Artificial intelligence is no longer a sideshow in corporate dealmaking. According to Morgan Stanley's top M&A bankers, it is becoming the main event.
Tom Miles, Morgan Stanley's Global Co-Head of M&A, laid out a blunt assessment of the current deal landscape during a recent appearance on Bloomberg Deals: companies that fail to articulate a credible AI strategy risk being left behind, both as acquirers and as attractive targets. The conversation with Bloomberg's Dani Burger touched on the broader revival of global M&A activity, but the through line was unmistakable. AI is reshaping how deals get done, which deals get done, and why.
Global merger and acquisition activity has been clawing its way back after a brutal 2023, when elevated interest rates, regulatory scrutiny, and macroeconomic uncertainty pushed deal volumes to their lowest levels in over a decade. Data from Dealogic showed worldwide M&A totals roughly $1.2 trillion in the first half of 2024, a modest improvement but still well below the peaks of 2021. The rebound is real but uneven, and AI-related transactions are shouldering a disproportionate share of the momentum.
What makes this cycle different from previous technology-driven M&A waves is the dual nature of AI's impact. It is not simply that big companies are buying AI startups, although that is happening at a furious pace. The deeper shift is that AI is forcing virtually every industry to reassess its competitive position, cost structure, and growth assumptions. As Miles indicated in the Bloomberg interview, boards and chief executives are now approaching deals with AI front of mind, whether they run a logistics company, a healthcare provider, or a financial services firm.
Consider what has happened over the past eighteen months. Companies like Databricks, Anthropic, and Mistral have commanded multi-billion-dollar valuations in funding rounds that look more like traditional M&A than venture capital. Thoma Bravo's acquisition of a majority stake in AI-powered legal research company CaseText, and Cisco's $28 billion proposed acquisition of Splunk, both signalled a broader appetite among large enterprises and private equity firms to pay significant premiums for AI-adjacent capabilities. These are not speculative bets on unproven technology. They are calculated moves to secure data infrastructure, machine learning talent, and automation tools that can drive real operational savings within tight timeframes.
For startups, this environment creates both opportunity and pressure. On one hand, AI-native companies with differentiated technology, strong product-market fit, and clean data pipelines are finding themselves in high demand. Strategic buyers are willing to pay up, and the universe of potential acquirers has broadened well beyond traditional tech giants. Industrial conglomerates, retailers, and pharmaceutical companies are all actively scouting AI targets. On the other hand, startups that merely wrap a thin layer of AI around conventional software, without clear technical moats, face a much harder road. The market is getting smarter at distinguishing genuine innovation from marketing, and acquirers are conducting more rigorous technical due diligence than ever before.
What Comes Next
There are risks worth watching. Regulatory agencies in both the United States and Europe have signaled increased scrutiny of AI-related acquisitions, particularly when large platforms acquire smaller rivals that could become competitive threats. The Federal Trade Commission's ongoing review of several major tech deals underscores this trend. Antitrust enforcement tends to lag market cycles, but the current political climate in both Washington and Brussels suggests that the next wave of blockbuster AI deals will face meaningful regulatory friction.
Interest rates remain another variable. While markets are pricing in gradual rate cuts through 2025, borrowing costs are still elevated relative to the cheap-money era that fuelled the 2020-2021 deal boom. That means buyers are being more selective, leaning toward targets with proven revenue models rather than speculative growth stories. For AI companies still in the pre-revenue or early-revenue stage, access to capital may tighten if the macro picture darkens.
The practical takeaway for founders and corporate development teams alike is straightforward. AI is no longer a feature you bolt onto a pitch deck. It is a fundamental lens through which every potential deal is being evaluated. Companies that can demonstrate how their AI capabilities translate into measurable efficiency gains, cost reductions, or new revenue streams will command premium valuations. Those that cannot will struggle to get serious attention in a market that is moving fast, and getting faster.
