When Singtel’s network went dark for eight hours on March 16, the ripple effects were immediate and far-reaching. Emergency services faltered. Digital payments stalled. Thousands of gig workers lost an entire day’s income. For a city-state that has staked its economic future on digital leadership, the outage was more than an inconvenience: it was a stress test the infrastructure did not entirely pass.
The incident arrives at a pivotal moment. Southeast Asian (SEA) telcos are bracing for mobile data consumption to surge to 40GB per user by 2030, driven in large part by the accelerating deployment of AI across every sector of the economy. At the heart of this transformation is a concept gaining urgent traction in boardrooms and policy circles alike: Sovereign AI — the principle that nations must own, operate, and govern the AI infrastructure that underpins their critical systems, rather than cede that control to foreign platforms or distant cloud providers.
For Singapore, Sovereign AI is not merely a geopolitical aspiration. It is an infrastructural imperative. As AI workloads demand always-on compute, low-latency data processing and ironclad network reliability, the question is whether the telco sector — the backbone of the digital economy — is architected for what comes next.
Mayank Srivastava, chief executive of BDx Data Centres, argues the Singtel outage carries a lesson that extends well beyond fault attribution. “As economies digitise, dependencies concentrate across networks, data centres, and cross-border links,” he said in an email interview with e27.
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The following is an edited excerpt of the conversation.
Can you explain what Sovereign AI means in practice, and why you believe Singapore and SEA risks “data colonisation” if it doesn’t act now?
In practical terms, Sovereign AI is about ensuring that value creation happens locally. In the AI economy, the data centre is the factory housing large‑scale GPU clusters. It is where data is processed, models are trained, and decisions are generated. Sovereign AI means that data, compute, and governance frameworks are aligned within national or regional jurisdictions, under local laws and accountability.
This is not about isolation or exclusion. It is about economic participation. Historically, economies that exported raw materials but imported finished goods captured less long‑term value. In the digital economy, data is the raw material. If it is consistently processed elsewhere, the economic and strategic value associated with it accumulates outside the region.
The implication is not just revenue, it is agency. Critical systems in healthcare, finance, and public services increasingly rely on AI‑driven decision layers. Ensuring that these systems are supported by trusted, locally governed infrastructure strengthens transparency, resilience, and public trust. When we use the term “data colonisation,” we are referring to this economic value‑capture dynamic, not a political concept.
Singapore is well positioned to lead in this area. Recent IMDA initiatives around trusted infrastructure, AI governance, and high‑efficiency data centres reflect a thoughtful, forward‑looking approach. By supporting secure, energy‑efficient AI infrastructure within its regulatory framework, Singapore can anchor value creation locally while remaining globally connected—benefiting enterprises, startups, and the broader digital economy.
With SEA telcos projecting 40GB of data per user by 2030, what does that demand curve actually mean for the physical infrastructure required to support it?
The headline number has indeed shifted. With 5G-Advanced, V2X, and persistent machine-to-machine traffic, SEA’s mobile data usage is now projected at around 38-40GB per user by 2030 per Ericsson and GSMA baselines, though aggressive AI/IoT scenarios could push toward 60GB+ in high-growth markets. But the real infrastructure implication isn’t just volume. It is the shape of the demand curve.
What matters is the nature of the data. More ultra‑high‑definition video, far more real‑time AI inference, and continuous IoT traffic fundamentally change infrastructure requirements. These are latency‑sensitive, always‑on workloads that stress power delivery, cooling, and network resilience in ways traditional mobile traffic never did.
Around 40GB per user is not a gentle increase; it is a vertical climb when translated into physics. Five years ago, a typical rack ran at 5kW. Today, NVIDIA DGX GB200-scale AI racks reach 120-200kW in production (scaling to 700-800kW in dense clusters). By 2030, 1-2MW per rack is realistic as power density becomes the limit.
Supporting 2MW racks requires an order‑of‑magnitude shift in cooling, including direct‑to‑chip and liquid‑immersion systems, along with redesigned power trains and grid interfaces. As highlighted in BDx discussions on AI‑first facilities, this represents 10× cooling retrofits compared to conventional designs.
Regionally, this demand cannot be met by any single market alone. It points to the need for coordinated capacity development across Singapore, Indonesia, Malaysia, Thailand, and Vietnam. Given the long lead times involved in power provisioning and construction, infrastructure planning must move well ahead of demand rather than react to it.
