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.
A lot has been written and spoken about DeepSeek since the release of their R1 model in January. Soon after, Alibaba, Mistral AI, and Ai2 released their own updated models, and we have seen Manus AI being touted as the next big thing to follow.
DeepSeek’s lower-cost approach to creating its model – using reinforcement learning, the mixture-of-experts architecture, multi-token prediction, group relative policy optimisation, and other innovations – has driven down the cost of LLM development. These methods are likely to be adopted by other models and are already being used today.
While the cost of AI is a challenge, it’s not the biggest for most organisations. In fact, few GenAI initiatives fail solely due to cost.
The reality is that many hurdles still stand in the way of organisations’ GenAI initiatives, which need to be addressed before even considering the business case – and the cost – of the GenAI model.
Real Barriers to GenAI
• Data. The lifeblood of any AI model is the data it’s fed. Clean, well-managed data yields great results, while dirty, incomplete data leads to poor outcomes. Even with RAG, the quality of input data dictates the quality of results. Many organisations I work with are still discovering what data they have – let alone cleaning and classifying it. Only a handful in Australia can confidently say their data is fully managed, governed, and AI-ready. This doesn’t mean GenAI initiatives must wait for perfect data, but it does explain why Agentic AI is set to boom – focusing on single applications and defined datasets.
• Infrastructure. Not every business can or will move data to the public cloud – many still require on-premises infrastructure optimised for AI. Some companies are building their own environments, but this often adds significant complexity. To address this, system manufacturers are offering easy-to-manage, pre-built private cloud AI solutions that reduce the effort of in-house AI infrastructure development. However, adoption will take time, and some solutions will need to be scaled down in cost and capacity to be viable for smaller enterprises in Asia Pacific.
• Process Change. AI algorithms are designed to improve business outcomes – whether by increasing profitability, reducing customer churn, streamlining processes, cutting costs, or enhancing insights. However, once an algorithm is implemented, changes will be required. These can range from minor contact centre adjustments to major warehouse overhauls. Change is challenging – especially when pre-coded ERP or CRM processes need modification, which can take years. Companies like ServiceNow and SS&C Blue Prism are simplifying AI-driven process changes, but these updates still require documentation and training.
• AI Skills. While IT teams are actively upskilling in data, analytics, development, security, and governance, AI opportunities are often identified by business units outside of IT. Organisations must improve their “AI Quotient” – a core understanding of AI’s benefits, opportunities, and best applications. Broad upskilling across leadership and the wider business will accelerate AI adoption and increase the success rate of AI pilots, ensuring the right people guide investments from the start.
• AI Governance. Trust is the key to long-term AI adoption and success. Being able to use AI to do the “right things” for customers, employees, and the organisation will ultimately drive the success of GenAI initiatives. Many AI pilots fail due to user distrust – whether in the quality of the initial data or in AI-driven outcomes they perceive as unethical for certain stakeholders. For example, an AI model that pushes customers toward higher-priced products or services, regardless of their actual needs, may yield short-term financial gains but will ultimately lose to ethical competitors who prioritise customer trust and satisfaction. Some AI providers, like IBM and Microsoft, are prioritising AI ethics by offering tools and platforms that embed ethical principles into AI operations, ensuring long-term success for customers who adopt responsible AI practices.
GenAI and Agentic AI initiatives are far from becoming standard business practice. Given the current economic and political uncertainty, many organisations will limit unbudgeted spending until markets stabilise. However, technology and business leaders should proactively address the key barriers slowing AI adoption within their organisations. As more AI platforms adopt the innovations that helped DeepSeek reduce model development costs, the economic hurdles to GenAI will become easier to overcome.
Barely weeks into 2025, the Consumer Electronics Show (CES) announced a wave of AI-powered innovations – from Nvidia’s latest RTX 50-series graphics chip with AI-powered rendering to Halliday’s futuristic augmented reality smart glasses. AI has firmly emerged from the “fringe” technology to become the foundation of industry transformation. According to MIT, 95% of businesses are already using AI in some capacity, and more than half are aiming for full-scale integration by 2026.
But as AI adoption increases, the real challenge isn’t just about developing smarter models – it’s about whether the underlying infrastructure can keep up.
The AI-Driven Cloud: Strategic Growth
Cloud providers are at the heart of the AI revolution, but in 2025, it is not just about raw computing power anymore. It’s about smarter, more strategic expansion.
Microsoft is expanding its AI infrastructure footprint beyond traditional tech hubs, investing USD 300M in South Africa to build AI-ready data centres in an emerging market. Similarly, AWS is doubling down on another emerging market with an investment of USD 8B to develop next-generation cloud infrastructure in Maharashtra, India.
