Chinese e-commerce giant Alibaba has made waves globally in the tech and business communities with its own family of “Qwen” generative AI large language models, beginning with the launch of the original Tongyi Qianwen LLM chatbot in April 2023 through the release of Qwen 3 in April 2025.Well, not only are its models powerful and score high on third-party benchmark tests at completing math, science, reasoning, and writing tasks, but for the most part, they’ve been released under permissive open source licensing terms, allowing organizations and enterprises to download them, customize them, run them, and generally use them for all variety of purposes, even commercial. Think of them as an alternative to DeepSeek. This week, Alibaba’s “Qwen Team,” as its AI division is known, released the latest updates to its Qwen family, and they’re already attracting attention once more from AI power users in the West for their top performance, in one case, edging out even the new Kimi-2 model from rival Chinese AI startup Moonshot released in mid-July 2025.

In partnership withMichigan Economic Development Corporation What if we could permanently remove the toxic “forever chemicals” contaminating our water? That’s the driving force behind Michigan-based startup Enspired Solutions, founded by environmental toxicologist Denise Kay and chemical engineer Meng Wang. The duo left corporate consulting in the rearview mirror to take on one of the most pervasive environmental challenges: PFAS. “PFAS is referred to as a forever chemical because it is so resistant to break down,” says Kay. “It does not break down naturally in the environment, so it just circles around and around. This chemistry, which would break that cycle and break the molecule apart, could really support the health of all of us.” Basing the company in Michigan was both a strategic and a practical strategy. The state has been a leader in PFAS regulation with a startup infrastructure—buoyed by the Michigan Economic Development Corporation (MEDC)—that helped turn an ambitious vision into a viable business. From intellectual property analyses to forecasting finances and fundraising guidance, the MEDC’s programs offered Kay and Wang the resources to focus on building their PFASigator: a machine the size of two large refrigerators that uses ultraviolet light and chemistry to break down PFAS in water. In other words, “it essentially eats PFAS.”
Despite the support from the MEDC, the journey has been far from smooth. “As people say, being an entrepreneur and running a startup is like a rollercoaster,” Kay says. “You have high moments, and you have very low moments when you think nothing’s ever going to move forward.” Without revenue or salaries in the early days, the co-founders had to be sustained by something greater than financial incentive.
“If problem solving and learning new talents do not provide sufficient intrinsic reward for a founder to be satisfied throughout what I guarantee will be a long duration effort, then that founder may need to reset their expectations. Because the financial rewards of entrepreneurship are small throughout the process.” Still, Kay remains optimistic about the road ahead for Enspired Solutions, for clean water innovation, and for other founders walking down a similar path. “Often, founders are coached about formulas for fundraising, formulas for startup success. Learning those formulas and expectations is important, but it’s also important to not forget that it’s your creativity and innovation and foresight that got you to the place you’re in and drove you to start a company. Ultimately, people still want to see that shine through.” This episode of Business Lab is produced in partnership with the Michigan Economic Development Corporation. Full Transcript Megan Tatum: From MIT Technology Review, I’m Megan Tatum. This is Business Lab, the show that helps business leaders make sense of new technologies coming out of the lab and into the marketplace. Today’s episode is brought to you in partnership with the Michigan Economic Development Corporation.Our topic today is launching a technology startup in the US state of Michigan. Building out an innovative idea into a viable product and company requires knowledge and resources that individuals might not have. That’s why the Michigan Economic Development Corporation, or the MEDC, has launched an innovation campaign to support technology entrepreneurs.Two words for you: startup ecosystem.My guest is Dr. Denise Kay, the co-founder and CEO at Enspired Solutions, a Michigan-based startup focused on removing synthetic forever chemicals called PFAS from water.Welcome, Denise. Dr. Denise Kay: Hi, Megan. Megan: Hi. Thank you so much for joining us. To get us started, Denise, I wondered if we could talk about Enspired Solutions a bit more. How did the idea come about, and what does your company do?

