Why India Hasn’t Built Its GPT Moment (Yet)

India has the world’s third-largest startup ecosystem, a thriving developer base, and a mobile-first population larger than the US and Europe combined. Yet, no GPT-4. No DeepMind. No Amazon-style platform. Why?

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Innovation Isn’t Accidental—It’s Engineered

The Zerodha Daily Brief recently asked why India hasn’t built a global product company like Apple. The key argument: India isn’t building for the world. It’s solving for local constraints, scale, and affordability—but global scale requires deep IP, design, and tech differentiation. It’s not just about software, it’s about systems thinking.

More importantly, it answers the question: Why do countries innovate? The answer isn’t just genius or ambition—it’s incentives and ecosystems. The U.S. Defense Department, for example, accounted for nearly 70% of federal R&D funding during the Cold War. China has pumped billions into semiconductors and AI with long-term national alignment. These aren’t short-term bets—they are strategic, deliberate acts of state-backed capitalism.

India, in contrast, spends less than 0.7% of its GDP on R&D—far below global innovation leaders. Until this changes, private startups alone can’t be expected to carry the weight of foundational research. A comprehensive breakdown of these enablers and impediments to innovation—ranging from catalytic leadership and policy regimes to talent and capital—is detailed in Table 1 of this policy paper by Sarthak Pradhan and Pranay Kotasthane.

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The Flywheel That Never Spun

In the US and China, tech giants built consumer businesses that became engines of innovation. Amazon didn't stop at e-commerce—it created AWS. Google scaled ads, then built TPUs, DeepMind, and open-sourced TensorFlow. Alibaba and Tencent invested in compute infra and national-scale AI labs.

This is the innovation flywheel:

Consumer Growth → Profit Flywheel → Infra Investment → Foundational Innovation

India’s flywheel spins only partially. We’ve nailed consumer growth. But the rest—profits, infra, and innovation—remains fragmented.

US/China tech ecosystems demonstrate a complete innovation cycle from growth to deep-tech.

India’s flywheel stalls after consumer growth, lacking reinvestment into infrastructure and foundational R&D.

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The Staircase Stall

India's top startups are still climbing the staircase to profitability. Without steady free cash flow, they can't reinvest in moonshots. US and Chinese firms reinvested into R&D, building compounding advantages.

The Walmart-Flipkart deal is a textbook example of where India’s climb stalled. Flipkart was the country's best shot at building an Amazon-style flywheel. It had the consumer base, data, logistics, and momentum. But when Walmart acquired it, strategic decision-making and IP moved abroad. Flipkart scaled consumption, but never birthed an AWS. The staircase ended at growth—without a leap into infrastructure or innovation.

However, there are signs of what the next step can look like. Ola has begun climbing further up this staircase—its push into electric vehicles and the recent launch of Krutrim, an Indian foundational AI model, signal intent to reinvest profits into infrastructure and deep-tech bets. It’s an early example of an Indian company trying to transition from consumer scale to ecosystem-scale innovation.

US/China startups scale all the way up to infrastructure and innovation layers.

India’s journey often halts at growth and early profitability, with fewer transitions to foundational tech bets.

But even among the few companies that reach the top of the staircase, the next leap—into deep-tech—is often derailed by structural constraints in capital control and strategic intent.

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Capital Yes, Control No

India has capital, but much of it is foreign and growth-driven. Foundational innovation demands patient capital—what some call "patient money"— and national alignment. Most Indian unicorns prioritize TAM expansion over deep tech bets. Paytm, for instance, scaled rapidly with strong VC backing, but has yet to convert that scale into long-term R&D or infrastructure plays—illustrating the challenge of capital without strategic control. Oyo follows a similar arc: once a poster child of India's startup boom, it raised billions in funding and expanded globally, yet has struggled to evolve into a platform company or reinvest meaningfully into foundational tech or infrastructure.