This focus on AI is not limited to the top hyperscalers; Oracle, for instance, is seeing rapid cloud growth, with 15% revenue growth expected in 2026 and 20% in 2027. This growth is driven by deep AI integration and investments in semiconductor technology. Oracle is also a key player in OpenAI and SoftBank’s Stargate AI initiative, showcasing its commitment to AI innovation.
Emerging players and disruptors are also making their mark. For instance, CoreWeave, a former crypto mining company, has pivoted to AI cloud services. They recently secured a USD 12B contract with OpenAI to provide computing power for training and running AI models over the next five years.
The signs are clear – the demand for AI is reshaping the cloud industry faster than anyone expected.
Strategic Investments In Data Centres Powering Growth
Enterprises are increasingly investing in AI-optimised data centres, driven by the need to reduce reliance on traditional data centres, lower latency, achieve cost savings, and gain better control over data.
Reliance Industries is set to build the world’s largest AI data centre in Jamnagar, India, with a 3-gigawatt capacity. This ambitious project aims to accelerate AI adoption by reducing inferencing costs and enabling large-scale AI workloads through its ‘Jio Brain’ platform. Similarly, in the US, a group of banks has committed USD 2B to fund a 100-acre AI data centre in Utah, underscoring the financial sector’s confidence in AI’s future and the increasing demand for high-performance computing infrastructure.
These large-scale investments are part of a broader trend – AI is becoming a key driver of economic and industrial transformation. As AI adoption accelerates, the need for advanced data centres capable of handling vast computational workloads is growing. The enterprise sector’s support for AI infrastructure highlights AI’s pivotal role in shaping digital economies and driving long-term growth.
AI Hardware Reimagined: Beyond the GPU
While cloud providers are racing to scale up, semiconductor companies are rethinking AI hardware from the ground up – and they are adapting fast.
Nvidia is no longer just focused on cloud GPUs – it is now working directly with enterprises to deploy H200-powered private AI clusters. AMD’s MI300X chips are being integrated into financial services for high-frequency trading and fraud detection, offering a more energy-efficient alternative to traditional AI hardware.
Another major trend is chiplet architectures, where AI models run across multiple smaller chips instead of a single, power-hungry processor. Meta’s latest AI accelerator and Google’s custom TPU designs are early adopters of this modular approach, making AI computing more scalable and cost-effective.
The AI hardware race is no longer just about bigger chips – it’s about smarter, more efficient designs that optimise performance while keeping energy costs in check.
Collaborative AI: Sharing The Infrastructure Burden
As AI infrastructure investments increase, so do costs. Training and deploying LLMs requires billions in high-performance chips, cloud storage, and data centres. To manage these costs, companies are increasingly teaming up to share infrastructure and expertise.
SoftBank and OpenAI formed a joint venture in Japan to accelerate AI adoption across enterprises. Meanwhile, Telstra and Accenture are partnering on a global scale to pool their AI infrastructure resources, ensuring businesses have access to scalable AI solutions.
In financial services, Palantir and TWG Global have joined forces to deploy AI models for risk assessment, fraud detection, and customer automation – leveraging shared infrastructure to reduce costs and increase efficiency.
And with tech giants spending over USD 315 billion on AI infrastructure this year alone – plus OpenAI’s USD 500 billion commitment – the need for collaboration will only grow.
These joint ventures are more than just cost-sharing arrangements; they are strategic plays to accelerate AI adoption while managing the massive infrastructure bill.
The AI Infrastructure Power Shift
The AI infrastructure race in 2025 isn’t just about bigger investments or faster chips – it’s about reshaping the tech landscape. Leaders aren’t just building AI infrastructure; they’re determining who controls AI’s future. Cloud providers are shaping where and how AI is deployed, while semiconductor companies focus on energy efficiency and sustainability. Joint ventures highlight that AI is too big for any single player.
But rapid growth comes with challenges: Will smaller enterprises be locked out? Can regulations keep pace? As investments concentrate among a few, how will competition and innovation evolve?
One thing is clear: Those who control AI infrastructure today will shape tomorrow’s AI-driven economy.
As AI evolves, the supporting infrastructure has become a crucial consideration for organisations and technology companies alike. AI demands massive processing power and efficient data handling, making high-performance computing clusters and advanced data management systems essential. Scalability, efficiency, security, and reliability are key to ensuring AI systems handle increasing demands and sensitive data responsibly.
Data centres must evolve to meet the increasing demands of AI and growing data requirements.
Equinix recently hosted technology analysts at their offices and data centre facilities in Singapore and Sydney to showcase how they are evolving to maintain their leadership in the colocation and interconnection space.
