#Data centers

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Impact and innovation of AI in energy use with James Chalmers

New Post has been published on https://thedigitalinsider.com/impact-and-innovation-of-ai-in-energy-use-with-james-chalmers/

Impact and innovation of AI in energy use with James Chalmers

In the very first episode of our monhtly Explainable AI podcas, hosts Paul Anthony Claxton and Rohan Hall sat down with James Chalmers, Chief Revenue Officer of Novo Power, to discuss one of the most pressing issues in AI today: energy consumption and its environmental impact.

Together, they explored how AI’s rapid expansion is placing significant demands on global power infrastructures and what leaders in the tech industry are doing to address this.

The conversation covered various important topics, from the unique power demands of generative AI models to potential solutions like neuromorphic computing and waste heat recapture. If you’re interested in how AI shapes business and global energy policies, this episode is a must-listen.

Why this conversation matters for the future of AI

The rise of AI, especially generative models, isn’t just advancing technology; it’s consuming power at an unprecedented rate. Understanding these impacts is crucial for AI enthusiasts who want to see AI development continue sustainably and ethically.

As James explains, AI’s current reliance on massive datasets and intensive computational power has given it the fastest-growing energy footprint of any technology in history. For those working in AI, understanding how to manage these demands can be a significant asset in building future-forward solutions.

Main takeaways

AI’s power consumption problem: Generative AI models, which require vast amounts of energy for training and generation, consume ten times more power than traditional search engines.

Waste heat utilization: Nearly all power in data centers is lost as waste heat. Solutions like those at Novo Power are exploring how to recycle this energy.

Neuromorphic computing: This emerging technology, inspired by human neural networks, promises more energy-efficient AI processing.

Shift to responsible use: AI can help businesses address inefficiencies, but organizations need to integrate AI where it truly supports business goals rather than simply following trends.

Educational imperative: For AI to reach its potential without causing environmental strain, a broader understanding of its capabilities, impacts, and sustainable use is essential.

Meet James Chalmers

James Chalmers is a seasoned executive and strategist with extensive international experience guiding ventures through fundraising, product development, commercialization, and growth.

As the Founder and Managing Partner at BaseCamp, he has reshaped traditional engagement models between startups, service providers, and investors, emphasizing a unique approach to creating long-term value through differentiation.

Rather than merely enhancing existing processes, James champions transformative strategies that set companies apart, strongly emphasizing sustainable development.

Numerous accolades validate his work, including recognition from Forbes and Inc. Magazine as a leader of one of the Fastest-Growing and Most Innovative Companies, as well as B Corporation’s Best for The World and MedTech World’s Best Consultancy Services.

He’s also a LinkedIn ��Top Voice’ on Product Development, Entrepreneurship, and Sustainable Development, reflecting his ability to drive substantial and sustainable growth through innovation and sound business fundamentals.

At BaseCamp, James applies his executive expertise to provide hands-on advisory services in fundraising, product development, commercialization, and executive strategy.

His commitment extends beyond addressing immediate business challenges; he prioritizes building competency and capacity within each startup he advises. Focused on sustainability, his work is dedicated to supporting companies that address one or more of the United Nations’ 17 Sustainable Development Goals through AI, DeepTech, or Platform Technologies.

About the hosts:

Paul Anthony Claxton – Q1 Velocity Venture Capital | LinkedIn

www.paulclaxton.io – am a Managing General Partner at Q1 Velocity Venture Capital… · Experience: Q1 Velocity Venture Capital · Education: Harvard Extension School · Location: Beverly Hills · 500+ connections on LinkedIn. View Paul Anthony Claxton’s profile on LinkedIn, a professional community of 1 billion members.

Rohan Hall – Code Genie AI | LinkedIn

Are you ready to transform your business using the power of AI? With over 30 years of… · Experience: Code Genie AI · Location: Los Angeles Metropolitan Area · 500+ connections on LinkedIn. View Rohan Hall’s profile on LinkedIn, a professional community of 1 billion members.

Like what you see? Then check out tonnes more.

From exclusive content by industry experts and an ever-increasing bank of real world use cases, to 80+ deep-dive summit presentations, our membership plans are packed with awesome AI resources.

Subscribe now

[exeton]

Data Centers in High Demand: The AI Industry’s Unending Quest for More Capacity

The demand for data centers to support the booming AI industry is at an all-time high. Companies are scrambling to build the necessary infrastructure, but they’re running into significant hurdles. From parts shortages to power constraints, the AI industry’s rapid growth is stretching resources thin and driving innovation in data center construction.

