Sector Spotlight: Generative AI & The Impact on Software M&A
Generative AI has moved from hype to hard cash. In 2025 the headline deals, from hyperscalers locking up capacity, to enterprise software acquirers buying specialist model and agent teams, are reshaping how buyers value, and integrate software targets. For mid-market founders and acquirers in North America (and Canada specifically), the shifting economics create both opportunity and new risks. Below we summarize a few of the large moves and unpack three practical implications for middle-market software M&A.
Recent large, market-moving deals
• CoreWeave signed a multi-billion dollar infrastructure agreement to supply Meta with GPU capacity through 2031, a deal reported at roughly $14.2B and signaling the strategic importance of scale compute capacity to large AI players.
• Microsoft and OpenAI formalized a next-phase memorandum of understanding in September 2025, underscoring ongoing strategic realignment between cloud providers and leading model creators as both seek favorable commercial and control terms.
• Strategic acquirers remain active: Salesforce’s mid-2025 acquisition of Convergence.ai (an AI agents specialist) is a clear example of enterprise software vendors buying capability to accelerate product roadmaps rather than build from scratch.
• Capital flows into standalone model builders and infrastructure players also continue: Anthropic’s multi-billion dollar collaborations and investments from major clouds illustrate how platform and compute partners are underwriting the model economy.
These headline moves, plus numerous other announcements and partnerships are compressing the competitive window for acquiring top AI talent and IP.
What this means for middle-market software M&A
1) Multiples and buyer sets are diverging
Generative AI capability can materially re-rate a target. Buyers are willing to pay premium multiples for companies with defensible models, proprietary datasets, or proven AI-driven workflows that materially increase customer ROI. That said, the buyer universe is bifurcating: deep-pocketed strategics and some PE sponsors chasing platform plays will pay up for AI-enabled differentiation, whereas traditional acquirers may be more conservative and disciplined. Expect valuation dispersion, as similar revenue profiles can attract very different prices depending on perceived AI upside in the current market.
2) Due diligence now must include compute, data & IP economics
Technical due diligence has expanded. Beyond code quality and architecture, buyers are focused on:
Model provenance and licensing (what training data was used; third-party model liabilities);
Compute & cloud economics (run costs for inference and R&D can be a material P&L item); and
Talent continuity and governance (keeping ML researchers and engineers post-close, plus reproducibility of models).
3) Deal structure and integration complexities increase
Earnouts, holdbacks and milestone payments are becoming common when AI ambitions are part-promise, part-execution. Buyers may prefer contingent payouts tied to product-level performance or customer outcomes (e.g., measured model accuracy, new ARR from AI features). Integration risk is also higher: merging models, data schemas and privacy regimes requires cross-functional execution and often regulatory review. For Canadian targets, cross-border data residency, privacy (PIPEDA & provincial regimes) and talent mobility must be addressed in integration planning.
Canada & cross-border considerations
Canada remains an important AI talent hub (research labs, startups, and service firms) and will continue to feed North American M&A activity. Canadian companies frequently attract U.S. strategic buyers and PE when they demonstrate model IP and enterprise traction; conversely, Canadian buyers are also consolidating local AI capabilities. Sellers in Canada should be especially mindful of data residency, bilingual product requirements, and immigration/retention strategies for ML talent, these are practical items that materially affect deal value and post-close execution.
Conclusion
Generative AI has rewritten the playbook for strategic acquirers and re-weighted what buyers prize in software targets. For middle-market players in North America and Canada, the opportunity is clear: companies that translate AI into durable customer outcomes, and that can demonstrate controlled, cost-effective operations around models and data, will see the most attractive exits. But those rewards come with heavier diligence, structural complexity and execution risk; being prepared on those fronts is now paramount for a successful deal.