CHART: Mining vs AI – Nvidia sneezes, sheds five BHPs

Frik Els | July 13, 2026 |

A pit battle. Image: Definitely AI generated

A year ago this week Nvidia became the first company to touch $4 trillion, and MINING.COM ran the numbers against the combined worth of the world’s 50 most valuable mining companies. It was not even close.

The chip designer was worth 2.7 times the MINING.COM TOP 50 (and since valuations quickly shrink outside that, probably twice the entire global mining industry).

This year Nvidia has been having the kind of quarter miners unfortunately know all too well. 

After peaking at roughly $5.5 trillion in the middle of May, the stock shed about $1 trillion over the next eight weeks. At the bottom of the slide, Nvidia was briefly trading at 18 times forward earnings – below the S&P 500 average, which made the poster child of the AI age, at least technically, a value stock.

The chipmaker’s P/E is now back near 20 times forward earnings, but mining’s majors change hands for barely 13 – cheaper on next year’s profits than Nvidia managed to look even at its bargain-bin bottom and despite all the criticality clamour surrounding mining. 

The TOP 50 had a quarter of its own. A record $2.4 trillion at the end of March, before gold’s slip back below $4,000 an ounce took $228 billion off the ranking, or for the Mag 7 slash Lag 7, a bad Friday afternoon. 

The scoreboard now reads $5.11 trillion versus $2.19 trillion. Nvidia is worth 2.3 times the top 50 miners, down from 2.7 times last July, and for a few sessions this month the multiple flirted with two – territory last visited when cheap and cheerful DeepSeek gave the AI trade its first proper scare. 

Over the past twelve months mining even outgrew the machine – up 47% against Nvidia’s 27% – a first in the short history of this exercise. 

But to return to theme of mainstream investors not valuing the production of copper the same way as the production of hallucinations about the production of copper, here’s a sobering thought:

What Nvidia shed between mid-May and early July – nearly five BHPs, and BHP has never been worth more – was about what the whole Top 50 was worth for most of this decade.

Renaming everything critical minerals was a good start (and kudos to met coal and lead for making the list), but it’s time to launch a worldwide public/investor awareness campaign. What about this old nugget for a tagline: If it can’t be grown, it has to be mined. 

That includes, for the record, the silicon, copper, gold, silver, tungsten, tantalum, titanium, cobalt, aluminium, tin, nickel, hafnium, ruthenium, molybdenum, indium, palladium, gallium, germanium, arsenic, antimony, bismuth, boron, phosphorus, cerium, lanthanum, yttrium, gadolinium, europium and praseodymium that Nvidia’s chips are made from.

Nvidia Backs 8-GW Ohio AI Campus for OpenAI With $1.5 Billion Investment

By Charles Kennedy - Aug 17, 2026

Nvidia is backing a massive artificial intelligence infrastructure development in Ohio that could ultimately require at least 10 gigawatts of new power generation, underscoring the rapidly growing impact of AI data centers on U.S. electricity demand.

The chipmaker said it has partnered with SB Energy to secure land, power and data center shell capacity at the PORTS-Pike Technology Campus in Pike County, Ohio, where OpenAI will be the customer under a 20-year lease.

The first phase is designed to provide 4.25 gigawatts of IT capacity, with Nvidia holding an option covering another 3.75 GW. At full buildout, the project would provide 8 GW of AI computing capacity.

SB Energy, which is backed by Japan's SoftBank Group and OpenAI, will build, own and operate the data center infrastructure. Nvidia will exclusively supply the site's AI computing systems, including GPUs, CPUs and networking equipment through its DSX AI factory platform.

The planned scale of the development highlights the increasingly close relationship between AI infrastructure and the energy sector. SB Energy and SoftBank plan to develop at least 10 GW of new electricity generation to support the campus and invest at least $4.2 billion in regional grid infrastructure through a partnership with AEP Ohio.

The companies said the arrangement is structured to prevent the costs of serving the data center campus from being shifted onto existing electricity customers.

Development is expected to proceed in phases beginning in 2028.

The campus is being built around the former Portsmouth Gaseous Diffusion Plant, a major Cold War-era uranium enrichment site in southern Ohio. The developers are working with AEP Ohio as well as the U.S. Departments of Energy and Commerce to redevelop the area into a large-scale technology and power hub.

