Charting India’s Path in the AI Wave (Part 1)

I’m an Indian tech entrepreneur who has lived through the rise of IT services, the SaaS boom, and the generative AI revolution. India has shifted from back-office programming to building global products with each wave. Today, as AI transforms industries, I am both excited and uneasy. Excited because India has never been better positioned to innovate, uneasy because we face headwinds unique to our context. How can we ride this AI wave to our advantage? Here’s my first-hand take on what’s holding us back, what could propel us forward, and how we should navigate the journey.

Unique Challenges in India’s AI Journey

Every ecosystem has hurdles, but India’s AI aspirants grapple with a distinct set of challenges:

• High Infrastructure Costs (in USD): Training advanced AI models or running large-scale services requires cloud computing and hardware (GPUs, TPUs), which are priced mainly in US dollars. For Indian startups earning primarily in rupees, this is a serious pain point. The rupee recently hit record lows of around ?86.7 per US$ (The rupee slide spells both gains and challenges for SaaS firms | YourStory), so every dollar spent on AWS, Azure, or NVIDIA hardware makes a more significant dent in budgets. Unlike earlier IT waves (where a talented engineer in Bengaluru could be 1/3rd the cost of one in the Bay Area), in AI, the playing field of cloud/server costs is flat and pegged to the dollar. A young founder in Delhi pays the same $2.50 per hour to fine-tune a model on a cloud GPU as a founder in Palo Alto – but in rupee terms, that cost has risen steadily due to currency depreciation.
• “Instrument of AI” Gap (Infrastructure Access): Beyond cost, there’s the sheer availability of infrastructure. As NVIDIA’s South Asia head, Vishal Dhupar, aptly noted, “The biggest challenge for startups in AI is the lack of infrastructure… Without the instrument of AI, how do you do AI?” (AI startups: Infrastructure gap a key challenge for AI startups: Nvidia’s Vishal Dhupar - The Economic Times). While a well-funded U.S. lab can access clusters of hundreds of GPUs on demand, an Indian startup often finds itself in a queue or throttled on cloud credits. On-premise high-end computing is scarce. In short, our innovators are trying to build cutting-edge AI with limited tools. Until this gap is closed, it’s like asking someone to run a Formula 1 race with a go-kart.
• Data Access and Quality: In the AI race, data is fuel – and here we hit another speed bump. The world’s most powerful AI models are trained on mountains of data, which are much user-generated on Western platforms. Google and Meta can train models off billions of daily data points from their search, social, and chat products. By contrast, many Indian companies (and even our government) don’t sit on such troves of usable data. The result is a global imbalance: “Companies that can’t train their AI models due to lack of data access sit elsewhere: in Europe and the global south… This makes for a very uneven distribution of AI providers” (AI Regulation: A Realist Perspective). Despite its 1.4 billion people, India has not yet fully unlocked its data advantage for AI. Much of our data is either siloed, not digitized, or in the hands of foreign tech firms. The lack of large open datasets – especially in local languages and domain-specific areas – is a bottleneck for our AI startups. For example, a healthcare AI startup here might struggle to obtain diverse medical imaging data due to hospital privacy rules and fragmented records, whereas a U.S. counterpart could leverage established research data partnerships. We need data-sharing frameworks that allow innovation while respecting privacy. Until then, many Indian AI teams are “flying blind” with limited data or resorting to public datasets that might not reflect Indian realities (e.g., training speech AI on American-accented voices, then wondering why it struggles with Indian names).
• Currency Mismatch and Rupee Depreciation: This ties back to the cost issue – a weaker rupee is a double-edged sword for Indian tech. On the one hand, a strong dollar can boost export revenue (?1 crore in U.S. sales converts to more rupees today than it did a couple of years ago, which can help pay local expenses) (The rupee slide spells both gains and challenges for SaaS firms | YourStory). On the other hand, if your customers are in India or your costs are dollar-denominated, you’re squeezed. Many SaaS platforms (even Indian-origin ones) are priced in USD globally. So, an Indian business paying for an AI tool finds that last year’s $1,000/month fee (?80k) is now ?86k or more (The rupee slide spells both gains and challenges for SaaS firms | YourStory). It's not great for driving local AI adoption. Likewise, essential AI infrastructure or software licenses often must be imported/purchased in USD. The rupee’s slide means imported tech and talent get pricier for Indian startups. This isn’t a trivial issue – it affects everything from buying high-end Nvidia GPUs (priced in USD) to paying for API calls to an OpenAI or Google model. The exchange rate effectively taxes us. Startups that earn in dollars (by selling abroad) can offset this, but those focused on the Indian market feel the pinch.
• Talent and Brain-Drain Pressure: It might seem paradoxical because India’s tech talent pool is world-renowned – yet finding AI-specific talent is a real struggle. We produce hundreds of thousands of engineers, but only a tiny fraction have hands-on experience in machine learning research, large-scale data engineering, or cutting-edge AI deployment. The competition for skilled AI/ML engineers, data scientists, and research scientists is fierce globally. An early-stage startup can’t easily match the salaries a Google AI or a global hedge fund’s AI team would pay. We’ve seen a lot of top AI researchers of Indian origin, but many of them are working in Silicon Valley, London, or Toronto. This talent crunch is “especially acute for early-stage startups that lack resources to compete with established companies” (From Talent Scarcity To Data Quality: Challenges Facing AI Startups In 2025). The result: either you struggle to hire (and possibly compromise on quality or speed), or you hire fresh grads and spend months training them, which slows your time-to-market. Initiatives to nurture AI talent domestically – from specialized master's programs to industry-academic collaborations – are just beginning and must scale quickly. The good news is that a global Indian diaspora of AI experts exists; the challenge is attracting some of that expertise back into Indian projects (even if only as advisors or via remote contributions).

