Centerview's Tony Kim Says AI's Speed Is Rewriting the Deal Playbook

AI deals are moving faster than traditional tech acquisitions, forcing investors and founders to compress due diligence and restructure negotiations entirely. Tony Kim, co-president of investment banking at Centerview Partners, told Bloomberg that the rapid pace of AI advancement has fundamentally altered how deals get done.

The acceleration isn't just about speed for speed's sake. Kim notes that AI capabilities evolve so quickly that standard 6-12 month deal timelines can render a company's technology obsolete by closing. This compression affects everything from valuation methodologies to risk assessment frameworks.

This shift represents a departure from traditional tech M&A, where buyers could leisurely evaluate market position and competitive moats. Now, the window for strategic advantage closes in months, not years.

Kim observes a consistent pattern: AI founders obsess over technical capabilities while missing the strategic positioning that actually drives acquisitions. Despite M&A activity increasing 12% in 2024, numerous startups with solid technology struggle to attract serious interest.

The disconnect stems from founders spending months perfecting pitch decks highlighting AI capabilities, data advantages, and technical moats. Yet they fundamentally misunderstand what acquirers actually want. Strategic buyers care less about breakthrough algorithms and more about integration potential, market timing, and defensive positioning.

This misalignment has created a two-tier market where well-positioned AI companies command premium valuations while technically superior but poorly positioned startups languish. The gap isn't in technical merit but in strategic narrative.

Traditional M&A structures are giving way to more flexible, milestone-based arrangements that account for AI's rapid evolution. Rather than fixed valuations, deals increasingly include earnouts tied to technical benchmarks or market adoption metrics.

Kim's experience advising on over $1 trillion in transactions, including Scale AI's $29 billion valuation from Meta and Disney's $28 billion Hulu acquisition, provides unique insight into these structural shifts. The new paradigm favors staged investments, option agreements, and contingent value rights that let buyers hedge against technological obsolescence.

This evolution mirrors broader market uncertainties. With AI capabilities advancing weekly rather than yearly, rigid deal structures become liabilities rather than assets.

The AI acceleration effect ripples beyond M&A into venture capital and public markets. Kim's recent commentary on CNBC about IPO markets coming back from weakness reflects how AI companies must now balance rapid scaling against public market scrutiny.

The compressed timelines affect everything from Series B to pre-IPO positioning. Companies that might have spent years building enterprise sales teams now need to demonstrate market traction in months to justify valuations that assume exponential growth.

This pressure creates a paradox: AI companies need more capital to compete, but must prove viability faster than ever. The result is a new breed of hybrid financing rounds that blend venture capital with strategic corporate investments.

AI's dealmaking revolution extends beyond AI-native companies. Traditional software firms, cloud providers, and even non-tech corporations now face the same accelerated timeline pressures when acquiring AI capabilities.

The phenomenon resembles the dot-com era's pace but with higher stakes. Companies that successfully navigate this environment, like those Kim has advised, create defensive moats through speed rather than traditional IP protections.

This shift has implications for everything from antitrust policy to employee retention strategies. When deals close in weeks instead of quarters, regulatory scrutiny and talent retention become simultaneous challenges rather than sequential processes.

The current trajectory suggests AI dealmaking will continue compressing, potentially reaching real-time valuations and instant acquisitions for strategic assets. Kim's observation that founders feel investors don't understand their positions indicates this disconnect may worsen before it improves.

The next evolution likely involves AI-powered due diligence itself, where algorithms assess acquisition targets faster than human teams. This meta-application of AI to AI deals could further accelerate the cycle.

For founders and investors, success increasingly depends on mastering the intersection of technical capability and strategic positioning. Those who can articulate both dimensions quickly will dominate the next wave of AI consolidation.