Big Tech’s AI M&A Strategies: What Do They Mean for VCs?
Artificial intelligence investments have moved to the very center of the technology ecosystem over the past two years. In 2024, AI accounted for approximately 34% of total global investments, a figure that increased to nearly 50% in 2025. Total AI investment volume, which stood at $114 billion just a year ago, reached $202 billion in 2025.
Artificial intelligence investments have moved to the very center of the technology ecosystem over the past two years. In 2024, AI accounted for approximately 34% of total global investments, a figure that increased to nearly 50% in 2025. Total AI investment volume, which stood at $114 billion just a year ago, reached $202 billion in 2025. This growth signals not only an expansion in investment volume, but also a fundamental shift in capital allocation priorities across the technology landscape.
Where Is the Capital Flowing?
A significant portion of this capital has been directed toward companies developing foundational AI models. In 2025, firms building large language models and general-purpose AI systems captured nearly 40% of total AI investments.
The rapid growth in investment volumes has also had a clear impact on how Big Tech approaches both investing and M&A. For global technology leaders such as Google, Microsoft, Meta, Amazon, and OpenAI, the focus is no longer simply on how much capital is allocated to AI, but rather on how that capital is integrated into product development processes, distribution channels, and long-term strategic objectives.
As a result, Big Tech’s AI-focused M&A strategy has moved away from traditional large-scale acquisitions toward a more targeted approach—one centered on acquiring specific technical capabilities, deepening ecosystem relationships, and accelerating product development cycles.
What Do 2025’s Moves Signal?
In 2025, Google acquired Galileo AI, a company operating in generative design, bringing both its technology and team in-house. Rather than a large-scale acquisition, this move stands out as a capability-driven strategic acquisition. Galileo AI’s ability to generate user interfaces from text and visual inputs enables Google to integrate AI models more effectively into the early stages of product development. This positioning also strengthens integration with other components of the Google ecosystem, including Firebase, Flutter, Material, and Google Cloud.
Google also submitted an approximately $32 billion acquisition offer for Wiz, a cloud security company, which was approved by competition authorities last November. Wiz provides security, visibility, and risk management solutions for multi-cloud environments, with a particular focus on data access and governance challenges that arise as AI applications are integrated into enterprise infrastructure. Through this acquisition, Google aims to ensure that AI models running on Google Cloud are more secure and controllable, while easing enterprise customers’ security concerns during AI adoption.
Meta
Meta’s relationship with Scale AI is one of the most striking examples of how Big Tech’s AI M&A strategies are evolving. In 2025, Meta invested $14.3 billion in Scale AI, acquiring 49% ownership, while also bringing Scale AI’s CEO into Meta as part of the deal. This move highlights Meta’s emphasis on high-quality training data and evaluation infrastructure for its AI models—and shows that the transaction extends beyond financial investment to include the transfer of knowledge and operational expertise.
This strategic move underscores a broader shift: value in AI is increasingly driven not by outright ownership, but by access to critical technical capabilities and the effective integration of teams that build them.
Meta’s acquisition of PlayAI, a voice-based AI startup, along with its aggressive recruitment of OpenAI talent through multi-million-dollar offers, further demonstrates that competition in AI is increasingly shaped by talent and specialized expertise, as much as by corporate entities themselves.
OpenAI
OpenAI acquired io Products for $6.5 billion, bringing the company’s design team and product development capabilities in-house. Focused on next-generation AI hardware and user interfaces, this acquisition reflects OpenAI’s ambition to move beyond being solely a model-development company and to play a defining role in the product layer that mediates human–AI interaction.
By internalizing hardware and design capabilities, OpenAI signals a future in which AI shifts from a background technology to a highly visible, continuously used product experience.
Amazon
Amazon acquired Bee, a startup developing AI-powered wearable devices. While Amazon has long been positioned in AI-enabled consumer hardware through Alexa—now running in approximately 97% of the company’s hardware devices via the upgraded Alexa+—the Bee acquisition extends the AI assistant experience beyond the home. It enables Amazon to engage users through a wearable device that remains with them throughout the day, reflecting a strategy of embedding AI across diverse moments of everyday life rather than limiting it to fixed devices.
Microsoft
Microsoft recently announced its acquisition of Osmos, a company developing an agentic AI-based data engineering platform. This acquisition supports Microsoft’s strategy to expand its Fabric platform by leveraging autonomous AI agents in data engineering workflows.
Osmos offers automated processes that prepare raw data for analytics and AI applications on OneLake, Microsoft Fabric’s unified data lake. Through this acquisition, Microsoft aims to simplify how customers consolidate data and analytics in a single, secure platform, while enabling autonomous AI agents to collaborate with humans, reduce operational workload, and improve efficiency in data-driven processes.
What Does This Mean for the VC Ecosystem?
Recent acquisitions and talent transfers point to a fundamental shift in how value is created in AI investing. Big Tech’s AI-focused M&A strategies increasingly prioritize specific technical capabilities, strong teams, and solutions that can be directly integrated into existing products and platforms.
This approach elevates the importance of understanding not only a company’s scalability, but also which strategic need it addresses and how closely it aligns with Big Tech’s long-term product roadmaps. Rather than acquiring companies outright, major technology players are focused on integrating the right teams and critical capabilities quickly into their existing ecosystems—accelerating product development, improving security and compliance, and transforming AI from an abstract technology into a core, embedded product component.
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