AI Is Turning Much of SaaS Into a Capital-Intensive Business

Conviction: 84% · Horizon: 2Y · 2026-05-01
Generic application software is losing its structural moat

AI lowers the cost of building software, compresses differentiation, and forces incumbents to spend heavily on compute, tokens, and product iteration just to defend existing revenue. Businesses without proprietary data, regulatory depth, or embedded execution risk weaker margins, lower cash conversion, and lower valuation multiples.

Software companies with proprietary data and zero-fault workflows can compound through AI

The strongest software businesses in the AI era will own unique data assets, sit inside mission-critical workflows, and monetize usage or execution rather than fragile seat-based access. In these cases AI improves throughput, customer value, and operating leverage instead of commoditizing the product.

Instrument Side Target Reason
3854.T Long The company is deeply embedded in customer operations, pairs software with customized implementation, and benefits from high retention and recurring revenue. Its workflow depth and industry know-how make AI an enhancement to customer value rather than a direct substitute, creating room for continued cash flow and margin expansion.
CLBT Long Digital forensics requires evidentiary integrity, auditability, and court-defensible outputs, which sharply limits the threat from generic AI tools. AI can instead increase analyst productivity on top of a trusted workflow, supporting stronger growth, operating leverage, and a potential valuation rerating.
RDVT Long The core asset is a proprietary identity graph built from hard-to-replicate data aggregation and refinement, while revenue is largely usage-based rather than seat-based. As automation spreads, query volume can rise, letting AI increase demand for the platform instead of eroding its moat.

Themes

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