AI megadeals reshape m&a, leaving SaaS in the dust

In 2026, strategic buyers overwhelmingly dominate the large-cap and megadeal M&A landscape, particularly in AI infrastructure assets such as data centers and power generation, driving record deal volumes and commanding premium valuations. This trend is exemplified by NextEra Energy’s landmark $67 billion acquisition of Dominion Energy, which strategically targeted power- and data-center-heavy regions, and the surge in hyperscalers moving beyond contracting power to owning generation capacity, including nuclear assets like small modular reactors. As PwC highlights, nearly a third of the 100 largest corporate deals in 2025 cited AI as a strategic rationale, underscoring the centrality of AI infrastructure in reshaping the M&A market.

The AI infrastructure supercycle is fueling unprecedented capital expenditures and strategic acquisitions, with hyperscalers such as Amazon, Microsoft, Google, and Meta collectively projected to invest $1.5 trillion in data center and chip infrastructure by 2027. This massive spending spree has driven quarterly data center costs up 72% year-over-year to $170 billion, while companies like Nvidia and TSMC report explosive revenue growth—Nvidia’s data center revenue soared 92% to $75.2 billion, and TSMC raised its full-year revenue growth guidance above 40%. These investments are underpinned by a $2.3 trillion backlog of customer cloud commitments, reflecting strategic buyers’ confidence in sustained demand and justifying premium valuations for physical AI infrastructure assets.

While direct acquisitions of AI application-layer companies continue, they occur at a much smaller scale compared to the megadeals centered on infrastructure, highlighting a clear valuation bifurcation within the AI value chain. Boston Consulting Group notes that infrastructure-layer assets—data centers, compute capacity, and power generation—command extraordinary valuations tied to visible, contracted demand, whereas application-layer AI companies face valuation corrections. This divergence is further reflected in North American TMT M&A, where strategic buyers accounted for 90% of deal value in 1H26, focusing on acquiring semiconductors, cloud platforms, and cybersecurity capabilities to rapidly build AI capacity rather than develop internally.

Strategic buyers’ ability to justify premium valuations stems from their pursuit of long-term operational synergies and strategic positioning, enabling aggressive investment despite near-term free cash flow pressures and AI-related valuation uncertainties that constrain private equity participation. As Covotta explains, these buyers value technology assets beyond pure financial returns, supported by expanding cloud/AI margins and triple-digit AI sales growth. This dynamic contributes to a bifurcated M&A market where megadeals and AI infrastructure transactions accelerate rapidly, while mid-market deals face valuation gaps and more cautious buyers, reinforcing the dominance of large corporates with strong balance sheets and access to private credit.

Application-layer software and SaaS companies are grappling with a pronounced valuation bifurcation driven by AI disruption, where infrastructure-layer assets like data centers and computing capacity command premium valuations tied to contracted demand, while application-layer firms face downward corrections. This dynamic is exemplified by major private equity players such as Apollo Global Management and Blackstone channeling $35 billion into Anthropic’s AI infrastructure expansion rather than traditional software acquisitions, reflecting a strategic pivot towards foundational AI assets amid skepticism about broader software business models.

Valuation pressures on application-layer SaaS are compounded by legacy financial structures and market shifts; many 2019-2021 vintage SaaS companies, once buoyed by zero interest rates, now confront soaring borrowing costs—some refinancing at rates as high as 11%—which exacerbate risk profiles amid AI-driven market uncertainty. This 'SaaSpocalypse,' triggered by innovations like Anthropic’s Claude Cowork plug-ins, has chilled mid-market M&A activity as investors struggle to identify defensible software businesses, leading to cautious private equity approaches that prioritize AI-native and highly embedded SaaS with proprietary data over generic horizontal software deemed increasingly obsolete.

Investor sentiment has shifted decisively from momentum-driven buying to fundamentals-based investing, demanding clear evidence that AI integration tangibly drives revenue growth, margin improvement, or defensible competitive advantages. As Don Zambarano of KPMG observes, private equity firms retain significant capital but concentrate on businesses with resilient earnings and value creation potential, while deal structures increasingly incorporate earnouts and seller notes to bridge persistent valuation gaps. This disciplined, selective approach underscores a market recalibration where application-layer software companies must present clean financials and realistic valuations to attract strategic buyers amid ongoing skepticism.

