Madrona Ventures anticipates a rise in private-to-private mergers as a key trend for 2026. With questions about the long-term durability of even fast-growing private AI companies, consolidation is seen as a primary way for winners to emerge and build more defensible businesses in a volatile market.
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While AI technology will achieve widespread adoption and major breakthroughs, the financial infrastructure supporting it will falter. Peripheral companies that jumped on the AI trend without a core business will face a significant market correction, creating a paradoxical "best and worst" year for the industry.
Because boards lack deep expertise in AI's seismic impact, they are pursuing scale-driven M&A. The goal is to accumulate diverse assets ('cards in a deck') to maintain flexibility and strategic options in an unpredictable, AI-driven future, rather than making specific bets on the technology itself.
Current M&A activity related to AI isn't targeting AI model creators. Instead, capital is flowing into consolidating the 'picks and shovels' of the AI ecosystem. This includes derivative plays like data centers, semiconductors, software, and even power suppliers, which are seen as more tangible long-term assets.
The current AI market is like hot, moving fat in a skillet—fluid and competitive. The key strategic question is predicting when "the heat comes off and then everything's fixed." This "congealing" moment will lock in market leaders and make disruption much harder, marking the end of the wild early phase.
With hundreds of unicorns and only about 20 tech IPOs per year, the market has a 30-year backlog. Consolidations between mid-size unicorns, like the potential Fivetran and dbt deal, are a necessary strategy for VCs to create IPO-ready companies and generate much-needed liquidity from their portfolios.
The rapid evolution of AI means traditional private equity M&A timelines are too slow. PE firms and their portfolio companies must now behave more like venture capitalists, acquiring earlier-stage, riskier AI companies to secure necessary technology before it becomes unaffordable or obsolete.
Widespread adoption of AI coding tools like Cursor dramatically increases code output, shifting the primary development bottleneck from writing to reviewing. This creates a market for collaboration tools like Graphite and drives consolidation as platforms race to own the end-to-end developer loop.
The dot-com era saw ~2,000 companies go public, but only a dozen survived meaningfully. The current AI wave will likely follow a similar pattern, with most companies failing or being acquired despite the hype. Founders should prepare for this reality by considering their exit strategy early.
Conventional venture capital wisdom of 'winner-take-all' may not apply to AI applications. The market is expanding so rapidly that it can sustain multiple, fast-growing, highly valuable companies, each capturing a significant niche. For VCs, this means huge returns don't necessarily require backing a monopoly.
While merging portfolio companies is strategically sound, it's often blocked by investor incentives (e.g., diluting a 20% stake in a winner down to 8%). The process is vastly simplified when a single firm, like Andreessen Horowitz in the Fivetran/dbt case, is a major investor in both companies, which aligns incentives.