Indian Developers: The Quiet Anxiety of AI Coding Tools

Deepak, a bright-eyed junior developer in Bengaluru, had dreamt of this moment since college.

Landing a role at a buzzing tech startup, he imagined long nights wrestling with elegant algorithms, debugging complex functions, and ultimately, building robust software from the ground up.

He pictured the whiteboard sessions, the eureka moments, the sheer satisfaction of crafting lines of code that brought ideas to life.

Yet, as he settled into his new role, the reality felt starkly different.

His primary companions weren’t just fellow human engineers, but AI coding tools like GitHub Copilot and Cursor.

He was told to use them, to ship faster, to try more designs.

The expectation was clear: Whatever coding you had to learn you learnt in college.

Now is the time to build and ship.

This sentiment, shared by an anonymous front-end developer at an AI startup in Bengaluru (AIM), echoes a growing quiet anxiety among Indian developers.

Companies are rapidly adopting AI coding tools, often mandating their use.

While this accelerates development and allows for faster shipping, many developers, particularly juniors, are sensing a reduced learning curve.

The concern isn’t about laziness; it’s about the erosion of fundamental skills and the uncertain future of software engineering itself.

In short: AI coding tools are accelerating development in India, but this rapid adoption is fueling anxiety among developers about skill degradation, limited learning opportunities, and the changing definition of software engineering, shifting focus to prompt quality and shipping efficiency.

Why This Matters Now: The Unseen Force Behind AI

The Indian tech landscape is dynamic, with companies rapidly integrating AI coding tools such as Cursor and GitHub Copilot into their workflows.

This widespread adoption is fundamentally changing how developers work, impacting traditional coding practices and skill development (AIM).

The race to innovate means companies are pushing for efficiency, driven by the capabilities of generative AI.

This rapid adoption often outpaces the underlying capabilities of existing skills.

Developers are realizing that extensive reliance on these AI tools is leading to a reduced learning curve, potentially worsening their skills (AIM).

This concern isn’t just theoretical; it raises fundamental questions about career progression, the depth of technical knowledge for future developers, and even employee retention (AIM).

Further compounding this unease, companies are now tracking AI tool usage and prompt quality via dashboards for employee assessment.

This shift in metrics creates anxiety, redefining performance expectations and raising fears of being replaced by those more adept with AI tools (AIM).

The Shifting Sands of Software Engineering: Why AI Changes Everything

For years, the industry preached against obsessing over lines of code.

Now, it seems, we are replacing one vanity metric with another: prompt quality and AI usage.

The very nature of coding itself is shifting.

Developers are writing less code from scratch.

They prompt, inspect, tweak, ship, and repeat.

Adam Wolff, creator of Claude Code at Anthropic, made a stark prediction on X, suggesting that

“maybe as soon as the first half of next year: software engineering is done.

Soon, we won’t bother to check generated code, for the same reasons we don’t check compiler output” (Adam Wolff, X).

This is a little scary, as programming might not be a big part of the job anymore.

Wolff adds, however, that

“coding was always the easy part.

The hard part is requirements, goals, feedback—figuring out what to build and whether it’s working” (Adam Wolff, X).

This sentiment mirrors the manager’s advice to Deepak: “Now is the time to build and ship” (AIM).

A Bengaluru Developer’s Quiet Concern

Deepak’s experience isn’t isolated.

The anonymous front-end developer in Bengaluru, for instance, recounted using Cursor for most of his code and Lovable for proof-of-concepts (AIM).

While this undeniably enabled faster shipping and more design iterations, he felt a significant loss of learning, apart from mastering the tools themselves (AIM).

When he raised this concern, his manager’s response was blunt: “Whatever coding you had to learn you learnt in college.

Now is the time to build and ship” (AIM).

This situation is particularly challenging for junior developers straight out of universities, who are handed AI tools directly.

Many are leaving companies early, either due to a lack of genuine skill development or being let go because their value became tied purely to AI tool usage rather than deep coding expertise (AIM).

As a Reddit user u/EdgeFamous377 on r/developersIndia succinctly put it,

“I think I’m writing less code… and companies seem weirdly okay with it.

Should we be worried?” (Reddit, u/EdgeFamous377).

The fear of being replaced by someone more proficient with these powerful AI coding tools is real.

The Debate: AI as Skill Erosion vs. Learning Accelerator

The Indian tech community is deeply divided on this issue.

Is AI a drug that erodes developer skills, or a catalyst that accelerates learning?