These challenges are very real – I’ve felt some of them first-hand. Yet, I’m optimistic because we’ve confronted “impossible” hurdles before and overcome them. Remember the skepticism around Indian companies building world-class products? A decade ago, many thought our startups couldn’t go beyond cost arbitrage and services. Today, that’s been proven wrong. In the same way, if we play our cards right, we can turn the current AI challenges into catalysts for innovation. How? By leveraging India’s unique strengths.

India’s Unique Advantages in the AI Era

If the above sounds doom and gloom, let me flip the coin. India also has inherent advantages in this AI race – strengths we can double down on:

• A Global Talent Network: While retaining top talent is challenging, we undeniably have a massive human capital advantage. India produces more engineers than most countries produce graduates. Our education system (for all its flaws) churns out problem solvers and mathematically trained minds at scale. And it’s not just quantity – quality is proven. Indians lead AI teams at Google, Microsoft, Meta, OpenAI – you name it. This means two things: first, we have a diaspora that can be tapped. It’s not far-fetched to imagine an Indian-origin researcher at Stanford or Facebook taking their expertise and starting a venture with operations in India. (This happens regularly now – see startups like Abacus.AI or Eightfold.ai, founded by Indians abroad but with India offices.) Two, culturally, Indians get tech – there’s widespread enthusiasm to learn and adapt. The number of Kaggle competition winners, top Stack Overflow contributors, and open-source maintainers from India is rising. So, even if we lack some experience in frontier AI research, we have a hungry base of young developers eager to catch up. Given the proper exposure and mentorship, they can surprise you. Personally, I’ve found that Indian engineers often excel at frugal innovation – finding clever solutions despite constraints – which is a handy skill in AI, where you often need to optimize code to run on fewer resources.
• Product-Building Experience: Ten years ago, skeptics would cite the lack of product culture if you asked whether India could build a globally successful software product. Today, names like Zoho, Freshworks, Postman, Druva, Zerodha, and many more have put that to rest. We’ve learned how to build products, not just projects. This matters for AI because success isn’t just about inventing an algorithm – it’s about packaging it into a product that users or businesses love. India’s new generation of founders has a playbook for this. We know how to do rapid iterations based on customer feedback, run efficient dev sprints, and scale a SaaS business. Culturally, we also value cost-efficiency as a core principle; Indian startups historically had to be scrappy (less access to easy capital in the early days), and DNA is powerful in the AI era where computing is costly. A study by Bessemer Venture Partners highlighted that “Indian SaaS companies already value efficiency on a cultural level; they can upstart and scale with less capital than startups elsewhere” (The Rise of SaaS in India 2023 - Bessemer Venture Partners). Companies like Zoho and Freshworks succeeded by building multi-product suites and cross-selling to customers at value prices (The Rise of SaaS in India 2023 - Bessemer Venture Partners). That experience in building and shipping products quickly and doing more with less is a huge edge. An Indian startup can find a clever workaround to deliver an AI solution cheaply, whereas a Silicon Valley one might throw money and compute at the problem. We’ve been forced to innovate in product building – now we can turn that into a competitive advantage.
