While AI expands software's capabilities, vendors may not capture the value. Companies could use AI to build solutions in-house more cheaply. Furthermore, traditional "per-seat" pricing models are undermined when AI reduces the number of employees required, potentially shrinking revenue even as the software delivers more value.

AI is becoming the new UI, allowing users to generate bespoke interfaces for specific workflows on the fly. This fundamentally threatens the core value proposition of many SaaS companies, which is essentially selling a complex UX built on a database. The entire ecosystem will need to adapt.

The AI productivity boom is not lifting all tech stocks. Instead, it's negatively impacting traditional software companies. The market is pricing this in, with software ETFs like IGV breaking down technically even before earnings reports reflect the anticipated decline in business.

The lucrative maintenance and migration revenue streams for enterprise SaaS, which constitute up to 90% of software dollars, are under threat. AI agents and new systems are poised to aggressively shrink this market, severely impacting public SaaS companies' incremental revenue.

Wall Street believes AI is 'eating' software, causing stocks for giants like Salesforce and Oracle to plummet. AI tools like Anthropic's Claude Code, which can create software from simple prompts, threaten to undercut the value proposition of traditional Software-as-a-Service (SaaS) companies by democratizing and simplifying software creation.

Incumbent software vendors face a crisis: customers aren't churning, but all new enterprise budget is directed at AI. This traps legacy platforms as stagnant 'systems of record' while AI applications built on top capture all future growth.

Companies no longer need SaaS products that simply analyze their data. They can now apply large language models directly to their data stores (like Salesforce or SAP) to generate insights, rendering an entire category of software obsolete and collapsing its pricing power.

EQT's Arvind Kumar predicts software vulnerable to AI disruption is centered on rules-based logic that LLMs can easily replicate. He specifically identifies BI tools, coding platforms, and CRM as at-risk. Defensible software relies on regulatory complexity, proprietary data, and deeply embedded workflows.

While Salesforce seems difficult to disrupt externally, its large Fortune 500 customers have the resources to build their own tailored solutions using AI. They can bypass paying for a bloated software suite they only partially use, posing a significant "insourcing" risk.