Nvidia will also invest $1.5 billion directly in SB Energy, joining SoftBank and OpenAI as investors in the infrastructure company. The funding will support SB Energy's broader expansion as demand grows for purpose-built electricity and data center infrastructure serving AI computing.

The project adds to a wave of multibillion-dollar data center developments that are transforming U.S. power demand forecasts. Hyperscalers and AI companies are increasingly seeking dedicated generation, transmission capacity and long-term power arrangements as access to electricity becomes one of the principal constraints on new computing capacity.

The PORTS-Pike development is particularly notable for its scale: 10 GW of generation capacity would be comparable to the output of several large nuclear power stations and represents a major new source of electricity demand concentrated at a single industrial site.

The developers also plan significant local investment. OpenAI has added $40 million to an existing $40 million community benefits fund established by SB Energy, bringing the initial fund to $80 million. The money is intended for programs including energy affordability, workforce development and regional economic development.

The companies said the project could ultimately support tens of thousands of jobs in Ohio.

By Charles Kennedy for Oilprice.com

 

The link between incoherent beliefs and conspiracism

PNAS Nexus

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Simultaneous belief in logically incompatible claims is linked to conspiratorial thinking, according to a study that models belief using a Bayesian framework.

The hypothesis that people who hold multiple logically incompatible beliefs are more likely to endorse conspiracy theories has been debated for over a decade. Alessandro Miani and colleagues use a new method to quantify the incoherence of an individual's beliefs. Rather than using a dichotomous classification system of belief or disbelief, the authors interpret agreement ratings as subjective degrees of belief expressed as probabilities within a Bayesian framework. It is coherent to entertain two incompatible possibilities, giving each a 50% probability of being true, but incoherent to put a 90% probability on both, because the probabilities assigned to incompatible claims cannot sum to more than 100%. The authors use this approach to analyze previously collected data from eight studies with 8,590 participants and to analyze new data from a preregistered online study of 469 Americans.

The results support the link between incoherent beliefs and conspiracism. In the reanalyzed data, the estimated probability of holding  incoherent beliefs rose from 3% among the weakest conspiracy believers to 91% among the strongest. In the new survey, participants were presented with16 fictitious scenarios—such as an 8-year-old girl, the daughter of a member of the European Parliament, going missing for three days. Participants who scored higher on a standardized 5-item conspiracy mentality questionnaire were more likely to rate it as highly probable that the girl had been secretly held by her father in order to misuse public funds and, also that she had escaped an environment in which she was secretly forced into child labor, so that the two probabilities summed to more than 100%. However, the relationship between incoherence and conspiracism did not extend to every kind of explanation. When the two explanations offered were mundane accidents that could not both be true—the girl fell from a nearby cliff, or she was caught in an avalanche— conspiracy mentality scores did not predict incoherence. Conspiracism did predict incoherence, however, when one statement was the strict negation of the other and the two probabilities therefore had to sum to exactly 100%, regardless of whether the content was conspiratorial.

According to the authors, incoherence in conspiracism may reflect two components: a general component—reduced logical monitoring—that surfaces with strict-negation pairs, and a content-specific component—the worldview-driven endorsement of stories that counter official or expected narratives—that surfaces when two different conspiratorial explanations are themselves incompatible.

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Journal

PNAS Nexus

Article Title

Still very much dead and alive: Incoherence in conspiracism as a departure from Bayesian rationality

Article Publication Date

18-Aug-2026

 

Assistance systems to fight shortage of skilled workforce: XR supports sight-impaired people at work

KIT and Fraunhofer IOSB are developing an assistance system that eliminates obstacles in daily work

Karlsruher Institut für Technologie (KIT)

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XR glasses can support visually impaired people at work. (Photo: Amadeus Bramsiepe, KIT)

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Credit: Amadeus Bramsiepe, KIT

For many skilled workers with a sight impairment, daily tasks can be full of obstacles: Reading a device label, deciphering a handwritten note, or finding their way in unknown spaces is time-consuming or not possible without help. Conventional aids often hit their limits beyond the computer workstation. This is where the AssistiveXR4Work project comes into play: “We’re developing a system that automatically recognizes or magnifies visual information, displays it with high contrast or reads it out loud, depending on the individual vision requirements,” said Professor Rainer Stiefelhagen from KIT’s Center for Digital Accessibility and Assistive Technologies (ACCESS@KIT). “For example, it can automatically recognize and magnify the contents of a whiteboard in a meeting or convert handwritten notes to plain text and further process it without human intervention.” In engineering working environments, on the other hand, the system can help with orientation and highlight relevant components. 