Despite strong growth and AI exposure, leading application-layer software companies like DataBricks and Snowflake face valuation ceilings as investor enthusiasm gravitates towards frontier AI labs such as OpenAI and Anthropic, risking a loss of attention for established enterprise software models. This nuanced investment landscape favors orchestration platforms and robotics-as-a-service models within warehouse automation—praised by Nick Banich for their recurring revenue and system utilization benefits—while hardware-centric and generic software models endure steep valuation compressions, with revenue multiples plummeting from 20-30x at market peak to around 3.2x in recent deals, reflecting a broader recalibration towards proven deployments with attractive unit economics.

By early 2026, private equity experienced a marked pullback in activity, with buyout volume declining 21% year-on-year to USD 168.5 billion and exit volume dropping 32% to USD 174.4 billion, as firms grappled with valuation mismatches and uncertainty fueled by AI’s transformative impact. This cautious stance is especially pronounced in the technology sector, where buyout volume plunged 78% to USD 10.9 billion amid a valuation reset, prompting sponsors to pause deals until clearer insights into AI’s effects emerge.

Despite abundant capital reserves, private equity leaders like Don Zambarano, US head of private equity at KPMG, emphasize a strategic pivot toward investments characterized by strong fundamentals, resilient earnings, and tangible value creation opportunities. This selective approach reflects a broader sector rotation, as capital flows increasingly favor resilient industries such as utilities and energy infrastructure, which surged to USD 64.6 billion in buyout volume during 1H26—nearly doubling last year’s figures and overtaking technology as the dominant buyout sector.

Venture capital investment in AI remains heavily concentrated in a handful of mega-rounds, with just 6% of deals capturing nearly 89% of the sector’s funding, exemplified by companies like OpenAI and Anthropic commanding over 70% of global AI capital. However, this concentration is driving investors to look beyond these giants toward AI-native infrastructure and embodied AI applications, as illustrated by Bezos-backed Project Prometheus’s $41 billion debut valuation and significant funding flowing into physical AI hardware, robotics, and data center infrastructure. This shift underscores the strategic importance of infrastructure assets that form the essential 'plumbing' of an AI-driven economy, spanning sectors from utilities to defense and financial systems.

Geographically, the AI investment landscape is diversifying, with Asia notably increasing its share of new AI unicorns to 24% of recent cohorts, surpassing its historical 15% share, signaling a broadening beyond traditional US hubs. Meanwhile, underfunded regions like Africa and the Middle East are emerging as attractive frontiers for capital deployment, focusing on real-economy assets such as energy infrastructure, electric mobility, and climate resilience. Supported by sovereign-backed industrialization programs in Saudi Arabia and the UAE, these regions offer strategic hedges against capital concentration risks, benefiting from improving exit environments and national transformation initiatives that prioritize AI infrastructure and energy transition.

Investors are increasingly targeting AI’s integration into traditionally slow-moving, large physical economy sectors—such as chemicals, transportation, and healthcare—that collectively represent $10 trillion in annual revenues. By focusing on AI-native infrastructure bottlenecks and niche applications like AI-controlled continuous-flow synthesis or self-maintaining APIs, investors can capture outsized value in markets historically neglected by software-centric capital. This approach leverages AI and robotics to scale intelligence over physical hardware, enabling productivity gains without proportional labor increases, and aligns with the evolving capital cycle that includes compute, data-center power, and networking throughput as critical investment nodes.

The frontier of AI investment is moving decisively from consumer-facing chat interfaces toward embedding AI within physical institutions and infrastructure—schools, defense, construction sites, and aging households—where trust, compliance, and API maintenance become as vital as the AI applications themselves. Notably, the American defense sector is opening up to startups faster than expected, dismantling traditional barriers and creating unique strategic opportunities for AI-native infrastructure investments. This institutional ambition to modernize legacy systems reflects a broader recognition that the foundational layers of AI infrastructure, including self-maintaining APIs and physical data pipelines, are critical and underpriced assets poised to become permanent fixtures across thousands of companies.