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At first glance, the most striking part of the SunRise, a recently redeveloped residential tower in Edmonton, Alberta, is the boldly colored facade, with strips of primary color and a lively mural. Called The Land We Share, the vibrant landscape sketch has sparkled on the skyline since its unveiling this past summer. But the mural is far more than a pretty picture. Covered on all sides in a kind of colored solar panel called BIPV made by Canadian firm Mitrex, the mural and the rest of the structure generate roughly 267 kilowatt hours, enough to cut the buildings carbon emissions in half. Typically, high-rises generate solar power primarily via their rooftops. But thats limiting, says Mitrex founder and CEO Danial Hadizadeh. High-rises are exposed to the sunlight, and we can infuse them with panels at a minimal cost, so why not? he says. [Photo: courtesy Mitrex] A smaller part of the cladding company Clarify, Mitrex (named after the Iranian god of the sun) launched five years ago, after solving some of the unique technical challenges around making these colorful panels work. The panels are safe and easy to hang and can be colored in numerous shades in addition to the standard bluish tint. They have been reformulated to be noncombustible and now are cost competitive with other facade choices. Hadizadeh says that next year the company will introduce a new model thats cost competitive with aluminum cladding, and he hopes to see larger real estate portfolios start coating multiple buildings in the panels to reduce their energy costs. [Photo: courtesy Mitrex] Increasing efficiency, lowering cost, and implementation on all elevations and every aspect of the building, thats where we are going, Hadizadeh says. While it is true that, say, a 10-square-foot section of a vertical array on the side of a skyscraper will generate less energy than a similar-size section on a rooftop panel, due to the latters ability to capture more direct sunlight, its still generating considerably more than an un-panelized facade. There might be some difficulty getting every side of a building to provide adequate generation in a super-dense collection of skyscrapers such as in Midtown Manhattan, but thats a relatively small part of the market. [Photo: courtesy Mitrex] In the case of SunRise, the buildings owner, Avenue Living Asset Management, needed the building upgrade to meet certain carbon emission reduction targets to qualify for retrofit funding, and the Mitrex panel made the project pencil out. In fact, Mitrex panels hang atop whats called the rainscreen, a waterproofing and insulating layer on the facade of the building; not only does this approach create power, but it also improves the buildings overall energy efficiency at the same time. Mitrex projects slated to open next year include a medical center on the University of Toronto campus and a series of high-end residential towers in Dubai.
Category:
E-Commerce
A cozy, neutral sameness defines our era of interior design. Velvet sofas. Bouclé armchairs. All-white living rooms. Beds layered with fluffy faux-fur blankets. Calming sage green kitchen cabinets. You see it in furniture catalogs, social media feeds, perhaps even your own home. And we’ve got algorithms to thank. A decade ago, social platforms shifted from chronological feeds to algorithmic ones, optimized to show users what they were most likely to engage with. As many cultural critics have pointed out, those systems reward what is broadly appealing and shareable. In interiors, that has meant rooms that are soothing and inoffensivebut largely devoid of personality. “Algorithms are a mathematical equation based on the statistical middle,” says Christiane Robbins, a founding partner of architectural firm MAP Studio, who has studied algorithms’ influence on design. “Over time, the middle becomes what everybody thinks they want.” [Photo: Lulu & Georgia] Over time, algorithmic aesthetics begin to feel familiar, then comfortable, then indistinguishable from your own taste. Its subtle, says Sara Sugarman, founder and CEO of Lulu and Georgia, a furniture brand that she launched in 2012, just before algorithms reshaped the internet. Your personal style is influenced by these trends whether you realize it or not. You might decide you like a shade of gray without realizing its because youve seen it hundreds of times. But experts like Katherine Lambert, Robbins’ business partner, believe that change is coming. Consumers are getting tired of the visual sameness all around them. Home brands are realizing that they no longer have a distinct point of view that sets them apart from competitors. “We’re seeing a ‘design resistance’ emerging,” says Lambert. “Designers are rebelling against the algorithm.” Sugarman considers herself a member of this resistance. At Lulu and Georgia, she’s pushing back against algorithm-inspired design across her business. Instead, she’s empowering designers who have a strong point of view to create idiosyncratic pieces that draw the customer in. The majority of the brand’s revenue comes from products that it designs and manufactures itself, allowing it to create an aesthetic that stands out from other brands. [Photo: Lulu & Georgia] This strategy has been good for Lulu and Georgia’s bottom line. The company, which is self-funded and profitable, has been growing at a rate of 30% year over year for the past few years. And customers tend to be loyal, with a repeat rate of more than 