Do you think telcos should stop worrying about power density and refocus on services that drive growth? What is the danger of telcos continuing to build and operate their own data infrastructure rather than partnering with specialist providers like BDx?
Telcos face a genuine strategic balancing act. Modern data centres have evolved into highly specialised environments requiring deep expertise in power engineering, thermal management, and increasingly AI‑optimised design. These capabilities sit alongside—but are distinct from—core telecommunications operations.
The question is less about capability and more about focus. As networks evolve and services become more sophisticated, tying up capital and leadership attention in highly specialised infrastructure can limit flexibility elsewhere.
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Partnership models offer an alternative. By working with specialist providers, telcos can access AI‑ready, future‑proof infrastructure while concentrating their investment and innovation efforts on network quality, platforms, and customer‑facing services. This separation of roles also lowers friction for startups and enterprises, who benefit when telcos focus on service innovation while infrastructure specialists focus on scale, efficiency, and resilience.
It is a collaborative approach that allows each participant to operate where they add the most value.
How should we think about the ROI of reliability? When a telco goes down, the costs are obvious, but what is the business case for investing heavily in resilience before a crisis happens, especially when margins are already under pressure?
Reliability investments are challenging to justify because their value is most visible in what does not happen—outages avoided, customers retained, regulatory scrutiny prevented. Traditional ROI models struggle to capture this.
A useful analogy is healthcare. There is an accepted baseline of reliability below which systems simply cannot operate. In a digital economy, communications infrastructure increasingly occupies that same category. As AI supports real‑time finance, healthcare, and public services, reliability becomes a prerequisite rather than a differentiator.
In that context, resilience is not defensive spending. It is a condition for participating in higher‑value use cases. Operators that can demonstrate consistent, measurable reliability operate in a different commercial and regulatory conversation than those competing solely on cost.
As Southeast Asian telcos consolidate to boost valuation, there is a tension between leaning out operationally and building the robust backbone an AI-native economy needs. How do telcos resolve that contradiction?
Consolidation can create scale, but scale alone does not solve architectural complexity. The opportunity lies in being precise about what to optimise internally and what to access through partnerships.
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AI is going to be a utility, like electricity or the internet. The backbone required for an AI‑native economy is layered. It includes networks, specialist infrastructure, cloud platforms, and regulatory frameworks working together. No single balance sheet needs to own every layer.
By treating infrastructure as something to access strategically rather than own entirely, telcos can redirect capital toward network quality and differentiated services while still supporting the depth and resilience AI workloads require.
If you were advising Singapore’s policymakers today, what are the two or three most urgent infrastructure decisions they need to make in the next 12 to 24 months to ensure the country is genuinely ready for an AI-driven future?
First, aligning power policy with AI timelines.
AI infrastructure investments move faster than traditional approval cycles. While Singapore’s regulatory rigour is a strength, there is scope for clearer, faster pathways for high‑efficiency, AI‑optimised capacity with defined sustainability standards. As AI adoption accelerates, the next 12 to 24 months become disproportionately important for setting these frameworks.
Second, strengthening trusted compute for critical sectors.
As AI becomes integral to finance, healthcare, and public services, ensuring that these workloads are supported by resilient, trusted infrastructure is essential. Periodic stress‑testing of dependencies and encouraging meaningful infrastructure diversity can further strengthen confidence.
Third, keeping regulations practical and enabling.
Singapore has a strong track record of using regulation to unlock innovation rather than constrain it—across sectors such as aviation, fintech, and border security. Changi Airport’s use of facial recognition is a clear example of regulation providing the clarity and confidence needed for large‑scale adoption.
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AI infrastructure requires the same seamless approach: clear rules, aligned incentives, and strong governance, implemented in a way that matches the pace and capital intensity of the technology. When regulatory frameworks are predictable and outcomes‑based, they enable infrastructure providers to take the long‑term investment risks required to keep Singapore—and the region—at the forefront of global AI development.
This balance between oversight and enablement is one of Singapore’s defining strengths, and applying it thoughtfully to AI infrastructure will be key to sustaining leadership as the ecosystem continues to evolve.
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Image Credit: Taylor Vick on Unsplash
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