Every major corporate strategy conversation now has the same uninvited guest: artificial intelligence. And where strategy conversations go, investment bankers follow.

Wally Cheng, Morgan Stanley’s Global Head of Technology M&A, is making the case that AI-related acquisitions are set to accelerate across industries and deal sizes. Not just mega-deals between tech giants, but transactions spanning sectors that historically had little to do with machine learning or neural networks. Think healthcare, manufacturing, financial services, and logistics, all racing to bolt on AI capabilities before their competitors do.

The AI acquisition arms race

Here’s the thing about corporate AI strategy in 2024: building it in-house takes too long. Training specialized models, hiring scarce talent, and assembling proprietary datasets can burn years. For companies feeling competitive pressure right now, buying is faster than building.

That dynamic is what Cheng is pointing to. The pressure on companies to enhance their AI capabilities has become a primary catalyst for M&A activity, creating demand from both strategic buyers and financial sponsors. Strategic buyers want the technology integrated into their existing operations. Private equity firms see AI-native companies as portfolio plays with outsized growth potential.

The result is a dealmaking environment where AI assets are attracting interest from a remarkably wide range of acquirers. A retailer looking at computer vision for inventory management and a defense contractor seeking autonomous systems both end up in the same M&A pipeline, competing for overlapping talent pools and technology stacks.

Cheng’s outlook also reflects improving financing conditions. After a period where rising interest rates and economic uncertainty froze much of the deal market, stabilizing capital costs are making leveraged acquisitions feasible again. When borrowing gets cheaper, more buyers can afford to come to the table. That’s true for billion-dollar platform acquisitions and for smaller tuck-in deals alike.

Why this goes beyond Silicon Valley

The most interesting part of Cheng’s thesis isn’t the volume prediction. It’s the breadth.

Previous waves of technology M&A, cloud computing in the 2010s, mobile in the early 2010s, social media before that, tended to concentrate among a relatively narrow set of buyers. Tech companies bought other tech companies. The AI wave looks different because the technology is horizontal, not vertical. It applies to almost every business function: customer service, drug discovery, supply chain optimization, fraud detection, content creation.

That horizontal applicability means a pharmaceutical company might be bidding against a fintech startup for the same natural language processing firm. It means industrial conglomerates are suddenly showing up at the same auctions as software giants. The competitive dynamics for AI targets get complicated fast when every sector considers itself an AI buyer.

For Morgan Stanley, this represents a significant advisory opportunity. The bank’s decision to promote Cheng to lead its global technology M&A practice as part of a broader leadership restructuring signals how seriously it takes the AI dealmaking pipeline. When a bulge-bracket bank reorganizes its leadership around a theme, it’s typically because the fee pool is already materializing.

Look, 2023 was already an active year for US M&A despite economic headwinds. Conditions heading into 2024 and 2025 appear even more favorable for a broader recovery. Cheng sees early evidence of this uptick, with AI serving as the through-line connecting otherwise unrelated transactions.

What this means for investors

The implications here ripple well beyond traditional M&A advisory fees. When acquirers compete aggressively for AI assets, valuations get bid up. That creates a halo effect across the entire AI ecosystem, from pure-play AI companies to data infrastructure providers to semiconductor firms supplying the compute layer.

For public equity investors, the takeaway is straightforward: companies with defensible AI capabilities, whether proprietary models, unique datasets, or specialized talent, carry acquisition premium optionality. Even if they never get bought, the fact that they could be bought at a premium creates a valuation floor. That’s particularly relevant for mid-cap AI companies that might be too small to dominate their markets independently but too valuable for larger players to ignore.

The digital asset space is worth watching here too. AI-linked tokens and blockchain-based data infrastructure projects could see renewed interest if traditional M&A activity validates the economic value of AI capabilities more broadly. When Morgan Stanley’s dealmakers are telling clients that AI assets command premium multiples, that narrative tends to seep into adjacent markets.

The risk, of course, is overpayment. M&A waves driven by technological anxiety have a long history of producing deals that look brilliant in the press release and terrible on the balance sheet three years later. The dot-com era and the social media acquisition spree of 2012-2014 both generated their share of write-downs. Companies buying AI assets under competitive pressure, with improving financing conditions making leverage easy, is precisely the cocktail that historically produces winner’s curse outcomes.

For investors watching this space, the signal isn’t just that more deals are coming. It’s that the competitive landscape for AI acquisitions is broadening in ways that could sustain elevated valuations across AI-adjacent sectors for several quarters, even as individual deal outcomes remain a coin flip.