Latest news with #JoelKjellgren
Forbes
17-04-2025
- Business
- Forbes
Where Chinese Or American Tech Is Used In Cloud Data Storage
TOPSHOT - Joel Kjellgren, Data Center Manager walks in one of the server rooms at the new Facebook ... More Data Center, its first outside the US on November 7, 2013 in Lulea, in Swedish Lapland. The company began construction on the facility in October 2011 and went live on June 12, 2013 and are 100% run on hydro power. AFP PHOTO/JONATHAN NACKSTRAND (Photo by JONATHAN NACKSTRAND / AFP) (Photo by JONATHAN NACKSTRAND/AFP via Getty Images) Cloud computing has changed the status of network hardware like servers and data storage from a common asset of organizations or companies to a type of infrastructure. With this wide-reaching change come many implications, not least which enterprise owns and operates this infrastructure and which country they are from. American and Chinese firms dominate the cloud computing and data center space, raising questions around data security and independence for third countries, many of which host data centers from American and Chinese companies and store their data and applications there. Major players include Amazon, Google and Microsoft from the United States as well as Huawei, Tencent and Alibaba from China. This chart shows the share of cloud availability zones in different countries, by national origin of ... More provider (in percent). Research from Vili Lehdonvirta, Boxi Wú and Zoe Hawkins at the University of Oxford and Finland's Aalto University shows which countries are home to which kind of data centers. While several European nations including Italy and Poland as well as Israel and Gulf nations Qatar and Bahrain host exclusively American-owned cloud infrastructure, the picture is more mixed in Germany, the United Arab Emirates and the United Kingdom as well as in France and the Netherlands. The countries have between 14% and 40% Chinese data center infrastructure, measured by the share of so-called cloud availability zones, which are one or several connected data centers. The paper is scheduled to be published in the Review of International Political Economy. The United States as well as Canada have small shares of Chinese cloud availability zones, ranging from 8% to 12%. The same number was as high as 17% in Australia and 30% in South Korea. Much higher shares can be observed in countries in developing Asia and in Latin America. Here, reliance on Chinese data center providers ranged from 25% in Brazil to 40% in Chile and 100% in Argentina, Mexico and Peru. Likewise, 55% of cloud computing clusters in Singapore, 57% in Indonesia and 100 percent in Thailand, the Philippines and Malaysia were owned by Chinese companies. The same was true for Saudi Arabia and Turkey. The countries in question were rated as seeking the affordability that Chinese infrastructure offers, while some are also in favor of the Chinese model of the controlled internet, the report concludes. Worldwide, 70% of cloud computing infrastructure is American owned, while almost all of the remaining 30% is in Chinese hands. While it is true that organizations can choose to host their data with a cloud provider all over the world, many use infrastructure that is close by or local. Government or corporate policy rules often stipulate that data centers within the same country or other type of jurisdiction are utilized. While this does have advantages in terms of legal recourse over data centers, questions remain regarding the ownership and country of origin of the technology used. While these concerns have in the past focused mainly on China, they could arise towards the United States more in the future as a consequence of the foreign policy actions of the current Trump administration. The researchers of the report warn that due to these ownership structures, cloud infrastructure could theoretically be weaponized. A total disruption was deemed very unlikely, but extremely wide-ranging by the authors, as it would not only affect many workplaces and public authorities, but also, for example, banking applications, smart home devices and parcel delivery warehouses. The research concludes that the attachment to one or the other country of ownership has to do with trade intensity between the nations but also with strategic choices made by the third country's government. However, owner nations have also been pushing their companies to expand overseas in the cloud sphere. While governments in Europe have made attempts towards cloud sovereignty—driven by suspicion of Chinese as well as American providers—, these initiative were rated as inefficient so far. Charted by Statista
Forbes
01-04-2025
- Business
- Forbes
AI's Energy Crisis: Can Next-Gen Chips Outrace Data Centers' Growing Carbon Footprint?
