Artificial Intelligence Q1 2026 Global Report

STRATEGIC M&A

• Strategic acquisitions of AI targets reached near-record quarterly deal count and, driven by SpaceX’s acquisition of xAI for $250 billion, pushed total value well above any prior quarter or full-year period.
• Excluding the xAI acquisition, Q1 strategic M&A deal value still rose  96% YoY, with five announced or completed deals above $1 billion in enterprise value during the quarter.
• Buyers are pursuing transformative deals, acquiring AI assets that enhance product offerings, workflow control, and proprietary data. Marvell’s acquisition of Celestial AI reflects this push to secure enabling technology.

PRIVATE EQUITY

• AI-related buyout activity remained healthy in Q1 2026, with deal count rising YoY even as total value declined, signaling continued interest alongside valuation discipline.
• Sponsors remain selective, with many favoring infrastructure and mature AI-enabled assets over emerging pure-play software developers.
• Activity spans platform buyouts and add-ons: Haveli’s acquisition of Sirion shows appetite for scaled AI-enabled platforms, while Fullbay’s acquisition of Pitstop highlights add-ons that can drive portfolio-wide AI enablement through predictive maintenance.

VENTURE CAPITAL

• Capital concentration intensified in Q1 2026, with deal count down sharply YoY but total deal value up meaningfully as funding pooled around several large rounds.
• OpenAI’s $122 billion round marked the largest VC deal in history and alone accounted for 36% of total VC investment in the quarter.
• AI captured 79% of global VC dollars in Q1 2026, and 55% of all global VC investment went to just four AI companies: OpenAI, Anthropic, xAI, and Waymo.
• Even excluding those four rounds, AI VC deal value still increased 116% as investors continued minting unicorns at the fastest quarterly pace since 2022.
• The concentration is not a temporary distortion but a defining feature of the current AI-driven VC market, with leading applications absorbing a disproportionate share of VC dollars.

Notable AI Transactions: Venture Capital

The AI Infrastructure Buildout

• The AI infrastructure buildout continues to accelerate, with ~$2.9 trillion in global CapEx expected through 2028.
• As end-market demand matures, bottlenecks are shifting from GPUs alone to power delivery, cooling, and execution. KKR’s agreed sale of liquid-cooling provider CoolIT to Ecolab for $4.8 billion underscores how cooling infrastructure is becoming investable in its own right.
• Hyperscalers are pursuing a dual strategy of self-build and third-party leasing, sustaining demand for both owned campuses and sponsor-backed platforms.

HYPERSCALERS COMMIT CAPEX

• Unlike previous bubbles, the largest builders are investing from large revenue and cash-flow bases, with spending supported by proven demand rather than speculative supply.
• Hyperscalers are also focused on capital efficiency. Alphabet reported that Gemini server unit costs declined 78% over 2025 through model optimizations, efficiency, and utilization improvements.
• Microsoft has indicated that customer demand continues to exceed available capacity and that much of the GPU capacity it is procuring is already committed.
• At the same time, partnerships are becoming more vertically integrated across power, silicon, and compute. OpenAI and Softbank each invested $500 million into SB Energy alongside a 1.2-gigawatt lease.

PRIVATE CAPITAL FILLS FINANCING GAPS

• Even with unprecedented hyperscaler commitments, the capital requirement is too large for corporate balance sheets and traditional bank lending alone. There is an estimated $800 billion opportunity for private credit through 2028.
• The financing stack is broadening, with private credit providing 60%-75% of early-stage development capital and moving from a supplemental source of funding to a core financing mechanism.
• Private capital is funding compute assets as well as shells and land, as illustrated by Apollo-backed funds leading a $3.5 billion capital solution for Valor’s $5.4 billion acquisition and lease of NVIDIA GB200 infrastructure to xAI using a triple-net lease.
• Private equity continues to back platform-scale data center assets. In January, KKR and Oak Hill committed nearly $2 billion to Global Technical Realty, and in February, CPP Investments and Equinix agreed to acquire atNorth for $4 billion.

