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Google’s estimate of AI resource consumption leaves out too much – Computerworld

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“If you, as a CIO, are not speaking with your operations and facilities teams around forecasting power requirements versus power availability, start immediately,” said Matt Kimball, VP/principal analyst for Moor Insights & Strategy. “Having lived in the IT world, I am well aware of how separate these organizations can be, where power is just a line item on a budget and nothing more. Talk to the team that’s managing power, cooling and datacenter infrastructure — from the rack out — to better understand how to use these resources most efficiently.”

It’s not just computing capacity that contributes to the cost of AI: IT needs to reexamine existing storage operations too, Kimball said.

“I would take a long look at my storage infrastructure and how to better optimize on and off prem. The infrastructure populating most enterprise datacenters is out of date and underutilized. Moving to servers that have the latest, densely populated CPUs is a first start,” he said. “Moving on-prem storage from spinning media to all flash has a higher up-front cost, but is far more energy efficient and performant. It’s easy to buy into the NVIDIA B300 or AMD MI355X craze. Or the Dell, HPE, or Lenovo AI factories. But is this much horsepower required for your AI and accelerated computing needs? Or are, say, RTX6000 PRO GPUs good enough? They are far more affordable and about 40% of the power consumption compared with a B300.”



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Leading Google UK & the AI Opportunity

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The UK has always had a special place in my story. Canary Wharf is where my career began in the 90s, during a period of profound transformation for the country’s financial sector. Reflecting on my first three months as Google UK lead, it’s clear that the pace of AI innovation is driving an even greater sense of historic opportunity, not just in the City, but across the entire country.

Recently, I attended a technology industry dinner at the historic Mansion House. The evening was an electric pairing of tradition and transformation – a blend that the UK has perfected. The room was filled with British business leaders, policymakers, and trailblazers across the tech sector, eager to uncover how AI-powered technologies could help solve some of the biggest challenges of our generation. This opportunity to build on the country’s rich heritage for pioneering world leading breakthroughs is why I’m excited to be back in the UK to lead Google’s operations here.

The UK: a hub for AI innovation & cultural influence

During my 15 years at Google, I’ve held a variety of regional and global roles, partnering with a diverse range of organisations to turn complex challenges into technological opportunities. Throughout that time, the UK has always stood out as a hotbed of innovation, a global epicenter for AI research — in particular, the work of our remarkable Google DeepMind colleagues — and a pioneer in the international advertising industry.

The UK has long been a nation of early adopters. This is why the UK was one of the first countries to roll-out new Gemini-powered products, such as AI Mode — a new way to search for information, developed to cater to the growing number of people asking longer and more complex queries.

UK consumer behaviour is constantly evolving, across streaming, scrolling, searching, and shopping. That’s why Google and YouTube are uniquely positioned to empower UK businesses to thrive, in a dynamic digital environment. It’s been inspiring getting to know the teams here in the UK who are helping businesses of all sizes meet the moment and use AI-powered tools to turn their online presence into real-world revenue, providing a vital engine for UK economic growth.

The UK’s cultural influence is also undeniable, as evidenced by well established homegrown British YouTube creators, such as Amelia Dimoldenberg and Brandon B who have become new media powerhouses in their own right. Or the England squad Lionesses, like Lucy Bronze who are both athletes and content creators in their own right, inspiring young female footballers to strive for excellence on and off the pitch, while winning for the UK. YouTube, which celebrated its 20th birthday earlier this year, is transforming how businesses use AI to reach new audiences. I’m proud of our leadership in this space, and the site’s potential to connect even more brands with a new generation of consumers.

Seizing the opportunity ahead

The construction of our first UK data centre in Waltham Cross, our new King’s Cross development and our AI Works initiative — our partnership with British organisations to help uncover the most effective ways to accelerate AI adoption and upskilling — are just some of the significant investments we’re making in the UK’s digital future. The UK is a country unlike any other and this is an incredible time to be back.



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AI hype has just shaken up the world’s rich list. What if the boom is really a bubble?

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Just for a moment this week, Larry Ellison, co-founder of US cloud computing company Oracle, became the world’s richest person. The octogenarian tech titan briefly overtook Elon Musk after Oracle’s share price rocketed 43% in a day, adding about US$100 billion (A$150 billion) to his wealth.

The reason? Oracle inked a deal to provide artificial intelligence (AI) giant OpenAI with US$300 billion (A$450 billion) in computing power over five years.

While Ellison’s moment in the spotlight was fleeting, it also illuminated something far more significant: AI has created extraordinary levels of concentration in global financial markets.

This raises an uncomfortable question not only for seasoned investors – but also for everyday Australians who hold shares in AI companies via their superannuation. Just how exposed are even our supposedly “safe”, “diversified” investments to the AI boom?

The man who built the internet’s memory

As billionaires go, Ellison isn’t as much of a household name as Tesla and SpaceX’s Musk or Amazon’s Jeff Bezos. But he’s been building wealth from enterprise technology for nearly five decades.

Ellison co-founded Oracle in 1977, transforming it into one of the world’s largest database software companies. For decades, Oracle provided the unglamorous but essential plumbing that kept many corporate systems running.

The AI revolution changed everything. Oracle’s cloud computing infrastructure, which helps companies store and process vast amounts of data, became critical infrastructure for the AI boom.

