Global M&A value in H1 2026
Transactions in the first half
Of M&A value from $1B-plus megadeals
Record average deal size YTD
01 Executive summary
The global dealmaking landscape has entered an era defined by scale. After years of building momentum, activity is now propelled by fewer but far larger transactions, with the biggest deals commanding the majority of capital in play. What began as a post-pandemic recovery has become a structural shift: Acquirers are consolidating aggressively to reach the scale needed to compete in an economy increasingly organized around artificial intelligence (AI). Scale is decisive here because building and deploying AI—its datacenters, power, compute, and specialized talent—carries costs only the largest players can absorb, making consolidation the entry price for competing in an AI-driven economy.
Two forces sit at the center of this movement. First, corporate strategic buyers, rather than financial sponsors, are steering the market, pursuing acquisitions that reinforce competitive position and extend AI capabilities across their supply chains. Second, capital is simultaneously flowing toward the physical infrastructure underpinning AI, datacenters, power grids, and manufacturing capacity, as well as toward the software and services layer that sits atop it. Much of the industry discussion today has pointed to a material shift toward more conservative investments, such as heavy assets, low obsolescence (HALO), but the data shows otherwise. Rather, capital is flowing into both asset-heavy and asset-light segments of the market.
Beneath the headline dynamism lies a quieter reality: Every one of these transactions rests on contracts. As deals grow larger, more complex, and more frequent, the documentary burden expands in lockstep, and the amendment cycles that follow each change in terms multiply it further. This is precisely the high-volume, high-stakes work that manual processes can no longer absorb. In a market moving at this velocity, Litera’s AI-enabled legal technology shifts from convenience to necessity, turning contract workflows from a bottleneck into a source of speed and confidence.
02 Global M&A dealmaking overview
Global M&A dealmaking in the first half of the year continued a three-year trend line toward larger deal execution. Global M&A totaled roughly $2.8 trillion across 19,110 transactions in H1 2026, and annualized figures suggest four consecutive years of M&A value year-on-year (YoY) growth if current investment momentum is sustained through the end of the year. The trend is driven primarily by the continued rise of megadeals, or transactions exceeding $1 billion. The share of M&A value originating from such deals has now captured 67.9% of total M&A value year to date (YTD), compared with 48.3% at the height of dealmaking in 2021.
Notably, the share of $5 billion-plus transactions has continued to materially increase. Transactions at this size have historically accounted for about one-third of total global M&A value, but this figure has far surpassed that precedent, capturing over half of global M&A value YTD. Of the $1.5 trillion invested across 75 $5 billion-plus M&A transactions, xAI’s $250 billion merger and Dominion Energy’s announced $118.5 billion sale collectively constituted almost 25%. Unsurprisingly, the sheer amount of capital funneled into a handful of deals translated into a record average global M&A deal size of $756.9 million YTD, compared with $409.1 million in 2025.
Global M&A deal volumes, on the other hand, tell a diverging story. While values rise, volumes are falling. Fewer deals being executed in the market indicate an increasing concentration of capital and investment bets in the global M&A market. Smaller fund managers are likely being pushed out of the market, while only the largest funds can execute in a larger-check world.
03 Falling multiples signal a shift toward a buyer’s market
While size drove the H1 2026 global M&A dealmaking story, the price at which these transactions closed shows that global M&A is becoming a buyer’s market. The median and average enterprise value (EV)/earnings before interest, taxes, depreciation, and amortization (EBITDA) multiples have fallen slightly from 2025. Falling multiples can mean one of two things: Either the M&A target’s earnings are outpacing its valuations, or the target’s valuation is shrinking in relation to earnings. In reality, acquisition targets are being undervalued because their growth prospects may be slowing or their debt burden is decreasing relative to their earnings.
