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Having $1 billion isnt enough these days. To be seen among the richest of the rich, you now need your own private sanctuary. For some, that means a sprawling compound. Increasingly, though, members of techs 1% are incorporating their own towns, giving them the power to set rules, issue building permits, and even influence education. Some of these modern-day land grabs are already functioning; others are still in the works. Either way, the billionaire class is busy creating its own utopias. Heres where things stand: Elon Musk Musk can lay claim to not one but two towns in Texas. In May, residents along the Gulf Coast voted to incorporate Starbase (though its worth noting that nearly all of them were SpaceX employees). Previously called Boca Chica, the 1.5-square-mile zone elected Bobby Peden, a SpaceX vice president of 12 years, as mayor. He ran unopposed. The vote stirred controversy. The South Texas Environmental Justice Network opposed the plan. The group wrote in a press release in May: Boca Chica Beach is meant for the people, not Elon Musk to control. For generations, residents have visited Boca Chica Beach for fishing, swimming, recreation, and the Carrizo/Comecrudo Tribe has spiritual ties to the beach. They should be able to keep access. Musk also controls Snailbrook, an unincorporated town near Bastrop, about 350 miles north of Starbase. The area includes a SpaceX site that produces Starlink receiver technology, sits just 13 miles from Teslas Gigafactory, and features housing and a Montessori school that opened last year. Mark Cuban In 2021, Cuban purchased Mustang, Texas (population: 23). The 77-acre town, an hour south of Dallas, was founded in 1973 as an oasis for alcohol sales in a dry county. The former Shark Tank star told CNN he has no immediate plans beyond basic cleanup. “It’s how I typically deal with undeveloped land,” he said. “It sits there until an idea hits me.” California Forever This project isnt tied to a single billionaire, but a collective. In 2017, venture capitalist Michael Moritz spearheaded a plan for a new city in Solano County, California, about 60 miles northeast of San Francisco. Backers included Marc Andreessen, Chris Dixon, Reid Hoffman, Stripes Patrick and John Collison, and Laurene Powell Jobs. Together, they spent $800 million on 60,000 acres. The plan proved unpopular. In November, California Forever withdrew its ballot measure to bypass zoning restrictions. (The land is not zoned for residential use.) It pivoted last month, unveiling Solano Foundry, a 2,100-acre project the founders say could become the nations largest, most strategically located, and best designed advanced manufacturing park. The group also envisions a walkable community with 150,000-plus homes. A Bay Area Council Economic Institute study released this week projected 517,000 permanent jobs and $4 billion in annual tax revenue if the revised plan goes forward. Larry Ellison Ellison doesn’t own a town, but he owns virtually all of one of the Hawaiian Islands. In 2012, he bought 98% of Lanai for about $300 million. He also owns the islands two Four Seasons hotels, most commercial properties, and serves as landlord to most residents. Lanai has become a retreat for the wealthy, hosting visitors from Elon Musk to Tom Cruise to Israeli Prime Minister Benjamin Netanyahu. Peter Thiel Thiel doesn’t own a city, per se, but he is part of a collective backing Praxis, a proposed “startup city” that is currently eyeing Greenland for its base of operations. Other investors include Thiel’s PayPal cofounder Ken Howery and Andreessen. The plan for Praxis is similar to California Forever. Founders hope to create a Libertarian-minded city that has minimal corporate regulation and focuses on AI and other emerging technologies. So far, however, no notable progress has been made on the project. Mark Zuckerberg Zuckerberg owns a 2,300-acre compound on the Hawaiian island of Kauai. Hes investing $270 million into Koolau Ranch, which will include a 5,000-square-foot underground bunker. Located on the islands North Shore, the property is also said to have its own energy and food supplies, Wired reports. While it’s not technically its own city, it will house more than a dozen buildings boasting upwards of 30 bedrooms and 30 bathrooms. There will be two mansions spanning 57,000 square feet, with elevators, offices, conference rooms, and an industrial kitchen. Those will be joined by a tunnel, which branches off into the underground bunker, which has a living space and a mechanical room as well as an escape hatch. Zuckerberg has posted on Instagram about the compound, saying he plans to raise Wagyu and Angus cattle. Bill Gates In 2017, Gates announced plans for Belmont, a smart city on 234 square miles near Phoenix. Designed to house 180,000 people, it promised autonomous vehicles and high-speed networks. There haven’t been any recent updates on the status of the Arizona development, however, and the project is considered dead in the water (well, desert) at this point.