50%, which is roughly double the industry standard. Lulu and Georgia offers a glimpse into how the world of mass-market interior design might be changing, as consumers want to break free from AI-generated sameness. The Democratization of Design Sugarman grew up immersed in design. Her grandfather, Louis Sugarman, founded Decorative Carpets in West Hollywood in 1955, catering to elite interior designers. As a child, she spent time in the showroom watching designers create custom pieces for wealthy clients. It was a closed system, where professionals controlled access and defined taste. That began to change in the 2000s, as the internet and social media gave a broader audience access to design inspiration. Mass retailers like Target, Ikea, and Wayfair made it possible to recreate high-end looks at lower prices. Sugarman didnt see this shift as a threat. It was incredible, she says. Design became more accessible, and it helped the industry overall. [Photo: Lulu & Georgia] She launched Lulu and Georgia as a digitally native rug brand before expanding into furniture and decor. But as platforms like Instagram, Pinterest, and later TikTok came to dominate visual culture, Sugarman noticed customers arriving with increasingly fixed ideas of what they wantedlabels like modern, coastal, or traditional that all pointed toward the same neutral, minimalist end point. For Robbins, this convergence makes sense. The rise of algorithmic feeds coincided with years of global upheavalfrom the pandemic to political instability. In uncertain times, people gravitate toward what feels familiar, she says. Sameness offers a subliminal sense of security. Algorithmic Design is Good for Business For home brands, flattened taste is operationally convenient. When consumers want the same sofas, colors, and textures, demand becomes easier to forecast and inventory risk shrinks. Searches for white sofas and bouclé furniture have steadily increased over the past decade, making those products reliable bets. If your business depends on scale and predictability, algorithmic sameness is incredibly efficient, Robbins says. You can optimize your supply chain, minimize risk, and flood the zone with products. [Photo: Lulu & Georgia] But Lambert is seeing signs of fatigue in her conversations with designers and clients. People sense that something is off, even if they cant articulate it yet, she says. Especially in [hotels and restaurants], everything looks interchangeable. Theres a global scroll nowwhere everything looks the same no matter where you are. In response, Sugarman has deliberately pushed back against algorithmic design. Lulu and Georgia does not use any trend-forecasting firms and resists letting past sales data dictate future products. This sets it apart from other furniture retailers. The forecasting agency WGSN has a robust interior design division which many manufacturers and brands (like LG and Knoll) use to decide what to make. Target, for its part, has built its own generative AI-powered forecasting platform called Target Trend Brain. By contrast, Sugarman empowers designers with distinct points of view to create pieces that dont yet exist in the market. Roughly 55% of the company’s revenue comes from products that it has designed and manufactured itself; the remaining 45% comes from products it has curated from other suppliers whose aesthetic fits in to Lulu and Georgia’s. The strategy is bearing fruit. Many of the designer collaborations sell out within days. Some of Lulu & Georgia’s bestsellers over the last few years look very different from the soft neutral styles that dominates our feeds: A red marble dining table with rounded leg, a wooden dining table with perforated holes on the base, dining chairs with unusual shapes cut out on the back. The brand collaborates with interdisciplinary designers including ceramicist Lalese Stamp, architect Ginny Macdonald, lighting designer Eny Lee Parker, textile designer Élan Byrd, and fashion designer Carly Cushnie, encouraging them to design what they genuinely want in their own homeseven if it means making a objects with no track record of selling. Products are often manufactured in small quantities to test demand. [Photo: Lulu & Georgia] One example is a small wooden vanity chair designed by longtime collaborator Sarah Sherman Samuel. Sugarman initially doubted it would sell. Most people dont have vanities anymore, she says. Still, they made a small run. The chair quickly sold out, with customers using it as a sculptural accent in living spaces. As with other furniture retailers, Lulu and Georgia also experiments with color through made-to-order pieces. A sofa designed by Macdonald is available in bold shades like mustard yellow and paprika red, produced only after a customer places an order. The approach allows the brand to test unconventional colors without overcommitting inventory. Sometimes, Sugarman says, those experiments become massive hits. [Photo: Lulu & Georgia] For Robbins and Lambert, this strategy works because it is rooted in specificity. Specificity is the secret sauce that throws off the algorithm, Lambert says. The more cultural, historical, and contextual knowledge you bring in, the harder it is for systems to flatten taste. As algorithmic sameness reaches its limits, they believe consumers will increasingly seek out brands willing to take risks. Were seeing fatigue percolate, Robbins says. I think were approaching a cultural tipping point. Designers who resist the algorithm are going to win.