The Skill Erosion Argument

Dhravya Shah, creator of Supermemory, a universal memory layer for large language models, openly shares his gratitude for having learned to code before AI tools became ubiquitous.

He describes AI as being “like a drug.

It’s always there” (AIM).

Shah observed that his friends currently learning to code struggle to do it properly due precisely to the existence of these tools.

His warning is clear: without learning to code without AI, one risks eventually writing bad code (AIM).

This viewpoint highlights the critical need for strong coding fundamentals.

The Learning Accelerator Argument

On the other side of the debate stands Adithya S Kolavi, founder of CognitiveLabs.

He firmly believes that AI should not be seen as a barrier to learning.

In fact, he found that he “started learning more when I started using AI to code” (AIM).

Kolavi advocates for a proactive approach: even when using AI for coding, developers should “read every single line, go through the documentation and understand what exactly is happening” (AIM).

This active engagement transforms AI into a powerful learning partner.

He even noted that interns trying to write all code from scratch sometimes held back others using AI, underscoring the importance of knowing how to use AI tools effectively (AIM).

Expert Usage: Bridging the Divide

Adarsh Shirawalmath, founder of Tensoic AI and an SGLang developer, offers a nuanced perspective.

He observes junior developers often use AI coding tools without truly understanding the output code (AIM).

However, he also praises the masterful way some experienced engineers leverage AI, particularly on large projects, achieving exponential shipping speed (AIM).

This suggests that for experienced professionals with strong foundational knowledge, AI serves as an incredible force multiplier.

Senior developers often use AI, but they don’t trust it blindly.

They prompt, inspect, tweak, and ship, but are ready to take control and write code themselves if the AI “spews nonsense.” This shows the evolving nature of software engineering and the necessity for adapting developer skills.

Navigating the New Normal: A Developer’s Playbook for the AI Era

The path forward for Indian developers in this AI-driven landscape requires deliberate strategy.

Here’s an actionable playbook:

1. Cultivate Strong Fundamentals: Never compromise on foundational coding knowledge.

   Regardless of AI tools, a deep understanding of programming languages, data structures, algorithms, and software design principles is essential.

   This ensures you can critically evaluate AI output and troubleshoot effectively (AIM, Dhravya Shah, 2025).

2. Master Prompt Engineering: The ability to articulate complex problems and guide AI effectively is a crucial skill.

   Learning to craft precise, detailed prompts to get the best out of generative AI tools is now as important as writing code itself (AIM).

   This new skill is central to AI impact on jobs.

3. Critical Code Review: Do not blindly accept AI-generated code.

   Adopt a disciplined approach to inspecting, understanding, and refining every line.

   Treat AI as a powerful assistant, not a replacement for your expertise (AIM, Adithya S Kolavi, 2025).

4. Embrace Continuous Learning: The tech landscape is evolving rapidly.

   Stay updated not only on the latest AI tools but also on new programming paradigms, architectural patterns, and Human-AI Collaboration best practices.

5. Focus on Problem Solving: Shift your value proposition from mere code generation to the more complex aspects of software engineering: requirements gathering, goal definition, system design, and ensuring the solution actually works (Adam Wolff, X).
6. Advocate for Balanced Metrics: Engage with your organisation to ensure performance evaluations balance shipping speed and AI usage with indicators of deep understanding, critical thinking, and code quality.

   This can influence career development pathways.

Risks, Trade-offs, and Ethics: The Human Side of AI Coding

The widespread adoption of AI coding tools comes with inherent risks and ethical considerations that demand attention.

Skill Stagnation for Juniors

The most immediate risk is the potential for skill stagnation among junior developers.

When mandated to use AI tools for most tasks without structured guidance on fundamental coding, new entrants may struggle to build the deep problem-solving muscles required for truly complex engineering challenges (AIM).

This could lead to a less skilled future workforce.

Job Security Anxiety

Developers’ anxieties about job security are valid.

As companies increasingly track AI tool usage and prompt quality, the fear of being replaced by those primarily adept at prompting, rather than traditional coding, intensifies (AIM, Reddit, 2025).

This raises questions about the long-term viability of pure coding roles and the need for new definitions of developer value.

Ethical Oversight and Quality

Adam Wolff’s prediction that we “won’t bother to check generated code” carries an inherent risk.

Unchecked AI-generated code could introduce subtle bugs, security vulnerabilities, or ethical biases that are hard to detect without strong human oversight (Adam Wolff, X).

The responsibility to ensure quality and integrity remains firmly with the human engineer.

Retention Challenges

The frustration of limited learning opportunities can lead to increased employee churn.