Category:
E-Commerce
In the late 1970s, a Princeton undergraduate named John Aristotle Phillips made headlines by designing an atomic bomb using only publicly available sources for his junior year research project. His goal wasnt to build a weapon but to prove a point: that the distinction between classified and unclassified nuclear knowledge was dangerously porous. The physicist Freeman Dyson agreed to be his adviser while explicitly stipulating that he would not provide classified information. Phillips armed himself with textbooks, declassified reports, and inquiries to companies selling dual-use equipment and materials such as explosives. Within months he had produced a design for a crude atomic bomb, demonstrating that knowledge wasnt the real barrier to nuclear weapons. Dyson gave him an “A” and then removed the report from circulation. While the practicality of Phillipss design was doubtful, that was not Dysons main concern. As he later explained: To me the impressive and frightening part of his paper was the first part in which he described how he got the information. The fact that a twenty-year-old kid could collect such information so quickly and with so little effort gave me the shivers. Zombie machines Today, weve built machines that can do what Phillips didonly faster, broader, at scaleand without self-awareness. Large Language Models (LLMs) like ChatGPT, Claude, and Gemini are trained on vast swaths of human knowledge. They can synthesize across disciplines, interpolate missing data, and generate plausible engineering solutions to complex technical problems. Their strength lies in processing public knowledge: reading, analyzing, assimilating, and consolidating information from thousands of documents in seconds. Their weakness is that they dont know when theyre assembling a mosaic that should never be completed. This risk isnt hypothetical. Intelligence analysts and fraud investigators have long relied on the mosaic theory: the idea that individually benign pieces of information, when combined, can reveal something sensitive or dangerous. Courts have debated it. It has been applied to GPS surveillance, predictive policing, and FOIA requests. In each case, the central question was whether innocuous fragments could add up to a problematic whole. Now apply that theory to AI. A user might prompt a model to explain the design principles of a gas centrifuge, then ask about the properties of uranium hexafluoride, then about the neutron reflectivity of beryllium, and finally about the chemistry of uranium purification. Each questionsuch as, What alloys can withstand 70,000 rpm rotational speeds while resisting fluorine corrosion?may seem benign on its own, yet each could signal dual-use intent. Each answer may be factually correct and publicly sourced, but taken together they approximate a road map toward nuclear capability, or at least lower the barrier for someone with intent. Critically, because the model has no access to classified data, it doesnt know it is constructing a weapon. It doesnt intend to break its guardrails. There is no firewall between public and classified knowledge in its architecture, because it was never trained to recognize such a boundary. And unlike John Phillips, it doesnt stop to ask if it should. This lack of awareness creates a new kind of proliferation risk: not the leakage of secrets, but the reconstitution of secrets from public fragmentsat speed, at scale, and without oversight. The results may be accidental, but no less dangerous. The issue is not just speed but the ability to generate new insights from existing data. Consider a benign example. Todays AI models can combine biomedical data across genomics, pharmacology, and molecular biology to surface insights no human has explicitly written down. A carefully structured set of prompts might lead an LLM to propose a novel, unexploited drug target for a complex disease, based on correlations in patient genetics, prior failed trials, known small molecule leads, and obscure international studies. No single source makes the case, but the model can synthesize across them. That is not simply faster searchit is a genuine discovery. All about the prompt Along with the centrifuge example above, its worth considering two additional hypothetical scenarios across the spectrum of CBRN (Chemical, Biological, Radiological, and Nuclear) threats to illustrate the problematic mosaics that AI can assemble. The first example involves questions about extracting and purifying ricin, a notorious toxin derived from castor beans that has been implicated in both failed and successful assassinations. The following table outlines the kinds of prompts or questions a user might pose, the types of information potentially retrieved, and the public sources an AI might consult: PromptResponsePublic Source TypeRicins mechanism of actionB chain binds cells; A chain depurinates ribosome, leading to cell deathBiomedical reviewsCastor bean processingHow castor oil is extracted; leftover mash contains ricinUSDA documentsRicin extraction protocolsHistorical