VCs Expect 2026 to Bring Concentrated AI Spending as Enterprises Cut Experimentation

Venture capitalists who focus on enterprise software expect the era of broad AI experimentation to wind down in 2026, with companies increasing overall AI budgets but directing more of that spend to a smaller set of vendors.

TechCrunch surveyed 24 enterprise-focused venture capitalists and found a strong consensus: enterprises will boost AI spending next year, but they will concentrate those funds on tools and vendors that demonstrate clear, measurable results. Investors described a pending consolidation in enterprise buying patterns as organizations move from pilot projects to scaled deployments.

Andrew Ferguson, a vice president at Databricks Ventures, said enterprises are currently running multiple proofs of concept for the same use case and will begin to rationalize overlapping tools. "As enterprises see real proof points from AI, they’ll cut out some of the experimentation budget, rationalize overlapping tools and deploy that savings into the AI technologies that have delivered," Ferguson said.

Rob Biederman, a managing partner at Asymmetric Capital Partners, predicted a sharp narrowing of the vendor set that captures enterprise AI budgets. "Budgets will increase for a narrow set of AI products that clearly deliver results and will decline sharply for everything else," Biederman said. He added that this will create a bifurcation where a small number of vendors capture a disproportionate share of enterprise AI budgets while many others see revenue flatten or contract.

Several investors highlighted the categories where enterprises are likely to concentrate spend as they move away from broad experimentation.

• Safety, oversight and governance. Scott Beechuk, a partner at Norwest Venture Partners, said enterprises are recognizing the importance of safeguards and oversight that make AI dependable. He expects spending to rise on the capabilities that reduce operational and compliance risk and enable scaled deployments.
• Data foundations. Harsha Kapre, a director at Snowflake Ventures, forecast that strengthening data infrastructure will be a primary area of investment, as firms seek reliable inputs for models and easier integration across systems.
• Model post-training optimization and tool consolidation. Kapre also identified investments in model optimization after training and in reducing software-as-a-service sprawl through consolidation toward unified systems that lower integration costs and improve return on investment.

Investors framed this shift as a move from trial-and-error toward procurement discipline. Many enterprises, they said, are actively reducing overlapping SaaS subscriptions and building toward intelligent, integrated stacks that can demonstrate measurable returns.

That shift will have implications for startups seeking enterprise customers. Investors suggested that companies with durable competitive advantages — for example, vertical solutions or products built on proprietary data — are more likely to sustain growth during a consolidation phase. By contrast, startups offering products similar to those of large enterprise suppliers may find pilot opportunities and funding more scarce.

When asked how to identify a defensible business in AI, multiple VCs pointed to proprietary data and offerings that are difficult for large technology providers or large language model companies to replicate. Those attributes, investors said, are evidence of a moat that could protect revenue as enterprises narrow their vendor lists.

The net effect, according to the surveyed investors, could be that overall enterprise AI budgets grow while the number of vendors capturing meaningful shares of that spend shrinks. "We expect a bifurcation where a small number of vendors capture a disproportionate share of enterprise AI budgets while many others see revenue flatten or contract," Biederman said.

If those predictions hold, 2026 may be a year in which enterprises accelerate spending on AI broadly but channel more of that investment into a concentrated set of proven technologies and partners. For startups, the period could separate those with distinctive, hard-to-replicate propositions from the larger field of broadly similar offerings.

Key Topics

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