The company announced the move on Wednesday during the ATx Summit in Singapore, where it also launched “OpenAI for Singapore”, a partnership with the country’s Ministry of Digital Development and Information (MDDI).

Under the initiative, OpenAI said it will commit more than S$300 million to Singapore and create about 200 technical roles over the next few years.

The company added that Singapore will become one of its global bases for Forward-Deployed Engineers, teams that work directly with businesses and public institutions to deploy AI systems.

The new lab will support projects tied to Singapore’s national AI priorities, especially in public services, healthcare, finance and digital infrastructure.

Denise Dresser, chief revenue officer at OpenAI, said the company sees Singapore as a key market because of its technical talent and long-term AI ambitions.

“We’re excited to partner with Singapore as it builds on its position as a global leader in AI,” she said.

“Singapore has strong technical talent, trusted institutions, and a clear ambition to use AI to drive long-term growth and improve people’s lives.”

She added: “Through OpenAI for Singapore, we want to help more organisations benefit from frontier AI, support the next generation of local AI talent, and widen access to these tools across the country.”

Singapore has spent the past few years positioning itself as a neutral and trusted centre for AI development in Asia. The government has steadily increased spending on AI research and infrastructure while encouraging global technology firms to expand operations in the country.

Authorities earlier pledged S$1 billion between 2025 and 2030 to strengthen public AI research capabilities. Tech giants including Google, Nvidia, AWS and Microsoft have also announced AI-related investments and partnerships in Singapore.

Alongside the OpenAI AI Lab deal, Singapore recently unveiled a National AI Partnership with Google focused on education, healthcare and enterprise innovation. Nvidia is also establishing a new AI research lab in the country to work with universities and government agencies.

The partnership with OpenAI will also include education and workforce programmes. OpenAI said it plans to work with Singapore’s Ministry of Education and GovTech on AI-powered learning tools, including support for Mother Tongue language learning.

The company will also launch a Singapore chapter of the OpenAI Academy, organise Codex hackathons for teachers and introduce a training programme for Forward-Deployed Engineers.

Singapore’s Permanent Secretary for Digital Development and Information, Chng Kai Fong, said the partnership shows the government’s drive to prepare its workforce and economy for AI adoption.

“With AI reshaping economies, businesses and the workforce, Singapore’s response has been deliberate: growing new sectors, anchoring global frontier companies here, and equipping our people with the skills to thrive in this new environment,” he said.

“This partnership with OpenAI reflects the Government’s commitment to developing Singapore’s AI capabilities, strengthening enterprise adoption of AI, and securing good jobs for Singaporeans.”

OpenAI said it also plans to support smaller businesses and startups through workshops, accelerator programmes and practical AI adoption initiatives.

Countries are currently competing to attract AI investment, talent and infrastructure. Singapore is not left out, standing alongside hubs such as London, Dubai and Silicon Valley to lead AI development.

Recent data from Slack’s Workforce Index showed that about 52% of workers in Singapore already use AI tools in their jobs, underlining how quickly adoption is spreading across the country’s economy.

The post OpenAI to Launch First Overseas Applied AI Lab in Singapore, Invest S$300 Million appeared first on Tech | Business | Economy.

For retailers and digital platforms, these are some of the most commercially significant days of the year. What bargain-hunting consumers don’t see is the behind-the-scenes AI and infrastructure that’s responsible for keeping these experiences running smoothly, or not. 

“At Africa Data Centres, we regularly see how moments of extreme demand test the limits of the digital infrastructure supporting modern retail platforms and AI-driven services,” Adil El Youssefi, CEO at Africa Data Centres, said.

Every step of the digital shopping experience is carefully curated, with search engines pushing relevant products, recommendation systems personalising offers in real time, pricing algorithms adjusting dynamically, fraud detection models screening transactions instantly, and AI-powered customer service tools handling thousands of queries simultaneously. 

However, the quest to improve customer experience and drive revenue introduces a new infrastructure challenge. When millions of users arrive at once, AI workloads spike, placing immense pressure on the infrastructure supporting them. 

Without the right foundation, even the most sophisticated digital platforms can struggle to cope. The same goes for the infrastructure that houses them.

The infrastructure stress of peak demand

Peak demand creates a very different operational environment compared with normal daily workloads. 

During major retail events, e-commerce platforms frequently report traffic spikes of three to five times normal levels. Data from Capitec, for instance, shows that South Africa’s Black Friday weekend alone generated 55 million transactions valued at R25.3 billion. And that’s just one institution.

