Why ICs are accidentally becoming tech leads much earlier in their career.
The lens problem
I’ve been watching developers struggle with AI for months now, and I think I finally understand what’s happening.
People keep asking: “What’s the best prompt?” or “Which AI tool should we use?” But that’s like asking “What’s the best email client?” when the real question is “How do I communicate effectively?”
The problem isn’t the tool. The problem is the lens.
Most developers are trying to use AI like a really smart autocomplete. They’re still thinking “I write code, AI helps me write it faster.” But that’s not what’s actually happening.
What’s actually happening is something nobody’s talking about: Individual contributors are being forced to develop Tech Lead capabilities to work effectively with AI.
The accidental tech-lead transformation
Here’s the interesting parallel: Good engineering managers face this exact tension:
- Delegate effectively to scale their impact
- Maintain enough technical depth to make sound decisions
- Spot problems before they become crises
- Stay credible with senior team members
This is exactly what P60 describes as the difference between Deep Engineers (‘doing’) and Tech Leads (‘defining technical direction and unblocking challenges’). AI is forcing ICs to make this transition faster than traditional career progression.
Most ICs have never had to develop these technical management skills. They’ve been individual contributors. They think in terms of “what do I need to do?” not “what problem needs solving and how do I verify the solution?”
Three scenarios I see when people work with AI:
| Profile | Approach | Outcome |
|---|---|---|
| The Micromanager | “AI, build me a login page” | Frustration, mediocre results |
| The Delegator | “AI, help me build user authentication with these requirements…” | Decent results, some iteration |
| The Tech Lead | “I need a secure login system for our web app. Must handle password resets, work with our existing user database, support single sign-on, and handle high traffic. Security requirements: industry standards, rate limiting, audit trails. Here’s our current user flow and system architecture…” | High-quality outcomes |
Profile #3 isn’t using better prompts. They’re using technical management skills.
The mindset shift that changes everything
The lens shift isn’t just philosophical – it has practical implications:
Old lens:
I am a code writer. AI helps me write code faster.
New lens:
I am a tech lead. AI is a capable team member who needs clear direction and verification.
This shift explains why some developers struggle while others thrive. The successful ones have learned to:
- Break down complex problems into clear, verifiable components
- Communicate rich context – current systems, constraints, success criteria
- Verify solutions effectively without redoing everything themselves
- Take accountability for outcomes they didn’t directly produce
When you delegate to a human, you don’t blame them if the outcome is wrong – you ask yourself: “Did I communicate clearly? Did I provide enough context? Did I verify the work properly?”
Same accountability applies with AI.
But here’s what’s interesting: This isn’t skill atrophy – it’s skill evolution.
Traditional IC skills:
- Write clean, efficient code
- Debug complex problems
- Understand system internals
AI-augmented IC skills:
- Design systems that AI can implement reliably
- Verify and improve AI-generated solutions
- Know when to override AI vs. when to trust it
- Maintain technical intuition through selective deep dives
This isn’t random skill development – it’s accelerated career progression. The P60 framework already recognizes this transition from Deep Engineer to Tech Lead. AI just makes it necessary for everyone to do that earlier.
The key insight? You can’t maintain these skills through delegation alone. You need deliberate practice what Amazon calls ‘Dive Deep’: staying connected to details while operating at higher levels. For AI-augmented developers, this means regularly implementing complex features manually to maintain technical judgment while scaling through AI delegation.
Start here: Learn to think like a tech lead
A litmus test: Can you explain a complex technical problem to a smart junior team member in a way that they can solve it independently and you can verify their solution confidently?
If yes, you’re ready for AI. If no, work on that first.
Here’s the thing: AI is powerful, but it’s not magic. It’s a very capable team member who never gets tired, never takes offense, and never asks for a raise. But like any team member, it needs clear direction, good context, and proper verification.
Start tomorrow:
- Pick one complex task you’re working on this week
- Write down the full context as if explaining to a smart junior team member
- Try delegating it to AI with that context
- Spend more time verifying than you normally would
- Ask yourself: What did I miss in my explanation? What would I do differently?
The developers who master this Tech Lead mindset – the same capabilities P60 already defines, just applied to AI systems instead of human engineers – will scale their impact beyond what any traditional individual contributor could achieve alone.
But maybe that’s okay. Maybe this is just the natural evolution of what it means to be a software developer.
What’s your experience? Are you learning to manage AI like you’d manage a junior team member, or are you still trying to use it like autocomplete? I’d love to hear your thoughts.