform, he fires them.
His AI payroll: ~$350/month (plus ChatGPT Pro). Human equivalent: ~$2,000.
That’s not the story, though.
The story is what happens when you try to replicate this without understanding the foundation.
Amit’s got four rules.
They are universally important to all fractional AI integration right now, not just building out an AI team.
Quality onboarding of AI tools (treat them like smart, inexperienced interns)
Define your privacy limits
Keep your differentiators human-led
Start with the problem, not the tool
But working with 300+ organizational leaders, we’ve seen something else that most people miss:
AI doesn’t impact all roles equally.
And autonomy is the pivot point.
Low autonomy roles (customer service, junior clerks): AI acts as an equalizer. The weakest performers catch up.
High autonomy roles (entrepreneurs, content managers): AI becomes an amplifier.
Top performers pull even further ahead. Why? Because success now hinges on judgement bandwidth—the ability to sort good AI outputs from mediocre ones.
So let’s revisit Amit’s rules through this lens:
1. Quality onboarding of AI tools
There needs to be a combination of two things.
Training the AI. And training your judgment.
The smart intern analogy only works if you’re developing the taste to know when they nailed it vs. when they missed. Most people can’t tell the difference yet.
That’s the gap.
2. Define your privacy limits
Yes. Establish AI assurance guardrails that let wins scale safely org-wide.
Privacy protects you from harm.
3. Know your differentiators and keep them human-led
This is where autonomy matters most.
Your differentiators live in high-judgment territory.
The moment you outsource judgment on what makes you unique, you’ve commoditized yourself.
4. Task framework: frequency + judgment
Amit’s matrix is sharp. We’d add one thing:
Don’t outsource thinking.
On high-judgment tasks, use AI for questions, critiques, feedback—things that sharpen YOUR thinking.
Offload tasks, not thinking.
That’s how you avoid cognitive atrophy.
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The real question isn’t “Can you build a team of AI tools?”
It’s: “Can you build the judgment capacity to make AI tools effective?”
Short-term, AI narrows performance gaps. Long-term, judgment drives inequality.
The U-curve effect.
The upskilling imperative isn’t about learning more AI tools. It’s about nurturing taste, curiosity, and autonomy—the human edge AI can’t replicate.
Amit’s succeeding because he’s operating with entrepreneur-level autonomy and judgment.
That’s the part most companies miss.
Are you building the judgment capacity to use AI well, not just use it often?