The system uses advanced XR glasses with a high-resolution camera image. They capture the surroundings continuously and digitally adapt the display to the individual vision requirements by enhancing contrast, magnifying contents, recognizing texts, and reading them out loud on demand. In addition, an integrated AI system helps with orientation or recognizes objects present in the surroundings. If needed, a human helper can be connected via a live stream. 

How Inclusion Helps Securing Skilled Workforce

The project not only helps visually impaired people participate in the labor market, but also addresses the shortage of skilled workforce. Actually, many highly skilled employees lose their jobs or have to shift their field of activity when their vision deteriorates. The goal behind AssistiveXR4Work is to help them pursue their career even then. According to the Institute for Employment Research (IAB), a research institution operated by the Federal Employment Agency, severe disabilities are widespread in Germany, the majority of which are occurring during the person’s professional life. The probability to still be part of the labor force five years after the disability strikes is reduced by roughly 16 percentage points for these persons.

It is planned to construct an operational demonstrator within the four years to come and then test it in practice. The Federal Ministry of Labour and Social Affairs is funding the project with around EUR 1.5 million. 

 

AI shares human tendency to infer character from facial features

PNAS Nexus

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Computer-generated images used in the study with questions about trustworthiness. The face on the right is usually seen as more trustworthy by both humans and LLMs. 

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Credit: Alex Todorov

Human beings have a tendency to infer personality or character traits from other people’s facial features, and these biases—ungrounded in any actual relationship between faces and behavior—lead to unfair outcomes.

Steven Lehr and colleagues explored whether AI models, which are trained primarily on text but have the ability to “see” images, share these biases. The authors asked GPT-4o to make over 4,500 forced-choice judgments between computer-generated faces, and presented thousands of additional forced choices to GPT-5, Gemini 3 Flash Preview, and Claude Sonnet 4.5. In some of the experiments, models were asked to choose which of two faces was more competent or more trustworthy. Other trials used related traits, including asking which face was more confident, smart, hardworking, lazy, inept, careless, warm, helpful, sincere, selfish, hypocritical, or aggressive. Some experiments asked LLMs to judge which computer-generated human face would be more likely to be a serial killer, to be arrested for human trafficking, or to defraud the public using a Ponzi scheme. Finally, LLMs were asked to choose between faces in the contexts of hiring a university president, investing in a tech startup, or selecting a financial manager. In all these cases, the models were willing to weigh in and in the majority of cases chose the same face that a human would typically see as more trustworthy or confident. Across studies, GPT-4o selected the face that would be expected based on human ratings 74.88% of the time. GPT-5 showed notably more bias than its predecessor. When GPT-5 advised on consequential decisions, the model recommended the more competent-looking individual fully 97.04% of the time, as compared to GPT-4o’s 75.19%. Models from other companies produced similar results.

If LLMs were free of human face-to-character bias, they could be used as a tool to help eliminate this form of bias in contexts such as job candidate selection or parole decisions. According to the authors, AI in its present form is instead likely to worsen unfairness if used in such contexts.

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Journal

PNAS Nexus

Article Title

Like humans, language models demonstrate face-to-character biases

Article Publication Date

18-Aug-2026

COI Statement

S.L. is affiliated with Cangrade, Inc., a company that works on de-biasing machine learning models. However, Cangrade, Inc. does not currently create generative AI models, did not fund this research, and is not expected to profit in any way from the results or their publication, other than by association. The other authors do not declare any competing interests.

Hanbat National University researchers reveal physics-informed AI for rapid optimization of thermal energy storage systems

The proposed physics-informed neural network framework enables rapid, autonomous design optimization of latent heat thermal energy storage systems

Hanbat National University Industry–University Cooperation Foundation

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The proposed framework is trained on 15 ground-truth datasets, enabling rapid, autonomous exploration of optimal latent heat thermal energy storage system designs.