The Parts Shortage Crisis

Data center executives report that the lead time to obtain custom cooling systems has quintupled compared to a few years ago. Additionally, backup generators, which used to be delivered in a month, now take up to two years. This delay is a major bottleneck in the expansion of data centers.

The Hunt for Suitable Real Estate

Finding affordable real estate with adequate power and connectivity is a growing challenge. Builders are scouring the globe and employing creative solutions. For instance, new data centers are planned next to a volcano in El Salvador to harness geothermal energy and inside shipping containers in West Texas and Africa for portability and access to remote power sources.

Case Study: Hydra Host’s Struggle

Earlier this year, data-center operator Hydra Host faced a significant hurdle. They needed 15 megawatts of power for a planned facility with 10,000 AI chips. The search for the right location took them from Phoenix to Houston, Kansas City, New York, and North Carolina. Each potential site had its drawbacks — some had power but lacked adequate cooling systems, while others had cooling but no transformers for additional power. New cooling systems would take six to eight months to arrive, while transformers would take up to a year.

Surge in Demand for Computational Power

The demand for computational power has skyrocketed since late 2022, following the success of OpenAI’s ChatGPT. The surge has overwhelmed existing data centers, particularly those equipped with the latest AI chips, like Nvidia’s GPUs. The need for vast numbers of these chips to create complex AI systems has put enormous strain on data center infrastructure.

Rapid Expansion and Rising Costs

The amount of data center space in the U.S. grew by 26% last year, with a record number of facilities under construction. However, this rapid expansion is not enough to keep up with demand. Prices for available space are rising, and vacancy rates are negligible.

Building Data Centers: A Lengthy Process

Jon Lin, the general manager of data-center services at Equinix, explains that constructing a large data facility typically takes one and a half to two years. The planning and supply-chain management involved make it challenging to quickly scale up capacity in response to sudden demand spikes.

Major Investments by Tech Giants

Supply Chain and Labor Challenges

The rush to build data centers has extended the time required to acquire essential components. Transceivers and cables now take months longer to arrive, and there’s a shortage of construction workers skilled in building these specialized facilities. AI chips, particularly Nvidia GPUs, are also in short supply, with lead times extending to several months at the height of demand.

Innovative Solutions to Power Needs

Portable Data Centers and Geothermal Energy

Startups like Armada are building data centers inside shipping containers, which can be deployed near cheap power sources like gas wells in remote Texas or Africa. In El Salvador, AI data centers may soon be powered by geothermal energy from volcanoes, thanks to the country’s efforts to create a more business-friendly environment.

Conclusion: Meeting the Unending Demand

The AI industry’s insatiable demand for data centers shows no signs of slowing down. While the challenges are significant — ranging from parts shortages to power constraints — companies are responding with creativity and innovation. As the industry continues to grow, the quest to build the necessary infrastructure will likely become even more intense and resourceful.

FAQs

1. Why is there such a high demand for data centers in the AI industry?

The rapid growth of AI technologies, which require significant computational power, has driven the demand for data centers.

2. What are the main challenges in building new data centers?

The primary challenges include shortages of critical components, suitable real estate, and sufficient power supply.

3. How long does it take to build a new data center?

It typically takes one and a half to two years to construct a large data facility due to the extensive planning and supply-chain management required.

4. What innovative solutions are companies using to meet power needs for data centers?

Companies are exploring options like modular nuclear reactors, geothermal energy, and portable data centers inside shipping containers.

5. How are tech giants like Amazon, Microsoft, and Google responding to the demand for data centers?

They are investing billions of dollars in new data centers to expand their capacity and meet the growing demand for AI computational power.

Muhammad Hussnain Facebook | Instagram | Twitter | Linkedin | Youtube

[el-ffej]

BTW, Ars Technica published a (IMO) much more informative -- and nuanced -- article re: predicted power usage due to AI.

The article is data-driven, and asks more questions than it answers; but I they're questions we should be considering re: the amount of energy used by data centers (not just AI):

After reading it, I feel like I have a much clearer perspective on the problem. Highly recommend giving it a look.

I don't know, how about switching it off?

[yourtechdietblog]

Top Data Center Trends to Watch in 2024

As technology continues to evolve, data centers play a crucial role in storing and managing the vast amounts of data powering the digital world. Here are the top 5 data center trends you should keep an eye on in 2024:

Edge Computing

Data processing closer to the end user for faster, more efficient services.

A decentralized approach to meet the growing demand for high-speed data handling.

Sustainability at the Forefront

Data centers adopting renewable energy and eco-friendly cooling solutions.

Sustainability isn’t just good for the planet; it helps reduce operational costs too!

