For a long time, Artificial Intelligence was spoken of as a “capability” — something a company could add on to make processes smarter, faster, or more efficient. That framing is now outdated.

Today, AI has shifted from being a tool to being the core operating system of modern businesses. Just as the internet redefined business models in the 1990s and cloud computing reshaped them in the 2010s, AI is now reshaping the DNA of companies across industries.

This shift isn’t theoretical — it’s visible in how the most promising startups are being built, scaled, and funded.

Why AI as “Core” Matters

1. Strategic Foundation, Not a Side Project
   Companies are no longer treating AI as an optional add-on. It is being designed into their products, pricing models, and customer journeys from day one.
   • Example: OpenAI built ChatGPT not as a “feature” but as a platform on which entire ecosystems (plugins, copilots, and vertical SaaS) are now thriving.
2. Moats Are Being Redefined
   The strongest defensibility now comes from data ownership, feedback loops, and AI infrastructure — not just distribution or brand.
   • Example: Glean (enterprise AI search) raised $200M+ at a unicorn valuation by leveraging proprietary organizational data, positioning itself as the “Google for enterprise knowledge.”
3. AI Drives Unit Economics
   AI is changing cost structures by automating tasks, personalizing at scale, and reducing marginal costs. Startups that embed AI in their core show better contribution margins and faster scalability.
   • Example: Zypp Electric (India) uses AI-powered fleet optimization for EV logistics. By reducing idle miles and predicting demand, they improve CM2 while keeping operating costs in check.
4. Governance as Core Strategy
   Bias, compliance, and privacy risks are no longer technical problems — they are boardroom issues. Startups that proactively integrate AI governance will be more attractive to institutional investors.
   • Example: Credo AI is building governance tools for enterprises to ensure AI models meet compliance and ethical standards, a space gaining strong VC attention.

 What Investors Should Look For

When evaluating startups today, the key questions have shifted:

• Data Moat: Does the startup have unique, defensible access to proprietary or hard-to-replicate data?
• Feedback Loops: Is there a mechanism for models to learn and improve continuously?
• Economic Lift: Can the AI materially improve margins, retention, or scale?
• Operational Readiness: Do they have MLOps in place, or is the AI still stuck in demo-land?

Startups that pass these filters are not “AI-enabled.” They are AI-first.

Case Studies from the Startup World

• HealthTech → Qure.ai (India)
  Uses AI for radiology diagnostics, reducing turnaround time for reports and making scans accessible in under-served geographies. Here, AI isn’t a “capability” — it is the diagnostic engine.
• FinTech → ZestMoney (before closure)
  Their failure underlines the opposite point: AI was used more as a front-facing credit scoring capability but lacked deep integration with underwriting risk management. Without AI as core infra, the moat was thin.
• RetailTech → Stitch Fix (US)
  Built its entire model around AI-driven personalization for fashion. Even though execution challenges remain, the company showed how AI can form the center of product, logistics, and consumer experience.
• DeepTech → Voltanova (BESS in India)
  In battery energy storage, AI-driven forecasting of demand and charge-discharge cycles is the core of efficiency and monetization. Without it, the hardware is commoditized.

The VC Perspective: Why This Shift Redefines Deal Flow

As venture investors, we need to recalibrate our screening lenses:

1. Market Thesis: Look for vertical AI platforms — healthcare, logistics, legal, agri-tech — where proprietary data creates durable moats.
2. Due Diligence: Go beyond demo decks. Ask for reproducible A/B tests, model refresh cadence, and cost-to-serve improvements.
3. Value Creation: Don’t just provide capital. Offer portfolio-wide shared services in AI/ML talent, MLOps infra, and governance frameworks.

The Takeaway

AI is no longer about adding a few smart features. It is about rewiring the company itself.

Startups that understand this are scaling faster, raising capital on stronger terms, and building moats that can last a decade. For founders, this means embedding AI into the architecture. For investors, it means changing how we evaluate, support, and govern.

When you look at your own business or portfolio, is AI at the periphery — or has it become the engine?