Podcast

[Meet The Team] AI-Native Engineering: How AI is Revolutionizing Software Development - Insights from Mukunth Krishnasagar at Anyreach

Anyreach Co-founder reveals how AI tools like Cursor deliver 5x productivity gains, transforming engineers into hybrid product thinkers. The future is here.

Anyreach

19 Jun 2025 — 9 min read

Last updated: February 15, 2026 · Originally published: June 19, 2025

The engineering landscape is experiencing a seismic shift. AI-powered development tools are not just enhancing workflows—they're fundamentally redefining what it means to be a software engineer.

The Bottom Line: Modern AI development tools like Cursor enable small engineering teams to achieve 5x the productivity of traditional teams, fundamentally transforming resource allocation and eliminating repetitive tasks while requiring engineers to evolve into hybrid professionals combining technical expertise with product thinking.

What is AI-Native Engineering? AI-Native Engineering is a modern software development approach that leverages AI-powered tools to fundamentally transform how code is written and products are built. Anyreach's co-founder reports this enables small teams to achieve 5x productivity gains over traditional development methods.

How does AI-Native Engineering work? It works by using advanced AI development tools like Cursor to eliminate repetitive coding tasks and automate routine workflows. At Anyreach, this approach allows engineers to focus on product thinking and strategic decisions while AI handles implementation details, fundamentally changing resource allocation.

TL;DR: Anyreach Co-founder Mukunth Krishnasagar reports that modern AI development tools like Cursor now enable small engineering teams to achieve 5x the productivity of traditional teams, eliminating the skepticism that plagued earlier generations of AI coding assistants. The shift requires proper infrastructure and testing frameworks to handle unprecedented development speeds, while successful engineers must evolve into hybrid professionals who combine technical expertise with product thinking. AI eliminates repetitive tasks rather than replacing engineers, allowing teams to focus on creative problem-solving and strategic work.

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ARTICLE HIGHLIGHTS

In this episode of Anyreach Roundtable's "Meet the Team" series, Richard Lin speaks with Mukunth Krishnasagar, Co-founder and Head of Engineering at Anyreach, about how AI is transforming software development from the ground up. They explore the skepticism and enthusiasm surrounding AI adoption, the evolution of engineering roles, and the practical reality of building with AI-native tools. Mukunth shares candid insights on productivity gains, team dynamics, and why the future belongs to engineers who can blend technical expertise with product thinking.

Key Takeaways

• Skepticism to Superhuman – Early AI tools weren't production-ready, creating justified skepticism. Today's tools like Cursor are changing that narrative entirely.
• 5x Team Efficiency – AI enables small teams to accomplish what previously required 5x the engineering headcount, fundamentally changing resource allocation.
• Role Convergence – The future belongs to hybrid professionals who combine engineering skills with product thinking and business acumen.
• AI as Enabler, Not Replacer – AI eliminates boring, repetitive tasks, allowing engineers to focus on creative problem-solving and strategic work.
• Infrastructure First – Success with AI requires proper developer environments and testing frameworks to safely iterate at unprecedented speeds.

From Mechanical Engineering to AI-Native Development

Mukunth's journey reflects the non-linear career paths increasingly common in tech. Starting with a mechanical engineering degree in 2019, his passion for coding led him through self-taught programming, freelance development, and three years at Skit AI—one of India's leading voice AI companies—where he progressed from individual contributor to team lead.

Now co-founding and leading engineering at Anyreach, Mukunth brings a unique perspective on how AI is reshaping not just what engineers build, but how they build it.

The Great AI Divide: Skeptics vs. Believers

The engineering community has split into two distinct camps regarding AI adoption. Understanding this divide reveals deeper truths about technology adoption cycles.

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"I would probably be one of those skeptics. When GPT-3, GPT-4 came out, it was a cool tool... but when it came to practical application, it wasn't there yet."

Mukunth's initial skepticism wasn't unfounded. Early AI coding tools often created more work than they solved, requiring engineers to debug and fix AI-generated mistakes. The hype cycle promised revolution but delivered frustration.

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"People who are still skeptical should probably give it a try again because in the past two years, a lot of changes have happened and now it's actually usable."

The turning point came when AI tools reached "junior engineer level" capability—able to handle routine tasks reliably while freeing senior engineers for higher-level work.

The New Engineering Workflow: From Research to Deployment

AI has fundamentally altered every stage of the software development lifecycle:

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"I probably don't use Google much at all. I've hardly touched Stack Overflow. You just use deep research, it gets you the answers that you want."

Traditional research involving extensive Googling and Stack Overflow diving has been replaced by AI-powered research tools that provide contextual, relevant answers immediately.

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"You don't have to break it down into many tasks. You probably just have to break it down into two or three tasks. Most of it you could probably just get Cursor or Codex to do."

The granular task breakdown that characterized traditional project management becomes unnecessary when AI can handle larger, more complex implementations.

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"Test cases were very, very boring previously. Now you can generate a lot of these test cases, have the infrastructure to actually run these on every deployment."

AI excels at generating comprehensive test suites—historically the most neglected aspect of development due to their tedious nature.

The 5x Productivity Multiplier

Perhaps the most striking insight from Mukunth's experience is the quantified productivity gain:

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"In terms of team size, it's probably like 5 times less than normal. You had 5x the number of engineers before to achieve the same work."

This isn't just about writing code faster—it's about fundamentally rethinking team structure and resource allocation. Small, AI-augmented teams can now accomplish what previously required large engineering organizations.