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The Role of India’s Incumbents: Missing in Action?

In the US and China, many of the foundational technology bets were either incubated within, or acquired by, large tech incumbents. In India, our industrial incumbents—Tata, Adani, Ambani—hold vast capital and influence but have largely stayed away from foundational tech plays in AI or semiconductors.

Reliance and Tata have made moves in digital infrastructure and EVs respectively, but not yet in frontier AI. Their scale, reach, and capital position them uniquely to fill the innovation gap—but we haven’t seen a Microsoft or Amazon-style moonshot from them yet.

It’s worth noting that similar patterns emerged elsewhere too. Japan and Korea built global hardware giants, but struggled to make the software leap. Europe, despite engineering excellence, hasn’t produced AI giants. The lesson? Without aggressive reinvestment by national champions, deep-tech ecosystems don’t just emerge—they must be built.

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Ice Cream, Chips & The Innovation Debate

Recent commentary from Commerce Minister Piyush Goyal reignited this debate. At Startup Maha Kumbh, he criticized Indian startups for focusing on grocery and food delivery instead of aiming for breakthroughs in AI, semiconductors, and EVs like their Chinese counterparts. His comments—"Do we have to make ice cream or chips?"—sparked intense reactions.

Leaders like Aadit Palicha (Zepto) and Mohandas Pai countered strongly. Palicha emphasized that building internet-first companies at scale is a necessary precondition to eventually fund deep tech. Pai questioned what structural support the government has actually put in place for semiconductor and AI startups.

In response to Goyal’s critique, Deep-tech founders highlighted bureaucratic hurdles, lack of funding clarity, and policy friction as key reasons why foundational innovation struggles in India. The message was clear: don’t just challenge entrepreneurs—enable them.

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Where Do We Go From Here?

1. Build Local Champions: Companies that scale and reinvest in infrastructure.

2. National Compute Infrastructure: AI needs access to GPUs, not just talent.

3. Shift from Valuation to Vision: Focus on compounding moats, not exit multiples.

4. Talent Repats + Open Source: Encourage diaspora and open ecosystem collaboration.

5. Policy as a Platform: Government must act not just as regulator, but as an enabler of deep-tech entrepreneurship.

India doesn’t lack the "what." It needs a shift in the "how" and "why."

Let’s stop waiting for a GPT moment. Let’s start building it.

From Stubborn to Smart: How I Learned to Use AI as a PM

Listen to the article in podcast format on PM-AI Diaries channel on Spotify!

Ever since I published "The Death of the Stubborn PM" back in February, my inbox has been buzzing with one big question: “Okay, I get that AI is the future for product managers—but how do I actually use it?” It’s a fair ask. In that piece, I argued that PMs who resist AI are doomed to fade away, like dinosaurs refusing to evolve. As I wrote, “The stubborn PM who clings to old ways will die out, replaced by those who harness AI’s power while leaning into what makes us human.” Now, people want the playbook. So, let’s walk through it with a story—my own journey of figuring this out, backed by some sharp insights from MIT Sloan’s "When Humans and AI Work Best Together—and When Each Is Better Alone".

The Wake-Up Call

Picture me a few months back: a PM buried in work, juggling a dozen tasks, and feeling like there weren’t enough hours in the day. Writing user stories, sketching ideas, tracking project timelines—it was endless. I was good at it, sure, but I was also exhausted. Then I wrote that article, and it hit me: if I was preaching adaptation, I’d better practice it. The MIT Sloan study I dug into sealed the deal. It showed that AI crushes it at repetitive, data-driven tasks, while humans shine in areas like emotional smarts and big-picture thinking. My workflows had to change.

