AI has become a battleground for geopolitical competition, national resilience, and societal transformation. The stakes are no longer theoretical, and the window for action is closing fast.
In March, the U.S. escalated its efforts to shape the global technology landscape by expanding export controls on advanced AI and semiconductor technologies. Over 80 entities – more than 50 in China – were added to the export blacklist, aiming to regulate access to critical technologies. The move seeks to limit the development of high-performance computing, quantum technologies, and AI in certain regions, citing national security concerns.
As these export controls tighten, reports have surfaced of restricted chips entering China through unofficial channels, including e-commerce platforms. U.S. authorities are working to close these gaps by sanctioning new entities attempting to circumvent the restrictions. The Department of Commerce’s Bureau of Industry and Security (BIS) is also pushing for stricter Know Your Customer (KYC) regulations for cloud service providers to limit unauthorised access to GPU resources across the Asia Pacific region.
Geopolitics & the Pursuit of AI Dominance
Bipartisan consensus has emerged in Washington around the idea that leading in artificial general intelligence (AGI) is a national security imperative. If AI is destined to shape the future balance of power, the U.S. government believes it cannot afford to fall behind. This mindset has accelerated an arms-race dynamic reminiscent of the Thucydides Trap, where the fear of being overtaken compels both sides to push ahead, even if alignment and safety mechanisms are not yet in place.
China has built extensive domestic surveillance infrastructure and has access to large volumes of data that would be difficult to collect under the regulatory frameworks of many other countries. Meanwhile, major U.S. social media platforms can refine their AI models using behavioural data from a broad global user base. AI is poised to enhance governments’ ability to monitor compliance and enforce laws that were written before the digital age – laws that previously assumed enforcement would be limited by practical constraints. This raises important questions about how civil liberties may evolve when technological limitations are no longer a barrier to enforcement.
The Digital Battlefield
Cybersecurity Threat. AI is both a shield and a sword in cybersecurity. We are entering an era of algorithm-versus-algorithm warfare, where AI’s speed and adaptability will dictate who stays secure and who gets compromised. Nations are prioritising AI for cyber defence to stay ahead of state actors using AI for attacks. For example, the DARPA AI Cyber Challenge is funding tools that use AI to identify and patch vulnerabilities in real-time – essential for defending against state-sponsored threats.
Yet, a key vulnerability exists within AI labs themselves. Many of these organisations, though responsible for cutting-edge models, operate more like startups than defence institutions. This results in informal knowledge sharing, inconsistent security standards, and minimal government oversight. Despite their strategic importance, these labs lack the same protections and regulations as traditional military research facilities.
High-Risk Domains and the Proliferation of Harm. AI’s impact on high-risk domains like biotechnology and autonomous systems is raising alarms. Advanced AI tools could lower the barriers for small groups or even individuals to misuse biological data. As Anthropic CEO Dario Amodei warns, “AI will vastly increase the number of people who can cause catastrophic harm.”
This urgency for oversight mirrors past technological revolutions. The rise of nuclear technology prompted global treaties and safety protocols, and the expansion of railroads drove innovations like block signalling and standardised gauges. With AI’s rapid progression, similar safety measures must be adopted quickly.
Meanwhile, AI-driven autonomous systems are growing in military applications. Drones equipped with AI for real-time navigation and target identification are increasingly deployed in conflict zones, especially where traditional systems like GPS are compromised. While these technologies promise faster, more precise operations, they also raise critical ethical questions about decision-making, accountability, and latency.
The 2024 National Security Memorandum on AI laid down initial guidelines for responsible AI use in defence. However, significant challenges remain around enforcement, transparency, and international cooperation.
AI for Intelligence and Satellite Analysis. AI also holds significant potential for national intelligence. Governments collect massive volumes of satellite imagery daily – far more than human analysts can process alone. AI models trained on geospatial data can greatly enhance the ability to detect movement, monitor infrastructure, and improve border security. Companies like ICEYE and Satellogic are advancing their computer vision capabilities to increase image processing efficiency and scale. As AI systems improve at identifying patterns and anomalies, each satellite image becomes increasingly valuable. This could drive a new era of digital intelligence, where AI capabilities become as critical as the satellites themselves.
