(HedgeCo.Net) — Artificial intelligence is no longer just a venture-capital story or a public-market momentum trade. It has become one of the most important forces reshaping mergers and acquisitions, with strategic buyers, private equity sponsors, infrastructure investors, and mega alternative asset managers all racing to secure positions across the AI value chain.
The most important shift is not simply that AI dealmaking is increasing. It is where that dealmaking is happening. The first phase of the AI boom was dominated by frontier model companies, consumer-facing applications, and public-market enthusiasm around a handful of mega-cap technology names. The next phase is moving deeper into software, infrastructure, data centers, workflow automation, cybersecurity, and the middleware layer that connects AI models to real business systems.
That shift explains why AI-related software deals have become one of the defining transaction themes of 2026. InvestmentNews recently reported that software accounted for nearly three-quarters of North American AI-related M&A activity, while AI-linked deals within software rose from roughly 7% of all software transactions in 2021 to nearly 30% in 2025.
For private equity and alternative investment firms, that statistic captures the new reality. AI is no longer a narrow technology category. It is becoming an embedded feature across software markets, operating systems, enterprise workflows, cybersecurity, analytics, and vertical applications. Buyers are not only acquiring “AI companies.” They are acquiring companies that can become AI-native platforms, AI distribution channels, AI data owners, or AI-enabled automation engines.
That is why the market has shifted toward what many investors now call the “picks and shovels” phase of AI M&A. The highest-profile consumer applications may still command attention, but dealmakers are increasingly focused on the assets required to make AI useful at scale: cloud infrastructure, data-management tools, workflow orchestration, security platforms, application programming interfaces, model-monitoring systems, and industry-specific software that can convert AI capability into measurable productivity gains.
In other words, AI M&A has become less about buying the flashiest demo and more about buying the operating layer.
That operating layer is especially attractive to private equity. Traditional software buyouts were often built around recurring revenue, high gross margins, customer retention, and the ability to improve sales efficiency. AI changes the equation. It can either strengthen a software company’s moat by embedding automation deeply into customer workflows, or weaken that moat by making legacy software easier to replicate. For sponsors, the challenge is to distinguish between companies that will be enhanced by AI and companies that will be disrupted by it.
PwC has described this as a fundamental change in software valuation, noting that the market narrative has shifted from “software eats the world” to “AI eats software.” PwC argues that private equity dealmakers should not treat this as a blanket retreat from software, but instead as a reason to assess defensibility, pricing models, and AI-enabled value creation more carefully.
That is precisely why software M&A is becoming more selective, even as activity rises. Buyers are no longer paying simply for subscription revenue. They are paying for data advantage, workflow control, integration depth, domain expertise, and the ability to turn AI from a feature into a product. A software company that merely adds a chatbot may not deserve an AI premium. A software company that uses AI to automate a mission-critical business function may.
This distinction is reshaping how alternative investment firms evaluate targets. In the old software playbook, a sponsor might underwrite growth, retention, margin expansion, sales productivity, and product bundling. In the AI playbook, the sponsor must also underwrite model dependency, data rights, compute costs, pricing power, customer adoption, regulatory exposure, and the risk that AI-native competitors will attack the business from below.
The result is a more complex but potentially more rewarding M&A environment.
The broader deal market is also helping. Morgan Stanley’s 2026 M&A outlook said the boom in AI infrastructure, financial sponsor activity, and cross-border deals has set the stage for another active year, with companies pursuing acquisitions in infrastructure, hardware, and software to meet surging compute demand. Bank of America’s recent hiring of veteran technology dealmaker Richard Hardegree as vice chair of M&A also reflects Wall Street’s view that technology dealmaking is rebounding, with Reuters noting that 2026 deal volume reached around $2 trillion, up 32% from the prior year, supported by regulatory improvements and AI investment.
The AI M&A surge is not confined to software alone. It is also pulling capital into data centers, power, chips, networking, and cybersecurity. Intralinks noted that technology continues to dominate U.S. M&A activity, with technology deal value reaching $150.4 billion, up 31% year over year, driven by AI-related consolidation and data centers. The same report highlighted power and utilities deal value of $50.5 billion, up dramatically, as AI-fueled demand for resilient power became a central deal driver.
That is why the AI M&A market increasingly looks like a convergence trade. Software investors need to understand infrastructure. Infrastructure investors need to understand software demand. Private equity investors need to understand AI disruption. Credit investors need to understand whether borrowers’ business models are becoming stronger or weaker because of automation. The lines between technology, real assets, private equity, and private credit are blurring.