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Credit: Assistant Professor Joo Hyun Moon from Hanbat National University

With intensifying climate change, decarbonization of the global building sector has become a key priority. A substantial portion of a building’s energy demands consists of heating and cooling needs. Consequently, developing efficient thermal energy storage systems is a crucial part of this effort. Among available options, latent heat thermal energy storage (LHTES) systems that utilize phase change materials offer unique advantages. These include a high energy storage density and the ability to release large amounts of thermal energy at a near-constant temperature, which is crucial for stable thermal management. Indeed, some studies have shown that LHTES systems can reduce heating and cooling energy consumption by up to 45%.

Despite these advantages, accurately modelling and optimizing LHTES systems remains a major challenge. The coupled heat-transfer and fluid-flow processes involved are difficult to simulate accurately. Although physical experiments provide reliable ground-truth data, they are generally limited to laboratory-scale systems. Computational fluid dynamics (CFD) simulations, on the other hand, can capture these complex physical processes across different scales. However, they are computationally expensive and time-consuming, making large-scale design optimization impractical.

In a new study, a collaborative team of researchers from the Republic of Korea, led by Assistant Professor Joo Hyun Moon from the Department of Building Systems Engineering at Hanbat National University in South Korea, has developed a hybrid physics-informed neural network (PINN) framework for optimization of LHTES systems. “The LHTES system utilizes a special wax-type material, called a phase change material, that soaks up a huge amount of heat when it melts and gives it back when it hardens, acting like a battery for warmth,” explains Dr. Moon. “Testing every new design using conventional computer simulations is slow and computationally intensive. In our PINN framework, we teach the AI model the governing laws of physics, enabling it to accurately reproduce the system's behavior and rapidly explore tens of thousands of possible designs.” Their study was made available online on May 05, 2026, and published in Volume 167 of the Journal of Energy Storage on July 30, 2026.

To develop the data-driven PINN, the researchers first created a high-fidelity ground-truth dataset that captures the physics of the LHTES system. For this, they developed a laboratory-scale experimental LHTES setup, and a corresponding CFD model. Experimental measurements were then used to validate the CFD simulations, which showed excellent agreement with only minor deviations. Once validated, the CFD model was used to generate a sparse dataset of 15 high-fidelity simulations that served as training data for the PINN.

The proposed PINN framework employs a zero-dimensional physical model and embeds the system’s governing physical equations directly as loss functions. A key innovation is that the PINN learns case-specific effective heat-transfer coefficients, while geometric effects are captured through a response surface model. By focusing only on discovering the laws of energy conservation, the PINN avoids overfitting despite being trained on sparse data. The response surface model (RSM) then enables the framework to predict heat transfer properties for any arbitrary, unseen geometry and flow condition during the optimization process. Together, the PINN and RSM form a fast digital twin of the LHTES system.

The digital twin is then coupled with a Non-dominated Sorting Genetic Algorithm II (NSGA-II) to perform multi-objective design optimization. The optimization simultaneously searches for designs that maximize total discharged heat and average discharge power while minimizing pumping power.

In numerical experiments, the PINN reproduced the behavior predicted by the CFD simulations with excellent accuracy while enabling rapid, autonomous design optimization. The optimal design obtained using the multi-objective design optimization process performed similarly to the best-performing baseline design, while significantly reducing pumping power. Moreover, the process also shows that flatter pipes are more favorable.

Beyond buildings, the same physics-informed design approach could help improve thermal management in electric-vehicle batteries, data centers, cold-chain logistics, and solar thermal systems. Earlier studies also suggest that smarter control of latent heat storage can cut electricity costs by more than 70%, showing the broader potential of this technology for reducing energy use and emissions.

“Our approach moves the LHTES design process from manual evaluation of discrete cases to autonomous exploration of the entire design space,” concludes Dr. Moon. “It provides engineers with a practical tool for developing more efficient thermal energy storage systems, helping reduce the energy consumption and carbon footprint of buildings while supporting a more sustainable energy future.”

