What apps did to websites, AI will do to software': Financial advisor flags big risks for India

 The statement, “What apps did to websites, AI will do to software,” is not just a catchy analogy—it’s a profound forecast of the coming technological upheaval. To understand its implications for India, a nation uniquely positioned at the intersection of a massive digital transformation and a burgeoning tech ecosystem, we must first revisit the app revolution. The rise of smartphones and mobile applications did not just make websites mobile-friendly; they fundamentally rewired user behaviour, business models, and economic value chains. Websites were largely destinations; apps became immersive, always-on companions. They democratized services (banking, shopping, travel), unleashed the gig economy, and created trillion-dollar platforms. Crucially, they shifted power from traditional, desktop-centric incumbents to agile, mobile-first disruptors. Artificial Intelligence, particularly generative AI and autonomous agents, is poised to enact a similar paradigm shift, but this time, the target is the very concept of “software” as we know it. For India, with its vast population, strategic "Digital Public Infrastructure" (DPI), and ambitious economic goals, this shift presents both unprecedented opportunities and significant, systemic risks.

 A financial advisor, looking through the lens of capital allocation, market disruption, and risk management, would flag several critical areas of concern and transformation. 1. Deconstructing the Monolith: From Software Suites to AI-Driven Workflows. Traditional software—whether enterprise ERP systems, desktop tools, or even complex mobile apps—is built as a defined set of features. Users navigate these features to accomplish tasks. AI inverts this model. The software becomes an intelligent, conversational interface that orchestrates tasks across multiple legacy systems. The Indian Impact: Consider a small business owner in Indore. Today, she might use UPI for payments, a GST portal for filing, Tally for accounting, and WhatsApp for customer service. Tomorrow, an AI agent, built on India Stack’s open APIs (like Aadhaar, UPI, and the upcoming NDC), could handle all these workflows through simple voice or text commands in her local language. 

The value migrates from the individual software licenses to the AI layer that integrates them. This threatens the business models of traditional software vendors serving India’s MSME sector while creating winners in the AI orchestration and integration space. 2. The Commoditization of Code and the Talent Disruption Apps lowered the barrier to creating a digital presence (no need to build a full website from scratch). AI is dramatically lowering the barrier to creating functional software. With prompt-based code generation (GitHub Copilot, Devin), the act of writing routine code is being commoditised. The Big Risk for India: India’s $250 billion IT/ITES industry and its vast pool of engineers are built on the global demand for software development, maintenance, and testing. While high-level design, architecture, and problem-solving will remain crucial, a significant portion of routine coding and QA work is susceptible to automation. The financial risk is a potential erosion of the country’s flagship service export model unless a massive, rapid upskilling toward AI-augmented development, AI system design, and domain-specific AI specialisation occurs. 

The human capital challenge is monumental. 3. The Rise of Hyper-Personalisation and the Data Sovereignty Imperative. Apps delivered personalised experiences based on user data. AI will deliver hyper-personalised, adaptive outcomes. An edtech app today suggests courses; an AI tutor will dynamically alter its teaching style, pace, and content in real-time. A fintech app shows your portfolio; an AI financial advisor will continuously adjust it based on global news, your spending habits, and life events. The Indian Risk and Opportunity: This hyper-personalisation requires vast, sensitive datasets. India’s proposed Digital Personal Data Protection Act (DP DPA) 2023 provides a framework, but the real risk lies in the creation of AI “walled gardens” by global tech giants. If dominant AI platforms (foreign or domestic) control the personalisation layer, they could extract disproportionate economic value and influence. India’s strategic response—through its DPI—could be to mandate open access to AI building blocks, fostering a competitive ecosystem of Indian AI applications that personalise atop sovereign, consent-managed data rails.

4. The Shift in Competitive Moats: From Distribution to Intelligence. In the app era, the moat was often distribution (getting your app on the home screen) and network effects. For AI, the primary moat shifts to proprietary data, contextual relevance, and real-time execution capability. Implication for Indian Companies: A giant like Reliance Retail’s success with JioMart was not just its app; it was its integrated supply chain. In the AI era, this integrated data (from production to last-mile delivery) becomes the untouchable asset to train superior AI for inventory prediction, dynamic pricing, and personalised commerce. Conversely, a pure-play app with great UX but shallow data will be easily displaced by an AI agent that can functionally fulfil the user’s need more holistically.

 For Indian startups, the pitch must evolve from “We have an app for X” to “We own the unique data and domain logic for X.” 5. Cybersecurity and Systemic Fragility: An Exponential Threat Surface. Apps introduced new vulnerabilities (data leaks, insecure APIs). AI introduces existential ones. AI systems are not just targets; they can become adaptive adversaries. AI-powered phishing, deepfake-driven fraud, and automated vulnerability discovery are already realities. An AI managing critical infrastructure or financial markets could make catastrophic decisions at speed, or be subverted by other AIs. As India pushes for AI adoption in governance (healthcare, agriculture, security), the systemic risk multiplies. A cyber-attack on a monolithic software system is containable; an attack that poisons or takes over the AI models driving multiple public services is a national security threat. 

Financial advisors would flag that investments in AI companies must be heavily weighted toward those with robust, explainable, and secure AI practices. Regulatory frameworks must evolve from IT Act compliance to rigorous AI auditing and resilience testing. Conclusion: Navigating the Transition – A Strategic Imperative. The app-to-website shift created winners and losers over a decade. The AI-to-software shift will be more compressed and profound. For India, the risks are not just corporate but civilizational: the risk of missing the upskilling curve, of having its economic patterns dictated by foreign AI platforms, and of introducing systemic fragility into its digitally-led growth story. However, the same DPI vision that made UPI a global marvel provides a unique foundation. By treating AI not just as a product but as a public utility layer—with open models (like Bhashini for language), standardised protocols, and strong governance—India can attempt to orchestrate this transition towards inclusive growth. The financial advisor’s final flag is one of urgency and discernment. The investment thesis for the next decade must move beyond “software-as-a-service” to “intelligence-as-a-service.” The businesses that will define India’s future are not those that simply bolt an AI chatbot onto an old app, but those that reimagine their core function as an intelligent, ambient service, built on India’s unique data and demographic strengths, while rigorously hedging the profound risks this new era unleashes. The AI wave is coming; it will not just reshape software, but the very structure of India’s economy and its position in the global tech order.