research articles and old patents describe protein purificationU.S. and Soviet-era patents (e.g., US3060165A)Protein separation techniquesAffinity chromatography, ultracentrifugation, dialysisBiochemistry lab manualsLab safety protocolsGloveboxes, flow hoods, PPEChemistry lab manualsToxicity data (LD50s)Lethal doses, routes of exposure (inhaled, injected, oral)CDC, PubChem, toxicology reportsRicin detection assaysELISA, mass-spec markers for detection in blood/tissueOpen-access toxicology literature It is apparent that while each individual prompt or question is benign and clearly relies on publicly available data, by putting together enough prompts and responses of this sort, a user could determine a crude but workable recipe for ricin. A similar example tries to determine a protocol for synthesizing a nerve agent like sarin. In that case the list of prompts, results, and sources might look something like the following: PromptResponsePublic Source TypeGeneral mechanism of acetylcholine esterase (AChE) inhibitionExplains why sarin blocks acetylcholinesterase and its physiological effectsBiochemistry textbooks, PubMed reviewsList of G-series nerve agentsHistorical context: GA (tabun), GB (sarin), GD (soman), etc.Wikipedia, OPCW docs, popular science literatureSynthetic precursors of sarinMethylphosphonyl difluoride (DF), isopropyl alcohol etc.Declassified military papers, 1990s court filings, open-source retrosynthesis softwareOrganophosphate coupling chemistryCommon lab procedures to couple fluorinated precursors with alcoholsOrganic chemistry literature and handbooks, synthesis blogs/tr>Fluorination safety practicesHandling and containment procedures for fluorinated intermediatesAcademic safety manuals, OSHA documentsLab setupInformation on glassware, fume hoods, Shlenk lines, PPEOrganic chemistry labs, glassware supplier catalogs These examples are illustrative rather than exhaustive. Even with current LLM capabilities, it is evident that each list could be expanded to be more extensive and granularretrieving and clarifying details that might determine whether an experiment is crude or high-yield, or even the difference between success and failure. LLMs can also refine historical protocols and incorporate state-of-the-art data to, for example, optimize yields or enhance experimental safety. God of the gaps Theres an added layer of concern because LLMs can identify information gaps within individual sources. While those sources may be incomplete on their own, combining them allows the algorithm to fill in the missing pieces. A well-known example from the nuclear weapons field illustrates this dynamic. Over decades, nuclear weapons expert Chuck Hansen compiled what is often regarded as the worlds largest public database on nuclear weapons design, the six-volume Swords of Armageddon. To achieve this, Hansen mastered the governments Freedom of Information Act (FOIA) system. He would submit repeated FOIA requests for the same document to multiple federal agencies over time. Because each agency classified and redacted documents differently, Hansen received multiple versions with varying omissions. By assembling these, he was able to reconstruct a kind of master document that was, in effect, classifiedand which no single agency would have released. Hansens work is often considered the epitome of the mosaic theory in action. LLMs can function in a similar way. In fact, they are designed to operate this way, since their core purpose is to retrieve the most accurate and comprehensive information when prompted. They aggregate sources, identify and reconcile discrepancies, and generate a refined, discrepancy-free synthesis. This capability will only improve as models are trained on larger datasets and enhanced with more sophisticated algorithms. A particularly notable feature of LLMs is their ability to mine tacit knowledgecross-referencing thousands of references to uncover rare, subjective details that can optimize a WMD protocol. For example, instructions telling a researcher to gently shake a flask or stop a reaction when the mixture becomes straw yellow can be better understood when such vague descriptions are compared across thousands of experiments. In the examples above, safeguards and red flags would likely arise if an individual attempted to act on this knowledge; as in many such cases, the real constraint is material, not informational. However, the speed and thoroughness with which LLMs retrieve and organize information means that the knowledge problem is, in many cases, effectively solved. For individuals who might otherwise lack the motivation to pursue information through more tedious, traditional means, the barriers are significantly lowered. In practice, an LLM allows such motivated actors to accomplish what they might already attemptonly with vastly greater speed and accuracy. Most AI models today impose guardrails that block explicitly dangerous prompts such as how to make a nuclear bomb. Yet these filters are brittle and simplistic. A clever user can circumvent them with indirect