The Q1 2026 data from NVCA-PitchBook Venture Monitor is now in, and we are squarely in the AI Power Law Era.
In a single quarter:
→ $267.2B invested — already 83% of all of 2025, in just 3 months
→ $347.3B in exits → 6 firms captured 76% of all VC fundraising
→ 3 deals (OpenAI, Anthropic, xAI) took 65% of all capital invested
→ AI now commands 89% of all US venture deal value
→ The SF Bay Area alone captured 83% of every VC dollar
The companies, funds, and geographies inside the power law are experiencing historic abundance. Those outside it are navigating a fundamentally different market.
Six takeaways from the data:
VC fundraising is back, but controlled by established funds. 6 firms captured 76% of all fundraising capital, reinforcing that access to capital is concentrated in a small set of established players.
From narrow slice to needle-point, capital is hyper-concentrated. The top 3 deals alone accounted for 65% of all VC dollars invested.
Capital is flowing only to AI companies. Everything else is competing for the leftovers. AI now commands 89% of all U.S. VC dollars, making it the dominant, and in many cases the only, category attracting meaningful investment.
AI as a gravitational force. Everything is happening in the SF Bay Area. The SF Bay Area alone captured 83% of all VC dollars, as AI pulls capital, talent, and ecosystems back into a few dense hubs.
Liquidity has returned, but only for mega-scale companies – overwhelmingly driven by a single transaction – the xAI deal, highlighting that liquidity is concentrated at the very top.
Venture is a bifurcated two-speed market. The winners are scaling faster than ever, while the rest face uncertainty.
In my three decades in venture capital and building startups, I have never seen a single quarter like Q1 2026. The next generation of iconic companies will be shaped by the forces this data reveals.
The power law is already playing out in the market. OpenAI securing 10GW of compute and moving beyond Azure exclusivity shows how frontier leaders are expanding both capacity and distribution at once. Meta locking in more than 1 GW of next-generation power, and KKR preparing a $10 billion-plus AI infrastructure venture, show that the real bottlenecks, and the real capital, are now concentrated in compute, energy, and the small group of players able to secure both. Anthropic exploring a potential $900 billion valuation is simply the financial expression of the same trend.
Full Weekend Edition below. 👇
Signals Shaping the Future of AI:
Infrastructure
OpenAI says it has secured 10GW of AI compute capacity ahead of its 2029 target. The company added 3GW in the past quarter and is increasingly relying on cloud partnerships to scale infrastructure. Click here
Microsoft and OpenAI end Azure exclusivity, enabling OpenAI to distribute models across multiple clouds. The shift reshapes enterprise AI competition by allowing OpenAI to expand beyond Microsoft while maintaining deep technical and commercial ties. Click here
Meta signs deals for over 1 GW of space-solar power and 100 GWh of long-duration storage for AI data centers. The agreements highlight energy as a core bottleneck for AI infrastructure and show hyperscalers securing next-generation power sources to sustain growth. Click here
NVIDIA’s B300 AI servers are now selling for about $1 million each in China. Prices for a B300 server loaded with eight Blackwell GPUs have nearly doubled to around 7 million yuan since late 2025 as US authorities crack down on smuggling and Chinese tech firms bid up scarce high-end compute. Click here
U.S. spending on power equipment for data centers is forecast to triple to about $65 billion by 2030. The Wood Mackenzie outlook shows AI data centers becoming a dominant buyer of transformers, switchgear, and related gear, turning electrical infrastructure into a core bottleneck and investment theme for AI expansion.Click here
Enterprise
LinkedIn’s AI recruiting agents are projected to generate $450 million in annual revenue. The disclosure provides one of the clearest signals of real monetization for agentic AI in enterprise workflows. Click here
Accenture rolls out Microsoft 365 Copilot to 743,000 employees. The deployment, Microsoft’s largest to date, is improving productivity and enabling internal AI agents across workflows. Click here
Mercedes-Benz partners with Liquid AI to deploy on-device AI assistants in future vehicles. The deal brings foundation models directly onto automotive hardware, enabling low-latency and privacy-preserving in-car AI systems. Click here
Anthropic launches Claude creative connectors that integrate directly with Adobe Creative Cloud, Blender and more. The connectors let Claude act as an agent across creative tools to query docs, run scripts, edit 3D scenes, and orchestrate multi-step asset workflows inside existing pipelines.Click here
Capital Flows
Anthropic explores a funding round that could value the company above $900 billion. The potential raise highlights extreme capital concentration in frontier labs and intensifying competition for model leadership. Click here
KKR is preparing a $10 billion-plus AI infrastructure venture led by former AWS chief Adam Selipsky. The platform will focus on data centers, power, and connectivity to support growing AI compute demand. Click here
Ineffable Intelligence raises $1.1 billion to build reinforcement learning-based AI models. The startup, founded by former DeepMind researcher David Silver, aims to develop systems that learn without human data. Click here
Aidoc raises $150 million Series E to expand clinical AI deployment across healthcare systems. The round supports scaling of regulated AI applications in diagnostics and hospital workflows. Click here
Parallel Web Systems raises $100 million Series B for AI agent-based search infrastructure. The funding supports development of autonomous browsing and retrieval systems for agent workflows. Click here
Avoca raises $125 million Series A to power AI “front-office” automation for home-services businesses. The round, led by Meritech and General Catalyst, backs an agentic platform that answers calls, books jobs, and manages customer communication for trades like HVAC, plumbing, and roofing. Click here
Sereact raises $110 million Series B to expand its Cortex AI “brain” for industrial robots. The funding, led by Headline, will accelerate development of Cortex 2.0 and global go-to-market for predictive, consequence-aware robotic manipulation in logistics and manufacturing.Click here
Research
