Unlike traditional acquihires that saved failing startups, today's AI acquihires are offensive moves where large companies pay billions for elite teams. The target's product is often irrelevant; the goal is to infuse the acquirer's existing products with top-tier AI talent, treating engineers like superstar athletes.

The PayPal bid exemplifies a new M&A trend: modern, AI-first companies are buying mature, founderless digital businesses. They see untapped potential in optimizing operations, networks, and products with AI, creating a playbook for reviving what they see as "flaccid" digital assets.

Perplexity, reportedly valued at $20B, is paying Snap—valued at half that—$400M for distribution. This inverted dynamic, where the less mature company pays for access, highlights how AI-related market caps are often detached from fundamental business performance like revenue and user base.

OpenAI's acquisition of media company TBPN doesn't make sense for user growth, as ChatGPT's audience is orders of magnitude larger. The rationale is likely strategic: gaining in-house media talent to shape public perception of AI, a technology facing significant public backlash.

Snap's $400M deal with Perplexity, paid largely in stock, pioneers a new strategy for consumer platforms. They can leverage their massive user bases as a capital asset, trading distribution for significant equity stakes in capital-rich AI startups that desperately need user growth.

If the AI market downturns and frontier models like OpenAI can't sustain their massive capital needs, they won't just disappear. A likely outcome is acquisition by a Big Tech giant like Microsoft or Apple at a fraction of their peak valuation, turning the AGI dream into a product feature.

In today's AI M&A market, the ease of replicating software has shifted acquisition focus away from pure technology. Buyers now prioritize targets with hard-to-replicate moats like brand reputation, established customer bases, and strong developer communities, as seen in the NVIDIA-Hugging Face deal.

In the current M&A landscape, data-centric startups are more valuable than application-layer companies. Acquirers, particularly large tech firms, need proprietary data sets to train, run, and customize their AI models. This demand makes companies with unique data assets highly attractive takeover targets, with some seeing a tenfold increase in inquiries.

Recent acquisitions of slow-growth public SaaS companies are not just value grabs but turnaround plays. Acquirers believe these companies' distribution can be revitalized by injecting AI-native products, creating a path back to high growth and higher multiples.