Policy, Power, and AI Sovereignty
Around the world, governments are waking up to the importance of AI sovereignty – ensuring that critical capabilities, infrastructure, and expertise remain within national borders. In Europe, France has backed Mistral AI as a homegrown alternative to US tech giants, part of a wider ambition to reduce dependency and assert digital independence. In China, DeepSeek has gained attention for developing competitive LLMs using relatively modest compute resources, highlighting the country’s determination to lead without relying on foreign technologies.
These moves reflect a growing recognition that in the AI age, sovereignty doesn’t just mean political control – it also means control over compute, data, and talent.
In the US, the public sector is working to balance oversight with fostering innovation. Unlike the internet, the space program, or the Manhattan Project, the AI revolution was primarily initiated by the private sector, with limited state involvement. This has left the public sector in a reactive position, struggling to keep up. Government processes are inherently slow, with legislation, interagency reviews, and procurement cycles often lagging rapid technological developments. While major AI breakthroughs can happen within months, regulatory responses may take years.
To address this gap, efforts have been made to establish institutions like the AI Safety Institute and requiring labs to share their internal safety evaluations. However, since then, there has been a movement to reduce the regulatory burden on the AI sector, emphasising the importance of supporting innovation over excessive caution.
A key challenge is the need to build both policy frameworks and physical infrastructure in tandem. Advanced AI models require significant computational resources, and by extension, large amounts of energy. As countries like the US and China compete to be at the forefront of AI innovation, ensuring a reliable energy supply for AI infrastructure becomes crucial.
If data centres cannot scale quickly or if clean energy becomes too expensive, there is a risk that AI infrastructure could migrate to countries with fewer regulations and lower energy costs. Some nations are already offering incentives to attract these capabilities, raising concerns about the long-term security of critical systems. Governments will need to carefully balance sovereignty over AI infrastructure with the development of sufficient domestic electricity generation capacity, all while meeting sustainability goals. Without strong partnerships and more flexible policy mechanisms, countries may risk ceding both innovation and governance to private actors.
What Lies Ahead
AI is no longer an emerging trend – it is a cornerstone of national power. It will shape not only who leads in innovation but also who sets the rules of global engagement: in cyber conflict, intelligence gathering, economic dominance, and military deterrence. The challenge governments face is twofold. First, to maintain strategic advantage, they must ensure that AI development – across private labs, defence systems, and public infrastructure – remains both competitive and secure. Second, they must achieve this while safeguarding democratic values and civil liberties, which are often the first to erode under unchecked surveillance and automation.
This isn’t just about faster processors or smarter algorithms. It’s about determining who defines the future – how decisions are made, who has oversight, and what values are embedded in the systems that will govern our lives.
The tech industry is experiencing a strategic convergence of AI, data management, and cybersecurity, driving a surge in major M&A activity. As enterprises tackle digital transformation, these three pillars are at the forefront, accelerating the race to acquire and integrate critical technologies.
Here are this year’s key consolidation moves, showcasing how leading tech companies are positioning themselves to capitalise on the rising demand for AI-driven solutions, robust data infrastructure, and enhanced cybersecurity.
AI Convergence: Architecting the Intelligent Enterprise
From customer service to supply chain management, AI is being deployed across the entire enterprise value chain. This widespread demand for AI solutions is creating a dynamic M&A market, with tech companies acquiring specialised AI capabilities.
IBM’s AI Power Play
IBM’s acquisitions of HashiCorp and DataStax mark a decisive step in its push to lead enterprise AI and hybrid cloud. The USD 6.4B HashiCorp deal that got finalised this year, brings Terraform, a top-tier infrastructure-as-code tool that streamlines multi-cloud deployments – key to integrating IBM’s Red Hat OpenShift and Watsonx AI. Embedding Terraform enhances automation, making hybrid cloud infrastructure more efficient and AI-ready.
