Venture Capital Intelligence Report
May 07, 2026 • Synthesizing insights from top-tier VCs
VCs are seeing strong fundamentals in AI and enterprise software but remain cautious on valuations after the recent correction. Focus has shifted to companies with clear paths to profitability and defensible moats.
Flight to quality continues with mega-rounds concentrated among proven teams and clear revenue models. Seed funding remains robust for AI infrastructure and vertical AI applications.
Down-rounds becoming normalized for 2021-2022 vintage companies. New deals pricing at 30-40% below peak valuations but still elevated vs pre-2020 levels.
The picks-and-shovels play for AI remains compelling as model training costs continue rising and enterprises need better inference infrastructure
AI copilots and automation tools for specific industries showing strong PMF and defensible data moats
Next-gen dev tools powered by AI showing early traction, but market timing questions remain
Policy tailwinds from IRA and corporate commitments creating sustainable demand for climate solutions
Companies building AI-native workflows from the ground up will displace incumbents who are retrofitting AI features
Post-ZIRP era demands fundamentally different company building - growth efficiency over pure growth
Europe's regulatory-first approach and domain expertise creates opportunities for B2B AI leaders
Tools and techniques to make AI models smaller, faster, and cheaper to run
Model costs becoming prohibitive for widespread deployment, driving demand for efficiency
$50B+ market as AI deployment scales
Early signals from: Kleiner Perkins, Greylock
Companies to watch: OctoML, Modular, Replicate
AI-powered compliance and regulatory reporting tools for complex regulations
Increasing regulatory complexity across AI, data privacy, and financial services
$25B market by 2028
Early signals from: Bessemer, Accel
Companies to watch: DataSnipper, Comply Advantage, Ayasdi
Previous: Red hot in 2020-2021 with multiple $1B+ rounds → Now: Limited new investment outside of AI-native apps
User acquisition costs spiking, Apple ATT impact, and platform risk concerns
What Changed: Shift from growth-at-all-costs to sustainable unit economics
VCs Cautious: Benchmark, a16z, Lightspeed
Previous: Peak buzz in 2021-2022 with massive rounds → Now: Selective investment in proven teams and clear use cases
Regulatory overhang and failed consumer adoption of most DeFi/NFT products
What Changed: Focus narrowed to payments, stablecoins, and institutional infrastructure
VCs Cautious: Sequoia, Accel, General Catalyst
Focus on inference costs, not just training - they'll dominate your P&L at scale
💡 Build cost optimization into your architecture from day one, not as an afterthought
— Benchmark
AI adoption in large enterprises taking 12-18 months vs 6-9 months pre-2024
💡 Plan for longer sales cycles but higher ACVs - enterprises buying fewer, larger solutions
— General Catalyst
Pure AI wrappers around OpenAI/Anthropic are struggling to raise Series A
💡 Build proprietary data flywheels or novel architectures for defensibility
— Lightspeed
Deal volume down 40% YoY but average deal sizes up 25% as VCs concentrate on highest conviction bets
Series C • Lead: Google • Others: Spark Capital, General Catalyst
Validates continued mega-investment in frontier AI models despite market correction
Foundation ModelsSeries F • Lead: Accel • Others: a16z, Index
Data infrastructure for AI training becomes critical bottleneck and valuable moat
AI InfrastructureIPO • Key investors: Kleiner Perkins, Index, Greylock
Design tools with AI features command premium valuations in public markets
Most AI startups are building features, not companies
Market believes AI will create many new unicorns
Reasoning: True differentiation requires proprietary data or novel architectures, which few possess
Their Bet: Investing in vertical software companies using AI as a tool, not AI-first companies
European enterprise software will outperform Silicon Valley in next cycle
US maintains software leadership
Reasoning: Regulatory complexity and privacy requirements favor European companies globally
Their Bet: Doubling down on European B2B investments with global ambitions
First $100B+ AI infrastructure company will emerge by 2027
HIGHSequoia Capital • Timeframe: Next 12-18 months
Implications: Validates AI as platform shift comparable to cloud/mobile
50%+ of enterprise software vendors will be acquired by 2028
MEDIUMa16z • Timeframe: 24 months
Implications: Massive consolidation as AI reshapes competitive landscape
Regulatory compliance will become $100B+ software category
HIGHIndex Ventures • Timeframe: 36 months
Implications: RegTech becomes as important as FinTech for enterprise budgets
Determines viability of AI application business models
Costs continue falling rapidly, enabling broader AI deployment
Costs plateau, limiting AI application scalability
Indicates speed of enterprise AI adoption
Enterprises streamline AI vendor evaluation and deployment
Risk aversion and long evaluation cycles slow adoption