 

Reference
DOI: https://doi.org/10.1016/j.est.2026.122514

 

About the institute
Established in 1927, Hanbat National University (HBNU) is a university in Daejeon, South Korea. As a leading national university in the region, HBNU strives to take lead in solving problems in the local community and solidify its cooperation with industries. The university’s vision is to become a ‘Global industry-university cooperation university creating future values’. HBNU has been chosen for a variety of nationwide-level projects such as An Autonomous Improvement University Project and Leaders in INdustry-University Cooperation+(LINC+), among others. With its focus on practical education and regional impact, HBNU continually advances technological solutions grounded in creative thinking and real-world relevance.

Website: https://www.hanbat.ac.kr/eng/

 

About the author
Dr. Joo Hyun Moon is an Assistant Professor of Building Systems Engineering at Hanbat National University in Daejeon, Republic of Korea. He received his Ph.D. in Mechanical Engineering from Chung-Ang University in 2017. Before joining Hanbat National University, he served as an Assistant Professor at Sejong University and as a postdoctoral researcher at the University of Texas at Dallas. His research spans thermal system optimization, phase-change heat transfer, and physics-informed, machine learning-based models for real-time energy-system optimization.

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Journal

Journal of Energy Storage

DOI

10.1016/j.est.2026.122514 

Method of Research

Computational simulation/modeling

Subject of Research

Not applicable

Article Title

Physics-informed neural networks for multi-objective design optimization of latent heat thermal energy storage systems

 

A single-source dual-drive flexible knee assistive exoskeleton for elderly daily locomotion

Beijing Institute of Technology Press Co., Ltd

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 (A) The prototype of the single-source dual-drive flexible knee assistive exoskeleton. (B) Diagram of the actuator structure and its working principles. The yellow arrow indicates that the moving mesh gear is engaged with the left-side drive gear; the blue arrow indicates that the moving mesh gear is engaged with the right-side drive gear. (C) Disc-type series elastomers based on linear springs. (D) Schematic diagram of the correspondence between the IMU layout and knee angle solution. DCCs, drive control components.

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Credit: Shisheng Zhang, Shenzhen Institutes of Advanced Technology.

The knee is a key weight-bearing and stabilizing joint in daily locomotion, supporting body weight, maintaining balance, and enabling flexible movement during level walking, ramp ascent and descent, and stair ascent and descent. With aging, older adults often experience declines in muscle strength and endurance, leading to weakened knee strength and insufficient body support, which can reduce daily mobility and quality of life while increasing fall risk. Against the backdrop of global population aging, developing knee assistive exoskeletons that can support older adults in real daily walking scenarios is highly important. Although various knee assistive exoskeletons have been developed, several practical challenges remain. Conventional motor-driven systems often add weight and inertia at the knee joint, increasing the burden on the wearer; pneumatic systems are compliant but limited by power supply, stability, and portability; and existing devices still struggle to recognize cyclic locomotion patterns such as level walking, ramps, and stairs in real outdoor environments. “Moreover, many assistance strategies are designed for specific locomotion modes and lack the flexibility to adapt to changing daily walking conditions.” said the author Shisheng Zhang, a researcher at Shenzhen Institutes of Advanced Technology, “Therefore, there is a clear need for a lightweight, comfortable, safe, and flexible knee assistive exoskeleton that can accurately recognize multiple daily locomotion patterns and provide appropriate assistive torque in real time.”

This study designed a single-source dual-drive flexible knee assistive exoskeleton (FKAE), using a single motor, clutch mechanism, and Bowden cables to provide time-shared assistance to the left and right knees. This design reduces the number of high-power motors and distal joint load, while a series elastic actuator improves interaction compliance and safety. The system consists mainly of a back-mounted drive control module, bilateral lower-limb exoskeletons, and a locomotion perception system. Four IMUs are mounted on the thigh and calf components to calculate knee angles in real time and extract gait-related features. For daily locomotion scenarios, including level walking, ramp ascent, ramp descent, stair ascent, and stair descent, the researchers proposed a dual-detection locomotion pattern recognition method combining fuzzy control and a finite state machine. Fuzzy control was used to recognize steady-state gait patterns, while the finite state machine detected transitions between different locomotion modes to reduce switching delay. Based on the recognized locomotion pattern, a finite-state time-shared assistive torque control strategy was further designed to generate assistive torque curves according to the real-time knee angle and provide knee-flexion assistance during level walking, ramp ascent, and stair ascent. Finally, 3 young participants and 3 older participants were recruited for outdoor locomotion recognition tests on level ground, ramps, and stairs, as well as indoor experiments using metabolic measurements and surface EMG signals to evaluate the effects of exoskeleton assistance on energy expenditure and muscle workload.