The DataStax acquisition strengthens IBM’s AI data strategy. With AstraDB and Apache Cassandra, IBM gains scalable NoSQL solutions for AI workloads, while Langflow simplifies AI app development. Together, these moves position IBM as an end-to-end AI and cloud powerhouse, offering enterprises seamless automation, data management, and AI deployment at scale.
MongoDB’s RAG Focus
MongoDB’s USD 220M acquisition of Voyage AI signals a strategic push toward enhancing AI reliability. At the core of this move is retrieval-augmented generation (RAG), a technology that curbs AI hallucinations by grounding responses in accurate, relevant data.
By integrating Voyage AI into its Atlas cloud database, MongoDB is making AI applications more trustworthy and reducing the complexity of RAG implementations. Enterprises can now build AI-driven solutions directly within their database, streamlining development while improving accuracy. This move consolidates MongoDB’s role as a key player in enterprise AI, offering both scalable data management and built-in AI reliability.
Google’s 1B Bet on Anthropic
Google’s continued investment in Anthropic reinforces its commitment to foundation model innovation and the evolving GenAI landscape. More than a financial move, this signals Google’s intent to shape the future of AI by backing one of the field’s most promising players.
This investment aligns with a growing trend among cloud giants securing stakes in foundation model developers to drive AI advancements. By deepening ties with Anthropic, Google not only gains access to cutting-edge AI research but also strengthens its position in developing safe, scalable, and enterprise-ready AI. This solidifies Google’s long-term AI strategy, ensuring its leadership in GenAI while seamlessly integrating these capabilities into its cloud ecosystem.
ServiceNow’s AI Automation Expansion
ServiceNow’s USD 2.9B acquisition of Moveworks completed this year, marking a decisive push into AI-driven service desk automation. This goes beyond feature expansion – it redefines enterprise support operations by embedding intelligent automation into workflows, reducing resolution times, and enhancing employee productivity.
The acquisition reflects a growing shift: AI-powered service management is no longer optional but essential. Moveworks’ AI-driven capabilities – natural language understanding, machine learning, and automated issue resolution – will enable ServiceNow to deliver a smarter, more proactive support experience. Additionally, gaining Moveworks’ customer base strengthens ServiceNow’s market reach.
Data Acquisition Surge: Fuelling Digital Transformation
Data has transcended its role as a byproduct of operations, becoming the lifeblood that fuels digital transformation. This fundamental shift has triggered a surge in strategic acquisitions focused on enhancing data management and storage capabilities.
Lenovo Scaling Enterprise Storage
Lenovo’s USD 2B acquisition of Infinidat strengthens its position in enterprise storage as data demands surge. Infinidat’s AI-driven InfiniBox delivers high-performance, low-latency storage for AI, analytics, and HPC, while InfiniGuard ensures advanced data protection.
By integrating these technologies, Lenovo expands its hybrid cloud offerings, challenging Dell and NetApp while reinforcing its vision as a full-stack data infrastructure provider.
Databricks Streamlining Data Warehouse Migrations
Databricks’ USD 15B acquisition of BladeBridge accelerates data warehouse migrations with AI-driven automation, reducing manual effort and errors in migrating legacy platforms like Snowflake and Teradata. BladeBridge’s technology enhances Databricks’ SQL platform, simplifying the transition to modern data ecosystems.
This strengthens Databricks’ Data Intelligence Platform, boosting its appeal by enabling faster, more efficient enterprise data consolidation and supporting rapid adoption of data-driven initiatives.
Cybersecurity Consolidation: Fortifying the Digital Fortress
The escalating sophistication of cyber threats has transformed cybersecurity from a reactive measure to a strategic imperative. This has fuelled a surge in M&A aimed at building comprehensive and integrated security solutions.
