Davos Is Looking at AI Backwards And Misunderstanding AI’s Most Important Market - India

Davos spoke about AI the way it always does: as efficiency, optimisation, polish on systems that already run smoothly. That lens is comfortable — and incomplete.

Making well-oiled economies 3–5% more efficient doesn’t change the centre of gravity. It compounds what already exists. The real discontinuity shows up elsewhere.

That place is India.

India is now the world’s fastest-growing large economy, with real GDP growth averaging ~6.5–7% over the last five years, even through global tightening cycles. It has ~65 million MSMEs, employs over 110 million people, and accounts for ~30% of GDP.

Less than 20% of Indian MSMEs have access to formal credit on reasonable terms. This is often framed as a documentation problem, a collateral problem, or a “financial inclusion” gap. That diagnosis is shallow.

The real issue is variance.

Small businesses in India don’t fail credit tests because they are unprofitable. They fail because their outcomes are unstable. Demand swings sharply by season, festival cycles, weather, local competition, and working-capital timing. Input costs move unpredictably. Collections are uneven. Quality depends on individuals rather than systems.

From a lender’s point of view, this variance is fatal.

Banks and NBFCs are not structurally set up to underwrite cash flows with high coefficient-of-variation, even if average profitability looks healthy. Credit models price tail risk, not mean outcomes. When volatility is high, interest rates spike, tenors shorten, collateral requirements rise — or credit disappears entirely.

This is why two businesses with identical EBITDA margins can face radically different access to capital. One is predictable. The other is not.

What AI changes is not the balance sheet — it changes the shape of the cash flow distribution. When demand is forecastable, throughput constraints are visible, failure modes are flagged early, and quality deviations are measurable, variance collapses before revenue meaningfully grows.

Once that happens, something counterintuitive occurs:Banks don’t need better promoters.Equity doesn’t need heroic growth stories.The same businesses suddenly look financeable. Even modest variance reduction — say a 20–30% reduction in cash-flow volatility — can have a larger impact on credit availability than a full percentage point increase in EBITDA margin.

This is why AI’s real leverage in India sits below the surface. It doesn’t make small businesses bigger first. It makes them legible. And legibility is what capital has always priced.

AI’s true role here is not automation. It is resolution.

When demand becomes forecastable, failure modes detectable, throughput measurable, and quality enforceable, the shape of cash flows changes. Businesses don’t need to reinvent themselves. They start being understood properly. Cash flows settle. Confidence builds. Capital begins to behave differently.

This is not a SaaS story, and it won’t look like a technology boom.

The most important AI outcomes in India will look almost boring: hospitals with steadier utilisation, manufacturers with fewer working-capital shocks, consumer businesses scaling without drama. Ordinary companies operating with unusual consistency.

As variance collapses, cost of capital falls. As capital reprices, duration extends. Scale becomes financeable.

By the time public markets recognise this pattern, the real work will already be done.

Disclaimer: In the article "Davos Is Looking at AI Backwards — And Misunderstanding AI’s Most Important Market" above - Any views, comments or communication (above or in the past) should not be construed to be investment advice by Alternative Growth (hereafter referred to as “AltG”) in any form whatsoever. AltG does not make an offer to sell or solicit to buy any securities.