Investment bankers are positioning themselves for a massive wave of AI-related mergers and acquisitions, with deal activity surging to $82 billion in 2024, up from approximately $55 billion in 2023 according to 451 Research. However, the focus isn’t primarily on pure-play generative AI companies—instead, dealmakers are targeting the “pickaxes and shovels” of the AI revolution: data infrastructure, management tools, and supporting technologies.
Neil Kell, Bank of America’s chair and global head of technology, media, and telecom for equity-capital markets, emphasized that effective AI deployment requires robust data management and integrity. Companies seeking acquisitions are increasingly focused on firms that specialize in managing, moving, and securing data—the foundational elements that enable AI applications to function effectively.
The surge in AI dealmaking is driven by several converging factors. Lower interest rates have reduced borrowing costs, making acquisitions more financially attractive. The gap between buyer and seller price expectations is narrowing as companies reset their valuations following earlier market exuberance. Additionally, political uncertainty has cleared with President-elect Trump’s selection of Andrew Ferguson to lead the Federal Trade Commission, a move viewed favorably by Big Tech companies eager to pursue M&A opportunities.
Brandon Hightower from tech-focused M&A advisory firm Axom Partners attributes the increase in AI-themed deals to “an arms race” around infrastructure and talent. Key acquisition targets include companies focused on developer tools, resource optimization, data analytics, and security. Major players like Databricks and Snowflake have made multiple acquisitions this year to strengthen their data infrastructure capabilities.
Notably, even AI giants are acquiring infrastructure companies. OpenAI’s purchase of Rockset earlier this year focused on enabling faster data retrieval and improving data pipelines. Nvidia acquired Seattle-based OctoAI to scale AI workflows more efficiently, and is pursuing Run:ai to optimize compute resources and reduce costs.
Jung Min, co-COO of Goldman Sachs’ TMT division, identified data infrastructure, management, analytics companies, and developer tools as the most interesting areas to watch. The focus on reducing inference costs—the expense of generating AI responses—is driving near-term M&A activity, with potential buyers including next-generation cloud companies like CoreWeave and Lambda.
Beyond pure technology sectors, AI dealmaking is expected to impact customer service, commerce, and industrial sectors. Companies that automate supply chains are particularly ripe for AI integration, while Salesforce’s pivot to AI agents is spurring competitive acquisitions among CRM providers like ServiceNow, Braze, and HubSpot.
Key Quotes
Everybody’s rightly caught up in the lore of AI and what the industry leaders are doing regarding building out platforms, but people forget; unless you have good data management and good data integrity, you can’t fully deploy any application of AI
Neil Kell, Bank of America’s chair and global head of technology, media, and telecom for equity-capital markets, emphasizes the critical but often overlooked foundation of AI success—quality data infrastructure rather than flashy AI models.
Two of the most interesting areas to watch next year will be data infrastructure, management, and analytics companies. The second is developer tools
Jung Min, co-COO of Goldman Sachs’ TMT division, identifies the key M&A targets for 2025, highlighting where investment bankers expect the most dealmaking activity in the AI ecosystem.
If you have data and if you own the models, those are two key components. How do I bring those together? And if you’ve got Databricks and Snowflake holding a lot of enterprise data, that’s a natural place for a future winner in the AI world
Alan Bressers, Axom Partners cofounder, explains why data infrastructure giants like Databricks and Snowflake are positioned to become dominant players in AI through their control of enterprise data assets.
Ferguson was tapped at a key time in the AI arms race in which we expect the strong to get stronger as Mag 7 gets the engines started up again on M&A
Wedbush Securities analysts highlight how the regulatory environment under Andrew Ferguson’s FTC leadership may enable Big Tech companies to pursue more aggressive AI-related acquisitions.
Our Take
This article reveals a crucial insight often missed in AI hype cycles: infrastructure matters more than applications in the near term. While headlines focus on ChatGPT and other consumer-facing AI tools, smart money is betting on the unglamorous but essential plumbing—data pipelines, storage, security, and optimization tools. This mirrors historical technology waves where infrastructure providers often captured more value than application developers.
The emphasis on reducing inference costs is particularly telling. As companies deploy AI at scale, they’re discovering that operational expenses can quickly spiral out of control. This creates immediate ROI opportunities for acquisition targets that can demonstrably reduce these costs, making such deals easier to justify than speculative bets on unproven AI startups.
The regulatory tailwinds under a potentially more permissive FTC could accelerate consolidation, allowing Big Tech to strengthen its AI moat through strategic acquisitions. This may benefit innovation in the short term but raises longer-term concerns about competitive dynamics in what may be the most important technology sector of the coming decade.
Why This Matters
This dealmaking surge represents a fundamental shift in how the AI industry is maturing. Rather than speculative investments in unproven generative AI startups, sophisticated buyers are targeting the essential infrastructure that makes AI deployment practical and scalable. This signals that AI is transitioning from experimental technology to enterprise-critical infrastructure requiring robust data foundations.
The $82 billion in AI-related acquisitions demonstrates massive capital commitment to building sustainable AI ecosystems. For businesses, this means the competitive landscape will increasingly favor companies with strong data management capabilities and efficient AI infrastructure. The focus on reducing inference costs and optimizing compute resources indicates that AI economics are becoming paramount—it’s no longer just about what AI can do, but whether it can be done profitably at scale.
For workers and society, this M&A wave will consolidate AI capabilities within larger organizations, potentially accelerating AI adoption across non-tech sectors like manufacturing, customer service, and commerce. The regulatory environment under Ferguson’s FTC leadership may enable Big Tech to strengthen its AI position through acquisitions, raising important questions about market concentration and competition in this transformative technology sector.
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