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Every year, companies and space agencies launch hundreds of rockets into spaceand that number is set to grow dramatically with ambitious missions to the Moon, Mars, and beyond. But these dreams hinge on one critical challenge: propulsionthe methods used to push rockets and spacecraft forward. To make interplanetary travel faster, safer, and more efficient, scientists need breakthroughs in propulsion technology. Artificial intelligence is one type of technology that has begun to provide some of these necessary breakthroughs. Were a team of engineers and graduate students who are studying how AI in general, and a subset of AI called machine learning in particular, can transform spacecraft propulsion. From optimizing nuclear thermal engines to managing complex plasma confinement in fusion systems, AI is reshaping propulsion design and operations. It is quickly becoming an indispensable partner in humankinds journey to the stars. Machine learning and reinforcement learning Machine learning is a branch of AI that identifies patterns in data that it has not explicitly been trained on. It is a vast field with its own branches, with a lot of applications. Each branch emulates intelligence in different ways: by recognizing patterns, parsing and generating language, or learning from experience. This last subset in particular, commonly known as reinforcement learning, teaches machines to perform their tasks by rating their performance, enabling them to continuously improve through experience. As a simple example, imagine a chess player. The player does not calculate every move but rather recognizes patterns from playing a thousand matches. Reinforcement learning creates similar intuitive expertise in machines and systems, but at a computational speed and scale impossible for humans. It learns through experiences and iterations by observing its environment. These observations allow the machine to correctly interpret each outcome and deploy the best strategies for the system to reach its goal. Reinforcement learning can improve human understanding of deeply complex systemsthose that challenge the limits of human intuition. It can help determine the most efficient trajectory for a spacecraft heading anywhere in space, and it does so by optimizing the propulsion necessary to send the craft there. It can also potentially design better propulsion systems, from selecting the best materials to coming up with configurations that transfer heat between parts in the engine more efficiently. In reinforcement learning, you can train an AI model to complete tasks that are too complex for humans to complete themselves. Reinforcement learning for propulsion systems In regard to space propulsion, reinforcement learning generally falls into two categories: those that assist during the design phasewhen engineers define mission needs and system capabilitiesand those that support real-time operation once the spacecraft is in flight. Among the most exotic and promising propulsion concepts is nuclear propulsion, which harnesses the same forces that power atomic bombs and fuel the Sun: nuclear fission and nuclear fusion. Fission works by splitting heavy atoms such as uranium or plutonium to release energya principle used in most terrestrial nuclear reactors. Fusion, on the other hand, merges lighter atoms such as hydrogen to produce even more energy, though it requires far more extreme conditions to initiate. Fission is a more mature technology that has been tested in some space propulsion prototypes. It has even been used in space in the form of radioisotope thermoelectric generators, like those that powered the Voyager probes. But fusion remains a tantalizing frontier. Nuclear thermal propulsion could one day take spacecraft to Mars and beyond at a lower cost than that of simply burning fuel. It would get a craft there faster than electric propulsion, which uses a heated gas made of charged particles called plasma. Unlike these systems, nuclear propulsion relies on heat generated from atomic reactions. That heat is transferred to a propellant, typically hydrogen, which expands and exits through a nozzle to produce thrust and shoot the craft forward. So how can reinforcement learning help engineers develop and operate these powerful technologies? Lets begin with design. Reinforcement learnings role in design Early nuclear thermal propulsion designs from the 1960s, such as those in NASAs NERVA program, used solid uranium fuel molded into prism-shaped blocks. Since then, engineers have explored alternative configurationsfrom beds of ceramic pebbles to grooved rings with intricate channels. Why has there been so much experimentation? Because the more efficiently a reactor can transfer heat from the fuel to the hydrogen, the more thrust it generates. This area is where reinforcement learning has proved to be essential. Optimizing the geometry and heat flow between fuel and propellant is a complex problem, invlving countless variablesfrom the material properties to the amount of hydrogen that flows across the reactor at any given moment. Reinforcement learning can analyze these design variations and identify configurations that maximize heat transfer. Imagine it as a smart