Trading a Blindfold for Sunglasses: Code Models, Managers, and Understanding the Why
Take your pick, people. You can either have:
- A jack-of-all-trades that barely completes tasks assigned to it.
- A single-task specialist who is an absolute knob on everything else.
AI models trained on code often feel sharper, more logical, and surprisingly good at problem-solving compared to general-purpose LLMs. But there is a catch. While code-trained models shine at execution, they can easily miss the "why" behind a product decision. Lemme explain.
Code Models as 2D Data
One big reason code models often outperform general-purpose LLMs is structure.
Unlike human language, which is messy and full of ambiguity, code lives in a tightly governed world:
- Syntax rules: Every function, variable, and bracket follows strict patterns.
- Dependencies: Functions rely on imports, classes, and logic chains.
- Deterministic execution: A program either runs or it doesn't.
Think of it as a 2D grid of data:
- One axis = tokens, variables, functions, keywords.
- Other axis = logic flow, nesting, indentation, dependencies.
As a result, code-trained models develop powerful reasoning and error-detection capabilities. They don't just memorize tokens; they learn structure. That's why many math- or reasoning-heavy LLMs borrow heavily from code datasets. But structure alone isn't enough.
Real-world products don't exist in 2D. Products exist in a multi-dimensional mess made of:
- Customer psychology
- Market dynamics
- Business constraints
- Team trade-offs
- Cultural nuance
A code model may generate a flawless function, but it won't question why that feature should exist at all. So to quote Jeff Goldblum from Jurassic Park:
Your scientists were so preoccupied with whether or not they could, they didn't stop to think if they should.
This gap is exactly where product managers and owners step in.
Online Code Templates and the Trap Within
Another layer of issue worth calling out is that most code on the internet, whether in Stack Overflow answers, GitHub gists, or boilerplate templates, isn't perfect. In fact, it might not even be remotely close to perfect.
One could argue that templates provide clean, efficient, and reusable solutions that accelerate development, but remember that many of the snippets used are context-specific hacks that are outdated or insecure.
When code models are trained on these sources, they can inherit both the brilliance and the blind spots. A function might run beautifully but silently carry forward bad practices like poor error handling, security flaws, or unnecessary complexity.
That's why feeding product context matters even more: it helps separate "technically correct but risky" from "strategically right for this product."
How Do Humans Fit Into All This?
Product managers hold the invisible knowledge that shapes decisions:
- What pain point are we solving?
- What trade-offs did we accept?
- How will success be measured beyond "it works"?
- What risks are we willing to live with?
Without this context, an AI system might optimize for speed, elegance, or efficiency but not for impact. In simpler terms, we want human intervention so AI doesn't go full Ultron on us.
If we want AI to build useful products, we need to feed it product context alongside code. That means going beyond repositories and also training on:
- Meeting notes and decision logs
- PRDs, product requirement docs
- Customer feedback loops
- Roadmap trade-off discussions
Ultimately, Why Does All This Matter?
If code models are 2D thinkers, then product managers are the third dimension, adding depth, direction, and relevance.
The future of AI in product development isn't just "ChatGPT that writes code." It's AI that understands and adapts:
- The why, customer problem
- The who, end user and market dynamics
- The how, technical execution
That's where things get truly exciting: an AI that doesn't just build what's possible but builds what matters.