NVIDIA launches Nemotron 3 Nano Omni, a multimodal model for AI agents. The open model combines vision, audio, and language capabilities into a single system to support tasks like document analysis, computer use, and audio-video reasoning. Click here
OpenAI releases Symphony, an open-source spec for orchestrating coding agents. The framework turns task trackers into control systems where agents autonomously pick up, execute, and manage multi-step software workflows. Click here
Nature Climate Change outlines the use of foundation models for integrated climate decision systems. The work proposes combining climate, economic, and societal data into unified AI models for large-scale policy and planning applications. Click here
Google Research advances AI-assisted scientific workflows through empirical research agents. The work highlights AI’s role in accelerating experimentation, analysis, and knowledge synthesis.Click here
Policy
Elon Musk’s lawsuit against OpenAI and Sam Altman proceeds to trial over governance structure. The case could reshape how frontier AI labs balance nonprofit origins with commercial expansion. Click here
US Department of Justice intervenes to challenge Colorado’s comprehensive AI law. The move signals federal pushback against state-level AI regulation and raises questions about national preemption. Click here
European Commission preliminarily finds Meta in breach of the Digital Services Act over minors. The enforcement action highlights increasing scrutiny of algorithmic systems and platform risk management in Europe. Click here
The White House drafts guidance to let federal agencies bypass Anthropic’s “supply-chain risk” flag and onboard new AI models such as Mythos. The draft executive action would give the administration a pathway to de-escalate its dispute with Anthropic over Mythos while reshaping how federal agencies vet and procure high-risk frontier models.Click here
Global AI Strategy
Pentagon signs AI deals with seven major tech companies for military use. The agreements include OpenAI, Google, Microsoft, Amazon, NVIDIA, and others to deploy AI tools across classified defense systems. Click here
DeepSeek slashes pricing for its V4-Pro model to aggressively undercut competitors. The move intensifies global competition in AI model pricing and positions China’s ecosystem as a low-cost alternative. Click here
Google DeepMind and South Korea agree to build a major AI campus in Seoul. The project strengthens regional AI capabilities and reflects strategic investment in global talent and research hubs. Click here
UK government moves to develop a national AI hardware strategy to secure domestic chip capabilities. The plan focuses on building sovereign capacity in semiconductors and AI infrastructure, reflecting growing concern over reliance on U.S. hyperscalers and global supply chains while positioning the UK to capture a share of the fast-growing AI chip market. Click here
Google advances a $15 billion AI data center project in India toward gigawatt-scale capacity. The project reflects geographic diversification of AI infrastructure and increasing investment outside core U.S. regions. Click here
Anthropic partners with NEC to deploy Claude across 30,000 employees and co-develop AI products. The deal expands Claude into Japan’s enterprise market and establishes a regional go-to-market channel for regulated industries.Click here
Talent Signals
Each week, we spotlight key roles tied to the themes shaping this week’s AI headlines, connecting talent to the companies driving the news.
@Supio builds AI software for legal teams, turning complex documents and case data into structured insights that support litigation and decision-making. As AI becomes embedded in high-value professional workflows, Supio is growing across engineering and product. Open roles are listed on its careers page. Click here
@AvocaAI builds AI agents that automate customer interactions across voice and chat, helping companies handle high-volume support and sales workflows. As businesses shift toward always-on, agent-driven engagement, Avoca is expanding across engineering, product, and operations. Open roles are listed on its careers page. Click here
@TogetherAI provides infrastructure and tools for building, training, and deploying generative AI models at scale. As companies move from experimentation to production AI systems, Together AI is expanding its engineering and platform teams. Open roles are listed on its careers page. Click here
You can see all the opportunities at Mayfield-backed AI companies here.
Social Signals
The most important conversations in AI are unfolding across social media, where top voices are shaping the next wave of signals and strategy. Here are some of the top social signals and their takes from the past week.
Andrew Ng (Click here) — “AI-native software engineering teams operate very differently than traditional teams. Engineers now play broader roles, partly product managers, designers, sometimes marketers. When we can build software 10x or 100x faster, everything else becomes the bottleneck.” Ng describes how AI is reshaping team structure, shifting value from pure coding to decision-making, communication, and cross-functional execution. As coding accelerates, bottlenecks move to product, design, marketing, and legal, favoring small, highly collaborative teams where individuals can operate across multiple roles.
John Collison (Click here) — “The obvious near-term effect is reduced transaction costs within companies. But inter-company transaction costs also reduce sharply. Agents make discovery easier, integration simpler, and contracting more straightforward. On net, we think the second effect is bigger, fewer people per firm, more output per firm, more firms, and more coordination through market-like mechanisms.” Collison frames AI through a Coasean lens, arguing that while internal efficiency improves, the bigger shift is lowering the cost of coordination between companies, enabling smaller, more numerous firms to operate and interact more fluidly.
Jonathan Ross (Click here) — “For 50 years, software engineering ran on code rationing. Writing code was expensive, so we rationed it through roadmaps, prioritization, and scope reviews. LLMs will be the end of code rationing. Code is cheap now, and while the No Engineer explains why something can’t be done, the Yes Engineer has already shipped three versions.” Ross argues that AI is flipping a core constraint in software development, where the cost of writing code is no longer the limiting factor. As that constraint disappears, speed of iteration and willingness to build become the new advantage, reshaping how teams prioritize and execute.
