Centerview’s Tony Kim Says AI Changes Pace, Structure of Deals

Artificial intelligence is changing how deals are structured and executed in the sector, according to Tony Kim of Centerview Partners. The article is a brief interview segment with no specific transaction, valuation, or financial metric disclosed. Market impact is likely limited, though it reinforces AI as a growing driver of deal activity and advisory workflow.

Analysis

The key read-through is not that AI is “changing dealmaking,” but that it is compressing the time and labor required to underwrite, diligence, and negotiate transactions. That tends to redistribute value away from large-firm process advantages and toward advisers, lawyers, and bankers that can codify repeatable AI workflows faster, while also putting pressure on fee pools in lower-complexity mandates. Over 6-18 months, expect greater pricing dispersion: bespoke, regulatory-heavy, or cross-border deals should remain resilient, while standardized growth-equity and mid-market transactions face fee compression and faster auction cycles.

A second-order effect is that AI raises the cost of being slow. Sellers can run more parallel processes, buyers can screen targets faster, and management teams can benchmark alternatives in real time, which should increase competitive intensity in private markets. That benefits platforms with proprietary data and execution scale, but hurts firms whose edge is relationship-based and whose revenues depend on manual screening and slide-deck production. The biggest economic winners are likely the software and data vendors embedded in the transaction stack, not the advisers themselves.

The contrarian view is that the current narrative may overstate near-term productivity gains while underestimating implementation friction. In regulated or high-liability processes, firms will adopt AI cautiously, so realized margin expansion may lag the rhetoric by multiple quarters. A more durable outcome is structural fee pressure: if AI lowers the marginal cost of originating and diligence work, clients will push harder on retainers and success fees, which is a medium-term headwind for investment banking economics even if headline deal volume holds up.

Catalyst-wise, watch for advisory firms to disclose AI-driven headcount leverage or margin improvement in the next 2-4 quarters; that would validate the thesis and likely rerate the data/software beneficiaries first. Conversely, any meaningful pullback in private-market exits or a risk-off funding environment over the next 6-12 months would dominate the AI-efficiency story and weaken the expected deal flow uplift.

Key Decisions for Investors

• Long MSFT / SNOW on a 6-12 month horizon: own the embedded workflow and data-layer beneficiaries of AI-enabled dealmaking; target upside from incremental enterprise adoption, with downside limited by recurring revenue and broad platform exposure.
• Long IREN-like pure-play AI infrastructure only if available via listed proxies; otherwise use a basket of PLTR / NOW on dips over the next 1-3 months to express the thesis that transaction workflows will become software-driven before banker economics improve.
• Short a basket of traditional advisory/consulting fee-exposed names on valuation strength over the next 3-6 months (regionally listed banks or publicly traded M&A advisers where available): AI should pressure labor-intensity and pricing before it boosts volumes enough to offset it.
• Pair trade: long high-quality private-market enablers / data providers vs. short late-cycle growth-financing intermediaries over 6-9 months, betting that AI accelerates diligence and sourcing for the strongest platforms but commoditizes lower-value intermediaries.
• Use upside calls on software vendors with dealflow exposure into next earnings cycle; if management teams explicitly discuss AI-led productivity gains, the market usually re-rates within 1-2 quarters before revenue shows up.