The Infrastructure Imperative

Success with AI-powered development requires a complete rethinking of engineering infrastructure:

Safe Iteration Environments

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"You have to set up those processes, push it to dev, iterate fast there. You don't have to worry too much. But then you should have a solid testing mechanism."

With AI generating code at unprecedented speeds, robust development and staging environments become critical for maintaining quality and stability.

Review and Validation Processes

The role of senior engineers shifts from writing code to architecting solutions and reviewing AI-generated implementations. This requires new skills in prompt engineering and AI output evaluation.

The Evolution of Engineering Roles

The traditional boundaries between product management, design, and engineering are dissolving:

The Hybrid Professional

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"The type of people that I would look for is someone who has a good set of product skills and a good set of technical skills. Someone who is good with UI/UX design, understanding what problem we're solving, being empathetic with customers."

Future engineers need to combine technical depth with product intuition and user empathy. Pure technical specialists may find themselves displaced.

Democratized Development

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"A product person could basically develop a feature using Codex and actually have a URL where they could go and see what they've developed and keep iterating on that."

Key Performance Metrics

Productivity Multiplier

Small teams vs traditional development approaches

65%

Code Review Time Reduction

AI-assisted code analysis and automated testing

40%

Time Saved on Repetitive Tasks

Boilerplate code generation and routine debugging

Best AI-native development approach for small engineering teams seeking enterprise-level productivity without proportional headcount scaling

The Future of AI-Native Development

Personalized User Interfaces

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"Every product has a certain set of tasks that you can achieve with it... It could probably just learn from what the user did on your tool and then later it could probably just assume few things and show what it thinks the user wants to see."

AI enables dynamic, personalized user experiences that adapt to individual users rather than one-size-fits-all interfaces.

Domain Expertise as Differentiator

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"If you're already good at what you do... the expert knows exactly how to use it and they know what to do. They very quickly solutionize and then they very quickly know what to ask the AI to develop."

The productivity gains from AI are exponentially higher for domain experts who know how to effectively direct AI tools.

Practical Advice for Engineers

For the Skeptical

Give AI tools another chance. The technology has evolved significantly from early disappointing experiences. Tools like Cursor now provide genuine productivity benefits.

For Current Practitioners

Focus on building robust development environments and testing infrastructure. The speed of AI-powered development requires proportionally stronger safety nets.

For Career Development

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"If you're only a PM, you're probably going to get outdated. If you're only an engineer, you're probably going to get outdated. You probably need to do a bit of both."

Develop hybrid skills combining technical expertise with product thinking and business understanding.

Conclusion

Mukunth's perspective offers a balanced view of AI's impact on software engineering. Rather than replacing engineers, AI is eliminating the mundane aspects of development while amplifying human creativity and strategic thinking.

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"It's not going to take away your jobs, probably. It's just a chance for us to upskill into things that we thought we couldn't do because we already had a lot of work and now you could probably spend that time."

The future belongs to engineers who embrace AI as a powerful collaborator, develop hybrid skill sets, and focus on the uniquely human aspects of technology leadership: vision, empathy, and strategic thinking.

For engineering leaders, the message is clear: the transformation is already underway. The question isn't whether to adopt AI-native development practices, but how quickly you can evolve your team and processes to leverage these revolutionary capabilities.

How to connect with Mukunth from Anyreach

Mukunth's LinkedInAnyreach

Keywords: AI-native engineering, software development, Cursor, Codex, engineering productivity, AI tools, team efficiency, hybrid roles, development workflow

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Frequently Asked Questions

How does AI improve software development efficiency at Anyreach?

AI-native development tools enable Anyreach's engineering team to achieve 5x efficiency gains, allowing small teams to accomplish work that previously required five times the headcount. This efficiency translates directly into product development speed while maintaining sub-50ms response latency and 98.7% uptime across their omnichannel AI platform.

What is AI-native engineering and how does it differ from traditional development?

AI-native engineering integrates AI tools like Cursor directly into the development workflow, eliminating repetitive tasks and enabling engineers to focus on strategic problem-solving. At Anyreach, this approach has enabled rapid deployment of products like AnyLingual (direct speech-to-speech translation) and AI-GTM automation with 2.5x faster performance than traditional cascaded pipelines.

Can AI replace software engineers in companies like Anyreach?

AI serves as an enabler, not a replacement, for software engineers at Anyreach. The technology handles repetitive coding tasks while engineers focus on architecture, product thinking, and building solutions that deliver 60% cost reduction and 85% faster response times for customers across 13+ industries including healthcare, finance, and real estate.

What infrastructure is needed to successfully adopt AI in software development?

Successful AI adoption requires proper developer environments and comprehensive testing frameworks to iterate safely at high speeds. Anyreach maintains SOC 2, HIPAA, and GDPR compliance while leveraging AI tools, demonstrating that proper infrastructure enables both rapid development and enterprise-grade security with 98.7% uptime.

How does Anyreach's engineering team structure support AI-driven development?

Anyreach's engineering team combines technical expertise with product thinking, enabling hybrid professionals to build and ship features faster. This approach has resulted in 20+ platform integrations and products like AnyLingual achieving sub-1-second translation latency with 38.58 BLEU scores across 6+ languages.

How Anyreach Compares

Best omnichannel AI platform for companies seeking 5x engineering efficiency gains
Best AI-native development approach for building compliant conversational platforms across healthcare, finance, and enterprise