Let AI Drive, You Steer

I started small, testing AI on the stuff that was dragging me down. Here’s what I found:

• Brainstorming Ideas: Need inspiration for a feature? AI can suggest ideas based on industry trends. Want a competitive analysis? AI will summarize reports in seconds.
• Writing Product Briefs? AI’s Got This. I needed a batch of user stories for a new feature. Normally, that’s an afternoon of brain-draining work. Instead, I plugged the basics into an AI tool, and bam—clean, usable drafts in minutes. “As a user, I want X so I can Y.” I’d tweak them a bit, but the slog was gone. Same with content—like drafting release notes or brainstorming taglines—AI churned out solid starters. Even sketches and inspiration images? AI whipped those up too, faster than I could grab a pencil.
• User Insights & Research Summaries: AI can sift through thousands of user reviews, feedback tickets, and survey responses to surface key trends. Instead of spending hours reading everything, you get clear, actionable insights.
• Visualizing Concepts: AI tools like Midjourney or DALL·E can create mockups from text prompts. No more waiting on designers for rough concepts—you can generate and test ideas instantly.
• Project Management Made Easy. I used to live in spreadsheets, chasing deadlines and nudging team members. Now, I have got AI which can automate meeting summaries, segregate action items and next steps, flag potential risks and even suggest adjustments. Think of it as your virtual Chief of Staff.

The MIT Sloan folks nailed it: “AI often outperforms humans in tasks that are repetitive or require processing large amounts of data.” For me, handing these off to AI was like shedding a heavy backpack—I could finally breathe.

Then there’s this sweet spot I stumbled into: working with AI. Take content creation—like writing a blog post (ahem, like this one). AI can spit out a rough draft fast, but it’s my voice, my tweaks, that make it sing. The MIT Sloan research calls this out: “Human-AI collaboration can be particularly powerful in creative tasks.” I’d let AI sketch the bones, then I’d flesh it out. Same with brainstorming—AI tossed out wild ideas, and I picked the gems. It’s not me vs. AI; it’s us, together, getting stuff done better.

Where I Stay in Charge (With a Little Help)

Not everything’s a job for AI, though. Some parts of being a PM are too human to outsource, and that’s where I leaned in:

• Emotional Quotient. Users were griping about a feature, and AI could analyze the feedback—count complaints, flag keywords. But figuring out what was really bugging them? That took me—reading between the lines, feeling their frustration, and knowing how to respond. AI gave me the data; I brought the heart.
• Decision Time. When it came to picking our next big move, AI handed me market trends and user stats. Cool, but deciding what aligned with our vision—or if it was even ethical? That was my call. No algorithm can wrestle with “should we?” like a human can.
• Heavy Context and Strategy. Market shifts were brewing, and AI spotted patterns in the noise. But tying that to our product’s story, our team’s goals, our users’ lives? That’s where my brain kicked in, weaving it all together into a plan.

The MIT Sloan study backs this up: “Humans excel in areas requiring emotional intelligence, creativity, and complex decision-making.” I didn’t ditch AI here—I used it as a wingman, feeding me insights to make my human skills sharper.

The New Me: A Smarter PM

Fast forward to today, my days look different now. AI handles the grind—user stories, content, project tracking, sketches—freeing me up to focus on users, strategy, and the big calls. I lean on AI for data and drafts, but I’m still the one steering the ship. I’m less frazzled, more creative, and honestly, having more fun. PM workflows needed to change, and this is how it’s working for me.

Your Turn: How to Use AI as a PM

So, here’s the straight-up advice, from my story to yours:

• Hand Off the Repetitive Stuff. Use AI for generating content, inspirations, images, sketches, user stories, and project management. It’s faster and doesn’t care about coffee breaks.
• Partner Up for Creativity. Team with AI on things like drafting or brainstorming—let it start, you finish. It’s a tag-team win.
• Lead with Your Human Edge. For emotional smarts, decisions, deep context, and strategy, take charge. Use AI to assist—data, insights, predictions—but you’re the boss.