thermostat but for a rocket engineone you definitely dont want to stand too close to, given the extreme temperatures involved. Reinforcement learning and fusion technology Reinforcement learning also plays a key role in developing nuclear fusion technology. Large-scale experiments such as the JT-60SA tokamak in Japan are pushing the boundaries of fusion energy, but their massive size makes them impractical for spaceflight. Thats why researchers are exploring compact designs such as polywells. These exotic devices look like hollow cubes, about a few inches across, and they confine plasma in magnetic fields to create the conditions necessary for fusion. Controlling magnetic fields within a polywell is no small feat. The magnetic fields must be strong enough to keep hydrogen atoms bouncing around until they fusea process that demands immense energy to start but can become self-sustaining once underway. Overcoming this challenge is necessary for scaling this technology for nuclear thermal propulsion. Reinforcement learning and energy generation However, reinforcement learnings role doesnt end with design. It can help manage fuel consumptiona critical task for missions that must adapt on the fly. In todays space industry, theres growing interest in spacecraft that can serve different roles depending on the missions needs and how they adapt to priority changes through time. Military applications, for instance, must respond rapidly to shifting geopolitical scenarios. An example of a technology adapted to fast changes is Lockheed Martins LM400 satellite, which has varied capabilities such as missile warning or remote sensing. But this flexibility introduces uncertainty. How much fuel will a mission require? And when will it need it? Reinforcement learning can help with these calculations. From bicycles to rockets, learning through experiencewhether human or machineis shaping the future of space exploration. As scientists push the boundaries of propulsion and intelligence, AI is playing a growing role in space travel. It may help scientists explore within and beyond our solar system and open the gates for new discoveries. Marcos Fernandez Tous is an assistant professor of space studies at the University of North Dakota. Preeti Nair is a master’s student in aerospace sciences at the University of North Dakota. Sai Susmitha Guddanti is a Ph.D. student in aerospace sciences at the University of North Dakota. Sreejith Vidhyadharan Nair is a research assistant professor of aviation at the University of North Dakota. This article is republished from The Conversation under a Creative Commons license. Read the original article.
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AI is rapidly changing the world around us, from the way we engage online to how we work. But while the technology is able to complete an astonishing number of tasks, humans are far from obsolete. A new report from McKinsey is shining light on why humans are still essential. According to the report, roughly 57% of work hours can be automated. Meanwhile, 70% of the skills employers look for can be used for both automated work and nonautomated work. This means over the next five years, humans will have to adjust their work habits to make room for automation. McKinsey designed an index to assess how automation will impact each skill used in the workplace today. According to the index, skills like digital and information processing, accounting, and coding are the most likely to be impacted by AI. Jobs requiring physical activity will see less of an impact, accounting for 35% of U.S. work hours. While robots have made huge strides in their ability to complete physical tasks, they cannot rival the “fine motor skills, dexterity, and situational awareness” of humans, the report says. Skills that rely on emotional awareness and personal connectionsuch as coaching, assisting, caring, or negotiatingwill see the least amount of impact. The report explains that “even under a full adoption” of AI, emotional skills will remain relevant in many roles. Currently about 75% of the demand for AI skills falls into one of three categories: computer or mathematical jobs (44%), management roles (19%), and business and financial operations (7%). However, the report goes on to highlight “nearly all occupations have at least one highly disrupted skilldefined as being in the top quartile of change by 2030, and that a third of occupations will see more than 10% of their skills highly changed.” While many jobs will change, new jobs will also be created where working alongside AI will become essential. In fact, the report says, demand for workers who understand AI is growing faster than any other skill set. “Workers will spend less time preparing documents and doing basic research, for example, and more time framing questions and interpreting results,” the report says. “Employers may increasingly prize skills that add value to AI.” Making space for AI in the workplace is key. The tech could unlock around $2.9 trillion in economic value in the U.S. if companies can utilize employees to work together with automation, the report projects. Either way, while humans are still necessary for most jobs, AI will inevitably continue to change how humans workno matter their role.