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E-Commerce
About a year ago, an advertisement caught the attention of Ashleigh Ruane, a PhD student in physics at the University of Cambridge. The ad was simple but unusual: Teach AI about physics. Curious, she clicked. She learned that experts across fieldsfrom physics and finance to healthcare and lawwere now being paid to help train AI models to think, reason, and problem-solve like domain specialists. She applied, was accepted, and now logs about 50 hours a week providing data for Mercor, a platform that connects AI labs with domain experts. Ruane is part of a fast-growing cohort of professionals who are shaping how AI models learn. According to Freelancer, thousands of new AI data training and annotation roles have appeared on their marketplace, with most of the growth taking hold in just the past 18 months. These roles range from highly technical expert tasks, like evaluating complex reasoning or diagnosing model errors, to nuanced judgment calls that large models still struggle with. Were entering a really interesting time period, says Freelancer CEO Matt Barrie. AI models need more and more data. Were seeing professionals from every field in every part of the world taking part in this AI data training work. The trend raises bigger questions: If AI models have already been trained on the open internet and vast corporate datasets, why do they still need human experts? What exactly are these experts doing? And how long will this new kind of work be around? AI has read the whole internet’and still needs real experts Theres a common assumption that todays largest AI models already know everything they need to know. After all, theyve been trained on millions of books, articles, papers, and posts. But industry leaders say domain experts are now more important than ever. Models trained on the entire internet can get you to an 80% answer, but in legal or tax, 80% isnt useful, explains Joel Hron, CTO of Thomson Reuters. Our customers demand a high level of accuracy and trust. Leveraging experts ensures accuracy to the highest degree that we can. Ana Price, vice president of supply at Prolific, which provides human data for AI labs, agrees that experts are becoming even more important as AI models move into regulated, high-stakes domains. The demand for human expertise and domain specific feedback from AI models is growing and growing and growing, says Price. As these models have gotten bigger, the errors are becoming harder to spot. Real expertise is needed to judge the substance of what models are producing, and not just the surface level correctness. In other words, the internet alone is not a substitute for structured professional knowledge. The more organizations rely on AI for serious, high-stakes work, the more they need experts to show models how real professionals think. What expert AI trainers actually do Linda Yu spent the last decade as an investor, deploying $4 billion of investments into technology enabled businesses. She started working with Mercor as an expert contributor a year ago, where typical projects involve coaching AI models to think like an investment professional. My role as a domain expert is to evaluate whether the model response is not just technically correct, but whether the complex reasoning behind the response is accurateincluding assumptions the model made, where it may have overreached, where it missed, and what a better answer would be, shares Yu. The work feels less like training an AI model, and more like mentoring a junior analyst. Experts like Yu say the work varies from project to project, and is being applied across industries from law, medicine, engineering, and beyond. Participants are typically paid hourly$85 per hour on averageand may be asked to evaluate a models reasoning on a technical question, rewrite incorrect answers into correct, step-by-step explanations, and compare multiple model outputs and choose which best reflects real-world practice. The output isnt generic content, but high-fidelity reasoning data designed to shape how AI systems operate. AI interviewers interviewing AI trainers The work requires real expertise, which means AI labs need data from experts who are vetted. To assist with the vetting, some platforms rely on AI interviewers to assess the actual expertise of potential AI trainers. Experts jump on a call, and they interview with AI, says Arsham Ghahramani, founder of Ribbon, an AI interviewer with more than 500 customers, including an AI training data provider who is interviewing more than 15,000 experts a month. Youll likely be asked the best interview questions youve ever been asked. AI interviewers assess experts for signals that would indicate red flags around expertise, like irregular response cadence, whether they respond naturally, and of course, whether they have the required expertise for a given domain. It was actually my first interview with not a real person, says Yu. It scanned my resume and came up with really relevant questions. After each answer, the AI interviewer acted like a real person and summarized what I said and asked a question that was a natural extension of our conversation topic. I was fascinated by the technology. AI now evaluates the humans teaching it, a reflection of just how far people have advanced model capabilities. The ‘last mile of information’ still belongs to humans One of the clearest explanations for why expert data remains essential comes from Mark Quinn, senior director of AI operations at Pearl and former head of Waymo engineering operations. He draws a connection between todays AI