AI-Powered Predictive Analytics

Using AI to predict and resolve potential issues before they disrupt operations.

Ensuring greater uptime and operational efficiency.

Hyperscaler Data Centers

Larger and faster data centers to handle the ever-increasing volume of data.

Providers like Amazon and Microsoft are leading the charge in this space.

Smart Monitoring Systems

Real-time monitoring with intelligent, automated systems.

Enhancing control over complex data center operations for better decision-making.

Final Thoughts

As we move into 2024, these trends are revolutionizing how data centers operate. Companies adopting these technologies will find themselves well-prepared for the demands of the digital future.

[danielkaufmanrealestate]

Bitcoin Nears $100K: What Trump’s Pro-Crypto Stance Could Mean for Investors

The cryptocurrency world is buzzing as Bitcoin surged to a new record high of $97,892, edging closer to the coveted $100,000 mark. This explosive growth comes amid speculation that President-elect Donald Trump’s administration will usher in a new era of crypto-friendly regulation. Trump’s Crypto Vision: A White House Crypto Policy Role? According to Bloomberg, Trump’s team is considering the…

[servermo]

Data Centers: Powering the Digital World 🌍💻

Data centers are the backbone of the digital age, storing and processing data for businesses and individuals alike. From cloud services to social media, they ensure the smooth operation of digital platforms across the globe 🌎💾. They are critical in securing and managing vast data with advanced technology and tight security measures 🔒. Understanding how they work can help us appreciate the tech that keeps us connected daily!

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Amazon Intensifies AI Chip Development Amid Nvidia Competition

Amazon has intensified its investment in artificial intelligence (AI) chip development, aiming to challenge Nvidia’s dominance in the sector. Through its subsidiary, Annapurna Labs, Amazon is advancing its custom AI chips, including the Trainium 2, designed to enhance the training of large AI models. Companies such as Anthropic, Databricks, Deutsche Telekom, and Ricoh are currently testing these…

[jcmarchi]

Elaine Liu: Charging ahead

New Post has been published on https://thedigitalinsider.com/elaine-liu-charging-ahead/

Elaine Liu: Charging ahead

MIT senior Elaine Siyu Liu doesn’t own an electric car, or any car. But she sees the impact of electric vehicles (EVs) and renewables on the grid as two pieces of an energy puzzle she wants to solve.

The U.S. Department of Energy reports that the number of public and private EV charging ports nearly doubled in the past three years, and many more are in the works. Users expect to plug in at their convenience, charge up, and drive away. But what if the grid can’t handle it?

Electricity demand, long stagnant in the United States, has spiked due to EVs, data centers that drive artificial intelligence, and industry. Grid planners forecast an increase of 2.6 percent to 4.7 percent in electricity demand over the next five years, according to data reported to federal regulators. Everyone from EV charging-station operators to utility-system operators needs help navigating a system in flux.

That’s where Liu’s work comes in.

Liu, who is studying mathematics and electrical engineering and computer science (EECS), is interested in distribution — how to get electricity from a centralized location to consumers. “I see power systems as a good venue for theoretical research as an application tool,” she says. “I’m interested in it because I’m familiar with the optimization and probability techniques used to map this level of problem.”

Liu grew up in Beijing, then after middle school moved with her parents to Canada and enrolled in a prep school in Oakville, Ontario, 30 miles outside Toronto.

Liu stumbled upon an opportunity to take part in a regional math competition and eventually started a math club, but at the time, the school’s culture surrounding math surprised her. Being exposed to what seemed to be some students’ aversion to math, she says, “I don’t think my feelings about math changed. I think my feelings about how people feel about math changed.”

Liu brought her passion for math to MIT. The summer after her sophomore year, she took on the first of the two Undergraduate Research Opportunity Program projects she completed with electric power system expert Marija Ilić, a joint adjunct professor in EECS and a senior research scientist at the MIT Laboratory for Information and Decision Systems.

Predicting the grid

Since 2022, with the help of funding from the MIT Energy Initiative (MITEI), Liu has been working with Ilić on identifying ways in which the grid is challenged.

One factor is the addition of renewables to the energy pipeline. A gap in wind or sun might cause a lag in power generation. If this lag occurs during peak demand, it could mean trouble for a grid already taxed by extreme weather and other unforeseen events.

If you think of the grid as a network of dozens of interconnected parts, once an element in the network fails — say, a tree downs a transmission line — the electricity that used to go through that line needs to be rerouted. This may overload other lines, creating what’s known as a cascade failure.

“This all happens really quickly and has very large downstream effects,” Liu says. “Millions of people will have instant blackouts.”