Each additional customer interaction triggers several AI processes in the background, which, when multiplied across millions of interactions, generate significant compute demand. 

Research from the International Energy Agency suggests that AI-driven data centre workloads are already contributing to rapid increases in power consumption globally, with demand expected to grow sharply as AI adoption accelerates.

For infrastructure operators, this translates into higher power needs, greater heat output, and dramatically increased data movement across networks during peak periods. If these are lacking, performance begins to degrade: latency increases, applications slow down, or services simply become unavailable. 

For businesses, that can translate directly into millions in lost revenue, while slow or unreliable platforms during major promotions can quickly erode trust and drive shoppers elsewhere.

Designing for performance under load

Maintaining consistent AI performance during peak demand, without sacrificing reliability, needs infrastructure designed specifically for these conditions. 

From ADC’s experience supporting high-performance digital environments, four elements consistently prove critical:

1. High-density compute capacity; the powerful GPU clusters capable of processing large volumes of requests simultaneously without bottlenecks. 
2. Power systems engineered for stability under sudden increases in demand, incorporating redundant power distribution and surge-handling capabilities.
3. Advanced cooling systems to deal with the significant rise in heat generation as servers operate at high volumes, maintain stable operating temperatures, and prevent thermal throttling, where hardware automatically reduces performance to avoid overheating.
4. High-bandwidth, low-latency data pipelines capable of moving vast amounts of information quickly and reliably to ensure that recommendation engines, search platforms, and pricing models respond instantly, even when demand surges.

Peak performance is an infrastructure challenge

With digital commerce expanding, high-demand events will only grow more intense, transforming peak performance from a software or cloud-scaling challenge into an infrastructure challenge.

Businesses are quickly recognising that the reliability of their AI infrastructure during peak demand times depends on the physical infrastructure supporting them. When infrastructure is designed to handle these demands, AI systems can deliver the consistent performance customers expect when it matters most.

In the digital economy, this is often the difference between revenue growth and missed opportunity, which is why infrastructure readiness should form part of AI strategy discussions at board level.

The post When AI Infrastructure Is Tested by Peak Demand appeared first on Tech | Business | Economy.

The company confirmed on Thursday that it will not proceed with the British phase of the project for now, saying work will resume only when conditions support long-term investment.

Stargate UK, developed with Nvidia and British developer Nscale, was announced in September 2025 as part of a plan to expand global data centre capacity.

The project was expected to deploy up to 31,000 AI chips and strengthen the country’s ability to run its own artificial intelligence systems.

That capacity, usually called sovereign compute, allows a country to manage sensitive data and AI workloads locally instead of relying on overseas providers.

OpenAI said in a statement: “We see huge potential for the UK’s AI future. AI compute is foundational to that goal, we continue to explore Stargate UK and will move forward when the right conditions such as regulation and the cost of energy enable long-term infrastructure investment.”

The decision is a setback for the UK government as Prime Minister Keir Starmer has made artificial intelligence central to his economic plans and wants Britain to attract more global tech investment.

Officials insist talks are still ongoing. A spokesperson said the government is “continuing to work with OpenAI and other leading AI companies to strengthen UK compute capacity”.

At the same time, they pointed to more than £100 billion in private investment that has flowed into the UK’s AI sector since 2024.

The cost of energy is also a big issue. Britain has some of the highest electricity prices in Europe, and large data centres require vast amounts of power to run and cool advanced chips. Regulation is another concern, especially those around data use and copyright.

OpenAI has been expanding its data centre footprint in other regions. Its Stargate programme includes projects in the United States, Norway and the United Arab Emirates. The first major campus is already underway in Texas.

The pause in the UK also comes as the company strengthens its focus. It has scaled back some side efforts and is concentrating more on core services like ChatGPT.

Competition is increasing, with companies such as Anthropic and Google pushing ahead with their own systems.

Despite the Stargate project delay, OpenAI says it will continue discussions with the UK government, including plans to support public services with its technology.

For now, the project is on hold, with no timeline for when work might begin.

The post OpenAI Pauses ‘Stargate UK’ Data Centre Project Over High Energy Costs, Regulation appeared first on Tech | Business | Economy.

Within that surge, the drive between Google and Microsoft has become one to pull focus on, not just for technology leadership, but for how artificial intelligence (AI) turns into profitable business, especially with committed spending.