prompts or by building the picture incrementally. There is no obvious reason why seemingly benign, incremental requests should automatically trigger red flags. The true danger lies not in the blatant queries, but in those that fall between the linesqueries that appear innocuous on their own but gradually assemble into forbidden knowledge. Consider, for example, a few hypothetical requests from the sarin, ricin, and centrifuge cases. Each could easily qualify as a dual-use requestone that a user without malicious intent might pose for any number of legitimate reasons: What are some design strategies for performing fluoride-alcohol exchange reactions at heteroatom centers? What lab precautions are needed when working with corrosive fluorinated intermediates? How do you design small-scale glassware systems to handle volatile compounds with pressure control? What are safe protocols for separating proteins from plant mash using centrifugation? How do you detect ribosome-inactivating proteins in a lab sample? How does affinity chromatography work for isolating specific plant proteins? What were USDA standards for castor oil processing in the 1950s? Which vacuum-pump designs minimize oil back-streaming in corrosive-gas service? Give the vapor-pressure curve for uranium hexafluoride between 20 °C and 70 °C. Summarize neutron-reflection efficiency of beryllium versus natural graphite. The requests evade traditional usage violations through a number of intentional or unintentional strategies: vague or highly technical wording, generic cookie-cutter inquiries, and interest in retrieving historical rather than contemporary scenarios. Because they are dual-use and can be used for any number of useful applications, they cannot simply be part of a blacklist. Knowledge enables access It is worth examining more closely the argument that material access, rather than knowledge, constitutes the true barrier to weaponization. The argument is persuasive: having a recipe and executing it are two very different challenges. But it is not a definitive safeguard. In practice, the boundary between knowledge and material access is far more porous than it appears. Consider the case of synthesizing a nerve agent such as sarin. Today, chemical suppliers routinely flag and restrict sales of known sarin precursors like methylphosphonyl difluoride. Yet with AI-powered retrosynthesis toolssystems that computationally deconstruct a target molecule into alternative combinations of simpler, synthesizable building blocks, much like a Lego house can be broken down into different sets of Lego piecesa user can identify a wide range of alternative precursors and synthetic pathways. Some of these routes may be deliberately designed to evade restrictions established under the Chemical Weapons Convention (CWC) and by chemical suppliers. The scale of such outputs can be extraordinary: in one study, an AI retrosynthesis tool proposed more than 40,000 potential VX nerve gas analogs. Many of these compounds are neither explicitly regulated nor easily recognizable as dual-use. As AI tools advance, the number of viable chemical synthesis and protein purification routes only expands, complicating traditional material-based monitoring and enforcement. In effect, the law lags behind the science. A parallel exists in narcotics regulation. Over the years, several novel substances mimicking fentanyl, methamphetamine, or marijuanainitially created purely for academic researchfound their way into recreational use. It took years before these substances were formally scheduled and classified as controlled. Even before AI, bad actors could exploit loopholes by inventing new science or repurposing existing technologies. The difference was that, historically, they could produce only a handful of problematic examples. LLMs and generative AI, by contrast, can generate thousans of potential confounders at once, vastly multiplying the possible paths to a viable weapon. In other words, knowledge can erode material constraints. When that occurs, even a marginal yet statistically significant increase in the number of motivated bad actors can translate into a measurable rise in success rates. Nobody should believe that having a chatGPT-enabled recipe for making ricin will unleash a wave of garage ricin labs across the country. But it will almost certainly lead to a small uptick in attempts. And even one or two small-scale ricin or sarin incidentswhile limited in terms of casualtiescould trigger panic, uncertainty, and societal disruption, potentially paving the way for destabilizing outcomes such as authoritarian power grabs or the suspension of civil liberties. The road ahead Heres the problem: we dont yet have a robust framework for regulating this. Export control regimes like the Nuclear Suppliers Group were never designed for AI models. The IAEA safeguards fissile materials, not algorithms. Chemical and biological supply chains flag material requests, not theoretical toxin or chemical weapon constructions. These enforcement mechanisms rely on fixed lookup lists