Turn/River Capital’s Security Acquisition
Turn/River Capital’s USD 4.4 billion acquisition of SolarWinds underscores the enduring demand for robust IT service management and security software. This acquisition is a testament to the essential role SolarWinds plays in enterprise IT infrastructure, even in the face of past security breaches.
This is a bold investment, in the face of prior vulnerability and highlights a fundamental truth: the need for reliable security solutions outweighs even the most public of past failings. Investors are willing to make long term bets on companies that provide core security services.
Sophos Expanding Managed Detection & Response Capabilities
Sophos completed the acquisition of Secureworks for USD 859M significantly strengthens its managed detection and response (MDR) capabilities, positioning Sophos as a major player in the MDR market. This consolidation reflects the growing demand for comprehensive cybersecurity solutions that offer proactive threat detection and rapid incident response.
By integrating Secureworks’ XDR products, Sophos enhances its ability to provide end-to-end protection for its customers, addressing the evolving threat landscape with advanced security technologies.
Cisco’s Security Portfolio Expansion
Cisco completed the USD 28B acquisition of SnapAttack further expanding its security business, building upon its previous acquisition of Splunk. This move signifies Cisco’s commitment to creating a comprehensive security portfolio that can address the diverse needs of its enterprise customers.
By integrating SnapAttack’s threat detection capabilities, Cisco strengthens its ability to provide proactive threat intelligence and incident response, solidifying its position as a leading provider of security solutions.
Google’s Cloud Security Reinforcement
Google’s strategic acquisition of Wiz, a leading cloud security company, for USD 32B demonstrates its commitment to securing cloud-native environments. Wiz’s expertise in proactive threat detection and remediation will significantly enhance Google Cloud’s security offerings. This move is particularly crucial as organisations increasingly migrate their workloads to the cloud.
By integrating Wiz’s capabilities, Google aims to provide its customers with a robust security framework that can protect their cloud-based assets from sophisticated cyber threats. This acquisition positions Google as a stronger competitor in the cloud security market, reinforcing its commitment to enterprise-grade cybersecurity.
The Way Ahead
The M&A trends of 2025 underscore the critical role of AI, data, and security in shaping the technology landscape. Companies that prioritise these core areas will be best positioned for long-term success. Strategic acquisitions, when executed with foresight and agility, will serve as essential catalysts for navigating the complexities of the evolving digital world.
When OpenAI released ChatGPT, it became obvious – and very fast – that we were entering a new era of AI. Every tech company scrambled to release a comparable service or to infuse their products with some form of GenAI. Microsoft, piggybacking on its investment in OpenAI was the fastest to market with impressive text and image generation for the mainstream. Copilot is now embedded across its software, including Microsoft 365, Teams, GitHub, and Dynamics to supercharge the productivity of developers and knowledge workers. However, the race is on – AWS and Google are actively developing their own GenAI capabilities.
AWS Catches Up as Enterprise Gains Importance
Without a consumer-facing AI assistant, AWS was less visible during the early stages of the GenAI boom. They have since rectified this with a USD 4B investment into Anthropic, the makers of Claude. This partnership will benefit both Amazon and Anthropic, bringing the Claude 3 family of models to enterprise customers, hosted on AWS infrastructure.
As GenAI quickly emerges from shadow IT to an enterprise-grade tool, AWS is catching up by capitalising on their position as cloud leader. Many organisations view AWS as a strategic partner, already housing their data, powering critical applications, and providing an environment that developers are accustomed to. The ability to augment models with private data already residing in AWS data repositories will make it an attractive GenAI partner.
AWS has announced the general availability of Amazon Q, their suite of GenAI tools aimed at developers and businesses. Amazon Q Developer expands on what was launched as Code Whisperer last year. It helps developers accelerate the process of building, testing, and troubleshooting code, allowing them to focus on higher-value work. The tool, which can directly integrate with a developer’s chosen IDE, uses NLP to develop new functions, modernise legacy code, write security tests, and explain code.
