Enterprise AI spending will increase in 2026 but flow to fewer vendors as companies shift from experimentation to consolidation, according to a TechCrunch survey of 24 enterprise-focused venture capitalists. The prediction signals a reckoning for AI startups lacking defensible moats as buyers rationalize overlapping tools and demand measurable returns on investment.
Andrew Ferguson, vice president at Databricks Ventures, characterized 2026 as the year enterprises start picking winners. "Today, enterprises are testing multiple tools for a single-use case, and there's an explosion of startups focused on certain buying centers like go-to-market, where it's extremely hard to discern differentiation even during proof of concepts," Ferguson explained. As enterprises see real proof points from AI, they will cut experimentation budgets, rationalize overlapping tools, and deploy savings into technologies that have delivered results.
The consolidation threatens startups offering products similar to capabilities from large enterprise suppliers like AWS or Salesforce. These companies may see pilot projects and funding dry up as enterprises concentrate spending with established vendors. When asked how they identify AI startups with defensible moats, multiple venture capitalists emphasized companies with proprietary data and products that cannot be easily replicated by tech giants or large language model companies.
Harsha Kapre, director at Snowflake Ventures, predicted enterprises will spend on AI in three distinct areas during 2026: strengthening data foundations, model post-training optimization, and consolidation of tools. "Chief investment officers are actively reducing software-as-a-service sprawl and moving toward unified, intelligent systems that lower integration costs and deliver measurable return on investment," Kapre stated. AI-enabled solutions are likely to benefit most from this shift.
The dynamics mirror the reckoning that hit SaaS startups several years ago. Companies operating hard-to-replicate products such as vertical solutions or those built on proprietary data will likely continue growing. Startups with generic horizontal capabilities face existential threats as enterprises consolidate around fewer, more comprehensive platforms.
Venky Ganesan, partner at Menlo Ventures, framed 2026 as the show me the money year for AI. "Enterprises will need to see real ROI in their spend, and countries need to see meaningful increases in productivity growth to keep the AI spend and infrastructure going," Ganesan told the publication. He predicted aggressive spending could bankrupt some major companies, though he also expects a major AI company to complete an initial public offering and GDP growth to increase by over 100 basis points in America.
James Brundage, leader of EY's global and Americas technology sector, said pragmatism will supplant optimism in 2026. "Boards will stop counting tokens and pilots and start counting dollars," Brundage explained, indicating the shift from measuring AI activity to measuring business outcomes.
The consolidation extends beyond vendor selection to how enterprises organize AI initiatives. Companies are moving from scattered pilot projects across departments to centralized AI strategies with clear governance and accountability. This organizational shift favors vendors that can serve multiple use cases rather than point solutions addressing narrow problems.
Vertical AI applications built on proprietary industry data emerge as one defensible category. Healthcare AI trained on clinical datasets, financial services AI with transaction history, and manufacturing AI with production data all possess advantages that generic models cannot replicate. These specialized applications justify premium pricing through demonstrable domain expertise.
The prediction carries implications for the venture capital ecosystem itself. Early-stage funds that deployed capital across numerous AI experiments may struggle to generate returns if their portfolio companies cannot secure follow-on funding or customer contracts. Growth-stage investors gain leverage to negotiate better terms as fewer startups demonstrate traction worth backing.
Enterprise buyers face pressure to make definitive vendor selections rather than maintaining optionality through multiple pilots. Chief information officers must balance the risk of betting on vendors that may not survive against the inefficiency of supporting redundant tools. This dynamic favors established players with broad product portfolios over startups with narrow capabilities, even when startup technology demonstrates superiority in specific functions.
