AI capital funding has dominated the venture capital landscape for the first half of the year, taking the lion’s share of deals. Q1 2026 VC deal volume surpassed an all-time high of $267.2 billion, with 88% attributed to AI and machine-learning companies. Into the second half of 2026, AI capital funneling has selectively scaled.
Investment that poured into raw compute during the semiconductor supercycle is shifting in favor of applications with high-ROI potential, sovereign infrastructure, and a new frontier of physical deployment. Broker research from the AlphaSense platform suggests the market is shifting from AI experimentation toward systemic value creation, and the chip-led boom of the past two years is entering its late stages.
The momentum behind the AI boom is indisputable. Findings from AlphaSense point to more than 16,000 AI-related deals across 2025 and 2026. Below, we explore recent trends that indicate capital is pooling in narrower channels from agentic workflows, to custom ASICs, to AI-driven energy systems. With the chip-buying boom winding down, the focus has shifted to procuring the energy needed to run GPUs; investors are taking note, deploying capital into targeted bets with promising returns.
Peak Capex and the Search for the Next Wave
For venture allocators, the biggest shift is the expected slowdown in hyperscaler spending. Broker research projects hyperscaler capex growth falling from roughly 70% a year to below 10% over the next three years as the supercycle rolls over. While spending is not expected to stop altogether, capital will be expected to work harder, and durable infrastructure will begin to matter more than sheer quantity. One analyst splits the picture geographically: U.S. technology sentiment is turning toward ROI discipline, while China stays focused on sovereign, on-premise models, and fast enterprise adoption.
As the demand for GPUs lessens, energy is taking its place. Broker research points to energy systems as the next primary bottleneck for AI scaling. Expert transcripts describe firms “land banking” power substations and standing up independent “neo-cloud” platforms to serve sovereign and inference workloads outside the usual hyperscaler orbit. In each case, capital is following the trends closely.
Three Pivotal Trends
Beyond raw deal data, management consulting earnings transcripts offer perspectives on pivotal direction. The first is the “AI control plane.” As agents multiply inside organizations, experts argue that companies will need a cross-enterprise management layer to monitor, govern, secure, and steer millions of digital workers in real time. Broker research suggests this gives rise to a new class of security vendors built to keep AI in check by enforcing policy and watching what agents can see and do, part of a wider case that AI is structurally bullish for cybersecurity.
The second theme is physical AI. Capital is shifting to bridge digital models and physical deployment, namely humanoid robotics and autonomous systems that rely on “multimodal perception fusion.” Per broker research, it blends vision, language, and force feedback, rather than clean structured data. The third theme is data itself. With algorithms increasingly open-source and compute no longer the main bottleneck in many non-LLM settings, experts argue that high-quality proprietary data has become the core factor of production.
Deal Flow Direction
The institutional and private investment landscape for early-stage through unicorn AI companies in 2026 is concentrated on embodied intelligence, agentic orchestration, and advanced infrastructure that addresses power and sovereign constraints.
As the market transitions toward specialized hardware, venture capital is increasingly funding companies like custom ASIC developers and robotics startups that offer long-term “autonomous intelligence” potential rather than just immediate software application margins.
| Company | Funding Stage | Amount Raised (USD) | Post-Money Valuation (USD) | Industry Focus | Lead Investor |
|---|---|---|---|---|---|
| Prometheus | Series B | $12B | $41B | Foundational AI & Machine Learning | N/A |
| DeepSeek | Series A | $7.40B | $50B | GenAI, Developer APIs & Software | Liang Wenfeng |
| Isomorphic Labs | Series B | $2.10B | $5.45B | AI in Life Sciences & Biotech | Thrive Capital |
| Ineffable Intelligence | Seed | $1.10B | $5.10B | Foundational AI & Software | Lightspeed, Sequoia |
| World Labs | Series B | $1B | $5B | 3D Technology & Foundational AI | N/A |
| Fluidstack | Series A | $830M | $7.50B | AI Infrastructure & GPU Cloud | Situational Awareness |
| Hark | Series A | $700M | $6B | Data Integration & Generative AI | Parkway Venture Capital |
| Recursive | Series A | $650M | $4.65B | Foundational AI & Data Mgmt | Google, NVIDIA |
| Apptronik | Series A | $520M | $5.50B | Robotics & Industrial Automation | B Capital, Google |
| MatX | Series B | $500M | $1B | AI Infrastructure & ASICs | Jane Street, Situational Awareness |
| Nexthop AI | Series B | $500M | $4.20B | Cloud Data & IT Infrastructure | Andreessen Horowitz |
| humans& | Seed | $480M | $4.48B | Foundational AI & Machine Learning | Georges Harik, SV Angel |
| CuspAI | Series B | $450M | $2.60B | AI for Advanced Materials | Kleiner Perkins, NEA |
| Rhoda AI | Series A | $450M | $1.70B | Foundational AI & Robotics | Premji Invest |
| Generalist AI | Series B | $400M | $2B | Generative AI & Robotics | Radical Ventures |
The table underscores the trend, highlighting late-stage megarounds for infrastructure (Prometheus, $12B; DeepSeek, $7.4B) alongside outsized early-stage rounds for specialized hardware and sovereign-AI plays.
Where Capital Flows Next
The next wave points to capital rotating out of general-purpose foundation models and into high-ROI verticals, physical AI, and specialized infrastructure such as custom ASICs and AI-optimized networking. According to experts, this is the “first quarter of the game,” with the next phase rewarding companies that can either scale through massive ARR growth or consolidate fragmented layers of the stack.
Four areas of concentration stand out.
Neo-clouds and custom ASICs. Experts expect activity over the next two to three years to hinge on deep pockets and aggressive scaling, favoring neoclouds and custom-silicon developers as positioned to disrupt a saturated inference market.
Vertical “flywheel” moats. VCs that felt they missed the infrastructure layer are moving to the apps layer, prizing systems of record and proprietary-data platforms that own the flywheel autonomous agents must run through, especially in areas where rules-based models can outperform general LLMs.
Sovereign and secure infrastructure. As AI models enter the territory of “national security,” there is a rising demand for sovereign data and on-premise residency. Capital is following this trend into specialized security firms that provide “command on capital intensity” while maintaining gross margins through efficient data orchestration.
Physical AI and humanoids. Investors are placing high-risk, long-duration gambles on autonomous intelligence layers beyond LLMs. Private market data reflects this trend, with early-stage capital flowing to robotics and specialized hardware names like Apptronik and Generalist AI.
The Next Frontier of VC AI Funding
For VC AI funding through the remainder of 2026 and beyond, the frontier has moved past the GPU to prioritize investment in power, packaging, agentic governance, and the physical world.
To stay ahead of trends, AlphaSense empowers investors with agentic AI workflows to automate analysis and synthesis across a broad universe of private companies. AlphaSense’s Deal Screener tracks customized targets across private funding rounds and M&A transactions.
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