@HPC Podcast Archives - OrionX.net - HPC News Bytes – 20260914

Episode Date: September 14, 2026

- The Rise and Rise of AI Doomerism - AI's Global Power Constrain - AI to Help Control Fusion Energy Process - AI Solves yet another hard math problem - Taiwan's AI-Server Boom [audio mp3="https://orionx.net/wp-content/uploads/2026/09/HPCNB_20260914.mp3"][/audio] The post HPC News Bytes – 20260914 appeared first on OrionX.net.

Welcome to HPC Newsbytes, a weekly show about important news in the world of supercomputing, AI, quantum computing, and other advanced technologies. Hi, everyone. Welcome to HPC Newsbytes. I'm Doug Black, and with me is Shaheen Khan. Shaheen, if I could start off this week's conversation with an observation, it stems from an event I went to early last month, a pretty big data center conference in northern Virginia. And the frequent comment we heard when people were comparing this year's conference to the previous year is that community issues related to data center. This is the growing resistance to data centers at the local, state, regional, and national levels was the biggest change for the industry over the past 12 months. That resistance is only growing stronger.

The latest development is that some of the most alarming AI warnings with calls for AI controls and pauses aren't. just coming from AI Dumeers in academia, industry analysts, and politicians, but from the industry itself. As the Wall Street Journal stated in a recent headline, last week was the week everybody started talking about AI Doomsday. We seem to have entered a new phase in the AI adventure we've been on for the last four years. Okay, so getting into more specific industry news, the U.S. Energy Information Administration, or EIA, expects electricity consumption to set records in both 2026 and 2027 with data centers a major contributor. But there's also a forecasting problem. Data Center demand for new grid connections reportedly exceeds 700 gigawatts, more than 10 times current U.S.

Data Center requests. As a result, some developers are applying for power at several possible sites before deciding where to build. Utilities are responding with deposits and other financial commitments that require developers to put real money behind a request. In some markets, projected demand has fallen substantially once speculative projects are screened out. It's hard to tell if the industry's warnings are motivated by the need to include all the downsides as they prepare to go public or as a way of slowing down open source alternatives in a competitive move or really only because of some new recognition that AI can cause harm. And crucially, whether the concern is shared globally.

But concern or not, like you were indicating, it doesn't look like anything is slowing down to the extent you can power it to begin with. The International Energy Agency projects data center electricity consumption through 2030, rising about 130% in the U.S., 170% in China, 70% in Europe, and 80% in Japan. China and the U.S. alone account for nearly 80% of the projected increase. It is especially an acute problem because power availability is now a competitive advantage alongside accelerators and capital. Governments will have to wrestle with AI sovereignty, also as you indicated, voter pushback,

policy complexities and roadblocks, and infrastructure build-out, starting with budgets, then power generation and distribution, securing the hardware, and developing indigenous AI models and applications. Many countries simply won't be able to build enough AI infrastructure, and just like many companies, will have to rely on access to cloud resources in other countries. In this case, on-prem will mean domestic, and cloud can mean offshore. The Tokomac is the most historically mature and heavily funded nuclear fusion design, using a donut-shaped magnetic field to trap plasma,

though it suffers from engineering challenges like plasma instability. Private companies are racing to commercialize it using next-gen compact magnets, while simultaneously funding three major alternatives to bypass its flaws. One is called Stellarators, which use twist-end, ultra-complex external coils for a stable continuous reaction. Another is inertial confinement, which uses lasers to implode fuel pellets, and magneto-inertial fusion, which uses colliding plasma rings for cheaper, smaller reactors. Driven by AI data centers, companies like Microsoft and Google have signed power purchase agreements with these private ventures, transforming fusion

from an academic pursuit into a multi-billion-dollar market race. Researchers at the Princeton Plasma Physics Lab have developed Pac-Man, short for prediction and control using machine learning. It's a modular AI control system for fusion experiments. They tested it in experiments on a large general atomic's Tokomac in San Diego. Pac-Man reads measurements from the plasma using AI models to predict its behavior, behavior and adjust the machine within safety limits. The loop runs about every 20 milliseconds. In one experiment, it predicted a potentially disruptive plasma instability about 200 milliseconds before it developed and changed the plasma conditions to avoid it.