The Bottom Line

Back in "The Death of the Stubborn PM", I said the future is for PMs who adapt. Now, I’ve lived it. AI isn’t here to replace us—it’s here to make us better. Let it sweat the small stuff, help with the creative, and back you up on the big stuff. That’s how you go from stubborn to smart—and trust me, it feels pretty darn good.

The Death of the Stubborn PM

Product Management is undergoing a seismic shift, much like programming did when compilers replaced assembly language or when Agile dismantled waterfall dogma. Stubborn PMs who cling to outdated rituals—like treating PRDs as sacred texts—will fade into irrelevance. The future belongs to those who embrace AI as a collaborator, not a threat.  

AI Will Disrupt the Tactics, Not the Thinking  

Historically, tools abstracted manual work: compilers automated code translation, A/B testing replaced gut-driven debates. Similarly, AI will automate tactical PM tasks—data aggregation, routine prioritization, even drafting specs. But this is liberating, not limiting.  

The stubborn PM obsesses over *how* to write a PRD; the adaptive PM focuses on *why* a product should exist. AI can’t replicate judgment calls that demand intuition: interpreting unmet customer needs, balancing ethics with growth, or navigating ambiguity when data is sparse. As AI handles execution, the PM’s role shifts to **curating problems worth solving**, not just prescribing solutions.  

The New PM Mindset  

1. Outcomes > Outputs: AI can generate feature ideas, but it can’t define what success looks like. PMs must obsess over outcomes (e.g., customer retention, not ticket counts).  

2. Human-Centered Strategy: AI reveals patterns, but empathy translates them into action. A chatbot can analyze feedback; a PM decides which pain point aligns with the product’s vision.  

3. Ethical Guardrails: AI might optimize for engagement, but PMs must ask, “*Should we?*”  

The Stubborn PM’s Downfall  

Resisting AI’s potential—or over-relying on it—spells disaster. Like programmers who dismissed high-level languages, PMs who conflate their value with documentation will be replaced by those who wield AI to sharpen their strategic edge.  

The future of Product Management isn’t about writing better PRDs. It’s about thinking harder.  

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Inspired by “The End of Programming as We Know It” (Tim O’Reilly) and “A Vision for Product Teams” (Marty Cagan). Both pieces remind us that progress favors adaptability, not stubbornness.

—This article was drafted with the assistance of multiple LLM tools.

Building Successful Products in the Maze of a Large Organization

 

*Image is generated using AI

Large organizations offer a treasure trove of resources and stability for product development. However, navigating their intricate landscapes can be a daunting task for product managers with innovative ideas. This guide empowers you, the young product champion, to navigate these complexities and build successful products within a large organization.

1. Charting Your Course: Building a Deep Understanding

Imagine a product launch party – everyone's excited, but celebrating different things. Stakeholders in a large organization often have diverse priorities and goals, which can lead to misalignment and missed opportunities. To ensure your product resonates with everyone, gain a deep understanding of their needs and objectives. For example, the marketing team might prioritize features that drive brand awareness, while the sales team focuses on functionalities that close deals. Align your product vision with these internal needs to ensure everyone celebrates the same success at launch. 

• Stakeholder Management: Juggling multiple stakeholders with varying priorities can feel like a three-ring circus. Effective communication is key. Go beyond formal meetings and scheduled presentations. Have informal conversations near the water cooler or in hallways to build rapport and understand their perspectives. This “hallway view” provides valuable insights that can strengthen your product roadmap.

2. Collaboration is King: Leverage the Collective Power

• Cross-Functional Teams: A large organization is like a well-stocked toolbox. You have design experts, engineering wizards, marketing gurus, and sales superstars. Build strong relationships with these teams. Don't just tell them what to do – collaborate! For example, involve designers early in the process to ensure a user-centric approach, and work with the sales team to understand customer pain points. This cross-functional collaboration fosters a cohesive product development process and a seamless user experience.
  
  

• Internal Communication: Silence breeds mistrust. Keep all stakeholders informed about your product's progress, challenges, and upcoming features. Regular updates through emails, internal wikis, or team meetings build trust and allow for proactive problem-solving.
  