Junior developers seeking to genuinely grow their programming skills may leave companies early if they feel their roles are diminishing into mere AI tool operators (AIM).

Companies must balance productivity gains with providing meaningful growth paths.

Ultimately, these are not just technical problems.

They have profound ethical and business implications for the Indian tech industry.

Responsible AI development is intrinsically linked to fostering an environment where developers can thrive, ensuring data integrity, ethical AI deployment, and sustainable talent growth.

Beyond the Code: Redefining Value and Career Paths

The discussions among Indian developers highlight a pivotal moment: the role of the software engineer is evolving.

It’s becoming less about the rote act of writing lines of code and more about architectural design, system integration, critical thinking, and human-AI collaboration.

The focus is shifting to “figuring out what to build and whether it’s working” (Adam Wolff, X).

This impacts career development significantly.

This means career paths will likely emphasize skills in complex problem-solving, system architecture, prompt engineering, and ethical AI deployment.

Developers who can articulate requirements clearly, understand system-level interactions, and effectively validate AI outputs will be highly valued.

This paradigm shift opens new avenues for professional growth, moving developers up the abstraction ladder.

Legacy code understanding also becomes crucial here.

FAQ: Your Quick Guide to AI Infrastructure

1. Q: Are Indian developers truly writing less code now?

   A: Yes, developers are reporting writing less code, not due to laziness, but because AI tools are integrated into workflows, enabling prompt-inspect-tweak-ship cycles.

   This is a common sentiment in the Indian tech community.

   (Ref: AIM, Reddit, 2025).

2. Q: How do companies measure developer performance when AI writes code?

   A: Companies are increasingly using dashboards to track prompt quality and the extent of AI tool usage by employees, shifting from traditional lines of code metrics.

   This indicates a move towards valuing efficient AI interaction.

   (Ref: AIM, 2025).

3. Q: Is learning to code without AI still important for new developers?

   A: Some experts argue that learning strong coding fundamentals without relying on AI is crucial.

   This foundational knowledge helps developers avoid writing bad code and enables them to take control when AI models produce errors or nonsensical output.

   (Ref: AIM, X, 2025).

4. Q: Can AI tools actually help developers learn more?

   A: Some believe AI can aid learning if developers diligently read every line of generated code, explore documentation, and understand the underlying logic rather than blindly accepting output.

   This approach turns AI into a learning partner.

   (Ref: AIM, 2025).

Conclusion: The Future of Coding: Adaptation, Not Replacement

Deepak, the junior developer, might still yearn for the days of pure, unassisted coding.

But the new reality is here.

The hum of AI tools will only grow louder, and ignoring it is not an option.

The fear of not coding enough is a valid one, reflecting a genuine concern for skill development and career trajectory in the Indian tech industry.

However, this isn’t necessarily a death knell for software engineering.

Instead, it marks a profound transformation.

In this evolving landscape, the infrastructure isn’t just hardware; it’s the foundational knowledge, critical thinking, and adaptability of the developers themselves.

AI gives you wings, but it also raises the bar.

Those who adapt, continually learn, and master the art of guiding AI, rather than being guided by it, will define the future of software development.

Let’s embrace this evolution, not with fear, but with a renewed commitment to continuous growth.

Glossary

• Edge Computing: Processing data closer to its source (e.g., factory floor, autonomous vehicle) rather than a centralized cloud or data center, to reduce latency.
• Data Fabric: An architecture that provides a single, unified view of data across diverse systems, breaking down silos and enabling consistent governance.
• Data Lakehouse: A hybrid data architecture that combines the low-cost storage of a data lake with the data management features and structure of a data warehouse.
• NVMe (Non-Volatile Memory Express): A high-performance storage interface that enables faster data transfer between enterprise and client systems and solid-state drives (SSDs).
• Zero-Trust Security: A security model that assumes no user, device, or application should be trusted by default, regardless of whether they are inside or outside the network perimeter.
• Immutable Storage: A data storage system designed to prevent the alteration or deletion of data once it has been written, ensuring data integrity and compliance.
• Generative AI: Artificial intelligence that can create new content, such as text, images, or code, rather than just analyzing or classifying existing data.
• DPDP Act (Digital Personal Data Protection Act, India): Legislation in India aimed at protecting the digital personal data of individuals, outlining requirements for data collection, processing, and storage.

References

• AIM. (2025). Indian Developers Fear They Aren’t Coding Enough Anymore.
• Reddit (u/EdgeFamous377). (2025). r/developersIndia post.
• X (Adam Wolff). (2025). Post on X.