I’m sure most of us have been affected by the recent Cloudflare outages — and apparently a few other outages across the hyperscalers too. They feel a bit more frequent than what we were used to earlier.

Most of these incidents already have published root causes: DNS-related issues, feature files ballooning in size, config drift… the usual. Nothing points directly to AI-generated code. But it’s been on my mind — what happens when 82% of developers are using AI code assistance every single day? When AI-generated or AI-touched code quietly enters production systems at scale? And when studies say this code is only 30–70% accurate depending on the tool?

what happens when we are all writing code faster than we can review it?

Now, personally, I don’t believe the recent outages were caused by AI. But I do think we’re entering a period where more outages will be influenced by it. And ironically, that might be the biggest signal that something truly transformative is happening underneath.

I’ll stress that again — the chaos itself is a sign of change.

The Silent Contributor

Source: Stackoverflow Survey

Eighty-three percent of firms now use AI to generate code, with 57% treating AI coding tools as standard practice. One in four enterprises with 100+ engineers has moved beyond testing into active production use. AI-generated code is approaching parity with human-written code in many development teams.

the alarming thing isn’t about ai generating so much code

but, developers are shipping this code anyway.
because it’s fast.
because it looks right.

Most importantly

because the pressure to deliver hasn’t decreased—it’s only increased now that everyone knows AI can write code in seconds.

The Twenty-Year Compression

Here’s the thought that keeps me up at night—and also gives me hope.

Image Source

The internet era took two decades to mature. From the chaos of Web 1.0, through the bubble, through the slow professionalization of web development, through the rise of cloud computing and SaaS, through DevOps and CI/CD and infrastructure as code—it took roughly twenty years for “building software” to become a reliable, repeatable, enterprise-grade discipline.

Twenty years of accumulated best practices. Twenty years of learning how to test, deploy, monitor, and roll back. Twenty years of building the institutional knowledge that makes modern software development possible.

AI-assisted development won’t take twenty years. I’d argue it will take two.

Not because AI is magic, but because the infrastructure already exists. The cloud is built. The deployment pipelines are mature. The monitoring tools are sophisticated. The feedback loops are measured in minutes, not months.

When AI writes bad code in 2025, we find out fast. When a deployment fails, we can roll back automatically. When an incident occurs, we have the observability to trace it to its root cause. The system is designed to surface problems quickly.

More importantly, AI learns from failure at a scale humans never could. Every bug report, every stack trace, every incident review becomes training data. The models that write code in 2026 will have learned from the mistakes of 2025. And the models of 2027 will have learned from both.

We’re not just building software anymore.

We’re building systems that build software.

And those systems improve on a timeline that isn’t bound by human learning curves.

The Chaos Is the Signal

So what should we expect?

In the next six to twelve months, we’ll likely see more incidents explicitly attributed to AI-generated code. Not because AI is getting worse, but because AI is getting everywhere. When AI-touched code reaches true parity with human-written code—when it’s in half of all production systems—it will statistically be involved in half of all incidents.

This will generate headlines. “AI Code Causes Major Outage.” “Company Loses Millions to AI-Generated Bug.” The narrative will be that AI is dangerous, that we’ve moved too fast, that we need to slow down.

The narrative will miss the point.

The point is that every major technological shift involves a period of instability. The early days of the automobile were more dangerous than horse-drawn carriages. The early internet was a security nightmare. Early cloud computing had spectacular failures that made enterprises swear they’d never trust their data to someone else’s servers.

And then, each time, the technology matured. Standards emerged. Best practices solidified. What was once risky became the new baseline of safety and reliability.

AI-assisted development is following the same pattern, just faster.

The Other Side

Here’s what I believe.

We’re not heading toward a world where AI makes software worse. We’re passing through a turbulent transition on the way to a world where AI makes software dramatically better.

The outages we’re seeing—whether or not they’re AI-caused—are symptoms of a system under stress. We’ve built infrastructure that connects everything to everything. We’ve created dependencies so deep that a DNS error in Virginia can ground flights in Alaska. We’ve concentrated critical services in a handful of hyperscalers whose mistakes ripple globally.

AI didn’t create that fragility. But AI will be part of the solution. Systems that can predict failures before they happen. Code that can heal itself. Infrastructure that can route around problems in real-time.

We just have to get through the messy middle first.

The internet took twenty years to grow up. AI-assisted software development will do it in two. And when we look back from 2027, the chaos of 2025 will look like exactly what it was: the growing pains of a technology that was about to change everything.