  

3. Prioritization is Key: Focus on What Matters Most

• Data-Driven Decisions: Data is your compass in the complex landscape of a large organization. Leverage market research data to identify user needs and understand competitor offerings. Analyze user feedback to pinpoint areas for improvement. Utilize internal analytics to measure feature performance and prioritize functionalities based on their potential impact.
  
  

• Market Savvy: Don't get lost in the internal world – keep your eye on the external market. Conduct thorough market research to validate your product concept. Identify user needs that your product can address and ensure it offers a unique value proposition compared to competitors.
  
  

4. Embrace Agility: Adapt and Iterate

• Minimum Lovable Products (MLPs): Don't try to build the Taj Mahal in one go. Start with a Minimum Lovable Product (MLP). An MLP is a basic version of your product with core features that allows you to gather early user feedback and validate core assumptions. This approach minimizes development time and allows you to iterate quickly based on user insights.
  
  

• Embrace Experimentation: Don't be afraid to experiment with new ideas and functionalities. A culture of experimentation encourages innovation and helps you identify what works and what doesn't. For example, A/B test different design layouts or marketing messages to see which resonates best with users.
  
  

5. Be a Champion: Advocate for Your Product

• Passionate Vision: Be your product's biggest cheerleader! Clearly articulate the product vision, its unique value proposition, and the positive impact it will have on users and the company's bottom line. Speak with passion and conviction to ignite excitement and secure buy-in from stakeholders.
  
  

• Data-Driven Advocacy: Passion alone won't win the day. Back your vision with data and evidence. Use market research, user feedback, and internal data to demonstrate the potential of your product and secure the resources needed to bring it to life.
  
  

By following these steps, you can navigate the complexities of a large organization and build successful products that meet user needs, deliver business value, and contribute to the company's overall success.

Bonus Tip: Leverage existing processes and tools within the large organization. Many large companies have well-established product development processes and tools in place. Learn to utilize these resources effectively to streamline your product development journey.

8 Product Management lessons from the movie 'Gold'

The movie 'Gold' starring Akshay Kumar, is an inspirational story of how passion, determination and persistence can help one achieve the Impossible.

The character of Tapan Das, who was the Team Manager of Indian Hockey Team, is a fine example of an ideal Product Manager.

Listing few of the core responsibilities/skillset of an ideal PM with examples from the movie (Spoiler Alert! ):

1. Vision: The foremost responsibility of a PM is to set the vision for the team. During 1936 Olympics in Berlin, Tapan clearly set the vision for Gold medal as free India as a Goal and Vision.
2. Hustle: As a PM, you need to hustle to get things done. Tapan, hustled to meet Mr Wadia to get things done over and over again through out the movie.
3. Stakeholder Management: Aligning different stakeholders to your vision and progress is critical. Tapan did a brilliant job of doing that with Mr Wadia, Imtiaz, Samrat and other members of the team. Be it a royal blood like Raj Pratap Singh or a village boy like Himmat, he aligned everyone to a common objective over and over again through out the movie. Post Independence, when Mr Wadia asked him, "If the current team can win a Gold Medal?". Though he knew it was not possible at the moment. He told "Yes" and than worked towards making it happen. These are some of the most important quality that a PM needs to demonstrate on a daily basis.
4. Expert Advice: Whenever, you start on a new project, it's best to take expert advice. Tapan at the start of his journey reached out to Samrat, the hockey sensation and not only got the important directions on how to start, but also a mentor and believer, who helped when all the guards were down.
5. Extrinsic Factors: There are always external factors at play which is beyond our control. Only way to sail through them is persistence and determination. When the team was ready, India and Pakistan partition led to it to break. It was determination and persistence of Samrat and Tapan that helped them achieve so much, in such a little time.
6. Limited Resource: You are always sort of resources. Only way to succeed is to figure out ways to work around by thinking out of box and come up with innovative solutions. Tapan, when sort of funds, got a Buddhist Monastery for setting up National Hockey Camp.
7. Multiple Hats: As PM, one needs to wear multiple hats, like from resourcing to logistics to planning. Given the needs of hour, resource availability, one needs to fill in different roles. Same was depicted by Tapan, when he went out scouting talent to build a team or took up logistics for the team stay post independence.
8. Influence, don't enforce: As a PM, when you work with other stakeholders, it important to know your boundaries and figure out ways to influence them. You should be persistent but never enforce your opinions. You need believers. Like when Tapan wanted Himmat to play in the finals, his approach was to convince the Captain, Vice-Captain and the team. As a PM one needs to take this up on a daily basis with Tech, Business, UX, Operations, etc.