Estimated $2.9 Trillion Global CapEx on Data Centers Through 2028 $1.4 trillion Covered by Hyperscaler Cash Flows $200 billion Corporate Debt Issuance $150 billion Securitized Credit Issuance $800 billion Opportunity for Private Credit $350 billion Other Capital (PE, VC, Sovereign)

Sources: Alphabet, Amazon, Apollo, Blackstone, Business Wire, Colliers, Equinix, JLL, KKR, Morgan Stanley, Microsoft, OpenAI

Agentic AI & Portfolio Integration

 

AGENTIC AI & ENTERPRISE AUTOMATION REPRESENT A GROWTH FRONTIER

• Agentic AI and enterprise automation are emerging as the next frontier of AI adoption, shifting the focus from generating answers to executing governed, multi-step workflows.
• Early traction is showing up in repeatable, high-value workflows where automation can be measured and controlled, as reflected in the representative deployments below.

 

• Private capital-backed companies are leading the broader market in AI adoption and have become a natural proving ground for that next phase of adoption, pairing sponsor urgency with repeatable rollout across governed operating environments.
• In PE portfolios, ambition continues to outpace execution: In a 2025 survey by Accordion of 200 senior PE executives and 200 CFOs at PE-backed companies, 98% of sponsors have directed CFOs to prioritize AI, but fewer than one-third report meaningful implementation and 68% say they do not know where to start.
• The strongest rollouts pair sponsor oversight with data readiness, governance, human-in-the-loop design, and clearly defined use cases in finance and operations, linking AI deployment directly to earnings, cash flow, and exit readiness.

Sources: Accordion, Anthropic, BASF, Cisco, Databricks, EY, Novo Nordisk, N26, OpenAI, Ramp

Segment Highlights

ROBOTICS & PHYSICAL AI HAVE ARRIVED IN INDUSTRIALS

• Integration partnerships are accelerating robotics commercialization. NVIDIA and robotics leaders ABB, FANUC, Yaskawa, and KUKA are embedding Omniverse, Isaac, and edge AI into two million over robots, pointing investors towards picks-and-shovels layers such as simulation, synthetic data, perception, inference, and fleet orchestration.
• Venture capital is concentrating in foundation model and deployment platforms rather than narrow point solutions. Apptronik extended its Series A to more than $935 million to scale Apollo humanoid deployments, while Skild AI raised nearly $1.4 billion to build a general-purpose robotics foundation model.
• Strategic M&A is clustering around enabling layers. For example, Ouster acquired StereoLabs to broaden its perception stack across lidar, cameras, compute, sensor fusion, and perception software.

"Physical AI has arrived — every industrial company will become a robotics company." — Jensen Huang, Founder and CEO of NVIDIA

ROI IN HEALTHCARE & LIFE SCIENCES

• HCLS has moved into AI execution, amid ROI visibility. In a NVIDIA survey of more than 600 respondents (comprised of HCLS management and AI practitioners), 85% of companies using AI reported that it is increasing annual revenue, 80% reported reduced annual costs, and 85% expected AI budgets to increase in 2026.
• HCLS companies are using partnerships and M&A to push AI deeper into workflows. NVIDIA and Lilly committed up to $1 billion over five years to a co-innovation lab for AI-driven drug discovery and manufacturing, while AstraZeneca agreed to acquire Modella AI to embed foundation models and AI agents in oncology R&D.
• Earendil Labs’ recent $787 million financing reflects broader HCLS momentum behind AI-native drug discovery platforms with clear clinical and commercial pathways.