Equinix is expanding in Latin America, Africa, the Middle East, and Asia Pacific. In Asia Pacific, they recently opened data centres in Kuala Lumpur and Johor Bahru, with capacity additions in Mumbai, Sydney, Melbourne, Tokyo, and Seoul. Plans for the next 12 months include expanding in existing cities and entering new ones, such as Chennai and Jakarta.
Ecosystm analysts comment on Equinix’s growth potential and opportunities in Asia Pacific.
Small Details, Big Impact
TIM SHEEDY. The tour of the new Equinix data centre in Sydney revealed the complexity of modern facilities. For instance, the liquid cooling system, essential for new Nvidia chipsets, includes backup cold water tanks for redundancy. Every system and process is designed with built-in redundancy.
As power needs grow, so do operational and capital costs. The diesel generators at the data centre, comparable to a small power plant, are supported by multiple fuel suppliers from several regions in Sydney to ensure reliability during disasters.
Security is critical, with some areas surrounded by concrete walls extending from the ceiling to the floor, even restricting access to Equinix staff.
By focusing on these details, Equinix enables customers to quickly set up and manage their environments through a self-service portal, delivering a cloud-like experience for on-premises solutions.
Equinix’s Commitment to the Environment
ACHIM GRANZEN. Compute-intensive AI applications challenge data centres’ “100% green energy” pledges, prompting providers to seek additional green measures. Equinix addresses this through sustainable design and green energy investments, including liquid cooling and improved traditional cooling. In Singapore, one of Equinix’s top 3 hubs, the company partnered with the government and Sembcorp to procure solar power from panels on public buildings. This improves Equinix’s power mix and supports Singapore’s renewable energy sector.
TIM SHEEDY Building and operating data centres sustainably is challenging. While the basics – real estate, cooling, and communications – remain, adding proximity to clients, affordability, and 100% renewable energy complicates matters. In Australia, reliant on a mixed-energy grid, Equinix has secured 151 MW of renewable energy from Victoria’s Golden Plains Wind Farm, aiming for 100% renewable by 2029.
Equinix leads with AIA-rated data centres that operate in warmer conditions, reducing cooling needs and boosting energy efficiency. Focusing on efficient buildings, sustainable water management, and a circular economy, Equinix aims for climate neutrality by 2030, demonstrating strong environmental responsibility.
Equinix’s Private AI Value Proposition
ACHIM GRANZEN. Most AI efforts, especially GenAI, have occurred in the public cloud, but there’s rising demand for Private AI due to concerns about data availability, privacy, governance, cost, and location. Technology providers in a position to offer alternative AI stacks (usually built on top of a GPU-as-a-service model) to the hyperscalers find themselves in high interest. Equinix, in partnership with providers such as Nvidia, offers Private AI solutions on a global turnkey AI infrastructure. These solutions are ideal for industries with large-scale operations and connectivity challenges, such as Manufacturing, or those slow to adopt public cloud.
SASH MUKHERJEE. Equinix’s Private AI value proposition will appeal to many organisations, especially as discussions on AI cost efficiency and ROI evolve. AI unites IT and business teams, and Equinix understands the need for conversations at multiple levels. Infrastructure leaders focus on data strategy capacity planning; CISOs on networking and security; business lines on application performance, and the C-suite on revenue, risk, and cost considerations. Each has a stake in the AI strategy. For success, Equinix must reshape its go-to-market message to be industry-specific (that’s how AI conversations are shaping) and reskill its salesforce for broader conversations beyond infrastructure.
Equinix’s Growth Potential
ACHIM GRANZEN. In Southeast Asia, Malaysia and Indonesia provide growth opportunities for Equinix. Indonesia holds massive potential as a digital-savvy G20 country. In Malaysia, the company’s data centres can play a vital part in the ongoing Mydigital initiative, having a presence in the country before the hyperscalers. Also, the proximity of the Johor Bahru data centre to Singapore opens additional business opportunities.
TIM SHEEDY. Equinix is evolving beyond being just a data centre real estate provider. By developing their own platforms and services, along with partner-provided solutions, they enable customers to optimise application placement, manage smaller points of presence, enhance cloud interconnectivity, move data closer to hyperscalers for backup and performance, and provide multi-cloud networking. Composable services – such as cloud routers, load balancers, internet access, bare metal, virtual machines, and virtual routing and forwarding – allow seamless integration with partner solutions.
Equinix’s focus over the last 12 months on automating and simplifying the data centre management and interconnection services is certainly paying dividends, and revenue is expected to grow above tech market growth rates.