Every time a company wants to train large language models or run machine learning algorithms, they need huge amounts of computing power and data storage. That’s precisely where Oracle excels.

When Oracle reported stronger-than-expected quarterly earnings this week, driven largely by soaring AI demand, its share price spiked.

That response wasn’t just about Oracle’s business fundamentals. It was about the entire AI ecosystem that has been reshaping global markets since ChatGPT’s public debut in late 2022.

The great AI concentration

Oracle’s story is part of a much larger phenomenon reshaping global markets. The so-called “Magnificent Seven” tech stocks – Apple, Microsoft, Alphabet, Amazon, Meta, Tesla and Nvidia – now control an unprecedented share of major stock indices.

Year-to-date in 2025, these seven companies have come to represent approximately 39% of the US S&P500’s total value. For the tech-heavy NASDAQ100, the figure is a whopping 74%.

This means if you invest in an exchange-traded fund that tracks the S&P500 index, often considered the gold standard of diversified investing, you’re making an increasingly concentrated bet on AI, whether you realise it or not.

Are we in an AI ‘bubble’?

This level of concentration has not been seen since the late 1990s. Back then, investors were swept up in “dot-com mania”, driving technology stock prices to unsustainable levels.

When reality finally hit in March 2000, the tech-heavy Nasdaq crashed 77% over two years, wiping out trillions in wealth.

Today’s AI concentration raises some similar red flags. Nvidia, which controls an estimated 90% of the AI chip market, currently trades at more than 30 times expected earnings. This is expensive for any stock, let alone one carrying the hopes of an entire technological revolution.

Yet, unlike the dot-com era, today’s AI leaders are profitable companies with real revenue streams. Microsoft, Apple and Google aren’t cash-burning startups. They are established giants, using AI to enhance existing businesses while generating substantial profits.

This makes the current situation more complicated than a simple “bubble” comparison. The academic literature on market bubbles suggests genuine technological innovation often coincides with speculative excess.

The question isn’t whether AI is transformative; it clearly is. Rather, the question is whether current valuations reflect realistic expectations about future profitability.

President and chief executive of Nvidia Corporation, Jensen Huang.
Chiang Ying-ying/AP

Hidden exposure for many Australians

For Australians, the AI concentration problem hits remarkably close to home through our superannuation system.

Many balanced super fund options include substantial allocations to international shares, typically 20–30% of their portfolios.

When your super fund buys international shares, it’s often getting heavy exposure to those same AI giants dominating US markets.

The concentration risk extends beyond direct investments in tech companies. Australian mining companies, such as BHP and Fortescue, have become indirect AI players because their copper, lithium and rare earth minerals are essential for AI infrastructure.

Even diversifying away from technology doesn’t fully escape AI-related risks. Research on portfolio concentration shows when major indices become dominated by a few large stocks, the benefits of diversification diminish significantly.

If AI stocks experience a significant correction or crash, it could disproportionately impact Australians’ retirement nest eggs.

A reality check

This situation represents what’s called “systemic concentration risk”. This is a specific form of systemic risk where supposedly diversified investments become correlated through common underlying factors or exposures.

It’s reminiscent of the 2008 financial crisis, when seemingly separate housing markets across different regions all collapsed simultaneously. That was because they were all exposed to subprime mortgages with high risk of default.

This does not mean anyone should panic. But regulators, super fund trustees and individual investors should all be aware of these risks. Diversification only works if returns come from a broad range of companies and industries.



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Virginia Tech expands faculty in AI, systems, and manufacturing

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The Grado Department of Industrial and Systems Engineering at Virginia Tech has added four new faculty members for 2025, part of a strategy to expand expertise in high-demand fields such as data-driven decision making, advanced manufacturing, and artificial intelligence.

The department prepares students for careers in engineering through research, teaching, and industry collaboration.

New faculty join the department

The new appointments are Alkim Avsar, assistant professor in systems engineering; Eric Brubaker, assistant professor in systems engineering; Kelsey Coleman, associate professor of practice and Learning Factory director; and Yuhao Zhong, assistant professor in manufacturing engineering.

Eileen Van Aken, professor and department head, says: “We’re thrilled to welcome these outstanding faculty to our team. Our students will benefit from their perspectives, and their work will expand our capabilities in areas critical to the future of engineering and the success of our graduates.”

Research and teaching areas

Avsar, who joins from Arizona State University, earned a Ph.D. in systems engineering from Stevens Institute of Technology. Her work combines systems engineering, behavioral science, game theory, and human factors to examine how social factors influence engineering decision making. She is currently teaching a graduate course in systems engineering.

Brubaker, previously a senior complex systems engineer at NASA, holds a Ph.D. in mechanical engineering from Stanford University. His research addresses human-AI collaboration and designs for complex systems that tackle challenges in water, energy, transportation, and healthcare. He is teaching AI for systems engineering.

Coleman, formerly at Eaton Corporation, manages workforce development programs and directs the Learning Factory, which provides students with hands-on training in areas such as additive manufacturing, robotics, and digital factory systems.

Zhong, who completed his Ph.D. at Texas A&M University, focuses on data science applications in manufacturing. His research develops explainable AI and computer vision tools to improve quality, safety, and performance in Industry 5.0 environments.

The ETIH Innovation Awards 2026



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