Amazon Q Business is an AI assistant that can safely ingest an organisation’s internal data and connect with popular applications, such as Amazon S3, Salesforce, Microsoft Exchange, Slack, ServiceNow, and Jira. Access controls can be implemented to ensure data is only shared with authorised users. It leverages AWS’s visualisation tool, QuickSight, to summarise findings. It also integrates directly with applications like Slack, allowing users to query it directly.
Going a step further, Amazon Q Apps (in preview) allows employees to build their own lightweight GenAI apps using natural language. These employee-created apps can then be published to an enterprise’s app library for broader use. This no-code approach to development and deployment is part of a drive to use AI to increase productivity across lines of business.
AWS continues to expand on Bedrock, their managed service providing access to foundational models from companies like Mistral AI, Stability AI, Meta, and Anthropic. The service also allows customers to bring their own model in cases where they have already pre-trained their own LLM. Once a model is selected, organisations can extend its knowledge base using Retrieval-Augmented Generation (RAG) to privately access proprietary data. Models can also be refined over time to improve results and offer personalised experiences for users. Another feature, Agents for Amazon Bedrock, allows multi-step tasks to be performed by invoking APIs or searching knowledge bases.
To address AI safety concerns, Guardrails for Amazon Bedrock is now available to minimise harmful content generation and avoid negative outcomes for users and brands. Contentious topics can be filtered by varying thresholds, and Personally Identifiable Information (PII) can be masked. Enterprise-wide policies can be defined centrally and enforced across multiple Bedrock models.
Google Targeting Creators
Due to the potential impact on their core search business, Google took a measured approach to entering the GenAI field, compared to newer players like OpenAI and Perplexity. The useability of Google’s chatbot, Gemini, has improved significantly since its initial launch under the moniker Bard. Its image generator, however, was pulled earlier this year while it works out how to carefully tread the line between creativity and sensitivity. Based on recent demos though, it plans to target content creators with images (Imagen 3), video generation (Veo), and music (Lyria).
Like Microsoft, Google has seen that GenAI is a natural fit for collaboration and office productivity. Gemini can now assist the sidebar of Workspace apps, like Docs, Sheets, Slides, Drive, Gmail, and Meet. With Google Search already a critical productivity tool for most knowledge workers, it is determined to remain a leader in the GenAI era.
At their recent Cloud Next event, Google announced the Gemini Code Assist, a GenAI-powered development tool that is more robust than its previous offering. Using RAG, it can customise suggestions for developers by accessing an organisation’s private codebase. With a one-million-token large context window, it also has full codebase awareness making it possible to make extensive changes at once.
The Hardware Problem of AI
The demands that GenAI places on compute and memory have created a shortage of AI chips, causing the valuation of GPU giant, NVIDIA, to skyrocket into the trillions of dollars. Though the initial training is most hardware-intensive, its importance will only rise as organisations leverage proprietary data for custom model development. Inferencing is less compute-heavy for early use cases, such as text generation and coding, but will be dwarfed by the needs of image, video, and audio creation.
Realising compute and memory will be a bottleneck, the hyperscalers are looking to solve this constraint by innovating with new chip designs of their own. AWS has custom-built specialised chips – Trainium2 and Inferentia2 – to bring down costs compared to traditional compute instances. Similarly, Microsoft announced the Maia 100, which it developed in conjunction with OpenAI. Google also revealed its 6th-generation tensor processing unit (TPU), Trillium, with significant increase in power efficiency, high bandwidth memory capacity, and peak compute performance.
The Future of the GenAI Landscape
As enterprises gain experience with GenAI, they will look to partner with providers that they can trust. Challenges around data security, governance, lineage, model transparency, and hallucination management will all need to be resolved. Additionally, controlling compute costs will begin to matter as GenAI initiatives start to scale. Enterprises should explore a multi-provider approach and leverage specialised data management vendors to ensure a successful GenAI journey.