Even if the system can handle a single downed line, Liu notes that “the nuance is that there are now a lot of renewables, and renewables are less predictable. You can’t predict a gap in wind or sun. When such things happen, there’s suddenly not enough generation and too much demand. So the same kind of failure would happen, but on a larger and more uncontrollable scale.”

Renewables’ varying output has the added complication of causing voltage fluctuations. “We plug in our devices expecting a voltage of 110, but because of oscillations, you will never get exactly 110,” Liu says. “So even when you can deliver enough electricity, if you can’t deliver it at the specific voltage level that is required, that’s a problem.”

Liu and Ilić are building a model to predict how and when the grid might fail. Lacking access to privatized data, Liu runs her models with European industry data and test cases made available to universities. “I have a fake power grid that I run my experiments on,” she says. “You can take the same tool and run it on the real power grid.”

Liu’s model predicts cascade failures as they evolve. Supply from a wind generator, for example, might drop precipitously over the course of an hour. The model analyzes which substations and which households will be affected. “After we know we need to do something, this prediction tool can enable system operators to strategically intervene ahead of time,” Liu says.

Dictating price and power

Last year, Liu turned her attention to EVs, which provide a different kind of challenge than renewables.

In 2022, S&P Global reported that lawmakers argued that the U.S. Federal Energy Regulatory Commission’s (FERC) wholesale power rate structure was unfair for EV charging station operators.

In addition to operators paying by the kilowatt-hour, some also pay more for electricity during peak demand hours. Only a few EVs charging up during those hours could result in higher costs for the operator even if their overall energy use is low.

Anticipating how much power EVs will need is more complex than predicting energy needed for, say, heating and cooling. Unlike buildings, EVs move around, making it difficult to predict energy consumption at any given time. “If users don’t like the price at one charging station or how long the line is, they’ll go somewhere else,” Liu says. “Where to allocate EV chargers is a problem that a lot of people are dealing with right now.”

One approach would be for FERC to dictate to EV users when and where to charge and what price they’ll pay. To Liu, this isn’t an attractive option. “No one likes to be told what to do,” she says.

Liu is looking at optimizing a market-based solution that would be acceptable to top-level energy producers — wind and solar farms and nuclear plants — all the way down to the municipal aggregators that secure electricity at competitive rates and oversee distribution to the consumer.

Analyzing the location, movement, and behavior patterns of all the EVs driven daily in Boston and other major energy hubs, she notes, could help demand aggregators determine where to place EV chargers and how much to charge consumers, akin to Walmart deciding how much to mark up wholesale eggs in different markets.

Last year, Liu presented the work at MITEI’s annual research conference. This spring, Liu and Ilić are submitting a paper on the market optimization analysis to a journal of the Institute of Electrical and Electronics Engineers.

Liu has come to terms with her early introduction to attitudes toward STEM that struck her as markedly different from those in China. She says, “I think the (prep) school had a very strong ‘math is for nerds’ vibe, especially for girls. There was a ‘why are you giving yourself more work?’ kind of mentality. But over time, I just learned to disregard that.”

After graduation, Liu, the only undergraduate researcher in Ilić’s MIT Electric Energy Systems Group, plans to apply to fellowships and graduate programs in EECS, applied math, and operations research.

Based on her analysis, Liu says that the market could effectively determine the price and availability of charging stations. Offering incentives for EV owners to charge during the day instead of at night when demand is high could help avoid grid overload and prevent extra costs to operators. “People would still retain the ability to go to a different charging station if they chose to,” she says. “I’m arguing that this works.”

[maryyeblog]

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[navyasri1]

Data Center Liquid Cooling Market - Forecast(2024 - 2030) - IndustryARC

The Data Center Liquid Cooling Market size is estimated at USD 4.48 billion in 2024, and is expected to reach USD 12.76 billion by 2029, growing at a CAGR of 23.31% during the forecast period (2024-2029). The increasing adoption of various liquid cooling strategies such as dielectric cooling over air cooling in order to manage equipment temperature is boosting the Data Center Liquid Cooling Market. In addition, the growing demand for room-level cooling for cloud computing applications is tremendously driving the data center cooling systems market size during the forecast period 2022-2027.

[oknews]

Cirion Technologies designa nuevo director de ciberseguridad para América Latina

Miami, FL – Cirion Technologies anunció la incorporación de Miguel Rodríguez como su nuevo Chief Information Security Officer (CISO) para América Latina. Rodríguez, un experto argentino en ciberseguridad, asumirá la responsabilidad de liderar la estrategia de protección digital en todos los mercados donde opera la empresa. Rodríguez llega a Cirion con una amplia trayectoria en la dirección de…