updated slowly and deliberately, often only after actual harm has occurred. They are no match for the rapid pace with which AI systems can generate plausible ideas. And traditional definitions of classified information collapse when machines can independently rediscover that knowledge without ever being told it. So what do we do? One option is to be more restrictive. But because of the dual-use nature of most prompts, this approach would likely erode the utility of AI tools in providing information that benefits humanity. It could also create privacy and legal issues by flagging innocent users. Judging intent is notoriously difficult, and penalizing it is both legally and ethically fraught. The solution is not necessarily to make systems less open, but to make them more aware and capable of smarter decision-making. We need models that can recognize potentially dangerous mosaics and have their capabilities stress-tested. One possible framework is a new doctrine of emergent or synthetic classificationidentifying when the output of a model, though composed of unclassified parts, becomes equivalent in capability to something that should be controlled. This could involve assigning a mosaic score to a users cumulative requests on a given topic. Once the score exceeded a certain threshold, it might trigger policy violations, reduced compute access, or even third-party audits. Crucially, a dynamic scoring system would need to evaluate incremental outputs, not just inputs. Ideally, this kind of scoring and evaluation should be conducted by red teams before models are released. These teams would simulate user behavior and have outputs reviewed by scientific experts, including those with access to classified knowledge. They would test models for granularity, evaluate their ability to refine historical protocols, and examine how information might transfer across domainsfor instance, whether agricultural knowledge could be adapted for toxin synthesis. They would also look for emergent patterns, moments when the model produces genuinely novel, unprecedented insights rather than just reorganizing existing knowledge. As the field advances, autonomous AI agents will become especially important for such testing, since they could reveal whether benign-seeming protocols can, unintentionally, evolve into dangerous ones. Red-teaming is far more feasible with closed models than with unregulated open-source ones, which raises the question of safeguards for open-source systems. Perfect security is unrealistic, but closed-source models, by virtue of expert oversight and established evaluation mechanisms, are currently more sophisticated in detecting threats through behavioral anomalies and pattern recognition. Ideally, they should remain one step ahead, setting benchmarks that open-source models can be held to. More broadly, all AI models will need to assess user requests holistically, recognizing when a sequence of prompts drifts into dangerous territory and blocking them. Yet striking the right balance is difficult: democratic societies penalize actions, not thoughts. The legal implications for user privacy and security will be profound. Concerns about tracking AI models ability to assemble forbidden mosaics go beyond technical, business, and ethical debatesthey are a matter of national security. In July 2025, the U.S. government released its AI policy action plan. One explicit goal was to Ensure that the U.S. Government is at the Forefront of Evaluating National Security Risks in Frontier Models, with particular attention to CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosives) threats. Achieving this will require close collaboration between government agencies and private companies to implement forward-looking mosaic detection based on the latest technology. For better or worse, the capabilities of LLMs are a moving target. Private and public actors must work together to keep pace. Existing oversight mechanisms may slow these developments, but at best, they will only buy us time. Ultimately, the issue is not definitive solutionsnone exist at this early stagebut transparency and public dialogue. Gatekeepers in both private and public sectors can help ensure responsible deployment, but the most important stakeholders are ordinary citizens who will useand sometimes misusethese systems. AI is not confined to laboratories or classified networks; it is becoming democratized, integrated into everyday life, and applied to everyday questions, some of which may unknowingly veer into dangerous territory. That is why engaging the public in open discussion, and alerting them to the flaws and risks inherent in these models, is essential in a democratic society. These conversations must focus on how to balance security, privacy, and opportunity. As the physicist Niels Bohr, who understood both the promise and peril of knowledge, once said, Knowledge itself is the basis of human civilization. If we are to preserve that civilization, we must learn to detect and correct the gaps in our knowledgenot in hindsight, but ahead of time.
