Artificial intelligence (AI) is swiftly establishing itself as a central pillar of the global digital economy. Businesses across various industries are integrating AI into their products, operations, and customer experiences to enhance efficiency and unlock new capabilities. For digital-native companies, this shift is transforming entire business models. AI-driven services, ranging from real-time video generation to cloud gaming, rely on robust computing infrastructure to provide the seamless experiences that users now demand.
As AI workloads expand and user demand escalates, the capacity to quickly and reliably scale high-performance computing has become a crucial competitive advantage. A report from the Infocomm Media Development Authority indicates that over 70 percent of companies in Singapore have adopted AI technologies, utilizing them to improve product development, streamline operations, and optimize supply chains. Notably, AI adoption among small and medium-sized enterprises (SMEs) surged to 14.5 percent in 2024, up from 4.2 percent in the previous year.
Despite this growing enthusiasm for AI, adoption among SMEs remains limited. Many smaller firms recognize AI’s potential but struggle to turn that vision into production-ready systems. One of the primary barriers is the computing infrastructure required. Establishing and managing high-performance systems in-house demands significant capital investment and specialized engineering expertise. In contrast, many enterprise-grade solutions cater to large organizations with dedicated IT teams and lengthy planning cycles, complicating their adoption by smaller, rapidly growing companies.
For SMEs and start-ups, the issue is not often a lack of ambition but rather access to the computing performance necessary to support modern AI workloads affordably, scalably, and in alignment with their growth stages. A new wave of specialized AI cloud platforms, often referred to as neocloud providers, is emerging to address these challenges. These platforms offer high-performance graphics processing units (GPUs) and scalable infrastructure, enabling businesses to run demanding AI workloads without the complexities of managing their own infrastructure.
One such platform, Bitdeer AI Cloud, is supporting companies in building compute-intensive AI applications. Leveraging existing data centers that are operational, Bitdeer AI provides SMEs with faster access to AI computing power, eliminating the long wait for new infrastructure. Combining global data center capacity with high-performance GPU resources, Bitdeer AI Cloud offers essential tools that cover the entire AI lifecycle—from model development and training to deployment and inference.
By integrating these capabilities, Bitdeer AI Cloud allows smaller teams to access enterprise-level computing and scale their AI applications as their businesses grow. This approach effectively lowers the entry barriers for AI innovation, enabling teams to focus on application development instead of the intricacies of infrastructure management. However, for many organizations, particularly SMEs, the balance between innovation and cost control remains a critical factor in AI adoption.
“Because AI adoption is still early, industry benchmarks for ROI are not yet well established,” explains Ms. Retainna Lin, marketing and commercial director for Bitdeer AI Cloud. This uncertainty underscores the importance of adopting AI in a manner that mitigates risk while allowing for gradual scaling. To facilitate this, Bitdeer AI enables customers to pilot, deploy, and expand AI applications in stages, increasing investments only as results become evident and requirements evolve.
Companies can utilize Bitdeer AI’s pay-as-you-go options during initial phases and transition to longer-term leasing as workloads stabilize and demand becomes easier to forecast. Over the next 18 months, Ms. Lin anticipates that advancements in AI will compel companies to undertake more complex workloads, moving beyond basic chatbots to sophisticated applications like agentic AI systems that can make and execute decisions.
“The sheer velocity of the industry is a double-edged sword,” notes Ms. Lin. “With new AI models and tools emerging daily, it is incredibly difficult for smaller teams to keep pace.” She emphasizes that SMEs should focus not only on whether to adopt AI but also on how to implement it 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,” she adds.
Bitdeer AI boasts access to 3 gigawatts of power capacity, which allows it to bring AI-ready facilities online more rapidly. The company is also among the early NVIDIA Cloud Partners in Singapore, with its infrastructure powered by high-performance NVIDIA GPUs designed to train and execute advanced AI models for clients. Backed by the broader Bitdeer Group, which has extensive experience in large-scale computing infrastructure through its involvement in crypto mining, the company is leveraging its established capabilities to accelerate the expansion of its AI-ready capacity.
Having developed expertise in managing high-density computing environments, Bitdeer AI benefits from its brownfield advantage, which facilitates the rapid launch of high-performance AI infrastructure. “We believe in democratising access to compute, ensuring that innovation isn’t gatekept by prohibitive pricing or artificial scarcity,” asserts Ms. Lin. Moreover, Bitdeer AI’s cloud platform offers rates that are up to 30 percent lower than those of hyperscalers, enabling smaller firms to begin experimenting with AI almost immediately while being able to scale up confidently as their requirements evolve.
As SMEs develop and refine their strategies, the need for robust AI infrastructure is expected to increase significantly. “We are preparing for this by following our own data center planning pace, ensuring we have the capacity ready when that maturity curve steepens,” notes Ms. Lin. Nevertheless, talent acquisition remains another significant hurdle for SMEs in their AI journey. Many smaller teams lack the specialized skills necessary for effective AI implementation, forcing them to choose between slow, in-house upskilling or the expense of external consultancy.
While Bitdeer AI’s platform offers a user-friendly interface and a comprehensive suite of tools, the company continues to provide SMEs with training and upskilling opportunities through collaborations with local partners. By fostering local research, training, and skills development, Bitdeer AI aims to bolster Singapore’s AI capabilities over time. “By partnering with local service providers, we help strengthen the domestic supply chain,” concludes Ms. Lin.
As the Singaporean market explores the potential of agentic AI, many business leaders acknowledge their teams are not fully prepared. A study by NTUC LearningHub revealed that three in four business leaders are engaged in AI initiatives, but many are uncertain about the implications for their operations. Through its alignment with Singapore’s National AI Strategy 2.0, Bitdeer AI is prioritizing local R&D, thereby enhancing the nation’s goal of tripling its AI talent pool. By hiring specialized engineers and providing essential GPU resources, Bitdeer AI is positioning itself as a critical player in advancing Singapore’s AI landscape.
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