Two Companies, Two Directions

Even before you look deeper, you’d notice both companies are building similar systems, but you’d see the difference in how those systems are used.

Google is pushing its models into products people already use every day, including Search, Android, and YouTube. Its Gemini platform has crossed 750 million monthly users, giving it reach that few competitors can match.

Microsoft is taking a different route which is more structured. Its Copilot tools are built into Word, Excel, Teams and other workplace software. The idea is to make businesses pay for productivity.

That difference is where we place our attention. Google has scale, while Microsoft has pricing.

The Competition is Infrastructure

It is easy to focus on apps and chat interfaces, but that is not where the case is being decided.

It is in infrastructure you’d find the competition; data centres, chips, and computing power.

Alphabet, Google’s parent company, plans to spend $175 billion to $185 billion in 2026 alone, largely on servers, networking and AI capacity.

Microsoft is also increasing spending, with its capital expenditure expected to move towards $100 billion or more, driven by demand for cloud and AI services.

This level of investment changes the nature of the industry. AI is not just software, it is capital-intensive, closer to energy or telecoms than traditional tech.

I would put it this way, whoever controls compute, controls the market.

Products: Gemini vs Copilot

The difference in strategy becomes better to grasp at the product level.

Google’s Gemini is built for wide use, sitting inside search results, mobile devices and developer tools. Updates have been frequent, with new versions released through 2025 and early 2026 to improve reasoning and performance.

Microsoft’s Copilot is more targeted, focusing on workplace tasks, writing documents, analysing spreadsheets, and summarising meetings.

But adoption?

Microsoft has around 15 million paid Copilot users, a small share of its Microsoft 365 base of hundreds of millions.

That gap stresses the fact that interest in AI tools is high. Paying for them is still limited.

Cloud: Where the Money Actually Comes From

The revenue engine is behind the scenes. Google Cloud has been expanding, with revenue growth close to 48% year-on-year, driven largely by demand for AI workloads.

Microsoft Azure is however a larger business, with strong growth tied directly to AI usage and enterprise demand.

This is where the competition becomes tougher because companies are not just using AI tools, they are renting computing power to run them.

Cloud turns AI into something billable.

Spending is Increasing Faster Than Returns

There is, nonetheless, an imbalance.

Microsoft is targeting $25 billion in AI-related revenue by 2026, supported by Copilot and Azure services.

Google is already seeing profits in advertising and cloud from its AI rollout.

But both are spending far ahead of what they are earning.

Even within Microsoft’s ecosystem, only a small percentage of users are paying for AI features, despite heavy investment and promotion.

So when does this start paying off?

It is Important to note that Investors are not ignoring the risk.

Google’s decision to increase spending has already triggered mixed reactions in the market, even as its core business stands strong.

Microsoft is facing a different issue, which is adoption. Copilot is growing, but not at a pace that fully justifies the scale of investment yet.

So the market is in a strange position, believing in the long-term potential, but watching the short-term numbers carefully.

Here the Bigger Question Comes

This has gone beyond a competition between two companies. Will the current level of investment produce the kind of productivity being promised?

The comparison with past technology cycles is unavoidable. Large amounts of capital are being deployed ahead of proven returns. That does not automatically mean a bubble, but it does introduce risk.

Right now, demand for computing power is strong, but what we don’t know is whether that demand will remain strong enough to justify the infrastructure being built.

Who is Ahead?

The answer depends on how you measure it.

Google is ahead when it comes to reach. Its products touch billions of users, and its AI systems are already embedded into everyday digital activity.

Microsoft comes top in structure. It has a clearer path to monetisation through enterprise software and cloud services.

Google and Microsoft are strong when it comes to AI, both are spending heavily, but neither has fully solved the same problem, which is turning scale into sustained profit.

So, let’s not look at who builds the better model between Google and Microsoft or who comes top in AI spending, but who can turn artificial intelligence into a reliable business before the cost of building it becomes harder to justify.

That is where this growth will be decided.

The post Google vs Microsoft: Big Tech & AI Spending in 2026 appeared first on Tech | Business | Economy.

Pang joined Meta in mid-2025 after leaving Apple, where he focused on AI model infrastructure.

At Meta, Pang oversaw key systems supporting next-generation AI models. His exit comes after months of recruitment by OpenAI, and sources said he left Meta last week.

Meta and OpenAI did not immediately respond to requests for comment.

The development reveals the scale of investment companies are making in AI talent. Reports indicate his compensation at Meta was more than $200 million over multiple years.

