Technology limitations in high demand scenarios

Surging adoption of digitalization and AI technologies has increased the demand for data centers globally. As also called out in our recent Global Energy Perspective, this rapid growth has changed forecast power demand trajectories. For the US, to keep pace with the current rate of adoption, the power needs of data centers are expected to grow to about 3x higher than current capacity by the end of the decade, going from between 3-4% of total US power demand today to between 11-12% in 2030. According to McKinsey analysis, the United States is expected to be the fastest-growing market for data centers, growing from 25 GW of demand in 2024 to more than 80 GW of demand in 2030. Skyrocketing compute and data demands are being accelerated by gains in computing capabilities alongside reductions in chip efficiency relative to power consumption. For instance, the amount of time central processing units need to double their performance efficiency has increased from every 2 years to nearly every 3 years. As the power ecosystem grapples with meeting data centers’ voracious need for power, it faces substantial constraints, including limitations on reliable power sources, sustainability of power, upstream infrastructure for power access, power equipment within data centers, and electrical trade workers to build out facilities and infrastructure. McKinsey research shows that time to power is the biggest consideration for data center operators when building new sites. Additionally, the industry faces the daunting challenge of decarbonizing its footprint to achieve the goal of 24/7 carbon-free energy usage by 2030. Most grid decarbonization timelines (if they exist) far exceed the targets set by major hyperscalers. There is a positive story that could be told: "Will the additional capabilities unlocked by AI help debottleneck some of the physical constraints of the energy transition?" At the same time, there is a real challenge that can be put forward: "Is the rapidly accelerating power demand another hurdle to be overcome on our path towards net-zero? Should we constrain demand for specific AI use cases in a world that is trying to meet net-zero by 2050?" While there was a real debate about the use of power for bitcoin mining (i.e., is the emissions impact worth the value of bitcoin), it would be worthwhile to have a conversation around the impact of AI on energy demand and resulting global emissions.

The semiconductor industry is one of the most vital sectors globally, being responsible for the development and manufacturing of the chips that power our computers, smartphones, and other electronic devices. However, the industry faces a series of significant challenges known as "walls," which obstruct further advancement. 🔹 The Memory Wall: This is the gap between the speed of the processor and the speed of the memory. As processors get faster, they need to access data from memory more quickly. However, the speed of memory is limited by the physical properties of the materials used to make it. This can lead to a bottleneck in performance, as the processor has to wait for data to be fetched from memory. 🔹 The Frequency Wall: This term refers to the limitations faced in increasing the clock frequency of microprocessors. With increased frequency, there comes increased power consumption and heat dissipation which is a fundamental challenge in semiconductor design. The "frequency wall" represents the point at which further increases in frequency yield diminishing returns or become unfeasible due to physical constraints. 🔹 The Power Wall: This is the limit on how much power a processor can consume before it becomes too hot and throttles its performance. As processors get faster, they consume more power. This can lead to problems with heat dissipation, which can damage the processor. 🔹 The ILP Wall: This is the limit on how much parallelism can be extracted from a program. As programs get more complex, it becomes more difficult to find opportunities for parallelism. This can limit the performance gains that can be achieved by increasing the clock speed or adding more cores. 🔹 The Network Wall: This is the limit on how fast data can be transferred between different parts of a computer system. As computers become more interconnected, the need for high-speed networking has increased. However, the physical limitations of the underlying technologies, such as copper wires and optical fibers, can limit the maximum bandwidth that can be achieved. These are just some of the walls that the semiconductor industry is facing. Researchers are working on new technologies to overcome these challenges and continue to push the boundaries of performance. #VLSI #semiconductorindustry #wall #challenges

I was just featured in Glossy discussing the challenges and strategies behind managing high-traffic e-commerce events. The article centers on the recent SEPHORA biannual sale, which, despite careful planning, left some loyalty members frustrated due to heavy website traffic. 🛒 The Challenge: High-traffic sales events like these pose a significant technological challenge. The spike in demand can overwhelm servers, lead to poor user experiences, and, in extreme cases, cause websites to crash. 🛠 The Solutions: The article delves into various strategies to mitigate these challenges—from simple queuing systems to more resource-intensive, user-friendly solutions. It's a fascinating look at the balance tech leaders must strike between scalability, profitability, and user experience. 👨💻 Personal Insight: Having faced similar challenges while managing Kanye West's now-defunct ecomm site YEEZY I can attest that it's a high-stakes game requiring creative, yet effective solutions. 📚 Why You Should Read This: Whether you're in e-commerce, tech, or any field requiring a strong digital presence, understanding these dynamics is crucial as online shopping continues to grow. Read the article here: https://lnkd.in/eTFDwqQy

The Conundrum in the #Datacenters market: AI use-cases are expanding across every industry, demanding more inference and training. These workloads run on datacenters which are challenging to build. An evidence of this can be seen in Microsoft's Q1'24 earnings: "high demand for artificial intelligence training and inferencing.....“[Data centers] don’t get built overnight,” Nadella said. “Even in Q2 for example, some of the demand issues we have, or rather our ability to fulfill demand is because of, in fact, external third-party stuff that we leased moving up. That’s the constraints we have.” Some of the prime reasons on why I think, it is happening: #1 The intersection of data and energy is a high growth space: The industry is poised to consume 35GW of energy by 2030; 2x from what it was in '22. At the same time, many hyperscalers have committed to green energy use, net positive water to name a few. Thereby, tethering from a grid that mostly burns fossil fuels to generate power defeats the purpose. Hence, organizations like Google, AWS have started to look at Small Modular Reactors as a source of power. #Gas #HVO could be transitory fuels as datacenter operators look to embrace #BYOP or on-site power, especially in the sub-500 MW band requirements. #2 AI workloads needs liquid cooling: nVidia Blackwell chips consume 60-120 kW/rack. Higher rack and power densities demand better thermal management in order to process the workloads faster. Air cooling, which has traditionally been the go-to, is now being transitioned to liquid cooling. There are different types of liquid cooling, with direct-to-chip cooling taking the lead today. Fluid management of the liquids (PG-25 is one such liquid that is used) is critical to ensure uptime. The supply-chain for this fluid management is weak today, thus unable to meet the market's demand. An example here is Schneider's acquisition of 75% controlling interest in Motivair for $850M in Oct'24. #3 DC operators thrive better with the shortest supply-chain: The value-chain to build out a DC is quite complex. Having the shortest and a contractually-bounded supply-chain ensures component supply and thereby a shorter time to market to build out these much needed infrastructures. No matter of the economic uncertainty, the demand curve for Datacenters is not showing a sign of let down, as per Frost & Sullivan analysis. We have an impressively unique and strong capabilities in this space. Feel free to reach out for a complimentary session with any one of our experts.