Headline M&A deals, which are priced in the billions, are but outliers in the story. Such outliers often relate to the AI spending spree, but acquirers have adopted more conservative investment strategies, as seen by the decline in global M&A deal volumes. The lack of volume is a sign of patience for a better market environment to execute, rather than an inability to execute. Overall, falling M&A multiples and transaction volumes resulted from fewer acquirers actively bidding. The recession of competition, which once pushed up prices, has eased, so sellers, especially those whose growth outlooks have cooled as AI challenges moats across industries—must increasingly settle for prices the market is willing to pay rather than what they want to receive.
04 Strategic acquisitions in the age of AI drive headline M&A figures
An examination of some of the industry’s largest M&A transactions this year offers a deeper perspective on how company growth prospects in the age of AI and the subsequent infrastructure build-out are evolving. Dominion Energy, which is in the process of being acquired by NextEra Energy for $118.5 billion, made the strategic decision to merge with another key energy player to bolster its competitive edge in the race to build AI datacenters and electric grid infrastructure. Its ability to outcompete others in a race already crowded with competitors shapes its own growth prospects and valuation. That said, to achieve growth, consolidation is essential, especially to operate at the scale necessary to build the future of an AI-led world. In the same vein, Elon Musk made the strategic decision to merge his AI company, xAI, into SpaceX to build an orbital AI datacenter.
Thus, the global M&A story, characterized by large outlier transactions and broadly undervalued investment targets, has been driven by corporate-led strategic acquisitions. Such acquisitions totaled $2.1 trillion YTD, or 75% of all M&A capital invested. Strategic acquisition deal volumes as a share of all M&A volume have also held steady at 12,456 deals, or 65.2%. This is telling of the outsized impact strategic acquirer capital in AI investments is having on driving the trend toward larger transactions and consolidation in AI and AI-adjacent supply chains.
Strategic acquisitions YTD, 75% of all M&A capital invested
Strategic acquisition deals,
65.2% of all M&A volume
LBO capital deployed in Q2 2026, down from $447B in Q1
Median global PE buyout size YTD, near the decade high
On the other hand, sponsor-led acquisitions are cooling even as headline M&A deal activity appears hot. The amount of capital deployed in leveraged buyouts (LBOs) fell from $447 billion in Q1 2026 to $288.6 billion in Q2. Transaction volumes remain soft, but PE capital deployment trends align more with the strategies of their corporate counterparts where capital deployment sizes are involved. PE take-private buyout activity has remained robust in terms of capital invested, while PE add-on activity has fallen short of 2025’s momentum. On the other hand, strong deal value in take-private activity—transactions that warrant much larger, heavily leveraged deals compared with smaller, less leveraged add-ons—is telling of how sponsors are executing in the current market environment. Most sponsors are not taking on the level of risk and leverage necessary to execute LBO deals, indicating a more conservative approach to dealmaking. However, a handful of sponsors—often among the industry’s largest and most established managers—remain risk-on. For instance, a consortium of investors led by BlackRock’s Global Infrastructure Partners and EQT agreed to take AES private in one of the energy industry’s largest buyout deals in history. The deal was made to strengthen AES’s competitive positioning in the AI datacenter race. With outlier deals of this caliber, the median global PE buyout size has held steady at $97 million YTD, near the decade high. The trend is undoubtedly biased toward large outlier deals, driven by the AI race.
05 Heightened M&A activity selectively presents opportunities across asset-heavy and asset-light investment targets
Further exploration into which investment targets warrant such large check sizes from strategic acquirers and sponsors alike reveals that AI-led infrastructure build-out, or the acquisition of AI capabilities, is driving capital on both sides of the aisle. The story revolves around AI, and a breakdown of global M&A financing trends by asset-heavy and asset-light acquisition activity shows that both are gaining momentum, driven by increasing pressure to adopt AI to stay competitive in the market.