His work at Meta was a major one in developing the company’s next-generation AI systems, part of its long-term strategy to compete with firms including OpenAI, Anthropic, Apple, and Google.

For OpenAI, Ruoming Pang strengthens its infrastructure and ability to scale. His expertise will support the training of larger multimodal models and more advanced AI workflows.

The hire occurs as governments strengthen review of AI development. Canada, for example, has recently urged OpenAI to increase safety measures or face regulatory consequences.

The post OpenAI Hires Former Meta AI Researcher Ruoming Pang appeared first on Tech | Business | Economy.

The agreement, which runs through 2028, is designed to speed up how ChatGPT and other OpenAI products respond to users, as competition gets stronger among the world’s largest technology firms to control the infrastructure behind advanced models. 

Cerebras will provide the capacity through its own cloud services, powered by its wafer-scale chips, with new data centres to be built or leased specifically for the contract.

Seven hundred and fifty megawatts is not incremental capacity, it shows industrial-level demand. 

OpenAI is no longer thinking only about training models but about inference, the everyday work of answering questions, reasoning through problems and serving millions of users at once. 

“Integrating Cerebras into our mix of compute solutions is all about making our AI respond much faster,” OpenAI stated. 

Talks between the two companies began last August after Cerebras showed that OpenAI’s open-source models could run more efficiently on its chips than on standard graphics processors. 

Months of negotiations followed. The result is a structure where Cerebras owns and operates the hardware, while OpenAI pays to access it as a service. Capacity will be added in stages over the next few years.

Cerebras is preparing to return to public markets after withdrawing its previous listing attempt in late 2024. It now plans to re-file for an initial public offering in the second quarter of 2026. This OpenAI contract helps address a long-standing concern among investors: dependence on a single customer. 

In 2024, UAE-based technology group G42 accounted for nearly 87% of Cerebras’ revenue. A multi-billion-dollar US client changes that picture.

The deal also fits neatly into OpenAI’s bigger strategy. The company is laying the foundation for a potential initial public offering that could value it at around $1 trillion. 

Its chief executive, Sam Altman, has publicly committed $1.4 trillion to building 30 gigawatts of computing capacity, enough to power roughly 25 million homes in the United States. 

Competition is high. Nvidia still tops the market for training chips, while Google and Microsoft are pushing their own custom hardware through cloud platforms. 

By turning to Cerebras, OpenAI is clearly trying to reduce reliance on a single supplier and gain an edge in speed, particularly for reasoning-heavy models.

But then, the spending spree is unsettling some observers, with warnings that the surge in valuations and capital commitments across the sector shows the excesses of the dot-com era. 

The counter-argument is equally strong, demand for inference compute is exploding as artificial intelligence moves from labs into daily use. 

The post OpenAI to Buy 750MW of Compute From Nvidia Rival Cerebras in $10 Billion Deal appeared first on Tech | Business | Economy.

Installed capacity, which sat at just under 70MW in 2024, is projected to reach about 330–340MW by 2026, a fivefold expansion that few emerging markets can reach. 

That scale of expansion results from high demand from cloud providers, financial institutions, digital platforms and AI-driven services that can no longer depend on offshore infrastructure without paying the price in latency, cost and compliance.

Valued between $280 to 300 million today, the market is expected to approach $670 million by 2030, with investments over $1.7 billion by 2027. 

Colocation revenue alone is forecast to grow from about $251 million in 2025 to almost $580 million by 2030, growing at a pace of over 18% a year. 

Hyperscale campuses, carrier-neutral sites and well-engineered Tier III and IV facilities designed for dense, powerful workloads are driving this growth. 

Interestingly, nearly 70% of new data centre capacity planned for West Africa is being developed locally, anchoring Lagos as the region’s primary hyperscale and interconnection hub.

Supported by nationwide fibre initiatives such as Project BRIDGE, which aims to lay 90,000 kilometres of fibre across the country, and a digital economy projected to contribute up to $180 billion to Africa’s GDP by 2026, the foundations are now in place.

Taken together, these explain why 2026 has a lot in store to look forward to. This is the year Nigeria’s data centre market gains larger scale and competitiveness.

These top data centre and cloud projects to watch in 2026 are laying the foundations for how data, cloud and AI will work in Africa’s largest economy.