The question isn’t whether AI will transform how we build software. That’s already happening. The question is whether we’ll navigate the transition with our eyes open—prepared for the turbulence, invested in resilience, and focused on the remarkable stability waiting on the other side.

Signing off this Friday with these thoughts!
Thank you for reading!
Have a happy weekend :)

Best Regards

Rohith Reddy Gopu!

Yet here we are in 2025, witnessing exactly that transformation.

The Great Equalizer Has Arrived

In my previous article, I shared my personal journey from writing my first HTML tags in Warangal to embracing the "vibe coding" revolution—using AI to generate 95%+ of code while humans focus on direction, purpose, and quality. I covered the tools, techniques, and incredible productivity gains we're seeing in development.

But there's a profound second-order effect I didn't fully explore: vibe coding isn't just changing how developers work—it's redefining who can build in the first place.

From Gatekeepers to Enablers

For decades, technical teams have functioned as the gatekeepers of creation within organizations. Have an idea? Get in line. Submit a request. Wait for prioritization. Hope it makes it into the next sprint. Convince a developer it's worth their time.

That power dynamic is now fundamentally shifting.

When I started my first consulting company, translating a founder's vision into working software required specialized skills accumulated through years of training. The technical gap between "having an idea" and "building the solution" was vast and expensive to bridge.

Today, that gap has narrowed to the point where it's nearly invisible for many use cases.

The New Organizational Capability Map

Here's what this shift looks like across different roles in your organization:

For Non-Technical Founders and Executives

You no longer need to wait for development resources to test market fit. That idea you've been explaining to your tech team for months? You can build a functional prototype this weekend. The barriers between your vision and its implementation have fallen dramatically.

For Product Managers

Your ability to validate hypotheses has expanded exponentially. Instead of creating wireframes and waiting weeks for implementation, you can rapidly iterate on actual working prototypes. The feedback loop that once took months now takes days or hours.

For Marketing Teams

Those campaign-specific landing pages and interactive content pieces you've dreamed up? No more waiting in the engineering queue. The specialized microsites, calculators, and visualization tools that drive engagement are now within your reach.

For Customer Support and Success

The custom dashboards, internal tools, and client-specific solutions you know would improve retention can now be built directly by your team. The insights trapped in your head about what customers need can flow directly into solutions.

For Operations Teams

Process automation that once required developer time can now be implemented by those who understand the processes best. The workflows and integrations that streamline operations are yours to create.

For Technical Teams

Far from making you obsolete, this shift elevates your role. You become the enablers, the architects, the quality guardians, and the scalability experts. Your value moves up the stack from implementation to vision and governance.

The Transformation I'm Witnessing

The shift is happening right before our eyes:

As I mentioned earlier, "Last week, my team prototyped an idea in just two days that would have taken us two months last year." This isn't hyperbole – it's the new reality of development velocity.

I've watched junior developers ship features that previously required senior-level expertise. The technical knowledge that once took years to accumulate is now augmented by AI assistants that understand system architecture and suggest optimizations.

Perhaps most revolutionary, I've observed non-technical founders build functional MVPs without writing code themselves. The technical barriers that once required specialized education or hiring scarce developer talent have largely evaporated.

Even within my own journey from that computer library in Warangal to building consulting companies, the contrast is stark. Projects that would have required months of work from my team in 2015 can now be completed in days or weeks, with MVPs created over a weekend.

The New Limiting Factors

With technical implementation no longer the primary bottleneck, organizations must reckon with new limiting factors:

1. Problem clarity — Can you articulate exactly what needs to be built and why?

2. User empathy — Do you truly understand the needs you're trying to address?

3. Vision alignment — Is everyone moving toward a coherent goal?

4. Quality standards — Do you have mechanisms to ensure what's built meets your bar?

5. Knowledge sharing — How does learning flow through your organization?

Technical limitations used to mask these more fundamental challenges. Now they're front and center.