challenges and autonomous driving. At Waymo, we worked towards the last mile of autonomous mobility. Now, were working towards the last mile of information, Quinn says. Even though AI systems are being developed to close the last mile of information, the reality is that people may still prefer human expert validation if they need an answer on what to do if their dog ate some chocolate. The metaphor resonates across the industry. Even as models get smarter and larger, theres a world full of edge casessituations that require judgment, ethical reasoning, or domain-specific logic that isnt easily captured in general datasets. Some leaders believe the last mile will shrink but never disappear entirely. Hron of Thomson Reuters notes, The base models still have a long way to go to be truly deep. Expert systems and expert knowledge will help models climb to the next level. Price of Prolific adds, Weve only scratched the surface in terms of what AI can do. Humans are a critical piece of the puzzle, especially in niche domains. In other words, the future isnt about replacing experts. Its about scaling the expertise thats essential to making AI models better and safer. A new kind of knowledge work For Ruane, the physics PhD student, expert data work has become a significant source of income. She recently accepted a full-time position, but notes that her new job will only be 38 hours per weekleaving time to continue contributing to AI training projects. What shes experiencing is quickly becoming common: skilled professionals treating AI training work as a supplemental career path, flexible side hustle, or even full-time job. The work plays an increasingly central role in how AI systems operate. As models get more capable, the value of real-world expertise is being redefined, not diminished. Experts arent just using AI. Theyre teaching it how to reason, think, and act like an expert.
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E-Commerce
As utilities struggle to keep up with surging energy demand, theyre starting to turn to an unexpected tool: windows that insulate like walls. Think of it like a thermos bottle in your walls, says LuxWall CEO and founder Scott Thomsen, who worked in the semiconductor and flat-panel display glass industry before taking on the challenge of windows. Energy-efficient windows arent new. But a radical design from LuxWall, a Michigan-based startup, goes further. Rather than relying on double or triple panes, it uses a vacuum to block heat transfer, the same way your Yeti tumbler can keep a drink ice-cold or steaming hot while the outside stays close to room temperature. [Photo: Charles Aydlett/LuxWall] Cutting energy bills in half A typical energy-efficient window might have an R-value (the measure of a material’s resistance to heat transfer) of R3. Luxwalls windows have a rating of R18, similar to a solid wall. When theyre used to replace single-pane windows, they can cut energy use by as much as 45%. Some of the startups first customers are large building owners, like JPMorgan Chase, looking for ways to slash energy bills. Homeowners are beginning to adopt the windows for the same reason. On large projects, the payback period for the windows can be three to seven years. Now, some utilities, like Con Edison and Eversource, are starting to offer incentives to use LuxWall as they look for new ways to help the power grid. When we go in and we retrofit a building from R2 to R18, the amount of kilowatt hours that we’re saving is dramatic, Thomsen says. Yes, we save energy efficiency and save costs for the property owner. But we’re realizing our biggest benefit is that we’re keeping electrons on the grid. … When you don’t send electrons to HVAC units, you’re sending electrons to data centers. Our theory is that you can retrofit buildings faster than you can build power plants. [Photo: Scott Thomsen/LuxWall] Making a super-insulating window The idea of vacuum-insulated glass isnt new, and first showed up in a lab in the 1960s. But unlike insulated bottles that can be mass-produced in a single size, windows of multiple sizes and shapes are difficult to scale. In my mind, the reason it had never been successfully commercialized was that you have to really blend material science with advanced manufacturing, Thomsen says. As the startup developed a feasible manufacturing process, it also got funding from Bill Gatess Breakthrough Energy Ventures and other VCs to build a factory. The company has raised $167 million to date. [Photo: Scott Thomsen/LuxWall] Inside a 217,000-square-foot factory in Litchfield, Michigan, a highly automated production line makes windows in custom sizes. (In one current project, they’re producing windows for a 40-story high-rise in New York City.) Large sheets of clear and lowemissivity glass are cut, edged, drilled, and tempered. Then theyre carefully joined on a vacuum assembly line, with tiny support pillars and sealants applied using lasers and heat. The air is removed between the panes, creating a vacuum that turns the window into a walllike insulator. Another 276,000-square-foot factory is under development in Detroit. The project previously won a $31.7 million grant from the Department of Energy that was canceled last year in a round of DOE funding cuts; the company is appealing the cancellation while still moving forward with construction. The building is complete, with some equipment on the way, and will be running early next year. “We’re cranking up the output,” Thomsen says. “So we’re going to really drive better unit economics. The goal is to start replicating this in multiple locations beyond Detroit.”