The data-center boom is the clearest example. A consortium including Nvidia, BlackRock, and Microsoft agreed to acquire Aligned Data Centers in a transaction valued at about $40 billion, according to the Associated Press. Aligned operates 50 data-center campuses with more than 5 gigawatts of capacity across the U.S. and Latin America, and the deal was described as part of the broader surge in infrastructure investment needed to support AI and cloud computing demand.
That kind of transaction shows how AI has expanded the definition of technology M&A. A data-center acquisition is not a traditional software deal, but it is fundamentally an AI deal if the investment thesis is tied to compute demand, model training, inference workloads, and hyperscaler capacity constraints. The same logic applies to power generation, cooling infrastructure, fiber networks, semiconductor supply chains, and specialized facilities designed for high-density computing.
For alternative investment managers, this creates a larger and more diversified opportunity set. The AI trade is no longer limited to owning public shares of chipmakers or funding early-stage model companies. It now includes buying enterprise software platforms, acquiring cybersecurity assets, financing data centers, investing in power infrastructure, backing semiconductor services, and consolidating fragmented vertical software markets.
Private equity firms are particularly well-positioned because AI creates both a threat and an operational improvement opportunity inside portfolio companies. On the threat side, sponsors must determine which businesses face margin pressure, product commoditization, or customer churn from AI disruption. On the opportunity side, sponsors can use AI to improve portfolio-company operations: automating customer service, speeding software development, improving procurement, enhancing financial planning, accelerating sales lead scoring, and reducing back-office costs.
That dual role makes AI both a diligence issue and a value-creation lever.
A software company that looks attractive on historical numbers may be less attractive if AI undermines its pricing model. Conversely, a company with modest growth today may become much more valuable if it owns proprietary data and can embed AI into a critical workflow. The best dealmakers are not simply asking whether a target “uses AI.” They are asking whether AI increases the company’s competitive advantage.
That is why middleware has become such an important category. Middleware is not always glamorous, but it is often where enterprise adoption happens. Companies do not replace all of their systems overnight because a new AI model appears. They need tools that connect models to databases, security protocols, compliance systems, customer records, enterprise resource planning platforms, and internal workflows. They need orchestration, monitoring, permissioning, auditing, and integration.
This layer is where many AI M&A targets may emerge. A middleware company that helps enterprises safely deploy AI across regulated workflows could become more valuable than a consumer AI application with viral growth but limited monetization. For financial services, healthcare, insurance, logistics, law, cybersecurity, and manufacturing, the ability to integrate AI safely into existing systems is essential.
That is also why cybersecurity has become one of the most important AI-adjacent M&A themes. AI increases both defensive and offensive cyber capabilities. Companies need tools to protect data, monitor model behavior, prevent leakage of confidential information, and detect AI-enhanced attacks. At the same time, AI can automate threat detection, accelerate incident response, and improve identity management. Buyers are likely to pay premiums for cybersecurity assets that sit directly in the path of AI adoption.
The strategic buyer universe is also expanding. Large technology companies are still active, but consulting firms, defense contractors, financial institutions, industrial companies, cloud providers, and private equity-backed platforms are all looking for AI capabilities. Accenture’s acquisition of UK AI start-up Faculty in a deal valued at more than $1 billion, reported by the Financial Times, reflected the consulting industry’s push to strengthen AI capabilities as clients demand help adapting to technological disruption.
The consulting angle matters because it reveals a broader truth: AI capability is becoming a service, not just a product. Companies do not only need AI tools. They need implementation, governance, process redesign, data strategy, security, training, and integration. That creates opportunities for M&A across professional services, systems integration, and enterprise technology consulting.
Private equity is likely to follow that demand. Sponsors may pursue AI implementation firms, data-engineering companies, industry-specific automation platforms, and managed-service providers that help enterprises adopt AI. These assets may not carry the same headline excitement as frontier model companies, but they may offer more predictable revenue and clearer paths to monetization.
The AI M&A surge is also changing exit strategy. For several years, private equity firms struggled with a slow exit environment, as higher interest rates and valuation gaps reduced IPO and strategic sale activity. AI is helping reopen parts of the exit market. A sponsor that owns a software company with credible AI capabilities may find a deeper buyer pool and stronger valuation support. A sponsor that owns a legacy software company vulnerable to AI disruption may face the opposite.
KKR’s reported exploration of a sale of BMC Helix is a good example of the market test. Reuters reported that KKR is considering a sale of BMC Helix, an AI-driven IT service management platform, in a transaction that could value the company at up to $1.5 billion. The platform helps enterprises automate service desks, manage incidents and assets, and monitor hybrid IT systems.