1. 21st Century Technologies’ 50MW Hyperscale Facility, Ikeja

What makes 21st Century Technologies’ Ikeja project impossible to ignore in 2026 is not just its size, but its intent. At 50MW, this is one of the largest hyperscale builds in West Africa, but its vision goes deeper. 

The facility is being designed for a phase where artificial intelligence, sovereign cloud and national data control are immediate needs. With demand for AI compute increasing across Africa, power and reliability have become the battlegrounds. This project faces that reality head-to-head.

The Ikeja site is engineered to Tier IV standards, with full N+2 redundancy across power, cooling and network layers. That is important because AI and mission-critical cloud workloads do not tolerate downtime. This facility will set a new benchmark for resilience in Nigeria’s data centre market. 

Its AI-ready architecture supports both hyperscale cloud providers and enterprises running heavy models locally, reducing dependence on offshore infrastructure and latency-prone routes. With open-access connectivity and a newly deployed regional network gateway, 21CTL is turning Ikeja into a serious interconnection hub.

This is a homegrown Nigerian hyperscale project at a time when most large facilities are foreign-owned. In 2026, data sovereignty will matter more than ever, especially for governments, banks and AI developers working with sensitive datasets. 

In combining scale, local leadership and global partnerships, 21st Century Technologies is going beyond adding capacity to laying down infrastructure that supports economic growth, skilled jobs and Africa’s long-term digital independence. That is why this project belongs strongly on any watchlist.

2. Airtel Africa’s 38MW Nxtra Hyperscale Data Centre, Eko Atlantic

Airtel Africa’s Nxtra facility at Eko Atlantic is unique because it is being built for the next wave of computing, not the last one. At 38MW, this carrier-neutral hyperscale data centre expands Nigeria’s capacity at a time when AI workloads are rewriting the laws of data centre design. 

High-density racks, GPU-ready halls and serious power planning are now highly indispensable. Nxtra is leaning fully into that transition.

Location is a strategic advantage here. Eko Atlantic provides direct proximity to major subsea cable routes and fibre corridors, translating into lower latency and stronger international reach. For cloud providers and enterprises, this is important. 

In 2026, the ability to deploy AI and cloud services locally, while staying tightly connected to global platforms, will define competitiveness. We expect this facility to attract both hyperscalers and regional platforms looking for neutral ground in Lagos’ fast-crowding data centre sector.

Beyond capacity, Nxtra’s importance lies in timing and scale. Backed by over $120 million in investment and scheduled to go live in early 2026, it arrives just as Nigeria’s cloud market enters its next growth phase. 

Its targeted power efficiency, multiple substations and regulatory alignment give it an edge with fintechs, telecoms and data-heavy enterprises under pressure to keep data onshore. 

This points to the fact that telecom operators now see data centres as core to Africa’s digital economy, not a side business.

3. Open Access Data Centres’ 24MW Hyperscale Expansion, Ilasan

Open Access Data Centres’ Ilasan project earns its place on this list because it solves one of Nigeria’s biggest digital problems, which is connectivity at scale. 

Expanding from 2MW to a planned 24MW by 2026, the Ilasan site is designed to serve hyperscalers, cloud platforms and AI-driven enterprises that demand both power and speed. Sitting directly next to the Equiano subsea cable landing station, it provides one of the lowest-latency environments in West Africa.

What truly differentiates this project is its open-access model. Carrier neutrality means choice, and choice drives competition, resilience and better pricing. Through its Open Access Fabric, OADC is effectively collapsing the distance between Lagos and Europe, making global cloud services feel local. 

With more workloads staying in-country to meet data protection regulations, facilities like Ilasan become strategic assets rather than simple colocation sites.

The scale of investment, $240 million committed as part of an African expansion plan, cannot be ignored. This project reveals a high confidence level in Nigeria’s digital growth. Sustainability is being built in from the start, with renewable energy integration and efficient design reducing long-term operating risk. 

In 2026, success will favour data centres that balance scale, connectivity and cost discipline. OADC’s Ilasan facility does exactly that, positioning Lagos as a regional hub ready to take its place in the global cloud and AI infrastructure map.

4. MTN’s Dabengwa Data and Cloud Centre, Lagos

MTN’s Dabengwa Data and Cloud Centre earns its place on this list of top data centre and cloud projects to watch in 2026 because it represents a transition in how large-scale digital infrastructure is being delivered in West Africa. 

Launched in 2025 and already seeing demand outpace supply, the facility is the region’s largest prefabricated modular data centre. That is important in 2026, when speed, flexibility and reliability are no longer nice-to-haves. At full build-out, the centre provides 9MW of Tier III capacity, designed to scale in phases as demand continues to rise.