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E-Commerce
When the heat index in Delhi, India, reached 125 degrees Fahrenheit this summer, so many people cranked up their air-conditioning that it led to power cuts. Its one fundamental problem with modern AC: Its least reliable when demand is highest. Surging power use from air conditioners also adds to emissions, and climate change leads to more heat waves that lead to more AC use, in an escalating feedback loop. [Photo: CoolAnt] Some startups are trying to shrink the power use and carbon footprint of modern air-conditioning. But others are using ancient cooling techniques as inspiration. In India, some architects are turning to a material thats been used for cooling for thousands of years: terra-cotta. [Photo: CoolAnt] The idea is inspired by nature, traditional techniques, and modern technology, says Pranjal Maheshwari, an architect at Delhi-based Ant Studio, the design firm that makes a terra-cotta cooling system it calls CoolAnt. Terra-cotta, made from unglazed clay, has a long history of use in hot climates. In India, traditional terra-cotta pots are still used to cool down water without refrigeration: Tiny pores in the terra-cotta let a little bit of water seep to the outside of the pot and evaporate, pulling heat away from the water. [Photo: CoolAnt] CoolAnt uses a similar approach. Water drips down a facade of terra-cotta tiles or pots and evaporates, cooling the surrounding air. The system can also be installed in windows, similar to latticework used in traditional Indian architecture. The intricate designs offer shade and ventilation. The first installation, in 2017, was inside a semiconductor factory that was struggling with heat from a diesel generator. The architects had been considering their design for an evaporative cooler and decided to test it. This gave us an opportunity to move beyond theory and try it hands-on, says Maheshwari. Since then, the company has designed around 50 different systems, from building envelopes to art installations. There are multiple variations on the idea. Several designs take inspiration from nature. One screen uses a beehive pattern to maximize surface area. Other tiles, shaped like leaves, mimic the way that trees use evaporative cooling. Some of the systems use sensors to monitor the temperature, and when it gets hot, a low-power pump begins dripping water over the terra-cotta. The water that doesnt evaporate is collected and recirculated. [Photo: CoolAnt] In a hot, dry climate, evaporative cooling can help reduce temperatures by as much as 18 degrees Fahrenheit. (On a humid day in India, its less effective, though the shade and ventilation from the system still help.) On its own, in most cases, it isnt enough to replace air-conditioning. But it can help reduce the use of AC. [Photo: CoolAnt] CoolAnt installations can precool outdoor air before it enters conventional HVAC systems, reducing the cooling load on traditional air conditioners and improving their efficiency, says Maheshwari, noting that buildings can also use CoolAnt in transitional spaces like lobbies or courtyards. The system can also make it possible to avoid using AC when its only moderately hot outside, or to use air-conditioning for a couple of hours rather than all day. [Photo: CoolAnt] Its one of several different passive cooling approaches that can be taken to respond to extreme heat, from ultra-white paint to new supercool cement. It makes sense, the architects say, to rethink our reliance on air-conditioningespecially when its use is growing so quickly around the world. In some Indian cities, sales of cheap AC units grew more than 1,000% last year.
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E-Commerce
Wind turbines usually jut up from the ground like giant pinwheels. Now a company in China is exploring a new form factor: flying wind turbines. These zeppelin-like aircraft float high in the sky, tethered to the ground only by cables as they generate a nonstop stream of power thanks to the strong winds present in the upper layers of the atmosphere. Rather than being throttled by capricious ground-level windsone of the main challenges of current stationary turbinesflying wind turbines power throughput will not fluctuate because the wind there is constant. The design could solve some of the biggest problems of wind power generation without having to invest in extensive infrastructure, reducing wind power’s environmental footprint in the process. The concept was first proposed by a Chinese engineer who was among the pioneers of NASA’s Jet Propulsion Laboratory in the mid-1940s. It never took off in the U.S., but the Chinese energy startup Sawes claims it is ready to deploy thousands of floating turbines that can produce 100 kilowatts, which is the same output of the ground windmills that now power everything from small to midsize commercial structures and agricultural operations to industrial facilities and even small municipal projects. Sawes is also working on a new model that will match the capabilities of typical ground windmills with a turbine that can generate more than 1 megawatt. [Photo: Sawes] A clever idea The origins of airborne wind power go back to Shanghai-born aerospace engineer Qian Xuesen. Qian had fled 1930s China to study at MIT before joining Caltechs famous