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Just a handful of years ago, the idea of one person creating a company worth over a billion dollars seemed like a pipe dream. Thanks to rapid advancements in AI, the possibility of a solopreneur unicorn is less a matter of “if and more a matter of when. Earlier this year, OpenAI founder Sam Altman told Reddit cofounder Alexis Ohanian that his group chat of tech CEO friends have a betting pool for when the world will see a one-person billion-dollar company. Ten months later, some experts suggest that the company could be founded in 2026, if it hasn’t been already, due to rapid advancements in agentic AI. The ability of a person to scale themselves, to automate their lives, has just become amazing, says Kyle Jensen, the director of entrepreneurship programs, associate dean and professor in the practice of entrepreneurship at Yale School of Management. If you’re very skilled with those tools, you can have the productivity of 10 people. Jensen adds that solopreneurship has historically been more akin to mom-and-pop-style small business ownership, with practitioners selling goods and services through the internet. While many solopreneurs still fit that mold, he sees another kind of solopreneur emerging; one that better resembles a high-growth startup, using new tools in lieu of hiring. There have been a handful of companies that had private market valuations in excess of a billion dollars with very small teamsWhatsApp is a very famous example, he says. What is the probability that you’ll see a solo-entrepreneur who’s like, some engineer from Google, who decided she doesn’t want to do that anymoreand she’s going to do her AI startup from home, and become the first unicorn? I think its a pretty decent probability. Unicorns are shrinking The first one-person company to surpass a billion dollars may not be all that far off. In recent years some one-person businesses have achieved smaller, yet still eye-popping valuations. At the same time the record for smallest unicorn company keeps getting broken. Earlier this year, for example, solopreneur Maor Sholomo sold his AI app-building platform Base44 to website builder Wix for $80 million, just six months after launch. Instagram had only 13 employees when it was sold to Facebook for a billion dollars in 2012. One of those cofounders, Mike Krieger, went on to found Anthropic in 2021, which was recently valued at $350 billion. Speaking with Fast Company earlier this year, Krieger suggested the one-person unicorn is closer than you think. It feels like every month were getting closer and closer, says Anthropics Head of Startup Sales, Jamie Neuwirth. Companies that we’re working with, for example, at Y Combinator who are very smallusually two or three peoplethey’re getting to market faster, and that opportunity for them to become a unicorn is very real. A virtual cofounder In the recent past, solopreneurs were able to automate certain operations, but it often required a high degree of technical know-how and many hours of building custom tools. Now, Neuwirth says AI tools like Anthropics AI assistant Claude can serve as a collaborator, taking on more advanced and critical tasks, without requiring founders to have a deep technical background. I think of AIand Claude in particularas everything from this virtual collaborator to a kind of the chief of staff, but the way I think about it when it comes to solopreneurs is more of your virtual cofounder, he says. You can have a less technical background, and there’s still a lot that can be achieved with these tools. Where to look for solo unicorns In recent months, AI companies like Base 44, Anthropic, and Swedish vibe coding app Loveablewhich lets users build apps and websites by describing it in plain languagehave dominated the headlines with eye-popping valuations, but Neuwirth says the first one-person unicorn wont necessarily emerge from the AI field. Thats because those very solutions are allowing small teams and individuals to build, test, prototype, and ultimately sell technical solutions without deep technical skills. As the model capabilities get a lot better, I think we’re going to see it come from different industries, he says. To me, it goes back to, where is the need immediate, and the market really big? One sector Neuwirth believes is ripe for a first solo-unicorn is healthcare, where he says there are lots of legacy practices and processes, and a massive, global market eager for innovation. Tim Cortinovis, speaker and author of The Single-Handed Unicorn, meanwhile, believes the first one-person billion-dollar company will offer an easy interface to a complicated process, or a simple solution to a universal problem. If you are able to put in a very easy interface between agents and the tasks at hand in, let’s say, a heavy machinery industry or the energy sector, I think this will solve a massive problem, he says. My advice is, dont try to create the first single-handed unicorn, but try to solve a huge problem. You wont win the game by thinking about winning. The first solo unicorn may have already been born Though it may take that solopreneur founder several years to reach a 10-figure valuation, Cortinovis says 2025 will go down as the year that the necessary tools to accomplish such a feat became available. In other words, it is possible that the first individual who will accomplish that feat has already begun building their business. In 2025 we reached the capacities of AI agents to accomplish these complicated tasks and orchestration, Cortinovis says, explaining that this year AI broke out of the chatbot box and is now able to work with other tools and apps to complete more complicated tasks, like build apps and websites, manage a marketing campaign, or handle more complicated customer service inquiries. Maybe we will see the first results at the end of ’26 or at the beginning of ’27 and then maybe two years later well get the first real single-handed unicorn on the market with that valuation. Whenever that one-person company does emerge, Cortinovis says the implications would extend well beyond the individual founder. I think it opens up the path for more people [to pursue entrepreneurship] because it proves you don#8217;t need an extensive team, you don’t need to start hiring massively to start an enterprise, he says. It symbolizes a new wave of founders. Even if youre not going after a massive valuation, it will make them more willing to found an enterprise, because it shows how much easier it is with the technology.
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