What’s most interesting is the build approach. Using 96 prefabricated containers across three floors, the centre was designed to deploy faster, expand cleanly and maintain high resilience under pressure. 

This is a practical response to Nigeria’s infrastructure situation. The modular design allows MTN to add capacity without long construction cycles, while Tier III certification ensures uptime for cloud, enterprise and public sector workloads. Early adoption by government agencies and enterprises shows that trust is already in place.

In 2026, the Dabengwa Centre will not be judged just by size, but by impact. It is already supporting cloud platforms, fintech services and government systems aligned with Nigeria’s digital economy agenda. 

With strong partnerships and deep local market reach, MTN is using this facility to anchor cloud services closer to users and institutions. That combination of scale, speed and adoption is why this project deserves close attention.

5. Kasi Cloud LOS1 Hyperscale Data Centre, Lekki

Kasi Cloud’s LOS1 project is ambitious by any standard, and ambition is exactly why it belongs on this watchlist. Planned as a 100MW hyperscale campus backed by a $250 million investment, LOS1 is designed to operate at a scale Nigeria has not seen before. 

In 2026, scale will be more important than ever. Cloud providers and large platforms are no longer looking for incremental capacity. They want room to grow, and LOS1 is being built to provide it.

Location and backing strengthen the case. Situated on the Lekki Peninsula and supported by the Nigeria Sovereign Investment Authority, LOS1 combines strategic geography with sovereign confidence. 

The facility is designed as a carrier-neutral interconnection hub, built to attract global cloud platforms while supporting local digital services. 

Nigeria is not just a consumer of cloud services, but also a host for regional digital infrastructure.

Sustainability pushes LOS1 even further ahead. With a target of 95% renewable energy usage, the project sets a new benchmark for green hyperscale development in Africa. In 2026, energy efficiency will be a deciding factor for hyperscalers weighing long-term operating costs and risk. 

LOS1’s focus on clean power, massive capacity and interconnection makes it a cornerstone project that will change how the world views West Africa.

6. Jovis Nigeria Data Centre, Victoria Island

The Jovis Nigeria Data Centre is among the top data centre and cloud projects to watch in 2026 because it shows where demand is heading, not where it has been. 

Located in Victoria Island, the country’s financial and commercial nerve centre, the project seeks to serve banks, fintechs, corporates and digital platforms that need low latency and local hosting. 

In 2026, proximity will be essential. Data-heavy services cannot afford distance when speed and compliance are on the line.

This project is also part of a growth wave changing Nigeria’s data centre market. With hundreds of megawatts of new capacity expected by 2026, competition will increase, and only well-located, well-built facilities will thrive. 

Jovis benefits from experienced delivery partners and a Tier III design approach that aligns with enterprise and regulatory needs. This is a measured, useful addition to Lagos’ fast-growing infrastructure base.

What makes Jovis one to watch is timing. As data localisation regulations tighten and open banking and digital public services expand, demand for secure domestic hosting will increase. 

Facilities like this help reduce reliance on offshore infrastructure while creating local jobs and skills. In 2026, the Jovis Data Centre will not just be another site in Lagos. It will be a pointer to how Nigeria’s digital economy is getting stronger, one project at a time.

7. Equinix LG3, Lagos

Equinix LG3 is key in 2026 because it marks a turning point, not an expansion. This is the first ground-up Equinix facility in West Africa, and it reveals a deeper, long-term commitment to Nigeria as a regional connectivity hub. 

While earlier presence came through acquisition, LG3 is purpose-built, designed from day one to integrate Lagos into Equinix’s global interconnection platform. That alone changes how international businesses view Nigeria.

Lagos already sits at the crossroads of multiple subsea cable systems, and LG3 is built to convert geography into economic advantage. By bringing global interconnection services directly into Victoria Island, Equinix is shortening the distance between Nigerian enterprises and global markets. 

This facility is expected to become a magnet for multinational firms, cloud platforms and fast-growing local companies that need secure, low-latency access to partners and customers worldwide.

What to watch in 2026 is not just occupancy, but influence. With LG3 going live in the first quarter and backed by a $100 million Africa expansion plan, Equinix is embedding Nigeria into its worldwide fabric of interconnected data centres. 

That pushes Lagos from a regional hosting location to a true global exchange point. For the cloud and enterprise market, this is a structural transition, not a headline project.