Suicide Squad, the rocket-obsessed engineers who laid the foundations of modern American spaceflight. He was a brilliant immigrant who helped build one of the most important American technological revolutions of all time, but Qians career in the U.S. ended in the shadow of McCarthy-era suspicions. After years under house arrest, he was deported to China in 1955, where he became the founding father of the Chinese missile and space program. His research was the foundation for the Long March family of rockets that has made Beijing a space superpower. That was also the time when he came up with the theories that make the current flying wind turbines possible. In 1957, Qian proposed what he called the ejector diffuser duct, a theory that the airflow through a turbine could be dramatically accelerated by adding a carefully designed circular housing around it. Instead of treating wind as a free-stream mass passing through open blades, Qians idea reframed the turbine as part of an aerodynamic system. The ring-shaped duct would create a pressure difference (low pressure behind the turbine, higher pressure in front) that pulled additional air into the blades. This effect, essentially a man-made throat for the wind, could increase efficiency significantly without requiring larger blades or taller towers. Whereas conventional ground turbines rely purely on swept blade area, constrained by physics and structure, Qians ejector diffuser model effectively multiplies the usable wind without increasing structural weight. Qians concept sounded outlandish at the time. Turbines were still niche engineering projects, and energy planners had little appetite for experimental designs. It wasnt until much later that they started to take shape. Crashing dreams It wasnt easy. Concepts are cool, but engineering and manufacturing them is often extremely difficult, which has resulted in a lot of roadkill. Over the decades, many have tried to turn flying wind turbines into working machines. The MIT spinout Altaeros built a helium-filled blimp with a wind turbine at its core, hoping it would hover at 2,000 feet to capture faster winds. We try to invent as little as possible, CEO Ben Glass told NBC News in 2014, explaining that Altaeros simply repurposed proven blimp and turbine tech. Despite promising tests in Alaska, the company abandoned the power dream and pivoted to wireless communication platforms to deploy local networks using blimps. Italys KiteGen built a kite-like system that flew in figure-eight patterns, pulling generators on the ground. It never moved beyond prototype stage. Makani Technologies, founded in the Bay Area and acquired by Google in 2013, tried a tethered glider equipped with rotors; it was shut down by Alphabet in 2020. Even NASA dabbled with concepts. None made it to large-scale deployment, although we may see a flying kite electricity generator on Mars or some other planet one day. All of them wrestled with the same problems: engineering complexity, flight stability in high winds, government permits, and natural gas undercutting wind costs. Plus, fixed wind turbines work great for large installations, despite their cost, time, and environmental impact. Why flying turbines? The allure of flying turbines lies in their access to stronger and steadier winds. Conventional towers can only reach up to about 650 feet above ground, where winds still fluctuate. But at 5,000 feet, air currents move three times faster and can generate up to 27 times more power. These turbines’ advantage is not only their theoretical constant power throughput but also their cost. Fixed wind turbines are enormously expensive and resource-intensive to build and install, far beyond the cost of manufacturing the rotor and tower alone. Each land-based turbine requires hundreds of tonnes of steel, concrete, and industrial components, plus massive construction sites, and, often, new roads or blasting of mountaintops to transport and position equipment. Offshore designs demand steel lattice towers weighing thousands of tonnes, specialized marine facilities, and complex logistics. These infrastructure needs translate to substantial environmental impacts: huge land footprints of up to 80 acres per turbine, habitat disruption, restricted land access, and months or years of planning, permitting, and construction. With restricted use around the turbines due to noise and safety hazards, much of the land becomes inaccessibleeven to the owners. In contrast, flying turbines like those developed by Sawes weigh less than a ton, require no permanent foundation or land clearance, and can be deployed rapidly where conventional power cannot reach with minimal disruption and cost: remote oil fields, small islands, or disaster zones where speed and mobility matter most. Sawes began research on its airborne generators using Qians ideas in 2017. The company considered using a main helium airbag integrated with a ring wing that accelerates airflow and channels it directly through the embedded generators, theoretically boosting efficiency by more than 20%. By October 2024, the companys S500 prototype reportedly hit 1,640 feet and generated 50 kilowattsbreaking global records for both altitude and output, which until then had belonged to a research team at MIT. In January 2025, the S1000 doubled that altitude and crossed