This was a fun attempt at explaining the role of Product Manager. Hope, you enjoyed reading this. Do comment!
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Did Snapchat CEO's comment on India being a poor country?

I would like to get the facts straight over here before we get more offended reading the headlines of all the major Indian bulletins.

Lets breakdown this incident:

• This is an allegation. There are no evidence yet to prove this yet.
• The allegation is made by an employee of Snapchat, Anthony Pompliano, who is currently engaged in lawsuit against Snapchat, accusing the company of misleading the investors by providing inflated statistics about user data.

Snapchat unredacted lawsuit by gmaddaus on Scribd

• According to the allegation, the statement was told in answer to a private company meeting, discussing the further expansion plan.
• India was ranked 126th in the world richest country. Total countries are 189. The ranking was created by adjusting GDP per capita to relative purchasing power. 
• Snapchat is a business. It earns money from advertising. The digital advertisement market of India is close to  Rs 7000 crore . Which roughly comes to $ 1 billion which is very low for the population of the country. It's 1/30th of China's digital advertisement market.

We as an Indian have few options here:

• Get offended, uninstall Snapchat, tweet, comment and troll Snapchat.
• Lets understand the facts about our nation. Learn why some countries are rich and some poor and work towards making our country great again by paying our taxes and electing the right candidate to run the country.

• Ignore it as there is nothing wrong with whatever being said.

And if you are feeling Rich then you can buy the snapchat spectacle over here



I have got lots of backlash for defending this. So i would like to add some more facts.
Approximately 23% of people in our country is below poverty line.
Moreover, India's per capita income (nominal) was $1,570 in 2013, ranked at 112th out of 164 countries by the World Bank, while its per capita income on purchasing power parity (PPP) basis was US$5,350, and ranked 106th. Other estimates for per capita Gross National Income and Gross Domestic Product vary by source.
Up to 95% of India still qualifies as poor or low-income, the vast majority of India's 1.2 billion citizens. For the globe, the equivalent proportion is 71%. As far as middle-income Indians go, only 2% of the country actually falls into this zone, compared to 13% of the globe, which is itself a disappointing number.

How different is Lalu Prasad Yadav and Donald Trump?

After my post on How similar is Lalu Prasad Yadav and Donal Trump? , I have been getting request to write a post on their difference. So lets see how different they are:

Money

Donald Trump has 300 times more money to screw up with as compared to Lalu Prasad Yadav.

The GDP of US is approximately 300 times of Bihar.

Power

Trump have control over nuclear weapons and can destroy the world while Lalu only had some rogues and destroyed just a state.

People

Trump have 3 times more lives at stake as compared to Lalu. Population of USA is approximately 3 times that of Bihar.

Comic

Lalu succeeded in making Bihar butt of all Jokes. Trump still has a great and enthralling task ahead of him. Lets all pray that he fails.

Ancestry

Lalu was born to a poor peasant family in Bihar. And his rise to power is a "rags to riches" story. While Donald Trump was born to a rich emigrant family in USA. Never the less his rise to power is more of "anything is possible" story.