8. Rack Centre LGS2 Expansion, Lagos

Rack Centre’s LGS2 expansion stands out because it combines scale, sustainability and neutrality in a way few projects in the region can match. At 12MW, it expands the company’s footprint and positions the campus as one of the largest carrier-neutral sites in West Africa. 

In 2026, capacity alone will not be enough. Data centres that succeed will be those that can scale responsibly and connect efficiently, and this expansion is designed with that reality in mind.

The sustainability angle is not to be ignored. LGS2 builds on Rack Centre’s green credentials, with energy- and water-efficient design that has already set regional benchmarks. This is becoming more important as operators face high energy expenses and pressure from enterprise customers to meet environmental targets. 

The site’s ability to host dense workloads while maintaining efficiency gives it an edge as demand for compute continues to climb.

What makes LGS2 particularly relevant in 2026 is its ecosystem role. With access to all major Atlantic subsea cables and dozens of carriers, Rack Centre is not just adding space, it is strengthening Lagos’ place as an interconnection hub. 

With more data required to stay within national borders, facilities like this will anchor Nigeria’s digital sovereignty while supporting cloud growth at scale.

9. Africa Data Centres (Pan-African Expansion)

Africa Data Centres earns its place on this list not because of a single site, but because of its reach. In 2026, the story will move from isolated facilities to networks, and ADC is building one of the largest carrier-neutral footprints on the continent. 

Its expansion across Nigeria, Kenya, South Africa, Morocco and other key markets makes it a backbone in Africa’s digital economy.

Demand is the driver. Internet usage, digital payments, enterprise cloud adoption and content consumption are all accelerating, and latency is no longer acceptable. Hosting workloads closer to users is becoming essential. 

ADC’s strategy is a direct response to that pressure. Creating interconnected hubs across multiple countries, it enables cloud providers and enterprises to deploy regionally while staying compliant with local data rules.

In 2026, ADC’s importance will be measured by how seamlessly it links markets. Its facilities are designed to support hyperscale platforms, financial services and governments that need reliability and choice. 

Africa’s data centre capacity is expanding, and ADC’s pan-African model doesn’t just make it a landlord, but an enabler of cross-border digital trade and growth.

10. Project BRIDGE – Nigeria’s National Fibre Backbone

Project BRIDGE belongs on this list because data centres do not operate in isolation. Fibre is the silent dependency, and in 2026 this project will determine how far Nigeria’s cloud ambitions can really go. 

By planning to roll out 90,000 kilometres of open-access fibre, BRIDGE addresses the single biggest limitation facing large-scale digital infrastructure, and that is national connectivity.

What makes BRIDGE different is reach. While most data centre projects are clustered around Lagos, this initiative extends high-capacity connectivity to all 774 local government areas. 

That changes the economics of cloud services. This is the moment when data centres stop serving only coastal markets and begin supporting nationwide digital services in health, education, finance and public administration.

In practical terms, BRIDGE is the foundation beneath every hyperscale build planned for 2026 and beyond. Without reliable fibre backhaul, scale is theoretical. 

With it, cloud platforms can provide consistent performance across the country. For investors and operators, this project is the infrastructure that makes every other project on this list viable.

The post Top Data Centre & Cloud Projects to Watch in 2026 appeared first on Tech | Business | Economy.

The deal places the Japanese group among the company’s most influential outside backers at a time when demand for advanced computing power is increasing.

Masayoshi Son, SoftBank founder, has pushed capital into OpenAI through a mix of direct funding and syndicated co-investment, spreading part of the risk to other investors while keeping strategic control close. 

Of the total sum, $22.5 billion was completed this week, following an earlier $7.5 billion injection in April. Other backers contributed an additional $11 billion through the syndicated structure.

The transaction raises OpenAI’s valuation far beyond where it stood earlier this year. In March, the business was priced at about $300 billion on a post-money basis. 

By October, a secondary share sale had pushed that figure to around $500 billion, according to PitchBook. That instantaneous re-rating shows how quickly expectations have changed across the sector.

Only days earlier, SoftBank agreed to buy DigitalBridge Group for $4 billion, adding a major digital infrastructure investor to its portfolio. 

Taken together, the two deals point to a deliberate strategy which includes control of the software platforms driving computing, while also owning the physical backbone that keeps them running. 

Son has previously described artificial intelligence as the “axis of global technology markets”, and this latest move reveals that belief in concrete financial terms.