TOPSHOT - Joel Kjellgren, Data Center Manager walks in one of the server rooms at the Facebook Data ... More Center on November 7, 2013 in Lulea, in Swedish Lapland. The environmental impact of data centers, mainly due to the growing demands of AI computing, is already huge, and is only bound to increase. Major tech companies are investing in renewable energy sources, but due to the frantic pace of development – AI-related demand for computing power is doubling every three to four months – that alone won't solve the problem. Currently, estimates from the International Energy Agency hold data centers responsible for 1% of the world's electricity consumption and 0.6% of global greenhouse emissions, figures that are projected to grow by 2030 to 2.8% and 1.9% respectively. Could the solution lie in reimagining computing architecture themselves, to make them more efficient and environmentally friendly? A new report by the World Fund, a European VC investing in clean tech companies, Intel's startup program Ignite, and Dealroom turns the spotlight on new material innovations that could provide alternatives to traditional silicon-based semiconductors, and next-generation computing paradigms, such as quantum computing and neuromorphic systems that could help mitigate the issue. The white paper focuses specifically on European startups and scaleups operating in these sectors and provides a comprehensive overview of the latest opportunities for clean tech investors. Funding in this sector is soaring: the white paper highlights 65 green computing startups that have already raised $1.5billion. However, it should be noted that none of the solutions presented is in itself a silver bullet to address AI's energy consumption, and that additional technical and geopolitical considerations beyond the scope of the report should be taken into account, when considering their implementation. Take, for instance, Gallium Nitride, or GaN, a wideband gap semiconductor (simply speaking, a material in which there is a wide energy difference between the band where electrons reside and the conduction band, where electrons can move freely), which has emerged as one of the most promising alternatives to silicon. GaN semiconductors promise significant efficiency gains – up to 40–50% compared to traditional silicon power transistors, and they also enable more compact designs; for instance, Texas Instruments demonstrated GaN-based adapters that are about 50% smaller, while still achieving power conversion efficiency. European GaN startups have collectively raised an impressive $70 billion over the last five years, with companies like GaN Systems, Cambridge GaN Devices and Hexagem leading innovation in this space. This clearly shows that there is a perceived market opportunity, as well as a potential benefit for the environment, as the widespread adoption of GaN could cut global CO2 emissions by up to 2.6 gigatons annually by 2050. However, scaling up GaN technology comes with manufacturing and supply-chain limitations. Wide-bandgap semiconductors like GaN (or Silicon Carbide, another alternative to silicon mentioned in the report) are currently more expensive to produce, can be prone to crystal defects, and have a less mature supply chain. In fact, the raw material gallium is a strategic choke point – China produces an estimated 98% of the world's gallium supply, raising concerns about resource constraints. These factors make it challenging to ramp up GaN production quickly, which in turn limits how rapidly the efficiency benefits can be realized at scale. Of course, these challenges can, and probably will, be overcome over time. The question is: when? As things stand, rather than adopting innovations that might require the rebuilding or restructuring of entire supply chains, in the short to mid-term, big tech companies are more likely to keep pace with the rushing advances in artificial intelligence by relying more on fossil-fuel or nuclear-generated energy. In the U.S., for instance, there are already cases of coal-fired power plants whose closure has been postponed, and there are plans to build more than 200 gas (natural gas) power plants in the next few years. This is not to downplay the promises of new material innovations, but to avoid creating overblown expectations either. Similar caveats apply to next-generation computing paradigms, such as quantum computing, of which Europe is a global leader. The continent's quantum startups raised $781M in 2023 - triple the amount raised in North America. The efficiency gains are potentially revolutionary: quantum systems can solve certain problems 100 million times faster than classical computers, translating directly to massive energy savings. Experts estimate that quantum-enabled innovations across various sectors could reduce global emissions by up to 7 gigatons annually by 2035—representing an 18% reduction in total global emissions. However, it's unclear whether and when quantum computing could become a general-purpose technology ready to speed up arbitrary AI workloads. Generally speaking, today's quantum computers are highly specialized machines that excel at specific optimization or simulation tasks, they also require complex cryogenics and control systems that themselves consume significant energy. It is true, though, that as the report notes, 'quantum simulations are already accelerating breakthroughs' in fields such as 'battery chemistry'. 'These advances lay the groundwork for next-generation high-performance batteries, which, if scaled effectively, could mitigate up to 14 gigatons of emissions annually by 2035,' the authors say. And of course, it's always possible that new breakthroughs change the rules of the game and quantum computing quickly becomes an all-purpose solution, applicable to all sorts of situations. I reached out to the report's authors for comment on a possible timeline for implementation of the new materials and techniques highlighted in the report. 'While it is true that some solutions explored in our report (e.g., biocomputing) are still in their early research and development phases, there are definitely solutions that can help limit the damage in the short-to mid-term. Technologies such as GaN and SiC are already available today, and as they scale, increased usage will help drive immediate efficiency savings and decreased power consumption. Software efficiency improvements, such as the model optimization recently seen by DeepSeek, also have the potential to make a more immediate impact. We also expect significant near-term positive impact from both quantum and optical computing,' a spokesperson for World Fund said. They also emphasize the need to deliver cleaner sources of energy alongside next-gen computing innovations, pointing at Google's collaboration with Fervo to provide clean base load power for its data centers or Amazon's investment into X-energy, to provide another source of clean baseload power from small modular reactors, as positive examples. There's much more information in the white paper that can be covered here; for instance, a chapter discusses software-based approaches – like data compression and server virtualization – as ways to curb energy use. An EPA report has found that aggressive virtualization and consolidation can reduce total server energy consumption by as much as 80% in certain data center setups. All in all, though, it seems fair to say that there's no magic plan, no silver bullet, that will solve AI's environmental footprint overnight; instead, a combination of hardware advances, software optimizations, and clean energy transitions could provide the best way to go. It is also clear that, due to the scale of the challenge and the pace of increase in data center energy consumption, even imperfect steps today are better than perfect solutions tomorrow.