That kind of asset sits directly in the enterprise AI workflow. IT service management is a natural area for automation because companies need to process tickets, resolve incidents, monitor infrastructure, and manage hybrid systems more efficiently. If AI can reduce human intervention and improve response times, the buyer universe expands. But the transaction would also test how much investors are willing to pay for AI-enabled software at a time when some legacy software valuations remain under pressure.
The valuation question is central. AI can justify premium multiples when it creates measurable growth, margin expansion, or defensibility. It can also become a buzzword that masks weak fundamentals. In a market where buyers are becoming more sophisticated, the difference matters. Deal teams are now expected to test whether AI features are proprietary, whether customers are paying for them, whether they reduce churn, and whether they require expensive compute that compresses margins.
This is where the diligence process is changing. Traditional financial diligence is no longer enough. Buyers need technical diligence, data-rights diligence, cybersecurity diligence, AI governance diligence, and product-roadmap diligence. They need to understand whether a company is building on third-party models or proprietary technology, whether its data can legally train models, whether its AI outputs are auditable, and whether customers will trust the system in regulated environments.
AI also affects workforce diligence. A company may claim productivity gains from AI, but buyers need to know whether those gains are real, whether employees are using approved tools, whether intellectual property is protected, and whether AI adoption creates compliance risk. In some sectors, AI can reduce headcount needs. In others, it may require new engineering, compliance, and monitoring teams.
The financing environment is another important factor. As deal activity returns, sponsors are looking for assets that can support leverage while still funding AI investment. Software companies with recurring revenue remain attractive to lenders, but AI-related business models may have new cost structures. Compute costs, cloud dependencies, and continued product development can affect free cash flow. Credit providers will increasingly evaluate whether AI spending is a growth investment or a margin drag.
That creates an opening for private credit. AI M&A requires financing, and private credit firms have become major lenders to sponsor-backed software and technology companies. But private credit lenders must also be careful. If AI disrupts a borrower’s product or weakens customer retention, the credit profile can deteriorate quickly. The same technology that creates acquisition opportunities can also create credit risk.
For mega alternative investment managers, the opportunity is integrated. A firm with private equity, infrastructure, real estate, private credit, and public-equity capabilities can invest across the AI stack. It can buy software companies, finance data centers, own power assets, lend to AI-adjacent platforms, and trade public-market beneficiaries. That cross-platform approach is becoming a competitive advantage.
BlackRock’s participation in the Aligned Data Centers transaction is an example of how large asset managers are positioning around AI infrastructure. Nvidia and Microsoft bring strategic technology demand, while BlackRock brings capital formation and infrastructure expertise. This is the kind of partnership structure likely to define the next phase of AI dealmaking: strategic technology companies working with large pools of private capital to build or acquire the physical backbone of AI.
The middleware and infrastructure focus also reflects skepticism about consumer AI monetization. Many consumer-facing AI applications have attracted enormous attention, but the business models remain uncertain. Customer acquisition costs can be high, competition can be intense, and users may switch tools quickly. Enterprise infrastructure and workflow software may offer a more durable path to revenue because they become embedded in business processes.
That is why investors are increasingly prioritizing AI that solves expensive enterprise problems. Automating legal review, reducing software-development time, improving fraud detection, optimizing supply chains, supporting medical documentation, accelerating customer service, and managing cybersecurity threats can produce measurable return on investment. In M&A, measurable ROI supports higher valuations.
There is also a vertical-software angle. Industry-specific software companies often sit close to proprietary workflows and data. If AI can be embedded into those workflows, the software provider may become more valuable. A healthcare software platform with access to clinical documentation workflows, an insurance platform with claims data, or a logistics platform with routing and pricing data may have AI potential that a generic software tool lacks.
This is why vertical specialization is becoming more important in technology M&A. IMAP’s 2026 technology M&A outlook noted that recurring revenue models, vertical specialization, applied AI capability, and digital infrastructure alignment are shaping the transaction landscape. For private equity, vertical software offers both consolidation potential and AI-enabled product expansion.
However, the market is not without risks. AI M&A can become overheated. Buyers may overpay for companies with limited defensibility. Sellers may attach AI narratives to ordinary software assets. Strategic buyers may pursue acquisitions out of fear of missing out. Private equity sponsors may underwrite aggressive growth assumptions that depend on AI adoption moving faster than customers are willing to pay.
Regulatory risk also looms. Large technology acquisitions are likely to receive scrutiny if they appear to consolidate control over AI infrastructure, data, or distribution. Cross-border AI transactions may face national security review, especially in semiconductors, cloud infrastructure, cybersecurity, and sensitive data markets. Data privacy, intellectual property, and model liability issues could also complicate transactions.