Category:
E-Commerce
Its a little-known fact that Columbia University, in Manhattan, was home to the first mining school in Americathe School of Minesfounded in 1864. For the past three decades, the university’s program has been mothballed. Parts of its curriculum were subsumed into the more fashionable subjects of earth and environmental engineering. But next fall, Columbia University will offer a bachelor of science degree in mining engineering once again. Other schools are barreling down, as well. The University of Texas at El Paso is also relaunching its mining engineering degree, starting in the fall of 2027, after a 60-year hiatus. The University of Texas system is providing $20 million to reestablish the program, which plans to produce up to 100 mining engineers annually. Existing programs at some of the top schools for miningincluding the Colorado School of Mines, the Missouri University of Science and Technology, and Montana Technological Universityare also reporting upticks in enrollment, reversing years of declines. Until the 1970s, most universities had pretty robust programs in mining engineering, says Greeshma Gadikota, professor of earth and environmental engineering at Columbia University, who will also teach in the revived mining program. This rebirth in mining education in the United States is happening for a reason. Its a response to a crisis thats been decades in the making. The underground scene In key measures of mineral wealth and production, the U.S. is failing to keep up. Rising global demand across clean energy, defense, and tech industries has driven prices for critical minerals like copper, silver, and tungsten to record highs. Geopolitical tensions have threatened access to many others. For decades, the U.S. had deprioritized mining and has instead come to rely on rare minerals produced in China. China dominates production of at least 15 critical minerals and mineral groups; it mines about 70% of the worlds rare earth elements and processes about 90% of the global supply. (The U.S. is entirely dependent on China to meet its demand for graphite, an essential component in lithium-ion batteries, for example.) But over the past year, in retaliation for Trumps tariffs, China has banned the export of three rare earth productsgallium, germanium, and antimonyto the U.S. And it has put export restrictions on many others, including ones for which China is the sole supplier, including dysprosium, essential for building superfast computer chips, and samarium, a rare earth metal used in many military applications. Last fall, prices for gallium (used in electronics, semiconductors, and batteries) and germanium (critical to infrared technology used in fighter jets and missiles) hit a 14-year high. Tapping into a domestic supply of rare minerals has become not just an economic imperative for the U.S. but a strategic one. Yet that requires rebuilding a declining workforce. More than half the people currently working in the U.S. mining industryroughly 221,000 workersare expected to retire or switch industries by 2029. The U.S. Bureau of Labor Statistics forecasts 400 annual job openings for mining engineers through 2034. That may not sound like a lotafter all, the Bureau of Labor Statistics anticipates about 5,500 annual openings for civil engineering technologists and technicians, and 17,500 openings for electrical and electronics engineers in the same period. But consider that in 2023, only 312 mining engineering degrees were awarded by U.S. universities. That means its a sellers market for new mining gradsa stark contrast to the outlook for computer science graduates and computer engineering majors, who faced 6.1% and 7.5% rates of unemployment, respectively, according to the Federal Reserve Bank of New York. (It’s no wonder Nvidia CEO Jensen Huang says he would study physical science if he were starting out today.) But the ability of the U.S. to mint new mining engineers is limited by the number of schools that still offer mining and mineral engineering programs, which has fallen from 25 in 1982 to about a dozen today. Edgar Mine field session [Photo: Colorado School of Mines] Those programs started shutting down one after the other, because so much of the work was getting shifted abroad,” Columbia University professor Gadikota says. Other countries took advantage of that, and they started building up capabilities. Today, China has more than 38 mineral processing schools and more than 44 mining engineering programs, according to the nonprofit Center for Strategic and International Studies. Chinas largest mineral processing program, at Central South University, alone has 1,000 undergraduates and 500 graduate students preparing for the field. Now, schools and businesses are trying to spread the word that the mining industry has well-paying jobs to filland that mining today is different. Graduates in mining engineering regularly earn $70,000 and up, right out of school. According to the U.S. Bureau of Labor and Statistics, the median annual pay for mining engineers is $101,200. Specific expertise in the extraction of rare earth elements, for example, and a willingness to work in remote locations can boost compensation. A new gold rush for mining engineers From aluminum and antimony to zinc and zirconium, there are currently 60 critical materials on the U.S. Geological Services list, minerals and rare earth elements that are vital to batteries, semiconductors, planes, lasers, medical imaging devices, cancer therapies, cars, electronics, nuclear power plants, and more.