Venture Capital Intelligence Report
February 28, 2026 • Synthesizing insights from top-tier VCs
VCs are seeing mixed signals - strong enterprise demand for AI solutions but public market volatility creating downstream pressure. Most firms remain selective, focusing on companies with clear paths to profitability.
Series A+ rounds remain competitive for quality AI companies, but Series B+ seeing 30-40% valuation corrections from 2025 peaks. LPs demanding stronger unit economics and faster revenue growth.
Down-rounds becoming more common; VCs negotiating stronger liquidation preferences and board control. Pre-seed/Seed relatively stable due to lower absolute check sizes.
Enterprise AI adoption creating massive demand for model deployment, monitoring, and optimization tools. Companies spending 40-60% of AI budgets on infrastructure vs models.
Moving beyond chatbots to AI agents that can execute complex workflows in specific industries. Healthcare, legal, and finance showing strongest adoption.
IRA funding catalyzing private investment. Focus shifting from R&D to commercialization and grid-scale deployment of clean energy solutions.
Embedded finance becoming table stakes. Banks and fintechs need better APIs, compliance tools, and real-time payment infrastructure.
AI-native development tools creating 10x productivity gains. Companies willing to pay premium for tools that accelerate shipping velocity.
Traditional SaaS will be disrupted by AI-first applications that reduce human workflow steps by 80%+
Best companies combine multiple AI capabilities (vision, language, reasoning) rather than optimizing single use cases
Monolithic SaaS platforms will be disrupted by specialized API-first tools that integrate seamlessly
Healthcare AI moving from diagnostic assistance to autonomous care coordination and treatment planning
Software layer needed to orchestrate the $2T in climate hardware investments over next decade
Tools for monitoring, auditing, and ensuring AI system safety and regulatory compliance
EU AI Act enforcement beginning, enterprise risk management concerns growing
$50B+ market as AI adoption accelerates
Early signals from: Accel, Index
Companies to watch: Robust Intelligence, Arthur AI
AI systems that seamlessly combine text, voice, vision, and action capabilities
Model capabilities reaching threshold for reliable multimodal reasoning
Could replace traditional app interfaces entirely
Early signals from: Benchmark, Greylock
Companies to watch: Adept, Rabbit
Government and enterprise-owned AI computing and model infrastructure
Data sovereignty concerns, national security considerations, cost optimization
Hundreds of billions in government spending globally
Early signals from: General Catalyst, Bessemer
Companies to watch: Cerebras, SambaNova
AI-generated training data to overcome data scarcity and privacy constraints
Real data becoming scarce, privacy regulations tightening, model training costs rising
$20B+ market by 2028
Early signals from: Lightspeed, a16z
Companies to watch: Synthesis AI, Mostly AI
Previous: Red hot in 2021-2023 → Now: Limited new investment
User acquisition costs skyrocketing, platform dependency risks, regulatory scrutiny on data privacy
What Changed: iOS privacy changes, TikTok uncertainty, and maturation of creator monetization tools reduced TAM expansion opportunities
VCs Cautious: Benchmark, a16z, Lightspeed
Previous: Extremely hot 2021-2022 → Now: Selective interest in specific niches
Regulatory uncertainty, institutional adoption slower than expected, limited real-world utility beyond speculation
What Changed: FTX collapse, banking sector restrictions, and focus shifting to AI reduced crypto prioritization across most mainstream VCs
VCs Cautious: Sequoia, Kleiner, General Catalyst
Previous: Hot during pandemic → Now: Avoid unless exceptional
Customer acquisition costs unsustainable, supply chain challenges, return to in-person shopping
What Changed: iOS 14.5 privacy changes made Facebook/Instagram advertising less effective, increasing CAC by 30-50% across most DTC categories
VCs Cautious: Bessemer, Accel, Index
Don't build AI features - rebuild workflows around what AI makes possible
💡 Map existing manual processes, identify 10x improvement opportunities, then design AI-native solutions
— Sequoia Capital
Lead with ROI metrics, not technology sophistication
💡 Quantify time savings, cost reduction, or revenue increase in first customer meeting
— Bessemer Venture Partners
Use the smallest model that solves your problem reliably
💡 Start with fine-tuned small models, only scale to larger models when necessary for accuracy
— Index Ventures
Show profitability path within 18 months of funding
💡 Include detailed unit economics and timeline to cash flow positive in pitch deck
— Accel Partners
Deal volume down 25% YoY but average deal size up 40%. Quality companies still commanding premium valuations, but Series B+ seeing significant markdowns.
Series C • Lead: Lightspeed Venture Partners • Others: Google Ventures, Spark Capital
Validates continued massive investment in frontier AI models despite public market volatility
Foundation ModelsSeries B • Lead: Sequoia Capital • Others: a16z, Benchmark
Largest robotics round ever, signals VC confidence in physical AI applications
RoboticsSeries C • Lead: Kleiner Perkins • Others: Greylock Partners, General Catalyst
Shows vertical AI agents can command premium valuations with strong enterprise traction
Legal AIIPO • Key investors: Accel, Index Ventures, Founders Fund
AI infrastructure companies can achieve massive scale and profitability simultaneously
Secondary Sale • Key investors: Blackbird Ventures, General Catalyst
Consumer productivity tools with AI integration maintain premium valuations
Foundation model companies will become low-margin utilities within 3 years
Most VCs believe model providers will maintain pricing power and high margins
Reasoning: Open source models improving rapidly, inference costs dropping exponentially, differentiation becoming harder
Their Bet: Avoiding pure-play foundation model investments, focusing on application layer
European AI companies will outperform US peers in enterprise markets
US companies have insurmountable lead in AI innovation
Reasoning: GDPR compliance, data sovereignty requirements, and cost advantages favor European AI companies
Their Bet: Doubling down on European AI infrastructure and application companies
Climate tech software margins will exceed traditional SaaS
Climate tech inherently lower margin due to hardware dependencies
Reasoning: Urgency of climate transition allows premium pricing, switching costs extremely high
Their Bet: Leading Series A rounds for climate software companies at traditional SaaS multiples