Denise: Well, my co-founder, Meng, and I had careers in consulting, advising clients on the fate and toxicity of chemicals in the environment. What we did was evaluate how chemicals moved through soil, water, and air, and what toxic impact they might have on humans and wildlife. That put us in a really unique position to see early on the environmental and health ramifications of the manmade chemical PFAS in our environment. When we learned of a very novel and elegant chemistry that could effectively destroy PFAS, we could foresee the value in making this chemistry available for commercial use and the potential for a significant positive impact on maintaining healthy water resources for all of us.Like you mentioned, PFAS is referred to as a forever chemical because it is so resistant to break down. It does not break down naturally in the environment, so it just circles around and around. This chemistry, which would break that cycle and break the molecule apart, could really support the health of all of us.Ultimately, Meng and I quit our jobs, and we founded Enspired Solutions. Our objective was to design, manufacture, and sell commercial-scale equipment that destroys PFAS in water based on this laboratory bench-scale chemistry that had been discovered, the goal being that this toxic contaminant does not continue to circulate in our natural resources.At this point, we have won an award from the EPA and Department of Defense, and proven our technology in over 200 different water samples ranging from groundwater, surface water, landfill leachate, industrial wastewater, [and] municipal wastewater. It’s really everywhere. What we’re seeing traction in right now is customer applications managing semiconductor waste. Groundwater and surface water around airports tend to be high in PFAS. Centralized waste disposal facilities that collect and manage PFAS-contaminated liquids. And also, even transitioning firetrucks to PFAS-free firefighting foams. Megan: Fantastic. That’s a huge breadth of applications, incredible stuff. Denise: Yeah. Megan: You launched about four years ago now. I wondered what factors made Michigan the right place to build and grow the company? Denise: That is something we put a lot of thought into, because I live in Michigan, and Meng lives in Illinois, so when it was just the two of us, there was even that, “Okay, what is going to be our headquarters?” We looked at a number of factors. Some of the things we considered were rentable incubator space. By incubator, I mean startup incubators or innovation centers. The startup support network, a pool of future employees, and what position the state agencies were taking regarding PFAS.While thinking about all those things and investigating our communities, in Michigan, we found a space to rent where we could do chemistry experiments in an incubator environment. Somewhere where we were surrounded by other entrepreneurs, which we knew was something we had to learn how to do. We were great chemists, but we knew that surrounding ourselves with those skills that could be a gap for us was going to be helpful.Also, we know that Michigan has moved much faster than other states in identifying PFAS sources in the environment and regulating its presence. This combination was something we knew would be the right place for starting our business and having success. Megan: It was a perfect setting for those two reasons. What were the first stages of your journey working with the Michigan Economic Development Corporation, the MEDC?
Denise: Well, both my co-founder, Meng, and I are first-time entrepreneurs. MEDC was one of the first resources I reached out to, starting from a Google search. They were an information resource we turned to initially, and then again and again for learning some fundamental skills. And receiving one-on-one expert mentorship for things like business contracts, understanding intellectual property landscapes, tracking and forecasting our business finances, and even how to approach fundraising. Megan: Wow. It sounds like they were an invaluable resource in those early days. How did early-stage research and development progress from that point? What were the key MEDC services and programs you used to get started?
Denise: Well, our business is based on cutting-edge science, truly cutting-edge science. Understanding the intellectual property landscape, which is a term used to describe intellectual property, patents, trademarks, trade secrets that are related to the science we were founding our business on, it was very important. So that we knew we were starting on a path, that we wouldn’t hit a wall three years from now. The MEDC performed an IP landscape survey for us. They searched the breadth of patents, and patent applications, and trademarks, and those things, and provided that for Meng and me to review and consider our position before really, really digging in and spending a lot of emotional time and money on the business. The MEDC also helped us early on create a model in Excel for tracking business financing and forecasting, forecasting our future financial needs, so that we could be proactive instead of reactive to financial limitations. We knew it wasn’t going to be inexpensive to design and build a piece of equipment that’s the size of two very large refrigerators that had never been built before. That type of financial-forward modeling helped us figure out when we would need to start fundraising and taking in investments. As we progressed along that, the MEDC also provided support of an attorney who reviewed contract language to make sure that we really understood various agreements that we were signing. Megan: Right. You mentioned that you and your co-founder were first-time entrepreneurs, as you put it. Tech acumen and business acumen are very different sets of skills. I wondered, what was the process like, developing this innovative technology while also building out a viable business plan? Denise: Well, Meng is a brilliant individual. She is a chemical engineer who also has an MBA. Meng had fantastic training to help understand the basis of how businesses function, in addition to understanding both the engineering and the chemistry behind what we were trying to do.I am an environmental toxicologist by training. I’ve had a longer career than Meng in that field. Over time, I have grown new offices and established new offices for different consulting firms I’ve worked for. I had the experience with people, space, culture, and running a business from that side. Meng has the financial MBA knowledge basis for a business. We’re both excellent chemists and engineers, and those types of things.We had much of the necessary knowledge, at least to take the first steps forward. The challenge became the hard limit of 24 hours in a day and no revenue to hire any support. That’s when the startup support networks like the MEDC became invaluable. It was simply impossible to do everything that needed to be done, especially while we were learning what we were doing. The MEDC and other programs provided support to take some of that load off us, but also helped us to learn to implement the new skills in an efficient manner, less stumbling.