the 100-kilowatt threshold. [Photo: Sawes] There are still challenges. Safety, for example, remains a constant question. At those altitudes, winds can quickly become violent. Weng Hanke, chief technology officer at Sawes, explained to the Hong Kong newspaper South China Morning Post that the companys dual system technologyradar on the ground and sensors in the airbagensures stability. In extreme conditions, the system can rapidly descend within five minutes, he said. Theres also the issue of helium. These things work in a similar way to weather balloons, and theres always leakage, which puts in question their durability. Dun Tianrui, founder and CEO of Sawes, told the South China Morning Post that the companys aerostats gas leakage has been reduced to the point where it can stay in the air for more than 25 years. According to Sawes, batch production has already begun in Yueyang, a city about 700 miles southeast of Beijing, with contracts worth more than $70 million. The companys ambition, meanwhile, stretches even higher. Next stop: The stratosphere Sawes is now preparing for a test flight of its new S1500 model. The newly developed S1500 system boasts a generation capacity of 1 megawatt, equivalent to that of a traditional 100-meter-high wind turbine tower [and] is scheduled for its test flight soon, Weng claimed. High-altitude wind is a powerful and mostly unused energy source. . . . Once these systems are built in large numbers, the power they produce could be as cheap as from normal wind turbines. The design has the same distinctive duct-ring airframe that accelerates airflow. Inside, 12 micro-generators made of carbon fiber operate in parallel, and will deliver utility-scale power from a unit weighing 90% less than a steel tower turbine. Tianrui envisions fleets of megawatt-class aerostats operating in the stratosphere, more than 32,000 feet up, where wind energy is said to be 200 times more powerful than at ground. At that time, he said, the cost of electricity will be one-tenth of what it is today. Such dreams underline both the promise and the challenge of airborne wind power. The world has seen airborne wind startups rise and fall. Whether Sawes can succeed where Google, MIT startups, and European engineers failed depends on scale, economics, and staying power. For now, they hold the record for the highest, most powerful flying turbine ever builta dream first imagined nearly 70 years ago that may finally be ready to take flight.
Category:
E-Commerce
There’s an oddly specific frustration to opening a Laffy Taffy Mini Bar. Its thin wrapper shreds a little too easily and tends to stick to the candy like tape. Finally, the maker behind the candy says it’s found a fix. @felisha_bubby Laffy taffy opening instructions #fyp #foryou #laffytaffy original sound – felisha_bubby Ferrara Candy Co., the Illinois-based confectioner behind Laffy Taffy and other popular sweets, including Nerds, Jelly Belly, and Red Hots, admits that for years customers have wanted better wrappers for the fruit-flavored taffy, and in response, the company tested more than 10 film options. The team behind the redesign won’t share technical information on how the new material was formulated, calling it “proprietary,” but they claim to have developed a “material orientation” that reduces the amount of shredding. “The new wrappers are made from new materials that create a smooth, one-pull opening,” Tricia Asbridge, Laffy Taffy brand manager, tells Fast Company in an email. [Photo: courtesy Laffy Taffy] The challenge in wrapping any taffy, including when you make it at home, is how sticky the candy is from its high sugar and moisture content. The trick usually suggested online is refrigeration. For Laffy Taffy, however, the trick is in the unwrapping technique: Thus the new wrappers include updated language on the back seal that says exactly where to pull to open them. “The process involved evaluating the existing wrapper, assessing alternative films, running trials, and testing the performance of new wrapper options compared to the existing wrappers,” Asbridge says. “At one point the brand tested over 10 different film options between the different variables under consideration in order to find the best solution.” Laffy Taffy wrappers are known for their dad jokes, and the new, improved wrappers will feature new crowd-sourced humor. The company ran a joke hotline, the “Laff Line,” where callers could submit jokes that could end up on a Laffy Taffy Mini Bar wrapper. Ferrara says the top 100 jokes, as judged on “humor, originality, creativity, and relevance to brand values,” like “multigenerational humor” and “being tastefully inclusive,” will be used, and winners will receive a custom bag of first-look candies. The packaging update comes at a time of expansion for Ferrara. In July, its European holding company announced it had entered into exclusive discussions to acquire the French candy company CPK Group, which has a portfolio of more than 30 brands. Meanwhile, it reports that sales of its Nerds Gummy Clusters have overtaken Mars Wrigleys Skittles to become the top sugar confection on the market. By improving the wrapper for its taffy brand, Ferrara could see its market share of the candy aisle grow even more.
Category:
E-Commerce
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