The experimental results showed that the single-source dual-drive flexible knee assistive exoskeleton could achieve high-precision locomotion pattern recognition in real walking scenarios while effectively reducing users’ movement burden. First, the switching time between left- and right-knee assistance was 0.10 to 0.12 s, satisfying the gait-cycle requirements under different locomotion modes and supporting the feasibility of the single-motor time-shared driving strategy. In outdoor locomotion recognition experiments, the dual-detection strategy combining fuzzy control and a finite state machine accurately recognized daily locomotion modes, including level walking, stair ascent, stair descent, ramp ascent, and ramp descent, with an overall average recognition accuracy of 98.89%; stair ascent recognition reached 100%. During mode transitions, the finite state machine substantially reduced detection delay, and in some transitions from level walking to stairs or ramps, the system could even anticipate the change by about half a gait cycle. Energy-consumption experiments further showed that, compared with the zero-torque condition, exoskeleton assistance reduced metabolic cost by approximately 5.4% to 12.8% during level walking, 11.9% to 28.2% during ramp ascent, and 10.9% to 18.8% during stair ascent. It also reduced surface EMG signals in multiple lower-limb muscles, with some muscle activation reductions exceeding 60%. These preliminary results indicate that the system can provide effective knee assistance across multiple daily locomotion tasks, with good recognition accuracy, interaction safety, and load-reduction potential.

The significance of this work lies in proposing a lightweight and flexible knee assistive exoskeleton for elderly daily locomotion, integrating structural design, locomotion pattern recognition, and assistive torque control to adapt to changing real-life scenarios such as level walking, ramps, and stairs. The single-source dual-drive architecture uses one motor to provide time-shared assistance to both knees, reducing the number of high-power motors and overall system weight. The Bowden cable and series elastic structure reduce distal joint load while improving human–robot compliance and safety. At the same time, the locomotion recognition method combining fuzzy control and a finite state machine achieved high accuracy and low transition delay in both steady walking and mode-switching situations, providing a basis for timely and appropriate exoskeleton assistance. The experimental results also preliminarily showed that the finite-state time-shared assistance strategy could reduce metabolic cost and lower-limb muscle activation, suggesting the system’s potential for supporting elderly daily mobility. However, this study remains an initial validation with a small number of participants. The single-source dual-drive structure cannot actuate both knees simultaneously and cannot cover all assistance demands across the full gait cycle. “In addition, the current recognition method mainly relies on limited kinematic features, and its robustness under complex terrain, turning, nonperiodic gait, and individual variability still needs further validation. Future work could expand testing in older populations, integrate multisource sensing such as foot pressure or EMG, and introduce adaptive control and human-in-the-loop optimization to improve personalized assistance and real-world applicability.” said Shisheng Zhang.

Authors of the paper include Shisheng Zhang, Yang Zhang, Yanzong Xu, Jinke Li, Yuquan Leng, and Xinyu Wu.

This work was supported in part by the National Natural Science Foundation of China (grants 62125307, 52175272, and 62403452), the Guangdong Basic and Applied Basic Research Foundation (grants 2024B1515020008 and 2023B1515130007), the Shenzhen Science and Technology Program (grants RCYX20231211090345058, JCYJ20220530114809021, and KCXFZ20230731093059012), and the Natural Science Foundation of Top Talent of SZTU (grant GDRC202328).

The paper, “A Single-Source Dual-Drive Flexible Knee Assistive Exoskeleton for Elderly Daily Locomotion” was published in the journal Cyborg and Bionic Systems on Aug 11, 2026, at https://doi.org/10.34133/cbsystems.0576.

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Journal

Cyborg and Bionic Systems