OpenAI now sits at the centre of an initiative to build capacity at huge scale. The company is working with Oracle and other partners on Project Stargate, a multi-year data-centre programme designed to support more powerful models and heavier workloads. 

The initiative is expected to cost tens of billions of dollars and ranks among the most ambitious infrastructure efforts the industry has seen.

Across global markets, competition for computing resources has strengthened. Large technology firms are committing vast sums to secure long-term access to data centres, specialised chips and energy supply. 

SoftBank’s wager is similar to that of competitors, but the size of its commitment differs from those typically associated with sovereign wealth funds.

Despite the scale of the spending, SoftBank has said its financial policies stand unchanged, including its approach to leverage and cash management. 

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Under the agreement, Anthropic will commit $30 billion to purchase Azure cloud services, including up to 1 gigawatt of compute capacity powered by Nvidia’s Grace Blackwell and Vera Rubin chips.

Nvidia will invest as much as $10 billion into Anthropic, while Microsoft’s stake reaches $5 billion. The companies have not disclosed exact timelines or detailed terms, but the scale makes this partnership as one of the largest infrastructure deals in the AI sector to date.

“We’re very excited to get additional capacity that we can use both to train our models to support Microsoft first party products and to sell together,” Anthropic CEO Dario Amodei said in a YouTube video accompanying the announcement.

This investment boosts the rivalry with OpenAI, which recently signed a $38 billion deal with Amazon Web Services. OpenAI has plans to expand computing resources, targeting 30 gigawatts, enough to power roughly 25 million U.S. homes. 

Anthropic’s cloud commitment places it as a multi-cloud alternative for enterprises seeking Claude alongside GPT-based services.

Analysts note that these deals show the current situation of circular investments in AI, where cloud providers fund startups that, in turn, commit to massive cloud spending. 

This has led to concerns about overvaluation and the sustainability of current growth, particularly as some investors exit their AI holdings. Peter Thiel’s hedge fund sold its Nvidia stake in Q3, and SoftBank CEO Masayoshi Son has also reduced exposure to Nvidia, though reinvesting in OpenAI.

Beyond financial backing, Nvidia and Anthropic will collaborate closely to optimise Claude models for next-generation chip architectures, ensuring maximum performance and efficiency. Microsoft’s investment expands its AI portfolio beyond OpenAI, a pivot to diversify partnerships as competition and evolving governance demands increase.

Anthropic’s Claude models are viewed as rivals to ChatGPT, particularly in enterprise applications where safety and reliability are essential. With heavy funding and multi-cloud partnerships, Anthropic is now among the most well-capitalised AI startups globally.

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His comments, given in an interview with the BBC, add urgency to high global concerns that the sector is inflating beyond what current adoption and revenue models can support.

Pichai described today’s AI bubble as an “extraordinary moment”, and also pointed to what he called “elements of irrationality” in the market. 

The concern is in line with earlier alarms sounded during the dotcom era, when valuations rose without clear foundations. Analysts now warn that a similar pattern is emerging in AI as venture capital builds startups and chipmakers at a pace that outstrips real-world usage.

He was explicit about the risks when asked how Alphabet would handle a severe downturn. “I think no company is going to be immune, including us,” he said. 

Alphabet’s stock has jumped 46% this year, buoyed by optimism over its capacity to challenge OpenAI and Microsoft in advanced model development. However, the same rally has led to talks in the United States and the United Kingdom over whether markets are pricing in far more than the technology can reasonably deliver.

In Britain, policymakers have already noted that the sector may be drifting into bubble territory. Despite these warnings, Alphabet has doubled down on its UK footprint. 

In September, the company committed £5 billion over two years to expand AI infrastructure, build a new data centre, and increase funding for DeepMind, its London-based research arm. 

Pichai also confirmed that Google will begin training models in the UK, an important step for Prime Minister Keir Starmer, who wants the country to become the world’s third AI superpower after the US and China.

The company’s aggressive UK plans align with that goal, but they also point to a bigger dilemma. Pichai admitted that AI’s “immense” energy requirements are slowing Alphabet’s progress towards its net-zero targets, as the growing demand for high-performance computing drives up power consumption across its operations. 

The admission shows an industry struggle on how to balance the speed of innovation, the AI bubble, with the environmental cost of the infrastructure required to support it.

Pichai’s warning lands as investors, regulators and governments are becoming more uneasy about the sustainability of AI valuations and the stress placed on energy grids. 

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