Building a Vibe Coding Culture

For organizations looking to harness this revolution, the playbook is still being written. But here's what I've seen work:

• Enable broad access to AI coding tools across departments

• Create knowledge-sharing mechanisms so solutions aren't trapped in silos

• Establish lightweight governance to maintain quality without stifling innovation

• Redefine technical team roles as enablers rather than implementers

• Reward cross-functional building to encourage innovation at the edges

• Emphasize rapid learning cycles over perfect first attempts

The Human Elements That Matter More Than Ever

As AI handles more of the implementation details, certain human capabilities become increasingly valuable:

• Systems thinking — Understanding how pieces fit together

• Aesthetic judgment — Recognizing what makes a good solution

• Communication clarity — Articulating exactly what needs to be built

• Empathetic design — Creating with the end user genuinely in mind

• Ethical consideration — Weighing the implications of what we build

The Great Opportunity

We stand at an inflection point similar to the early days of the internet or the mobile revolution. The democratization of building means:

• More ideas will be tested

• Solutions will emerge from unexpected places

• The gap between imagination and implementation will continue to shrink

• Organizations that embrace this shift will outpace those that don't

The kid from Hanamkonda who spent hours in the central computer library could never have imagined a world where the technical barriers to creation would fall so dramatically. Yet here we are, witnessing the most significant democratization of building capability in our lifetime.

What This Means For You

If you've been putting off that project, delaying that feature, or believing you needed technical co-founders to build your vision—it's time to reconsider.

The cost of creation has never been lower. The speed of iteration has never been faster. The opportunity to innovate has never been greater.

The question is no longer "do we have the technical resources to build this?" but rather "why aren't we building this already?"

What are you waiting to create?

What's your experience with vibe coding in your organization? How has AI-assisted development changed what's possible for your team? I'd love to hear your stories in the comments below.

I was born in Hanamkonda, Warangal in 1989, but my story with coding didn't begin until much later. While the tech world was evolving through the 90s with the rise of the internet and the dot-com boom, I was just a kid growing up in India, unaware of how computers would shape my future.

The 90s were the era of "code warriors" – developers working with C and C++, managing memory manually, and celebrating all-nighters to ship products on physical disks. Software development was still seen as a mysterious art practiced by a select few. But that world was about to change dramatically.

My First Encounter with Computers (2002-2003)

My journey began during summer holidays in 2002-2003, when my dad introduced me to computers. I still remember the fascination I felt seeing that beige box whirring to life, the distinct sound of dial-up internet, and the magic of moving a cursor around the screen.

What started as summer holiday exploration quickly grew into genuine interest. While my friends were playing cricket in summer holidays, I started setting up my blogs, understand how to build websites, I found myself drawn to this machine, internet and its seemingly infinite possibilities.

Learning to Code from the Library (2006-2007)

By 2006, I was spending hours at the central computer library in Warangal during my summer holidays.

The library became my sanctuary – a place where I could learn, experiment, and grow. I'd walk there after classes, eager to get more screen time and absorb everything I could about programming.

I still remember poring over thick manuals, following tutorials, and the pure joy of making something work after hours of troubleshooting. The internet was becoming more accessible, forums were virtual discussion rooms for troubleshooting issues.

A pivotal moment came when I was introduced to Ubuntu. Getting my hands on this Linux distribution opened up an entirely new world of possibilities. Then came PyCon India - my first real exposure to the vibrant community behind open source. These experiences weren't just about learning new technologies; they connected me with a global movement and philosophy. Suddenly, I wasn't just coding in isolation anymore – I was part of something larger, with countless resources and minds to learn from. The open source community created opportunities I couldn't have imagined when I was walking to that library in Warangal.

Navigating Through Different Tech Stacks

As my skills grew, so did my toolkit. I found myself working with WordPress, Drupal, Ruby on Rails, PHP, Yii, and Moodle. Each new technology was a world to explore, with its own philosophy and approach to solving problems.

What made my journey unique was that I experienced it through two lenses: as a developer writing code and as an entrepreneur building consulting companies. Helping founders turn their visions into reality gave me a perspective many developers missed – understanding not just how to code, but why we were coding and what problems we were really solving.

Those dual perspectives taught me that great software isn't just about clean code or clever algorithms, but about understanding human needs and building solutions that actually make a difference.

The Evolution I Witnessed First-Hand

Through the 2010s, I watched and participated as development tools grew increasingly sophisticated:

• IDEs transformed from glorified text editors to intelligent assistants

• GitHub changed how we collaborated on code

• The cloud revolutionized deployment, freeing us from worrying about hardware

• Mobile development exploded, creating new platforms and paradigms

• JavaScript evolved from a simple scripting language to powering complex applications

Each shift made development more accessible and powerful, but still required writing most code by hand. Even with frameworks and libraries, the core implementation work came from our fingers hitting keyboards.