&nbs; As defined by the Energy Act of 2020, these materials are essential to the economic or national security of the U.S.; have a supply chain that is vulnerable to disruption; and serve an essential function in the manufacturing of a product, the absence of which would have significant consequences for the economic or national security of the U.S. Many of these materials exist in the U.S., but most of them are still stuck in the ground. Thats starting to change, as big mining companies and startups alike race to develop new domestic sources. MP Materials, a rare earth mining and processing facility on the Nevada-California border, signed a guaranteed-pricing contract in 2025 with the Pentagon and saw its stock surge more than 240% for the year. MIT-founded startup Phoenix Tailings raised $76 million in venture funding last year, supporting the build-out of a next-generation rare earth processing facility in New Hampshire. In December, Ionic Mineral Technologies announced it had discovered rare earth and critical technology metals, including gallium, germanium, cesium, and tungsten, that it says are comparable to Chinas deposits. Global mining giants like Glencore, BHT, and Rio Tinto are also developing critical mineral assets in the U.S. Each of these companies employs its own mining engineersand most of them also contract with other companies that employ them. The growth in critical minerals is creating new kinds of opportunities for young people getting into the industry. And schools are scrambling to revamp curricula to reflect the shifting industry landscape. Kwame Awuah-Offei, who leads the Missouri University of Science and Technologys Department of Mining and Explosives Engineering, says the schools graduates typically fall into three career buckets: construction aggregate materials (a $35 billion-a-year business in the U.S.), mineral mining, and mining services (working for equipment makers, software companies, and others that support the mining industry). Even though U.S. coal mines still employ some 44,000 people, Awuah-Offei says, coal recruiters are having a tough go of it with new grads. There is concern among students that if they want to have a 30- or 40-year career, it’s not in coal. Whether its true or not, the numbers have shrunk quite a bit. Interest in critical minerals is a big factor contributing to larger recent class sizes, Awuah-Offei says. Domestic need for resources is just in the news morehe mentions Trumps talk of invading Greenlandand it drives curiosity on the issue. While undergrad mining engineering enrollment is still small compared with mechanical engineering, electrical engineering, civil engineering, and fast-growing nuclear engineering, it has grown over the past couple of years. Awuah-Offei is confident that graduates will find jobs when they graduatethanks to the new demand in rare metals mining and processing, coupled with very strong job opportunities in the construction materials and aggregate side of the business.” The latter type doesnt pay as much as metal mining jobs, but the attraction is that they tend to be around metro areas. Lifestyle is an important factor for this generation of students, Awuah-Offei says. Even if a job in Bagdad, Arizona”a remote copper mining hubis paying $10,000 more, theyd rather live in Dallas than be in Bagdad. Things come in waves, says Columbia University professor Gadikota. We had a wave around climate. Right now we have a wave around metals and foundational materials. Of course, the two things arent unrelatedwhich might be key to mining engineerings widening appeal. Sustainability and social considerations increasingly define industry practices. Mining meets AI, entrepreneurship, and environmentalism When people see today’s mining tech, they are surprised, Awuah-Offei says. This includes not only massive excavators and tunnel boring machines, but also increasingly common autonomous trucks and robotic equipment. Advances in technology have led to changes in mine design and operation, which in turn have created new challenges that require engineering-based solutions. For example, says Sebnem Düzgün, associate department head of mining engineering at the Colorado School of Mines, one of my students recently analyzed problems with BEV [battery electric vehicle] operations in underground environments. Its highly interconnectedthere’s a societal need for these critical minerals, and mining itself also needs them, to electrify the mines. Sebnem Düzgün [Photo:Colorado School of Mines] Düzgün recently led a recent curriculum update at the school, which included adding classes in things like data science, AI and machine learning, robotics, and autonomous operations. All engineering departments have an industrial advisory committee, she says, and we frequently reflect their requests in our curriculum. Modern mining involves using AI models to analyze geological and satellite data during the exploration phase, deploying predictive analytics to improve mine traffic