27 November, 2025

Software engineering is entering a new era where ideas can be translated into code at unprecedented speed. AI-native tools no longer just autocomplete lines - they generate entire modules, allowing teams to move from idea to working prototype in minutes. This “engineering at the speed of thought” is transforming not just development velocity but who can build, how products are tested, and the economics of innovation. AI-Native Software Engineering (or “Vibe Coding”) is not just about speed — it’s about democratising who can build, lowering experimentation costs, and giving engineers superpowers to go from idea to prototype rapidly.. As Andrew Ng famously put it, “A strong predictor of a startup’s success is its speed of execution.” This speed matters most in IoT, where rapid iteration and integration are critical to staying ahead of competition.

In this blog, we will explore how we can foster innovation across IoT industries with Vibe Coding.

It is a paradigm where English becomes the new programming language. It allows developers (and even non-coders) to simply express what they want and get functional, testable, and modular code in return with the help of AI coding tools like Claude Code, Cursor, Replit, Windsurf etc

As Armin Ronacher, creator of Flask, said: “It feels like I’ve gained 30% more time in my day because the machine is doing the work.” Vibe coding shifts the emphasis from keystrokes to creativity; you think to code, focusing on context and problem-solving rather than syntax..

If you could turn your next product idea into code using just plain English,what would you build first?

Building software once required years of training and high costs to deliver meaningful output. AI tools are now compressing this timeline, enabling up to 10x faster engineering.

Prototyping that once cost tens of thousands of dollars can now be done in hours, ensuring more ideas survive the feasibility filter.. A vibe-coded prototype can now convey intent, nuance, and vision far better than a thousand words. 

Today, a solo developer or a small team can build, test MVPs, and even productionize systems with ease.

Are you ready to lead in this new paradigm?

Vibe coding amplifies, not replaces, engineers.. It is offloading boilerplate, providing suggestions, and transforming the engineer’s role from builder to thinker. Engineers now act as curators, strategists, and validators — focusing on architecture and intent rather than manual typing. Jobs aren’t being lost; they’re evolving. The core skill? Not syntax, but context engineering i.e. providing AI with everything it needs to succeed at software development.

Your ideas will earn the pay checks, not your keystrokes!

In IoT, where speed-to-market is critical, AI-native coding brings several benefits:

• Faster Time to Market: Compresses development cycles by 3–5x.

• Increased Demand: Stimulates demand.

• Customer Value: Product delivers customer value much sooner.

• More Innovation: More good ideas see the light of the day

• Expanded Talent Pool: Designers, analysts, and PMs etc can now contribute directly to building products. 

• Increased Full-Stack Capability:  Certain full stack expensive services are now available to all.

Adoption – State of AI Coding Today
The data reveals not just adoption but reliance and hints at how AI is reshaping the way code gets written, reviewed and shipped. As per survey done by qodo.ai:

• 82% of developers now use AI coding tools weekly.

• 65% say AI contributes to at least a quarter of their codebase.

• 78% developers report productivity gains

These statistics show that AI coding is no longer experimental — it is embedded into daily workflows.

Even with all its promise, AI-assisted coding today hits limits in the cases like:

• Fixing a small specific thing on large codebases.

• Delivering context-aware, bug-free code without human review and oversight. 

• Interpreting ambiguous requests without structured context.

The real bottleneck? Lack of context. That’s why “prompt engineering” is evolving into “context engineering.” 

How are you bridging the context gap in your AI-powered development workflow?

Moving from the world of deterministic coding to using LLMs to write code, takes effort to figure out the sharp and soft edges. Following are my suggestions:

1. Preparing for the transformation: Upskill teams, Redesign product workflows for agility. 

2. Context is King: Project-wide context engines are essential for reducing hallucination and getting correct codes. Context engineering covers everything from Features, examples, online documentation for RAG, prompt engineering, state, history, memory, Instructions, best practices etc. 

Abrahim Lincon once said “Give me six hours to chop down a tree and I will spend the first four sharpening the axe. That exactly what context engineering does. 

1. Prompt the Plan: Project Planning with Product Requirement Prompt. Ask AI to assess and generate step by step prompts for generating a comprehensive architecture and detailed project plan with tasks and subtasks. 

2. Create Shared context across team members.

3. Establish guardrails and SOPs to ensure quality and safety

4. Build One Brick at a Time: Build and test one feature at a time.

5. Isolate for Safety: Separate sandbox, dev, prod environments and databases.

6. Design for Modularity to simplify integration.

7. AI-Powered Code Reviews: Always-on, opinionated, context-aware review is the force-multiplier.

8. Avoid Code Bloat: Remove the things that did not work, as it adds non-working context and confuses the AI coding tool. 

9. Pick the Right Model for the Right Job: Token context windows are powerful—use them wisely. For example, Claude 3.5 Sonnet (200k context) may outperform GPT-4o (128k) for long-context tasks.

Are your teams just prompting—or truly engineering software with AI?

Based on research from Stanford’s Software Productivity Research Group, productivity gains are as follow:

• Task Complexity: Green field projects gain 30-35% on simple tasks and 10-15% on complex ones verses 15-20% and 5-10% in brownfield projects.

• Language Popularity: AI boosts productivity by 10-25% in popular languages, but can decrease productivity in niche languages

• Codebase Size and Maturity: As AI code base size increases, productivity gain from AI decreases

• Context Length: State of the art coding performance drops with context length. Claude 3.5 

Key Takeaway: AI does improve productivity, but not uniformly. Strategic targeting is essential to maximize ROI from AI-native workflows.

Are you deploying AI where it delivers the most value, or just where it’s easiest to try?

Directing AI coding tools is much like directing people. The more intentional you are about guiding it, the more powerful and aligned its output becomes.

Treat AI as a collaborator, not a replacement, using it to ideate, architect, debug, and iterate. In IoT-driven software product development, where scale is huge, timelines are tight, competition is cut-throat and systems must scale across devices and data streams, this partnership becomes even more crucial. It’s not just about coding faster it's about solving the right problems more intelligently.