Artificial intelligence (AI) is rapidly becoming a foundational layer of the global digital economy.
Across industries, businesses are embedding AI into products, operations and customer experiences to improve efficiency and unlock new capabilities.
For digital-native companies, this transformation is reshaping entire business models. AI-powered services, from real-time video generation to cloud gaming and intelligent digital platforms, depend on powerful computing infrastructure to deliver the instant, seamless experiences users now expect.
As AI workloads expand and user demand grows, the ability to scale high-performance compute quickly and reliably has become a key competitive advantage.
A report by the Infocomm Media Development Authority shows that over 70 per cent of companies in Singapore have already adopted AI technologies, using them to enhance product development, streamline operations and optimise supply chains.
AI adoption among small and medium-sized enterprises (SMEs) rose to 14.5 per cent in 2024, up from 4.2 per cent the year before.
Despite growing interest in AI, adoption among SMEs remains relatively limited. Many smaller companies recognise the potential of AI but turning that ambition into production-ready systems is not always straightforward.
One of the most significant barriers lies in computing infrastructure. Building and managing high-performance systems in-house requires substantial capital investment and specialised engineering expertise.
At the same time, many enterprise-grade solutions are designed for large organisations with dedicated IT teams and long planning cycles, making them difficult for smaller, fast-growing companies to adopt.
For SMEs and start-ups, the challenge is rarely a lack of vision. Instead, it is gaining access to the level of computing performance required to support modern AI workloads in a way that remains cost-effective, scalable and aligned with their stage of growth.
A new generation of specialised AI cloud platforms is emerging to address these challenges, often referred to as neocloud providers.
These platforms provide high-performance graphics processing units (GPUs) compute and scalable infrastructure, allowing companies to run demanding AI workloads without the complexity of owning and managing infrastructure.
Bitdeer AI Cloud is among the platforms supporting companies that build compute-intensive AI applications.
By using its own data centres that are already up and running, Bitdeer AI allows SMEs to access AI computing power much faster, without waiting months for new infrastructure to be built.
PHOTO: BITDEER AI
Bitdeer AI Cloud combines global data centre capacity and high-performance GPU resources with an integrated full-stack AI cloud platform. It also provides essential tools that support the full AI lifecycle, from model development and training to deployment and inference.
By bringing these capabilities together, Bitdeer AI Cloud enables smaller teams to access enterprise-grade computing and scale their AI applications as their business grows. This lowers the barrier to entry for AI innovation, allowing teams to focus on building and scaling their applications instead of managing complex infrastructure.
For many organisations, especially SMEs, balancing innovation with cost control remains an important consideration when exploring AI adoption.
“Because AI adoption is still early, industry benchmarks for ROI are not yet well established,” says Ms Retainna Lin, marketing and commercial director for Bitdeer AI Cloud.
That uncertainty, she adds, makes it all the more important for firms to adopt in ways that manage risk and scale gradually.
To de-risk that journey, Bitdeer AI enables customers to pilot, deploy and scale AI applications in stages, increasing investment only as results are proven and needs evolve.
Firms can tap Bitdeer AI’s pay-as-you-go options in the early stages and shift to longer-term leasing when workloads become consistent and demand is easier to predict.
Over the next 18 months, AI advancements will push companies to run heavier workloads than before, says Ms Lin. Companies will be moving beyond simple chatbots to heavier work, such as agentic AI systems that can make and execute decisions, and compute-intensive tasks like simulating factory operations or accelerating drug discovery.
“The sheer velocity of the industry is a double-edged sword. With new AI models and tools emerging daily, it is incredibly difficult for smaller teams to keep pace.
Ms Lin says that the most important question SMEs should ask themselves is not just whether to adopt AI, but how to do so in a way that delivers clear business value.
“The strain shows most when there is no coherent AI strategy that maps technology choices to business outcomes,” says Ms Lin.
Ms Retainna Lin, marketing and commercial director for Bitdeer AI Cloud, says many firms struggle not with ambition, but with knowing when – and how – to invest in AI infrastructure.
PHOTO: BITDEER AI
Without such alignment, AI adoption can remain fragmented. Teams pick tools on an ad hoc basis, leaders struggle to anticipate how much computing power they truly need, and it becomes difficult to justify investment with measurable returns – a challenge that is especially acute for SMEs operating with lean budgets and small teams.
Bitdeer AI has access to 3 gigawatts of power capacity and land resources, allowing it to bring AI-ready facilities online more quickly. The company is also among the early NVIDIA Cloud Partners in Singapore, with its infrastructure powered by high-performance NVIDIA GPUs used to train and run advanced AI models for customers.
Backed by the wider Bitdeer Group, which has extensive experience operating large-scale computing infrastructure through its long-standing involvement in crypto mining, the company is drawing on capabilities built over the years to accelerate the expansion of its AI-ready capacity.
Operating high-density computing environments at scale has enabled the group to develop expertise in infrastructure management, energy optimisation, and system reliability, which now underpins the development and operation of its AI data centre infrastructure.
Larger companies can also secure long-term resources or access the latest hardware for demanding AI workloads, such as next-gen AI GPUs like NVIDIA’s GB200 NVL72 servers.
“Our brownfield advantage allows us to bring high-performance AI infrastructure to market at a speed and scale that is difficult to replicate,” says Ms Lin.
“We believe in democratising access to compute, ensuring that innovation isn’t gatekept by prohibitive pricing or artificial scarcity.”
Raw computing power is just one part of the equation. Bitdeer AI’s cloud platform is up to 30 per cent more affordable compared to the hyperscalers’, says Ms Lin, allowing smaller firms to start experimenting with AI on Day 1 and scale up confidently as their needs grow.
“As SMEs mature and consolidate their strategies, the need for robust AI infrastructure will grow exponentially,” she notes. “We are preparing for this by following our own pace of data centre planning, ensuring we have the capacity ready when that maturity curve steepens.”
Besides budget constraints, another obstacle SMEs face in their AI journey is talent, says Ms Lin. Many smaller teams lack the specialist skills to implement AI effectively, leaving them to choose between slow, in-house upskilling or costly external help.
While Bitdeer AI’s cloud platform offers a user-friendly interface and a comprehensive set of tools, the company continues to provide SMEs with training and upskilling opportunities through its local partners.
Beyond infrastructure, Bitdeer AI supports local research, training and skills development to help Singapore businesses build AI capability over time.
PHOTO: BITDEER AI
“By partnering with local service providers, we help strengthen the domestic supply chain,” says Ms Lin.
“A robust local ecosystem naturally attracts foreign talents and investments, creating a virtuous cycle for Singapore.”
According to a study conducted by NTUC LearningHub in October last year, three in four business leaders in Singapore said their organisations were already exploring, testing or deploying agentic AI, but many felt their teams were not ready. In fact, three in five do not understand the impact of agentic AI on their business operations.
This is where Bitdeer AI can plug the gap, aligning its efforts with Singapore’s National AI Strategy 2.0.
By localising research and development in Singapore, it is building more local capabilities to advance the country’s goal of tripling its AI talent pool.
Bitdeer AI’s R&D team in Singapore is building local technical capability by hiring and developing specialised engineering talent, and supports research and development efforts by providing much-needed GPU computational resources to research institutes here.
“For a country looking to move fast with AI, Singapore would benefit from more diverse AI vendors offering strong choices for businesses seeking an AI partner to grow their operations,” says Ms Lin.
“If this leads to more successful proofs of concept and adoption, Singapore benefits by strengthening its position in AI globally.”
Learn how Bitdeer AI Cloud can support your AI journey.
