The potential of AI in the data center industry during the post-cloud era
The growing use of artificial intelligence (AI) indicates a significant increase in data demand and a new era of possible data center business development over the next two years and beyond.
After a decade of industry expansion fueled by cloud and mobile platforms, the “AI Era” comes to an end with this transformation. Over the past decade, the top public cloud service providers and internet content providers pushed data center capacity expansion to a world level, culminating in a frenzy of activity from 2020 to 2022 due to an increase in online service consumption and low-interest-rate project financing.
Nonetheless, substantial changes have occurred in the sector over the last year, including a rise in finance prices, project costs, and development delays, as well as acute power limitations in core regions. In many worldwide areas, for example, standard greenfield data center development timeframes have increased to four or more years, approximately twice as long as a few years ago when electricity and land were less restrictive.
Big internet companies are racing to secure data center capacity in important locations while balancing AI potential and concerns. The instability and uncertainty will raise the level of risk and make navigating the industry more difficult.
The automated procurement of data center capacity has come to an end
Cloud service companies enhanced demand forecasting and automated capacity buying throughout the Cloud Era. They had to return for extra capacity since demand surpassed expectations. Customers’ willingness to accept larger deals and lease capacity at higher costs has risen over the last two years, especially in areas with more available power.
Expansion of Self-build data center building strategies
For efficient market access, hyperscale purchasers in the data centre business are adjusting their self-build approach to rely on leased capacity from third parties. They recognise that self-building is unfeasible and are proposing smaller self-builds to meet future demand. This transition may result in a more diversified mix of self-built and leased capacity, necessitating the assessment of possible migration risks by third-party providers.
Increasing power demand for AI workloads, liquid cooling
AI workloads need high power density in data centres owing to the use of GPUs. Nvidia controls 95% of the GPU market for machine learning, resulting in high-end AI workloads operating on comparable technology. This leads to rack densities of 30-40kW, compared to 10kW/rack for public cloud applications. To solve this, hyperscalers and data center operators are focused on effective cooling systems, with some large hyperscalers proposing to move to liquid cooling solutions or increasing data datacrentre temperatures.
ESG (Environmental, Social, and Governance) standards
The industry of data centres The primary emphasis of ESG concerns is sustainability. The data center business stresses sustainability via renewable energy, water consumption, and carbon footprint reduction, employing a variety of ways to accomplish these objectives.
Enhancements to Efficiency
Energy-efficient designs such as free cooling, efficient power distribution, and efficient lighting systems must be recommended.
1. Use of renewable energy
- Using the grid to obtain renewable energy.
- Solar and wind are the best renewable resources.
- Power purchase agreements (PPAs) specify the volume and price of long-term renewable energy.
2. Water consumption
- Systems that are cooled by air.
- Closed-loop water systems can reduce water consumption.
- Water recycling and rainwater harvesting can reduce water consumption.
- Waterless cooling technologies, like evaporative or adiabatic cooling, can assist in cooling systems.
3. Carbon balance
- The heat from IT equipment is used to recover energy.
4. Waste minimization
The capacity to implement these solutions will vary greatly by market, based on local climate, energy mix, and other considerations such as worker safety.
AI Plugins: The Future Generation of Ecosystems
Numerous companies have offered third-party service plugins, allowing developers to connect additional data sources into their language model, possibly reshaping data center ecosystems around certain sectors or data sources.
Conclusion
Since demand for data storage is anticipated to surpass supply, the data centre sector must adopt flexible methods to manage the AI revolution and add capacity in the right markets.
