Venture Capital Intelligence Report
December 24, 2025 • Synthesizing insights from top-tier VCs
VCs are navigating a bifurcated market where AI infrastructure continues to attract massive capital while traditional SaaS faces valuation pressure. Quality over quantity becoming the dominant theme as LPs demand more disciplined deployment.
Selective funding environment with longer due diligence cycles. Series A becoming the new Series B in terms of milestones required. Flight to quality accelerating.
AI infrastructure maintaining premium multiples (15-25x revenue), traditional enterprise software compressing to 5-8x revenue, consumer plays struggling below 3x revenue
The picks-and-shovels play for the AI gold rush. Focus on developer productivity tools, model deployment infrastructure, and AI observability platforms.
AI-native software for specific industries replacing traditional incumbents. Higher switching costs and defensibility than horizontal AI tools.
Hardware and infrastructure plays for decarbonization, driven by IRA incentives and corporate sustainability mandates.
Dual-use technologies benefiting from increased defense spending and space commercialization trends.
The next wave of AI will be agentic - autonomous software that can execute complex workflows without human intervention
Companies built AI-first from day one will displace incumbents who retrofit AI features
The infrastructure layer serving AI workloads will be larger than the application layer
Post-ChatGPT evolution toward AI that can plan, reason, and execute multi-step tasks autonomously
Chain-of-thought and tree-of-thought breakthroughs enabling more complex reasoning
$500B+ TAM across all knowledge work
Early signals from: a16z, Sequoia, Greylock
Companies to watch: Anthropic, Adept, Inflection AI
Nation-states and enterprises building their own AI infrastructure for data sovereignty and security
Geopolitical tensions and data privacy regulations driving localization needs
$200B+ government and enterprise spending
Early signals from: Index Ventures, General Catalyst
Companies to watch: Mistral AI, Aleph Alpha, Together AI
Programming biology to manufacture everything from materials to medicines
CRISPR maturation, AI-driven protein design, and sustainability pressures
$1T+ potential across chemicals, materials, and pharma
Early signals from: a16z, Kleiner Perkins, Flagship Pioneering
Companies to watch: Ginkgo Bioworks, Modern Meadow, Bolt Threads
Previous: Red hot 2020-2022 with TikTok competitors → Now: Significantly cooled
User acquisition costs skyrocketed, Apple's ATT impact, and market saturation concerns
What Changed: Focus shifted from growth-at-all-costs to sustainable unit economics
VCs Cautious: Benchmark, Sequoia, Greylock
Previous: Pandemic darling with e-commerce boom → Now: Funding dried up significantly
Customer acquisition costs unsustainable, return to physical retail, margin compression
What Changed: Realized most DTC brands lack defensible moats
VCs Cautious: Forerunner, First Round, Accel
Previous: Mega-rounds and unicorn valuations common → Now: Selective interest only
Rising interest rates hurt lending models, increased regulation, market saturation
What Changed: Focus shifted to embedded finance and B2B infrastructure plays
VCs Cautious: Ribbit, QED, Kleiner Perkins
Focus on workflow transformation, not feature addition
💡 Map existing workflows, identify AI-native alternatives, build for 10x improvement not 10% enhancement
— Sequoia Capital
Enterprise AI sales cycles are 2-3x longer due to compliance and integration concerns
💡 Build compliance and security documentation early, create sandbox environments for easy pilot programs
— Bessemer Venture Partners
AI talent market bifurcating between research scientists and application engineers
💡 Hire application engineers early, partner with universities for research talent, consider acqui-hires
— Greylock Partners
AI companies need different metrics - focus on inference costs and model performance over traditional SaaS metrics
💡 Track cost per query, model accuracy over time, and customer workflow completion rates
— Lightspeed Venture Partners
Deal volume down 40% YoY but average deal size up 25% - flight to quality accelerating with concentration in AI infrastructure and vertical applications
Series C • Lead: Google • Others: Spark Capital, Salesforce Ventures
Validates massive capital requirements for frontier AI development
Foundation ModelsSeries I • Lead: T. Rowe Price • Others: a16z, NEA
Shows continued strength in AI data infrastructure despite market headwinds
Data InfrastructureAcquisition by Microsoft rumored • Key investors: Accel, CapitalG, Kleiner Perkins
Process automation remains strategic priority for tech giants
Current AI application layer is overhyped - real value in picks-and-shovels infrastructure
Most VCs chasing AI applications and consumer AI products
Reasoning: Applications are easily replicated, but infrastructure creates lasting moats
Their Bet: Heavy investment in developer tools, model serving, and AI observability
European AI companies will outperform US counterparts due to regulation-first approach
US maintains AI leadership through big tech and talent concentration
Reasoning: European focus on trustworthy AI creates sustainable competitive advantages
Their Bet: Doubling down on European AI infrastructure and compliance tools
AI infrastructure will consolidate into 3-4 major platforms by end of 2025
HIGHSequoia Capital • Timeframe: 12 months
Implications: Winner-take-most dynamics in model serving, vector databases, and AI orchestration
First AI-native unicorn IPO will happen in H2 2025
MEDIUMa16z • Timeframe: 6-18 months
Implications: Will establish valuation benchmarks and validate AI-first business models publicly
Traditional SaaS margins will compress 20-30% as AI features become table stakes
HIGHBessemer Venture Partners • Timeframe: 24 months
Implications: Legacy SaaS companies must reinvent or face continued multiple compression
Will determine if current AI valuations are justified
High conversion rates validate enterprise AI market size
Low conversion rates suggest AI still too early for mainstream enterprise
Determines unit economics viability for AI applications
Costs drop faster than performance gains slow - expands TAM
Diminishing returns on performance while costs remain high
Will unlock or constrain enterprise AI adoption
Clear frameworks accelerate enterprise deployment
Restrictive regulations slow AI adoption and innovation