Sources: Apptronik, Barclays, Business Wire, Earendil, Modella AI, NVIDIA, Ouster, PR Newswire

HCLS Segments See ROI 57% Of adopters from the MedTech segment have seen an ROI with AI for medical imaging 46% Of adopters from the BioTech segment have seen an ROI with AI for drug discovery

Source: NVIDIA Survey of 600+ HCLS Leaders and AI Practitioners

AI Frontier Model Race Accelerates

EFFICIENCY BECOMES CRITICAL FOR HIGHLY INTELLIGENT MODELS

• Frontier intelligence is tightly clustered: The leading models from Google, OpenAI, and Anthropic (Gemini 3.1 Pro, GPT-5.4, and Claude Opus 4.7) are all tied at 57 on the Artificial Analysis Intelligence Index. This suggests the race is now defined by narrow performance gaps rather than a single runaway leader.
• Equal intelligence does not mean equal economics: Despite identical Intelligence Index scores, the estimated cost to run the full benchmark differs substantially, with Google’s Gemini 3.1 Pro Preview costing materially less to run than GPT-5.4 and Claude Opus 4.7.
• The strongest value may sit just below the frontier: MiniMax’s M2.7 and Xiaomi’s MiMo-V2-Pro score strongly on the Intelligence Index while costing far less to run than many frontier leaders.
• The frontier race is shifting from pure model quality to cost-efficient deployment: As performance converges at the top, competition to deliver the best economics on the most capable models will intensify and shape investment decisions.

MARKET TRENDS

• The AI market is maturing from a model race into an execution era, but the gap between adoption and transformation remains meaningful. In a Deloitte survey of over 3,000 business leaders, only 34% of respondents say AI is being used to deeply reshape core processes or business models, which keeps agentic AI and enterprise automation at the center of the next wave of AI-driven value creation.
• Control, trust, and sovereignty remain key hurdles to scaling AI. Trusted infrastructure, legal clarity, and continuous assurance are becoming commercial differentiators.
• While record capital continues to flow to scaled frontier labs and infrastructure, value capture remains uneven across and within sectors; emerging AI builders will need clearer differentiation across the stack. Value continues to concentrate around the platforms that convert model capability into governed production value, particularly compute, data infrastructure, orchestration, cybersecurity, workflow software, and simulation layers.

DEALMAKING OUTLOOK

• Venture capital should remain abundant but highly concentrated. Megarounds are likely to continue driving headline value, but capital should still be available for differentiated next-wave companies beyond the frontier lab cohort.
• Strategic M&A will remain active as AI enters a more industrial phase, with companies using acquisitions to accelerate scale, secure strategic capabilities, and close infrastructure, data, and workflow gaps faster than they can build internally.
• Private equity is likely to remain focused on infrastructure, consortium-style financings, and mature AI-enabled assets with clear value-creation levers. AI is shaping both where capital is deployed and how deals are underwritten.
• Alongside growing CapEx from hyperscalers, private credit will remain a major driver in AI infrastructure, particularly in early-stage, structured, and asset-backed financings.
• Amid geopolitical volatility, security-sensitive and sovereignty-driven dealmaking should become more important through the balance of the year. With the U.S. and China capturing ~65% of aggregate global AI investment, trusted shared infrastructure, localization, and deployment control are becoming more central to cross-border strategy.
• Companies with governed rollout playbooks, explainable systems, and continuous monitoring should command a premium over peers still running fragmented pilots, particularly where AI touches pricing, underwriting, or customer-facing decisions.

For Investors… “Our recommended strategy for 2026: focus on beneficiaries as nations pursue self-sufficiency in energy, critical materials, manufacturing capacity, and AI capabilities; invest in AI infrastructure, given accelerating AI capabilities and the massive excess demand for compute relative to supply; own AI adopters with pricing power, given the market fails to appreciate that the non-linear increase in AI capabilities magnifies adoption benefits; and be positioned, both on offense and defense, for AI-driven disruptions including labor dislocation and life sciences advances.” — Stephen Byrd, Head of Global Thematic Research, Morgan Stanley Research

Sources: Apptronik, Barclays, Business Wire, Earendil, Modella AI, NVIDIA, Ouster, PR Newswire

Public Company Performance