The Indian AI funding market hit $253 million in Q1 2026 — a 73% increase over the same quarter last year. That number sounds like validation. It is also a warning.

When capital floods a sector this fast, it funds both the genuinely transformative and the dangerously superficial. And in AI, the superficial can look almost identical to the real thing — at least for the first twelve months.

The Collapse Pattern Nobody Talks About

Sixty-three percent of Indian AI startups pivoted their core model in the past twelve months. That is not iteration. That is a structural problem with how these companies were built in the first place.

The failures did not announce themselves. Builder.ai was valued at $1.5 billion. It collapsed. Ola Krutrim, backed by one of India's most visible entrepreneurs, quietly took down its Kruti chatbot. CodeParrot peaked at $1,500 in monthly recurring revenue before shutting down. Each of these companies had a product demo, a funding round, and a plausible story about market opportunity.

What they did not have was a real business.

Two distinct failure waves. Wave One collapsed when foundation model costs fell. These were companies whose entire value proposition was access to an AI model — a thin application layer on top of an API provider. When model costs dropped and every competitor could access the same capability for less, the "product" evaporated overnight. No proprietary data. No switching costs. No reason for a customer to stay.

Wave Two is still unfolding. These companies were technically credible. Good engineers, solid architecture, real product thinking. They failed at enterprise sales. They could not articulate why a large organisation should trust them with critical workflows. They could not answer questions about data governance, audit trails, or what happens when the system gets something wrong.

The market is not punishing bad AI. It is punishing AI that cannot explain itself.

The Five Questions

Good diligence on an AI company — and by extension, good procurement diligence on an AI vendor — comes down to five questions.

1. Is the Market Real?

Not: is there a market for AI? Of course there is. The question is whether the specific problem this company is solving is large enough, acute enough, and currently underserved enough to justify the investment being made.

If a vendor cannot tell you precisely who their customer is, what those customers currently do instead, and why that alternative is painful enough to switch, the market thesis is incomplete.

2. Do the Unit Economics Work?

Revenue is not the number that matters. The question is: what does it cost to serve each customer, and does that cost go down or up as the company scales?

A business that charges enterprise pricing but has no path to margin improvement is not a software business. It is a services business with a software-shaped exterior.

3. Can the Team Execute?

The technical capability to build an AI system and the organisational capability to sell it into a risk-averse, procurement-heavy enterprise are almost entirely different skills.

Ask for reference customers — not logos, but conversations. Ask how long the sales cycle took. Ask what objections came up and how they were handled.

4. What Is the Moat?

This is the question where most AI vendors will give you a confident, polished answer that means nothing.

What does not constitute a moat in 2026: API access. Prompt engineering. First-mover advantage. A beautiful interface. These are features, not moats.

What does constitute a moat: Proprietary data that cannot be reconstructed from public sources. Deep workflow embedding that creates switching costs. Regulated-vertical positioning with genuine compliance infrastructure. Audit and governance infrastructure. Distribution — relationships with the buyers, not just the technology.

5. What Is the Exit?

For enterprise buyers, the question is: what happens if this company disappears in eighteen months? Can you extract your data? Are your workflows portable? Have you created a critical dependency on a system whose operational continuity you cannot guarantee?

Auditability Is the New Gross Margin

The new question that will define which AI companies survive the next twenty-four months is: if this AI system were audited tomorrow, could the company show how every output was produced?

This matters because enterprise AI is moving out of experimental pilots and into operational workflows. When AI touches a customer communication, a compliance decision, a legal document, or a financial calculation, the organisation that deployed it is accountable for the output.

The AI systems that will earn and retain enterprise trust are those that can show their work — that maintain clean, queryable records of what data was used, what logic was applied, what the system concluded, and why.

The way gross margin once told you whether a software business was real or illusory, auditability will tell you whether an AI deployment is sustainable or a liability waiting to surface.

What the Next Wave Rewards

The first wave of Indian AI startups rewarded speed. Get to market fast, raise a round, show ARR, raise again. The underlying business model could be sorted later.

It is not working now. The market has developed memory. Buyers who got burned by Wave One vendors — who watched their AI implementation become shelfware, or who got locked into a system they could not audit or exit — are approaching Wave Two with legitimate scepticism.

The next wave will reward defensibility: proprietary data moats, genuine workflow integration, regulated-vertical expertise, and the infrastructure to demonstrate accountability when the system is questioned.

If you are evaluating an AI vendor, run the five questions. Push hard on the moat analysis. And ask the auditability question directly: show me how you would reconstruct the reasoning behind this output six months from now.

The answer — or the inability to give one — will tell you most of what you need to know.