• Distribution & Customer Knowledge: One thing Indian startups have honed is distribution – both globally and domestically. On the global front, we mastered the art of inside sales and content-driven marketing to sell software worldwide from a home base in India. Even 5 years ago, it wasn't uncommon to see a 50-person SaaS startup in Chennai with 90% of its customers in North America or Europe. Founders banded together to share tips on cracking global markets (communities like SaaSBoomi are a testament to this). The results? By 2022, roughly 30% of India’s tech unicorns were primarily targeting international markets (India Startups are (finally!) building Global Brands - Lightspeed Venture Partners) – meaning they went global early and successfully. On the domestic front, companies that build for India have learned how to scale to hundreds of millions of users in a challenging environment – low-bandwidth networks, many languages, and price-sensitive consumers. If you can get adoption in India, you can probably get it anywhere. Think of the India stack of digital infrastructure (Aadhaar, UPI payments, etc.) – our startups know how to leverage these for rapid user acquisition (eKYC in minutes, instant bank transfers, etc.). Distribution knowledge also includes a secret weapon: world-class customer service. Culturally, Indian firms often go the extra mile for customers. For instance, many Indian SaaS companies have won deals against U.S. competitors regarding price by offering superior support and customizations. One observer noted that “Indian software companies can compete and win globally by providing white-glove service… Freshworks, for example, leveraged large support teams in India to offer hands-on help to even mid-market US customers” (Do Indian startups have a right-to-win in robotics? – An Operator's Blog). This willingness to “do the unscalable” (like tailor solutions for each client, hand-hold deployments) gives us an edge in enterprise AI deals where clients might be intimidated by the tech. We turn support and services into a strength, not just a cost center. It’s something Western competitors, operating with higher labor costs, struggle to match.
• Diverse Data and Use-Cases: Yes, I lamented the lack of data access – but conversely, India itself is an incredible data goldmine waiting to be tapped. We’re a country of 1.4 billion people, 22 official languages, and dozens of industries. If we figure out how to gather and harness this data ethically, the variety and volume can fuel AI models that are truly world-class and inclusive. We have the opportunity to lead in areas like multilingual AI (because an AI assistant that works in India’s linguistic cacophony will work pretty much anywhere), low-resource AI (apps that run on $100 Android phones of the kind hundreds of millions of Indians use), and verticals like agriculture, fintech, and healthcare for the developing world. Our constraints and diversity force us to innovate in ways a homogenous market doesn’t. For example, an Indian fintech AI startup dealing with thin credit files and multiple regional languages might develop alternate credit scoring algorithms or OCR tech that could be revolutionary in other emerging markets. In short, India’s complexity is a training ground for robust AI solutions. If we embrace this and gather domain-specific data (with consent and privacy), we can create AI products that global companies find hard to build or even conceive.

None of these advantages are automatic trump cards – we must consciously leverage them. But they give me confidence that India’s AI story can be unique and impactful, not just a derivative of Silicon Valley’s. And we’re already seeing glimpses of success that point the way.

Beyond Cost Arbitrage: Compete on Value, Innovation, and Scale

Historically, India’s tech success (especially in IT services and even early SaaS) was built on cost arbitrage – essentially being cheaper than Western counterparts. Labor arbitrage gave Indian IT companies (Infosys and Wipros) their big break. Even product companies like Zoho and Wingify benefited from being able to undercut competitors on price while still maintaining margins due to lower costs in India (Do Indian startups have a right-to-win in robotics? – An Operator's Blog). However, I would argue that?cost arbitrage alone is not a sustainable strategy in the AI era. The gap is closing: salaries for top engineers in Bangalore are not far off from those in California now, significantly when adjusted for experience and quality (Do Indian startups have a right-to-win in robotics? – An Operator's Blog). And regarding AI infrastructure, everyone pays the same provider rates as discussed. In some cases, running an AI startup in India could be?more?expensive due to inefficiencies (import duties on hardware, higher interest rates for capital, etc.). So, while efficiency remains crucial, “we’re cheap, deal with us” cannot be the main pitch going forward.

