How AI Venture Capital Firms Build Moat-Based Portfolios

In a market where technological breakthroughs can quickly become commoditised, AI-focused venture capital firms are prioritising one thing above everything else: defensibility. Building resilient portfolios today is less about chasing innovation alone and more about backing startups that can sustain long-term competitive advantages.

This shift is visible in India as well. Venture capital investment in AI reached approximately $1.2 billion across 188 deals in 2025, reflecting strong momentum. However, alongside this growth, investors are becoming increasingly selective, placing greater emphasis on economic moats to navigate intensifying competition and global interconnectedness.

Moats in AI Investments

Traditional moats such as proprietary technology are becoming less durable in the AI era, where advancements are rapidly replicated. As a result, AI venture capital firms in India are adopting a more layered approach to defensibility, focusing on data advantages, domain expertise, regulatory barriers, and distribution strength.

AI-focused VCs typically evaluate early-stage startups across five key dimensions: data advantage (30%), model superiority (25%), product lock-in (20%), distribution (15%), and unit economics (10%). Across funded AI startups, average defensibility scores tend to hover around the mid-range, indicating significant room for differentiation.

Among these, proprietary data access has emerged as one of the strongest early moats. Data flywheels, where products continuously improve through real-world usage, create compounding advantages, particularly in vertical AI applications such as healthcare and financial services.

In India, investors are also building ‘trust-by-design’ moats, focusing on privacy, security, and multilingual capabilities. With over 800 million internet users, the Indian market offers a unique testing ground for scalable, globally relevant AI solutions.

Screening for Defensibility

Advances in AI are not just shaping startups, they are also transforming how venture capital firms in India operate. Tools like ChatGPT are helping investors compress weeks of due diligence into days, enabling faster and more data-driven decision-making.

Today, VCs prioritise factors such as intellectual property strength, clear technical differentiation, and access to exclusive datasets. Increasingly, funding is being structured around milestone-based outcomes, linking capital deployment to product performance and execution benchmarks.

There is also a sharper focus on avoiding over-reliance on commoditised, open-source models that could weaken long-term defensibility. Instead, firms are backing teams that demonstrate strong founder–market fit and the ability to execute consistently in competitive environments.

This is particularly relevant in enterprise AI, where long-term value is driven by workflow ownership, governance, and integration depth, factors that are difficult to replicate at scale.

India-Specific Strategies

In India, AI venture capital strategies are evolving in response to both opportunity and constraint. While funding for foundational AI models remains relatively limited, investors are increasingly focusing on sovereign infrastructure and sector-specific applications across BFSI, healthtech, and govtech.

Government-backed initiatives and incentives are also playing a role in strengthening ecosystem-level moats, particularly in areas such as deep tech and defence. These ‘walled garden’ environments often create structural advantages for domestic players.

At the same time, AI venture capital firms are moving away from broad, high-volume investment strategies. Instead of a ‘spray and pray’ approach, many are concentrating capital more aggressively, allocating a significant portion of funds to a smaller set of high-conviction bets. This allows for deeper support, faster scaling, and improved capital efficiency.

Portfolio Construction Tactics

At a portfolio level, VCs balance breadth in early-stage exposure with depth in follow-on investments, overweighting startups with stronger defensibility metrics to maximise long-term outcomes.

Key Moat Types Prioritised

• Data Gravity: Proprietary data loops that are difficult for competitors to replicate
• Platform Lock-in: High switching costs embedded within core workflows
• Brand and Trust: Critical in regulated sectors such as banking, insurance, and finance
• Execution Speed: Faster product-market fit driven by strong founder expertise

In an environment where AI capabilities are rapidly commoditising, these layers of defensibility are becoming the defining factor between short-term traction and long-term success.