Behind the product

AI vs Humans in Product Development

A Techscale experiment comparing approach, and quality of outcome between AI and Human Developers.

Nikolaj Gaba
September 30, 2025
Behind The Product

TL;DR

We put AI (Cursor + ChatGPT) and one of our developers (Kainat) to the test, building the same front-end module: a responsive tab carousel with animations, parallax effects, and auto-rotation. Here’s what we found:

90%

Faster prototyping using AI for raw unrefined draft version.

Faster load time using human-crafted streamlined architecture.

40%

Less time spent on project management for human developers with only one brief.

100%

Increased SEO visibility and indexing using human-crafted development.

Kainat Aslam
Frontend Developer @ Techscale

Why We Did This Experiment

At Techscale, we’re genuinely curious about the role of AI in product development. With tools like Cursor, GitHub Copilot, and ChatGPT making headlines, we wanted to see how they stack up against our developers in a real-world use case.

The exercise challenge

Build a responsive tab section with smooth transitions, auto-rotation, parallax background, and a mobile-friendly design. The final version must be easy to apply, fast and scalable without clashing classes.

Getting started

Deciding on the approach

To get a thorough assessment, we used 2 AI tools .
One to do the work, and one to assess the AI-generated code, and optimize where possible.

AI's approach

Began development immediately, and built the first prototype in < 1 min.

Used vanilla JS (Basic Javascript with no stack), as nothing else was instructed in the brief, making it non-scalable and harder to manage.

4 hours and 30+ prompts to achieve the desired outcome.

We leveraged ChatGPT to conduct QA from the AI-generated code against the original brief, and passed it back to the vibe coding tool to fix and optimize.

Kainat's approach

Spent 30 min. understanding scope, scalability, and applied critical thinking beyond the brief.

Decided on Nuxt JS - a stack that makes it easy to mange, SEO optimised with server-side rendering, scale better so we can apply it anywhere.

Upon finishing the architecture assessment, Kainat presented her plan and finished the build in 8 hours and managed the project and outcome on her own.

Kainat leveraged our independent QA team to test and review the module before handing it over. In development, ~20% more bugs tend to slip through when you do QA on your own work.

Code without architecture is like walls without foundations — it stands, but it won’t last.

- Katinat Aslam , Frontend Developer @ Techscale

Comparing the code

The hidden risks of AI generated code

AI is exceptionally fast for prototyping, but for an actual product every output needs to be heavily monitored by an engineer to avoid major risks:

Quick fixes that break scalability —AI patches problems (e.g. !important - in CSS), making future changes harder.

Performance hits —Inefficient logic that slows down under real users.

No product context —Hardcoding, ignoring standards, or misaligned features.

Technical debt —Small messy fixes add up to big maintenance costs.

Security risks —While our experiment focused on a basic web element, larger applications are at greater risk from a security POV.

Leveraging AI in the right way

Consider AI a power tool - great for small tasks at home. But when you want to build a house, you still you bring in the experts.

Where AI shines

Speed at prototyping — can spin up functional code in seconds.

QA automation & test generation — great at repetitive validation tasks.

Code suggestions & refactoring — saves time on boilerplate.

Idea exploration — helps teams quickly test “what if” scenarios.

Developer productivity boost — acts like an extra set of hands.

Where Developers Are Essential

Architecture & scalability — ensuring code won’t break when the product grows.

User experience & design thinking — aligning the product with human behavior.

Complex integrations — handling APIs, databases, and business logic.

Security & performance optimization — critical for production readiness.

Context & vision — aligning tech decisions with business strategy.

How techscale uses AI

AI is an incredibly powerful tool to leverage - especially when it's prompted by an individual with code knowledge.

There are areas where we can trust AI to do the bulk work to help us save time, but it is still thoroughly reviewed by our teams:

QA and Automation:
We have trained AI specifically for every project, which helps us process QA 40% faster.

Code Suggestions & Refactoring:
Quick improvements like renaming variables, formatting, or simplifying functions.

Prototyping & Wireframes:
Fast draft code for UI elements or layouts (carousel, forms, dashboards).

Routine DevOps Tasks:
Writing boilerplate scripts for CI/CD, log parsing, or alerts.

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