Does that mean India’s famed cost advantage is dead? Not precisely – it’s just shifting. Instead of pure cost arbitrage, I see efficiency arbitrage and scale arbitrage as the new play:

• Efficiency arbitrage: This is about doing more with less – something Indian startups excel at culturally. As cited earlier, Indian SaaS firms often operate with leaner burns and still grow well (The Rise of SaaS in India 2023 - Bessemer Venture Partners). In AI, this might mean finding creative ways to reduce compute costs (like optimizing code or using knowledge distillation to run models with fewer resources) or leveraging a hybrid global team (e.g., a small client-facing team abroad with an engineering powerhouse in India) to balance costs. Our ability to stretch a dollar can attract investment and customers – especially in a post-2022 world where investors care about profitability. Bessemer’s report predicted that this efficiency will aid Indian companies on the path to global leadership (The Rise of SaaS in India 2023 - Bessemer Venture Partners). I’ve already seen AI startups here use clever tricks – like scheduling training jobs during off-peak cloud hours for cheaper rates or using India’s lower-cost electricity in their own data centers – to save money. It’s penny-pinching, but it adds up to a strategic edge at scale.
• Superior customer experience: I touched on this in advantages, but it’s worth reiterating as a conscious strategy. Many Western product companies hesitate to provide heavy customization or extensive support – they build one-size software. Indian companies can fill that gap by pairing products with a services layer. For AI, this is huge: enterprises might be afraid to adopt an AI solution without hand-holding. An Indian firm can say, “We’ll not only sell you the model, we’ll integrate it for you, train your staff, tweak it for your needs, and be on call 24/7.” That white-glove approach (Do Indian startups have a right-to-win in robotics? – An Operator's Blog) wins deals. It’s not as sexy as a pure self-serve SaaS model, but it works, and Indian labor costs make it viable at scale. We turn our workforce size into a feature, not a bug. We’ve seen how this helped SaaS companies win mid-market clients. This can be a make-or-break factor in AI, where trust is paramount. I believe service could become an India-based AI company’s competitive moat. Once customers are used to your high-touch support, they’ll be reluctant to switch to a faceless competitor, even if the core algorithms are similar.
• Domain and context specialization: Instead of trying to be everything for everyone, Indian AI startups can leverage deep domain knowledge in areas others overlook. For instance, consider vernacular AI – building NLP models that excel at Hindi, Tamil, Bengali, etc. The big AI labs focus on English, Mandarin, and maybe a bit of French/Spanish. There is an opportunity to be the world’s best AI for low-resource languages (we have dozens to practice on). Companies like Reverie (language tech) and AI4Bharat (a government-backed NLP mission) are examples of pushing in this direction. Similarly, for agriculture, who better than Indian agritech startups – dealing with millions of smallholder farmers – to build AI models for crop disease detection or yield prediction? They have ground truth data and an understanding of farming constraints that a Stanford lab might not. If we pursue these niches – which are huge markets when you consider all developing countries – we won’t compete head-on with Google’s or OpenAI’s models; we’ll beat them in corners they haven’t optimized for. This strategy requires investing in gathering proprietary data (e.g., an agritech might collect drone images of Indian farms, or a speech tech firm might compile audio of diverse Indian dialects) – but that becomes your moat. It’s not cost arbitrage; it’s context arbitrage (you have the context others lack).
• Original IP and R&D: Traditionally, India hasn’t been known for breakthrough research in AI – our strength was implementation. But going forward, we should aim to create differentiated intellectual property. This means encouraging more research labs, publishing papers, filing patents, and developing novel algorithms that solve problems in new ways. Admittedly, this is a long game – you can’t flip a switch and have a dozen Geoffrey Hintons appear. But we can incentivize PhDs to come back from abroad (or not leave at all) by creating AI research hubs in Bengaluru, Hyderabad, etc., possibly funded by a mix of government and industry. The payoff is that Indian companies have homegrown tech that can outperform or undercut foreign tech. For example, if an Indian team invents a new model compression technique that makes models 5x faster on commodity hardware, that’s IP that can be embedded in all our products, giving an edge globally (and that IP can itself be licensed out). I think of it this way: cost arbitrage shrinks over time, but intellectual arbitrage can grow. The more unique know-how we accumulate, the stronger our position. We’ve started seeing signs of research focus – the government’s AI mission explicitly talks about funding foundational AI research – but the industry needs to chip in, too. Indian tech giants and unicorns could do more to fund AI fellowships, open-source projects, and research competitions locally. It’s an investment in the future “brain trust” of India.

In summary, competing on value (not just price) and innovation (not just labor quantity) is the way forward. The good news is that our tech ecosystem is maturing in that direction. Many new startups I meet aren’t positioning themselves as “cheaper than X”. Instead, they say, “We solve Y problem which others haven’t cracked,” or “We add value through service/vertical integration.” That’s heartening. Cost will always matter (we’re a frugal lot, and that’s good), but it shouldn’t be our primary differentiator in AI. Let’s leave the pure cost arbitrage model to the past and focus on unique value arbitrage.