Krupal Chaudhary

India doesn’t have a work ethic problem. It has a work worth problem. When Narayana Murthy defends a 72 hour work week by pointing to China’s 996 culture where people worked 9am to 9pm, 6 days a week, the debate becomes about hours. But the real issue isn’t hours. It’s value. China’s 996 worked because the system paid for those sacrifices: high salaries, fast growth, clear rewards. In India, the equation is different. We ask people to work like China, but pay them like it’s still 2003. We demand global output, but run on local inefficiencies. We glorify hustle, but ignore burnout until it collapses a team. The truth nobody wants to admit... Indians aren’t tired of work. They’re tired of work that extracts more than it returns. As a founder, I respect Narayana Murthy immensely. But I don’t want to build a company that survives on exhaustion. I want to build one that scales on clarity, autonomy, and meaningful work. India doesn’t need longer weeks. It needs systems that reward output, not presence. Hours don’t create excellence. Energy does. And nothing drains energy faster than mistaking sacrifice for progress.

Most people are reading the India-EU deal as cars, alcohol, tariffs. But that’s surface level. The real shift is happening in IT and digital services. And it’s subtle. We already know that Europe isn’t just another market but it’s the hardest market to earn trust in. If your product works there, it works anywhere. For years, Indian IT did three things really well: Cost efficiency Scale Execution But Europe doesn’t reward speed alone. It rewards compliance, privacy, ethics, and long term reliability. And that’s where this deal quietly changes the game. So here’s what actually flips for Indian IT companies 👇 📌 From vendor to partner Indian IT firms won’t just deliver projects anymore. They’ll co-build platforms, digital public infrastructure and AI systems with European enterprises. That’s a perception shift. And perception compounds. 📌 EU compliance becomes a superpower GDPR, AI regulation, data sovereignty are painful today. But once Indian companies master them, they become globally unbeatable. Because the US follows Europe and emerging markets follow whoever looks legitimate. 📌 Talent flows both ways This isn’t about brain drain but it’s about brain circulation. Indian engineers learn inside EU systems. European firms tap India for innovation, not just manpower. That’s how ecosystems mature. But this deal won’t help companies that still sell cheap hours, generic services and same as everyone else tech It rewards: >Product thinking IT >Compliancefirst SaaS >AI, cybersecurity, fintech, gov-tech >Companies that think globally from day one And if you can crack this for your business then this deal might be the biggest opportunity Indian IT has seen in years. What do you think?

A developer recently shared that he deleted 3 months of AI generated code. And that's a bigger warning than most companies realize. Right now, a lot of teams are measuring AI success like this: More code shipped Faster delivery Higher output And those things matter. But they can also hide a problem. Because software isn't just code. It's accumulated decisions. - Why this pattern? - Why this architecture? - Why this tradeoff? - Why this constraint? When developers make those decisions themselves, they build context. When AI makes them repeatedly, teams can slowly lose it. And the risk doesn't show up immediately, it shows up months later. When a critical system needs to evolve. When a key engineer leaves. When a customer asks for something the original design never anticipated. And suddenly nobody is confident in changing what already exists. That's why I think the real challenge with AI generated code isn't quality. It's knowledge transfer. The code gets written. But the reasoning often doesn't. And for agencies and businesses implementing AI, that changes how AI should be used. 📌 AI should generate implementation Humans should generate intent. The moment teams outsource intent, architecture starts drifting. 📌 Every generated system needs documented reasoning Not just what the code does. Why does it exist? Why was this approach chosen? And what tradeoffs were accepted. Future maintainability depends on those decisions being visible. 📌 Optimize for organizational memory Because codebases don't become fragile when code breaks. They become fragile when understanding disappears. At our AI and software development agency, we've found that the highest leverage use of AI isn't replacing engineers. It's helping engineers spend less time writing and more time thinking about systems, tradeoffs and outcomes. Because in the long run, code is easy to regenerate. Context isn't.