The AI Assistant Revolution (2022-2024)

Around 2020, something fundamental began changing. GitHub Copilot appeared, offering suggestions that seemed almost prescient. ChatGPT and other LLMs emerged as debugging partners. The line between human and machine contributions started blurring.

Tasks that once took days now took a few hours. But this was just the beginning.

The Vibe Coding Era (2025….)

And now, here we are in 2025 – the era of "vibe coding." As Andrej Karpathy describes it, this means "fully giving in to the vibes," a complete paradigm shift in how we approach development.

What Is Vibe Coding?

For me, vibe coding has meant:

• Embracing AI to write 95%+ of my codebase rather than treating it as an occasional helper

• Shifting from identifying as a programmer to functioning as a "product engineer"

• Achieving 10-100x productivity gains by fully leaning into AI coding tools

My Essential Toolkit

My current tools of choice are decidedly streamlined:

• Replit - An all-in-one, browser-based development environment

• Claude 3.7 - As I explore this potent combination in March 2025

Right now, I'm putting this stack to the ultimate test - building an entire ERP system through vibe coding. It's ambitious, but that's exactly why it's the perfect project to push the boundaries of what's possible with this approach.

Other popular tools in the vibe coding ecosystem include:

1. Cursor - A popular IDE with comprehensive code understanding

2. Windsurf - For indexing and navigating entire codebases

3. Claude 3.5 Sonnet/3 Opus - Reliable models for everyday coding tasks

4. Claude 01/03 - Specialized reasoning models for complex debugging

5. ChatGPT - Particularly useful for reasoning through problems

My New Workflow

Vibe coding hasn't just changed my tools – it's transformed my entire approach:

• Roll not fix - I've learned rewriting is often faster than debugging; I just retry from scratch

• Prompt in parallel - Running multiple AI sessions on different features simultaneously

• Rapid iteration over perfection - Getting something working quickly and refining later

• Human taste over coding skill - My judgment and product sense matter more than typing speed

The productivity difference is staggering. Projects that would have taken my team months in 2015 now take days or weeks. MVPs can be created in a weekend.

Where Vibe Coding Works Best

From my experience building consulting companies and helping founders, I've found this approach works best for:

• Zero-to-one product development when speed is everything

• Feature prototyping and rapid iteration

• Projects where scale isn't an immediate concern

• Simple to moderately complex applications

• Modern, well-documented frameworks

The Human Element: Still Irreplaceable

Despite how much AI can do, my journey has taught me that certain human skills remain essential:

• System thinking and architecture - AI still struggles with big-picture design

• Product taste and user empathy - Understanding what people actually need

• Quality assessment - Judging whether generated code is truly good

• Debugging - Finding and fixing subtle issues

• The "why" and "what" versus the "how" - Purpose over implementation

Scaling Beyond the MVP

As a consultant helping founders, I've learned important lessons about scaling applications built with vibe coding:

• Technical debt accumulates faster - Plan strategic refactoring

• Complex infrastructure needs systems thinkers - Some specialties remain critical

• Critical systems may need traditional approaches - Not everything should be "vibed"

• Balance speed with architecture - Quick development must consider the long term

The Future of Development

Looking ahead from 2025, I see fascinating changes coming to our industry:

• Technical interviews will transform - I expect questions like "What's your vibe coding velocity?" replacing algorithm puzzles

• The definition of "good developer" will shift - From coding mechanics to system thinking and prompt engineering

• AI collaboration will become more intuitive - The human-AI interface will grow increasingly seamless

• Programming education will be reimagined - Teaching how to direct AI rather than syntax details

From Warangal to the World

When I started writing code in my early days, I could never have imagined the journey ahead – from writing my first HTML tags to directing AI systems to build entire applications.

The constants through this journey have been curiosity and adaptation. The willingness to learn new approaches, to throw away old habits when better ones emerge, and to focus on creating value rather than clinging to familiar tools.

That kid from Hanamkonda who fell in love with computers during summer holidays has witnessed an incredible transformation in how we build software. And while the tools and methods have changed dramatically, the core remains the same: using technology to solve human problems.

As we embrace the vibe coding era, I'm more excited than ever about what we can build. The barriers to turning ideas into reality have never been lower. The future belongs to those who can collaborate effectively with AI while maintaining the human judgment, creativity, and empathy that gives technology its purpose.