The antitrust environment remains important, even if regulatory conditions have improved enough to support a broader deal rebound. Buyers cannot assume every AI acquisition will sail through approval. Assets tied to data concentration, cloud dominance, chip supply, or national infrastructure may face a higher bar.
Still, the direction of travel is clear. AI has become one of the most important strategic rationales in global M&A. The M&A Advisor recently argued that the SpaceX-xAI and Google-Wiz transactions bracket a broader 2026 shift, with roughly one-third of the 100 largest corporate M&A transactions in 2025 explicitly citing AI as part of their strategic rationale, especially in technology, manufacturing, and power and utilities.
That breadth is the key point. AI is not just driving technology deals. It is influencing industrials, utilities, financial services, healthcare, defense, consulting, and infrastructure. Every sector is asking the same question: do we build AI capability internally, partner with a provider, or buy it?
M&A is often the fastest answer.
For alternative investment firms, the opportunity is to identify where AI creates durable value before it becomes fully priced. That requires avoiding the most superficial version of the trade. The winners will not be the firms that simply buy anything with AI in the pitch deck. The winners will be the firms that understand the difference between AI features and AI moats.
An AI feature can be copied. An AI moat is harder. It may come from proprietary data, embedded workflow, regulatory trust, distribution advantage, technical depth, compute access, or customer dependency. In software M&A, that distinction will determine whether today’s premium multiple becomes tomorrow’s value creation story or tomorrow’s impairment.
The best sponsors will also use AI inside their own deal processes. AI tools are already being used to screen targets, analyze contracts, summarize diligence materials, identify comparable transactions, model customer churn, and accelerate market mapping. Dealmaking itself is becoming more automated. UniCredit, for example, has expanded use of its AI-driven DealSync platform to identify small and mid-sized M&A opportunities, and Financial News London reported that the platform had generated 4,500 deal leads and facilitated two transactions.
That matters because AI is changing both the object of M&A and the process of M&A. Firms are buying AI assets, but they are also using AI to find, diligence, and execute deals. Over time, that could compress timelines, expand buyer universes, and increase competition for high-quality targets.
The private markets implications are enormous. If AI improves deal sourcing, smaller targets may become easier to identify. If AI improves diligence, sponsors may evaluate more opportunities with fewer resources. If AI improves portfolio operations, value creation plans may become more measurable. But if everyone has access to similar tools, information advantages may shrink, and competition may intensify.
That is why proprietary judgment still matters. AI can accelerate analysis, but it cannot fully replace investment discipline. In a market filled with AI narratives, the ability to say no may become as valuable as the ability to move quickly.
The AI M&A surge is ultimately about control. Corporations want control over capabilities that may determine future competitiveness. Private equity firms want control over platforms that can be transformed by AI. Infrastructure investors want control over the assets that power compute. Credit providers want control over risk exposure to borrowers facing AI disruption. Asset managers want control over the capital flows attached to the biggest secular investment theme of the decade.
Software has become the center of that control battle because it is where AI meets the enterprise. Infrastructure provides the compute, but software determines how AI enters workflows, creates productivity, and captures revenue. Middleware provides the bridge between models and real-world adoption. Together, those layers explain why software is capturing such a large share of AI M&A activity.
The next stage will be more competitive and more discriminating. As more capital enters the market, valuations will rise for the best assets. Weak assets will attempt to rebrand themselves. Buyers will need deeper diligence. Regulators will pay closer attention. Public-market volatility will influence private valuations. And the distinction between AI hype and AI earnings will become sharper.
For investors, that is where the opportunity lies. AI M&A is not a single-cycle fad. It is a structural reallocation of corporate and private capital toward the infrastructure, software, data, and automation systems that will define the next generation of business operations.
The firms that understand this early are not just buying growth. They are buying position. They are securing the operating layer of an AI-enabled economy.
That is why the AI M&A surge has become one of the defining alternative investment stories of 2026. Software is capturing the largest share of deals, but the real prize is broader: the infrastructure and middleware that make AI usable, secure, scalable, and profitable. Consumer applications may dominate public attention, but the serious capital is increasingly moving underneath the surface.
In the next phase of the AI boom, the most valuable deals may not be the loudest. They may be the companies that quietly sit between models and customers, between data and decisions, between infrastructure and productivity. That is where private equity, strategic acquirers, and alternative asset managers are now placing their bets — and where the next wave of AI-driven value creation is likely to emerge.
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