30.6% Asset-heavy share of global M&A value
Asset-heavy M&A activity, which focuses on equipment-based, manufacturing-dependent, and infrastructure-heavy industries across energy, transportation, mining, telecommunications, and information technology (IT), has steadily gained momentum in funding. By the first half of 2026, capital invested in asset-heavy M&A activity rose to constitute 30.6% of global M&A value, continuing its third consecutive year of YoY share growth. Such assets are also known by the popularized moniker HALO. HALO assets have become increasingly popular investment targets because they serve as hedges and beneficiaries to the AI revolution. HALO can hedge against the volatility surrounding asset-light business models, such as Big Tech companies, that have ironically become the largest spenders in the ongoing AI revolution. At the same time, the AI revolution requires heavy assets, such as datacenters, semiconductor manufacturing facilities, and grid infrastructure, to continue its upward trajectory. HALO investments possess this unique advantage in the current market and have driven valuation and return gaps relative to their asset-light counterparts. While asset-heavy M&A activity in 2026 thus far has seen its largest deals concentrated around AI capacity and the electrical grid, acquisition activity in this space has also been driven by a broader hunt for inflation-resistant business models with physically anchored cash flows, such as TK Elevator's announced $34.3 billion acquisition by KONE and Restaurant Depot's $29.1 billion merger.
32.1% Asset-light share of global M&A value
Asset-light M&A activity, which focuses on software, financial services, and IT services acquisition targets, has seen even stronger momentum, accounting for 32.1% of global M&A capital invested YTD and continuing its four-year climb. HALO investments entering the mainstream may suggest a zero-sum game in which market participants prefer either HALO or asset-light investments. However, this has not occurred. Rather, AI has offered value-creation opportunities for sponsors investing across both business models: asset-light and asset-heavy. For asset-light acquisition targets, the largest M&A deals are strategic acquisitions that allow corporates at the forefront of the AI revolution to fortify their competitive moat. Aside from SpaceX’s $250 billion acquisition of xAI, the company also announced plans to acquire AI company Cursor for $60 billion; a consortium of investors acquired Aligned Data Centers in a $40 billion buyout deal; and Alphabet acquired Wiz for $32 billion, almost $9 billion more than the original offer price two years prior. In short, asset-light targets offer value at two ends of the spectrum. Among the AI winners, that value sits in the software layer, where strategic buyers pay up to consolidate AI capabilities or layer in cybersecurity capabilities. At the other end, asset-light companies that have struggled to keep pace with AI have themselves become attractive PE take-private targets.
Whether the market veers toward asset-heavy or asset-light acquisition targets, it is clearly consolidating, check writing has shown no signs of slowing, and documentary burdens will only grow from here.
The transfer of intangible intellectual property and data or heavy, tangible assets requires thorough documentation, especially in a fast-moving M&A landscape with ever-growing transaction sizes and record-high transaction volumes compared with pre-pandemic levels. The paperwork around those assets, combined with the amendment wave that follows when deal terms change, is exactly the high-volume, high-stakes work ripe for automation.
Every one of these
transactions rests on contracts.
06 Methodology, about, and disclaimers
Reports are prepared in accordance with PitchBook’s methodology, which is described in detail on the PitchBook report methodologies page.
M&A is defined as the substantive transfer of control or ownership. We track only completed and announced control transactions. Eligible transaction types include control acquisitions, LBOs (including asset acquisitions), corporate divestitures, corporate asset purchases, spin-offs, and asset divestitures.
Debt restructuring or any other liquidity event, self-tenders (in which a company typically undertakes an offer for a limited number of its own shares to ward off a hostile takeover), or internal reorganizations are not included.
- Completed and announced M&A transactions were included.
- Rumored or canceled deals are not included. Aggregate transaction value is extrapolated using known deal values, unless otherwise noted as estimated.
Data provided by
PitchBook a Morningstar company
When deals reach this size and speed, the paperwork behind them becomes the bottleneck, and the amendment cycles that follow every change in terms only compound the pressure. This is where the choice of AI matters most. Most tools return probabilistic answers, which means every output is essentially a guess, and a guess on high-stakes transactional work demands long hours of verification before anyone can rely on it.
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