Open AI, meanwhile, says an internal AI system has solved one of the Millennium Prize problems. These are seven of the most famous and difficult math problems. each with a million dollar price for the first correct solution. It is organized by the Clay Mathematics Institute, a quiet philanthropic foundation that was founded in 1998, right next to Harvard University, before moving its operations to the University of Oxford in the UK in 2012 when a professor there became its president.

The specific problem is known as the Navier-Stokes' existence and smoothness problem, referencing a set of equations, named after a fact that, French civil engineer and an Irish mathematician, which, roughly speaking, describe and predict how fluids move. Navier-Stokes are notoriously difficult to calculate. The problem asks whether these equations can break down mathematically, producing quantities that become infinite after starting with perfectly well-behaved conditions. So I see it fundamentally as a search for whatever pathological conditions you can devise to make the equations go haywire. And Open AI says its AI has found such a condition and successfully proved the equations can, in fact,

break down. We've talked about AI solving math problems a couple of times now, and I've been calling the impressive part of what AI does a perceptive shot in the dark, and then the logic to follow a good lead and synthesize the results. This sounds like the same type of thing. Now, nothing is suddenly changing in fluid dynamics R&D. The Navier-Stokes equations remain an incredibly effective model for predicting fluid flow. So this would be a major mathematics results rather than a change to fluid dynamics engineering.

It would largely be in the good-to-know category for engineering practitioners. But while we wait for the proof to be reviewed and accepted, there is already controversy. It turns out that NYU mathematician Tristan Buckman, Buckmaster, an anthropic researcher Levant Alpogi, had independently been making massive progress in the area, using AI tools, including OpenAI's coding models. Because Buckmaster had a year's worth of research draft sitting in OpenAI systems, he has questioned whether information from that work contributed to OpenAI's sudden sprint. OpenAI says it had not accessed their specific data,

but acknowledges that it cannot rule out the possibility that de-identified data derived from users helped improve its core models. The dispute raises major questions about AI-assisted research, training data, attribution, and ownership. And recall that under current US law, and AI itself cannot be the inventor on a patent

or the author of a copyrighted work. As AI moves from answering questions towards discovering, designing, and acting, the potential scientific value rises, while errors, misuse, and loss of control become more consequential. Really interesting to track. Taiwan's major server manufacturers are showing extraordinary revenue growth as AI infrastructure deployments scale. Foxconn remains the largest, with January through August revenues of roughly 206 billion. Quanta reported about 84 billion over the same period, up 103% year over year, while Wistram reached about 79 billion, up 99%. August in and of itself was a remarkable

month. Quanta generated about 13.4 billion up 178%, while Wistram reached roughly 14.6 billion up 166%. These companies have evolved considerably beyond the old image of low-margin PC assemblers. They increasingly integrate accelerators, CPUs, memory, networking, storage, power, and liquid cooling into complex rack-scale systems. For scale, NVIDIA generated about $216 billion in its latest fiscal year, and AMD had about $35 billion in 2025. Foxconn has already done roughly in VEVILA's full year revenue in eight months, although its business and margins are obviously very different. This is a reminder that Taiwan's strengths are not limited to TSM and chip manufacturing.

They extend all the way up to systems. The geopolitical wrinkle is the pressure to move more of that manufacturing from chips through complete systems to the U.S., while much of the engineering expertise and supplier coordination remains concentrated in Taiwan. All right, that's it for this episode. Thank you all for being with us. HPC Newsbytes is a production of OrionX. Shaheen Khan and Doug Black host the show. Every episode is posted on Orionx.net.

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