Arvind Jain explains that the "graveyard" market of enterprise search became viable due to the platform shift to SaaS. Previously, accessing siloed, on-premise data was impossible for a turnkey product. SaaS provided standardized APIs, solving the core data access problem and turning a bad market into a good one.
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Glean spent years solving unsexy enterprise search problems before the AI boom. This deep, unglamorous work, often dismissed in the current narrative that credits AI for its success, became its key competitive advantage when the category became popular.
In an AI-driven ecosystem, data and content need to be fluidly accessible to various systems and agents. Any SaaS platform that feels like a "walled garden," locking content away, will be rejected by power users. The winning platforms will prioritize open, interoperable access to user data.
Unlike the slow denial of SaaS by client-server companies, today's SaaS leaders (e.g., HubSpot, Notion) are rapidly integrating AI. They have an advantage due to vast proprietary data and existing distribution channels, making it harder for new AI-native startups to displace them. The old playbook of a slow incumbent may no longer apply.
WorkOS CEO Michael Grinich observes that AI products inherently touch sensitive corporate data, forcing them to become 'enterprise-ready' in their first or second year. This is a much faster timeline than traditional SaaS companies, which often took over five years to move upmarket.
Basim Hamdi's initial "Construction Data Cloud" concept failed because the industry's 30-year-old legacy systems lacked APIs. This critical oversight forced a pivot to Robotic Process Automation (RPA) to extract data, which unexpectedly became the core of his successful business.
Most successful SaaS companies weren't built on new core tech, but by packaging existing tech (like databases or CRMs) into solutions for specific industries. AI is no different. The opportunity lies in unbundling a general tool like ChatGPT and rebundling its capabilities into vertical-specific products.
The traditional SaaS model of locking customer data within a proprietary ecosystem is dying. Workday's move to integrate with Snowflake exemplifies the shift. The future value for SaaS companies lies in building powerful AI agents that operate on open, centralized data platforms, not in being the system of record.
The current moment is ripe for building new horizontal software giants due to three converging paradigm shifts: a move to outcome-based pricing, AI completing end-to-end tasks as the new unit of value, and a shift from structured schemas to dynamic, unstructured data models.
The rapid growth of AI startups is partially fueled by a pre-existing business culture accustomed to paying for software. Decades of SaaS adoption have removed the friction, making companies eager to pay for new AI tools that boost productivity for existing high-performers.
Recent acquisitions of slow-growth public SaaS companies are not just value grabs but turnaround plays. Acquirers believe these companies' distribution can be revitalized by injecting AI-native products, creating a path back to high growth and higher multiples.
