Venture Capital Intelligence Report
December 31, 2025 • Synthesizing insights from top-tier VCs
VCs are increasingly selective, focusing on companies with clear paths to profitability. The era of growth-at-any-cost is definitively over, with emphasis shifting to unit economics and sustainable business models.
Series A+ rounds seeing 40-60% longer close times. Seed still competitive for AI infrastructure, but Series B/C experiencing significant valuation compression. Flight to quality evident across all stages.
Public market multiples compressing (SaaS trading at 6-8x revenue vs 15x+ in 2021). Late-stage private valuations down 30-50% from peak, but seed/Series A holding steady for AI-first companies.
The picks-and-shovels play for the AI gold rush. Infrastructure layer seeing massive demand as enterprises move from experimentation to production deployment.
AI-native software for specific industries, offering 10x better solutions than incumbents by embedding AI deeply into workflows.
Physical infrastructure for the energy transition is finally investable at scale, with clear policy tailwinds and corporate demand.
AI-augmented development tools creating massive productivity gains. The next generation of developer experience is being built now.
Next-gen financial rails for AI-first businesses, embedded finance, and real-time payments infrastructure.
We're in the early innings of a massive infrastructure cycle. Companies building the picks and shovels for AI will capture disproportionate value.
The biggest opportunities are in applying AI to transform existing industries, not building AI for AI's sake.
Europe's regulatory-first approach to AI gives European startups an advantage in healthcare, finance, and other regulated sectors.
Best SaaS companies are returning to focusing on core metrics: ARR growth, net retention, and path to profitability.
Autonomous AI agents that can complete multi-step business tasks with minimal human oversight
LLMs finally reliable enough for production use, and businesses desperate for productivity gains
$50B+ TAM in process automation
Early signals from: Greylock, Lightspeed, General Catalyst
Companies to watch: Adept, Embra, Lindy
Technology to help businesses and communities adapt to climate change impacts rather than just prevent them
Climate impacts accelerating faster than mitigation efforts, creating massive adaptation needs
$1T+ adaptation market by 2030
Early signals from: Breakthrough Energy, Lowercarbon Capital
Companies to watch: Jupiter, Cervest, Climate Engine
Backend services and tools for AR/VR and mixed reality applications
Apple Vision Pro and Meta's continued investment creating developer ecosystem
$100B+ by 2030
Early signals from: a16z, Kleiner Perkins
Companies to watch: Niantic, 8th Wall, Clay AIR
AI-first drug discovery and development platforms that can dramatically reduce time and cost
AI models now sophisticated enough to predict molecular behavior with high accuracy
$200B+ pharma R&D spend addressable
Early signals from: a16z Bio Fund, GV, Kleiner Perkins
Companies to watch: Recursion, Insitro, Genesis Therapeutics
Previous: Red hot 2020-2022 → Now: Significantly cooled
User acquisition costs skyrocketed, Apple's iOS changes hurt attribution, and ad market contracted. No clear path to monetization for new entrants.
What Changed: Platform risk from Apple/Google, difficulty competing with TikTok/Instagram for attention
VCs Cautious: Benchmark, Greylock, General Catalyst
Previous: Extremely hot 2021-2022 → Now: Ice cold
Speculative bubble burst, regulatory uncertainty, and lack of real utility beyond speculation became apparent.
What Changed: Market realized most NFT projects lacked sustainable value propositions
VCs Cautious: a16z, Paradigm, Coinbase Ventures
Previous: Very hot 2019-2021 → Now: Significantly cooled
Customer acquisition costs increased 3-5x, supply chain disruptions, and difficulty achieving profitability at scale.
What Changed: iOS 14.5 changes made performance marketing much more expensive and difficult
VCs Cautious: Forerunner, Lightspeed, Accel
Don't build your own foundation model unless you have $100M+ and a very specific use case. Focus on fine-tuning and application layer.
💡 Use APIs from OpenAI, Anthropic, or Google initially. Build your moat in data, workflow, or distribution.
— Sequoia Capital
Enterprise buyers are more educated about AI but also more skeptical. They want proof of ROI, not demos.
💡 Lead with business outcomes, not technology features. Have clear ROI calculations and reference customers.
— Bessemer Venture Partners
Raise 24+ months of runway. Funding cycles are longer and more difficult than 2021-2022.
💡 Start fundraising 6 months before you need money. Have multiple scenarios planned based on different raise amounts.
— Lightspeed Venture Partners
AI talent is still extremely scarce and expensive. Consider remote-first to access global talent pool.
💡 Offer equity packages competitive with big tech. Consider hiring from academia and providing industry transition support.
— Index Ventures
Deal volume down 40% YoY but average deal size up 15% as VCs concentrate on higher-conviction investments. Series A success rates dropped to 1 in 200 pitches vs 1 in 100 in 2021.
Series C • Lead: Google • Others: Spark Capital, Salesforce Ventures
Validates continued massive investment in AI foundation models despite questions about profitability
Foundation ModelsSeries B • Lead: Valor Equity Partners • Others: a16z, Sequoia Capital
Elon Musk's AI play shows continued appetite for contrarian bets on AI architectures
AI InfrastructureSeries D • Lead: BlackRock • Others: Neuberger Berman, Type One Ventures
Hardware-software co-design for AI inference gaining traction as alternative to NVIDIA dominance
AI HardwareAcquisition • Key investors: Accel, CapitalG, Kleiner Perkins
Even in tough market, high-quality automation companies with strong financials can command premium valuations
IPO • Key investors: Sequoia Capital, a16z, D1 Capital
Path to profitability and strong unit economics matter more than growth rate in current market
Most AI startups will fail because they're solutions looking for problems, not the other way around
AI is transformative and any AI company can find product-market fit
Reasoning: True innovation comes from deep domain expertise + technology, not technology looking for applications
Their Bet: Investing in vertical software companies that happen to use AI, not AI companies looking for verticals
Climate tech will see faster adoption than AI in enterprise
AI is the fastest-growing enterprise category
Reasoning: Regulatory pressure and consumer demand creating pull, not just push from technology
Their Bet: Doubling down on climate tech infrastructure with $2B+ committed