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India Is the World's Biggest AI Adopter. It's Also Nearly the Worst at Using It.

#Entrepreneurship #AI #Digital Marketing
8 min read
Raj Sharma
Raj Sharma Tech Entrepreneur & Digital Marketing Maverick

A founder I mentor in Hyderabad proudly told me last month his company had “fully deployed AI across all departments.” When I asked what that meant in practice, he said: “Everyone has a ChatGPT subscription.”

That right there is the gap I want to talk about today.

India’s Astonishing AI Paradox
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This week, two datasets dropped that, when you read them together, form one of the sharpest contradictions in the global tech story.

First, Deloitte published its State of AI in Enterprise report, dated March 22, 2026. The headline should have made every Indian entrepreneur proud: India leads the world in large-scale AI deployment across major business functions. 62% of Indian companies are deploying AI at scale in product development—versus a global average of 28%. Marketing and sales: 55%. Strategy and operations: 56%. Supply chain: 48%. By every measure of “have you switched on AI?”, India is first.

Then Anthropic released its March 2026 Economic Index, tracking actual Claude usage patterns across the globe. India’s per-capita AI usage index sits at roughly 0.2 to 0.36—compared to Israel’s 5 to 7 times the global average, and Singapore’s 4 to 5.5 times. We are in the bottom tier of depth while sitting at the top tier of declared adoption.

Translated into plain language: India’s businesses are buying AI. They are not learning it.

What “Power User” Actually Means
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Anthropic’s report introduced a specific term I want you to sit with: learning curves. The research documented a consistent pattern—users who have engaged with Claude for six or more months develop habits that qualitatively transform how they use the tool. Compared to newer users, 6-month-plus veterans have a 10% higher success rate in their conversations, work on tasks with 6% higher educational complexity, and spend 10% less time on personal or low-value queries.

That doesn’t sound dramatic until you understand what it means compounded across a year.

Power users don’t just write better prompts. They’ve restructured their workflows around AI. They’ve learned which tasks benefit from the most intelligent model tier and which don’t. They’ve built feedback loops that make each AI interaction more productive than the last. They’re attempting harder work with AI—and succeeding at it. The 9-12x speed-up Anthropic documents for complex, college-level tasks? That’s the power user operating in their lane. The casual user who types a question and reads the answer? They’re getting perhaps a 1.5x benefit, and mostly for simple things.

The gap between those two users is not about subscription level. It is not about access. It is about investment in depth.

A two-tier structure: the surface level showing rows of AI subscription icons with check marks, while beneath the surface a much smaller number of roots go deep into the ground — representing adoption vs. mastery.
India leads the world in checking the AI adoption box. The power-user depth tells a different story.

The Evidence Is Not Subtle
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Let me give you a concrete example of what happens when per-capita AI mastery gets high enough. Israel has the highest per-capita Claude usage of any country in the world—roughly 7x the global average. It is also the country that produced Maor Shlomo and Base44.

Shlomo built an AI-powered app-creation platform essentially solo, using Claude and the AI coding editor Cursor to write the vast majority of the technical work. He launched in late 2024. Three weeks later: 10,000 users and ₹8 crore (approximately $1 million) in annualised revenue. Six months later: 250,000 users, ₹29 crore in ARR, and Wix acquired Base44 for $80 million cash. With less than ten employees.

I am not saying every Indian founder needs to build the next Base44. I am saying that the relationship between a country’s depth of AI mastery and its capacity to produce outcomes like that is not accidental. When your population is using AI 7 times more intensely per capita than the global average, you produce founders who know what these tools can actually do. Israel doesn’t have better engineers than India. It has engineers who have spent more hours in genuine depth with AI—and the results compound.

At Meta, Zuckerberg has been tracking the same phenomenon internally. Power users of Meta’s AI coding tools report 80% productivity gains—not 80% more satisfied with their tools, but 80% more output. Average users are seeing 30%. The gap between a Meta power user and a casual adopter is wider than the gap between a 2024 developer and a 2022 developer.

The Uncomfortable Truth in the Deloitte Data
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Here is the part of the Deloitte report that most commentators skipped past. When Indian businesses were asked about their barriers to AI integration, cost came in at 12%. Infrastructure at 5%.

The top two barriers? Regulatory and compliance concerns at 39%. And resistance to change at 34%.

Read that again. Indian businesses are not failing to become AI power users because they can’t afford it or because the internet is too slow. They are failing because of organizational culture and risk aversion. The tools are bought. The subscriptions are active. The AI is switched on. But the actual workflow transformation—the deep, uncomfortable, iterative process of learning how to use AI as a genuine extension of your capabilities rather than a search engine upgrade—that work is not happening.

And this is precisely the 0-4% high-expertise figure that makes the 40%-deployment headline so hollow. Only 0-4% of Indian companies have high AI expertise, compared to a global average of 2-8%. We are not underperforming slightly. We are underperforming by a factor of two to eight against the world benchmark while simultaneously outperforming on deployment.

This is the equivalent of buying a state-of-the-art commercial kitchen and then using it to heat readymade meals.

The Compounding Problem
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The word Anthropic uses in their report that I keep returning to is “self-reinforcing.” Early adoption benefits are self-reinforcing. The 10% success rate advantage that 6-month users have over new users doesn’t stay at 10%. It compounds. Power users attempt harder tasks, succeed at more of them, build more sophisticated workflows, and pull further ahead every month.

The Anthropic CEO has warned that up to 50% of entry-level white-collar jobs could be automated within five years. India has the largest young workforce entering white-collar employment of any nation on earth. If those workers are not accumulating genuine AI fluency—not subscriptions, not occasional prompting, but the six-month compounding learning curve—the timing is genuinely dangerous.

For entrepreneurs, the business implication is simpler and more immediate. The competitor in your space who committed to becoming an AI power user eighteen months ago is not 18 months ahead of you in time. They are operating at a qualitatively different level—attempting harder problems, succeeding more often, and compressing the work of teams into the output of individuals. That gap does not close by buying a subscription.

What Jugaad Actually Requires Here
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I have built things with no money. I sold chai. I got fired seven times. The jugaad philosophy—doing more with less, finding a way when the obvious way is blocked—is not about cutting corners. It is about applying maximum intelligence to the actual constraint.

The actual constraint here is not access. India has access. The constraint is depth of engagement. And that is both the hardest and the most solvable problem, because it requires nothing but time and intentionality.

Here is what the path from subscriber to power user actually looks like, based on what I have watched work with the founders I advise:

Commit to one domain first. Pick the single area of your business where AI has the most to offer—content, customer research, financial modeling, code, whatever it is—and go deep in that area specifically. Not broad. Not “I’ll try AI for everything.” Deep.

Use the highest-capability model for your hardest problems. One of the documented behaviors of power users is that they consistently choose more intelligent models for more complex tasks. Most casual users use the default setting for everything. That’s like using a scalpel for every task because you only read about one type of knife.

Measure your output, not your prompts. The proxy metric casual users track is “did I get an answer?” Power users track “did this help me accomplish something I couldn’t have accomplished otherwise, or couldn’t have accomplished as fast?” Build that measurement habit from day one.

The businesses that will define Indian tech in 2030 are not going to be the ones with the most AI subscriptions. They will be the ones that ran the learning curve while everyone else checked the adoption box.

The subscriptions are table stakes. The six months of deep work? That’s the actual investment.

References:

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This article was created using artificial intelligence technology. While we strive for accuracy and provide valuable insights, readers should independently verify information and use their own judgment when making business decisions. The content may not reflect real-time market conditions or personal circumstances.

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#AI adoption #power users #India #startup productivity #AI strategy