flow and minimize equipment downtime, and creating digital twins to process real-time sensor data and optimize processes. If you go to the control room of a modern mining processing plant, all you see is big banks of computer screens with someone monitoring data streaming in from sensors,” Awuah-Offei says. “They dont necessarily need to walk out there to see whats going on. Technology has enabled a new breed of mining startups to flourish, which has prompted another change to the traditional curriculum. Mining is mainly governed by large industry, Düzgün says. But as new businesses have emerged, weve started incorporating entrepreneurship into our curriculum, and now some of our graduates are entrepreneurs. Some technology-enabled mining startups are even being funded at levels typically associated with AI companies. In January 2025, KoBold Metals, an AI-powered U.S. mining startup backed by Bill Gates and Jeff Bezos, raised a $537 million Series C round. Another part of the mining engineering syllabus is environmental stewardship. To be honest, weve been incorporating the social and environmental aspects of mningthings like mine closure and reclamation issuesinto our curriculum for almost 20 years, Düzgün says. But the industrys handling of these concepts became more pronounced. At Columbia, Gadikota says the mining program had morphed into earth and environmental engineering as the public became more focused on minings environmental footprint. We went so much toward managing environmental impacts that it reached the point where we didn’t even want [mines] in our backyard. Now, the pendulum is swinging back. We are rediscovering and repurposing our mining roots and bringing back all of that knowledge, but not just in the same outdated manner. We need the metals. We also need to clean up the tailingsmaterials left over after ore has been extracted from rockand the emissions, and develop sustainable water systems. We want to be conscious about managing tomorrows liability today, she says. Gadikota oversees a sponsored research agreement, announced in November, between Columbia University and Locksley Resources, which is targeting rare earth elements and antimony (used in energy storage) in California. Students at Columbia will explore approaches including AI-driven ore analysis, innovative electrochemical recovery, and carbon-dioxide-assisted mineral processing to help the company develop sustainable practices that improve upon current methods. If we wanted to build metal recovery capabilities based on technology that exists in other countries, we can certainly do that, Gadikota says. But we know that some of those mining pathways are not as energy-efficient, theyre not as material-efficient. They contribute to a lot of emissions. Then there is the processing side. How do we process the material in a way that allows us to produce not just one product, but multiple co-products? And how can we lower the environmental footprint of doing that? These are all key factors to consider, and that’s why we do what we do. Government spends heavily, but gaps remain Last March, President Trump signed an executive order, Immediate Measures to Increase American Mineral Production, that outlined numerous steps to increase funding and cut red tape for domestic mining and metals processing projects. The government responded: The U.S. Department of Energy announced in August that it would issue nearly $1 billion in funding to advance and scale mining, processing, and manufacturing technologies across critical minerals and materials supply chains. Three months later, the Energy Department’s Office of Fossil Energy announced that it would provide up to $275 million to build U.S. industrial facilities capable of producing valuable minerals from existing industrial and coal byproducts, and $80 million to establish Mine of the Future proving grounds to test next-generation mining technologies. But there hasnt yet been much federal funding specifically earmarked for mining education. We’ve seen an uptick in research funding for faculty to go after, Awuah-Offei says. Traditionally, when theres a lot of funding, universities are more willing to hire people in that area. So that has been good. But apart from programs in some states, like Texas, there hasnt been direct investment in education, necessarily. Introduced in the House of Representatives in March, the Technology Grants to Strengthen Domestic Mining Education Act of 2025 (aka the Mining Schools Act) would establish a grant program to support schools in recruiting and educating future mining professionals, including engineers. It is currently awaiting action by the House Committee on Natural Resources. Most of us support that because it will put direct funding into schools, Awuah-Offei says. Training mining engineers is expensive. You have to have an experimental mine. It’s lab-based and hands-on. Theres little time to waste. We’ll work on cute little mining projects, Gadikota says. But if you want to scale them up and you need a domestically trained workforce to implement and grow them, does that workforce actually exist? The answer to that question is: We are behind, and we are doing everything that we can to develop that talent and get them out again.
Category:
E-Commerce