I drafted this post in early 2023 when AI was just getting traction. Scan it quickly and lets see if the premise still holds.
Evaluating performance isn’t about tracking hours or micromanaging, it’s about looking at two simple things: What you deliver and How you work with the team.
Both matter equally. Delivering great results with a toxic attitude doesn’t work, and being super friendly without ever finishing tasks doesn’t work either.
Here is what defines great results:
1. Great Outcome (The “What”)
Great outcomes mean delivering work above expectations without causing headaches down the line.
- Done on time: You hit agreed deadlines without major delays or needing a rewrite.
- Tested and code-reviewed: It actually works, and a teammate has reviewed it.
- Documented and communicated: You explain what was built, why, and how to use it so nobody has to guess.
- Clear value: The end goal or user value is obvious to anyone looking at it.
2. Great Behavior (The “How”)
How you show up every day sets the culture for everyone around you.
- Team-first mindset: You care about the team’s overall success, not just your own ticket.
- Strong communication: You keep people updated early and often—especially crucial in a WFH setup.
- Solution-oriented: You follow a simple loop: Plan -> Execute -> Refine.
- Real ownership: You take responsibility for your work, and you’re genuinely glad to jump in and help others when they get stuck.
- Learn and lift: You pick up new things fast and share that knowledge with the team.
What still holds today in the AI era?
What Has Shifted in the “What” (Deliverables)
Today producing outcomes is GenAI salt and butter. AIs own training and harness have huge impact on outcome. It’s really good at executing, drafting and prototyping, but it lacks human judgement and accountability. Lets see how outcome goals changed in AI era.
“Tested and code-reviewed: It actually works…”
- The Reality: AI generates plausible, syntactically correct code that can contain subtle edge-case bugs, security vulnerabilities, or anti-patterns.
- The Shift: Writing the code is no longer the main bottleneck; verifying, validating, and reviewing it is. High performance here isn’t just using AI to write unit tests—it’s having the domain knowledge to know what edge cases the AI missed and ensuring system architecture isn’t deteriorating under the hood.
“Clear value: The end goal or user value is obvious…”
- The Reality: AI can generate code, draft documentation, and outline features based on prompts, but it doesn’t understand product vision or business context.
- The Shift: The human role shifts from builder to curator/director. Defining the right problem, validating that the solution actually solves a real user pain point, and trimming AI-generated feature bloat are distinctly human responsibilities.
“Done on time…”
- The Reality: The baseline speed for standard tasks has increased across the board.
- The Shift: Hitting deadlines is less about manual keyboard time and more about task decomposition, prompt engineering, and rapid iteration loops.
The “How” (Behavior) is non-negotiable
Culture is human: AI cannot make for psychological safety, facilitate cross-functional discussions, mentor a junior engineer or align across teams when priorities clash.
Communication in remote or hybrid teams: Since AI speeds up code generation, async sharing and proactive updates matter more. A team coding 3x faster without tight communication leads to 3x chaos faster.
The “What” vs. “How” balance
High performance still requires both pillars. Generating 10 pull requests a day with AI while bulldozing team processes is just automated toxicity.
For the reference where this method is in use across top companies
| Company Name | Project | Application |
| Netflix | Performance vs. Values Matrix & “No Brilliant Jerks” Policy | Evaluates technical delivery alongside cultural adherence. High delivery combined with toxic behavior gets zero tolerance. |
| Project Oxygen & “What & How” Rating Scale | Uses 360° reviews to rate tangible output (“What”) equally alongside collaborative behaviors, coaching, and communication (“How”). | |
| Stripe | Engineering Operating Principles & Leveling Grid | Rates engineers on Execution Rigor (code quality, testing standards, documentation) paired with Team Impact (ownership and mentoring). |
| GitLab | Async-First & Values-Based Evaluation | Evaluation assesses performance through documented output, asynchronous communication rigor, and self-directed ownership in a distributed environment. |
| Amazon | Leadership Principles + Delivery Metrics | Performance reviews split focus 50/50 between hitting concrete delivery targets and demonstrating behaviors like Ownership and Bias for Action. |
| General Electric | Jack Welch 2×2 Performance Grid | Pioneered the classic 2×2 matrix evaluating staff on two independent axes: Results (Outcome) vs. Values (Behavior). |
Summary
High-quality deliverables combined with a strong collaborative culture define high-performing engineering organizations.
It holds in AI era too, but require a subtle shift in your works processes. It makes the work more demanding by extra skills like project management, QA assistant and architect, but it also makes the work more interesting and comprehensive.