What's your coding journey been like? Have you started embracing vibe coding in your work? I'd love to hear your stories in the comments below.

Credit: Many of the vibe coding concepts shared in this post were originally found in a LinkedIn forward that has been tremendously helpful in my understanding and adaptation to this new paradigm. The original breakdown of vibe coding, essential tools, workflow changes, and human elements has provided a valuable framework for my current experiments with this approach.

What is an API?

Think of an API (Application Programming Interface) as a waiter in a restaurant. It’s the middleman that lets different programs talk to each other without exposing all the inner workings of each system. For example:

• In a Weather App:
  The app asks a weather service for the latest forecast. The service sends the data back, and the app displays it—just like ordering food from a menu. You don’t need to know how the kitchen prepares your meal.

• In Everyday Tech:
  When you log in with your Google or Facebook account, the app uses an API to verify your details. The app doesn’t need to know how Google or Facebook manages that info; it just asks for what it needs.

Real-world analogy:

Imagine being at a restaurant. The menu lists what you can order, and when you decide, the waiter takes your order to the kitchen and brings back your meal. Similarly, an API provides a standard way for one piece of software to request data or actions from another.

In simple terms, APIs are how software talks to other software, making it easier to build applications by reusing existing services.

Meet MCP: A Special Kind of API for AI

Now that we understand APIs, let’s dive into Model Context Protocol (MCP). At its core, MCP is an open standard that connects AI models with the data sources and tools they need—think of it as a universal adapter for AI.

Why MCP Matters

• Universal Connection:
  Instead of creating custom integrations for each data source (like Google Drive, Slack, or GitHub), MCP provides one standard method. It’s like having one charger that works for all your gadgets.

• Easier Integrations:
  With MCP, an AI assistant doesn’t need to know the unique way each tool operates. It sends a standardized request, and the tool’s MCP connector translates that into the tool’s native language. This makes it simpler and faster for developers to connect multiple services.

• Real-Time Data:
  Today’s AI often works with outdated or static data. MCP allows AI to fetch current information from various sources on demand, meaning responses can be more relevant and timely.

How MCP Works in Practice

Let’s say you have an AI assistant, Ava, and you want it to pull information from your Google Drive and Slack. Here’s a step-by-step of what happens with MCP:

1. The Request:
   You ask Ava: “Show me the latest project plan and any recent team discussions about it.”

2. Standardized Communication:
   Ava sends a request using the MCP protocol. It doesn’t need to know how Google Drive or Slack works internally—just that they’re connected via MCP.

3. Fetching Data:

   • The Google Drive MCP connector gets the latest project plan.

   • The Slack MCP connector gathers the recent team messages.

4. Unified Response:
   Both data sources return the information in a common format. Ava then uses this data to compile a clear summary for you.

Real-World Example

Imagine your company uses various tools for work. Instead of building custom code for every tool:

• Developers write one MCP integration.

• Your AI assistant can then easily pull in everything from project documents to team chat logs.

• This streamlined process saves time, reduces errors, and makes the assistant much more useful.

Why Developers and Users Love MCP

• Simplified Code:
  One standard protocol means fewer unique integrations to maintain.

• Plug-and-Play Flexibility:
  New tools with MCP connectors can be added seamlessly, much like adding new USB devices to your computer.

• Better User Experience:
  With access to real-time, relevant data, AI assistants can give answers that are much more useful and personalized.

• Enhanced Security:
  Organizations can host MCP connectors internally, keeping sensitive data secure while still enabling AI to access only what it needs.

In a Nutshell

Model Context Protocol (MCP) is to AI what an API is to traditional software—it’s a universal way for systems to communicate. By standardizing how AI connects to various data sources and tools, MCP makes integrations simpler, faster, and more effective. Whether it’s fetching the latest project plan or summarizing team discussions, MCP helps break down data silos, paving the way for smarter, more connected AI assistants.

MCP isn’t just another tech buzzword. It’s a practical solution that could transform how AI interacts with our everyday tools, making our digital lives smoother and more integrated.

I always wanted to be a Technology Evangelist and I’m embarking on that journey today!

I’d come up with one piece of content every week around SaaS, Cloud Native and AI!

That’s precisely my goal for this year - write 50 pieces of content on Substack.

I'd be writing all things technology - mostly around SaaS, Cloud Native, AI & Platforms :) is that everything under the sun? I don't think so.

Sincerely
Rohith #GitHappens

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