Megan: So many things to juggle, isn’t there, in starting a company. I wondered, in that vein, could you share some successes and highlights from your journey so far? Any partnerships or projects that you’re excited about that you could share with us? Denise: As people say, being an entrepreneur and running a startup is like a rollercoaster. You have high moments and you have very low moments when you think nothing’s ever going to move forward. I’d love to talk about some of the highlights. Our machine, which we call the PFASigator. First of all, coming up with that name has a fun story behind it. The machine is, like I said, about the size of two large refrigerators. It’s very large, and it breaks down PFAS in water. The machine takes in water that has PFAS in it, we add a couple of liquid chemicals, then a very intense ultraviolet light shines on that water, which catalyzes a chemical reaction called reductive defluorination. When all of this is happening and the PFAS molecules are being broken apart to nontoxic compounds, to an outsider, it all still just looks like water with a light shining on it. But the machine is big, and it essentially eats PFAS. Meng and I were bantering, and her young, six-year-old son was in the background at the time. We were throwing names around. Thomas called out, “The PFASigator!” We were like, “Ooh, there’s something there.”
Megan: It’s a great name. Denise: It matches what we do, and it’s a memorable name. We’ve really had fun with that throughout. That was an early highlight, and we’ve stuck with that name. The next highlight I’d say was standing next to our first fully functioning PFASigator. It was big. It was all stainless steel. Meng and I had never been part of building a physical, large object like that. Just standing there, and the picture we have of us, it was exhilarating. That was a magnificent feeling. Selling our first machine was a day that everyone in the company, I think we were about eight at that point, received a bottle of champagne. Megan: Fantastic. Denise: For a startup to go from zero to one, they call it, you’ve sold nothing to you’ve sold something. That’s a real strong milestone and was a celebration for us. I’d say most recently, Enspired has been awarded a very exciting project in Michigan. It is in the contracting phase, so I can’t reveal too many details. But it is with a progressive municipality that will have our PFASigator permanently installed, destroying PFAS. That kind of movement from zero to one, and then a significant contract that will raise the visibility of the effectiveness of our approach and machine, has really buoyed our energy and is pushing us forward. It’s amazing to know we are now having an impact on the sustainability of water resources. That’s what we started the company for. Megan: Awesome. You have some incredible milestones there. But it’s a hard journey, as you’ve said as well, being an entrepreneur. I wondered, finally, what advice would you offer to burgeoning entrepreneurs given your own experience? Denise: I would advise that if problem solving and learning new talents do not provide sufficient intrinsic reward for a founder to be satisfied throughout what I guarantee will be a long duration effort, then that founder may need to reset their expectations, because the financial rewards of entrepreneurship are small throughout the process.Meng and I put [in] some of our personal funds and took no salary, and worked harder than we ever had in our lives for at least a year and a half before we were able to take a small salary. The financial rewards are small throughout the process of being a startup. The rewards are delayed, and in many cases, for many startups, the financial rewards never materialize.It’s a tough journey, and you have to love being on that journey, and be intrinsically rewarded for that for the sake of the journey itself, or you’ll be a very unhappy founder.Megan: It needs to be something you’re as passionate about as I can tell you are about the work you’re doing at Enspired Solutions. Denise: There’s probably one other thing I’d like to add to that. Megan: Of course. Denise: Often, founders are coached about formulas for fundraising, formulas for startup success. Learning those formulas and expectations is important, but it’s also important to not forget that it’s your creativity and innovation and foresight that got you to the place you’re in and drove you to start a company. Ultimately, people still want to see that shine through.” Megan: That’s fantastic advice. Thank you so much, Denise. That was Dr. Denise Kay, the co-founder and CEO at Enspired Solutions, whom I spoke with from an unexpectedly sunny Brighton, England.That’s it for this episode of Business Lab. I’m your host, Megan Tatum. I’m a contributing editor and host for Insights, the custom publishing division of MIT Technology Review. We were founded in 1899 at the Massachusetts Institute of Technology. You can find us in print, on the web, and at events each year around the world. For more information about us and the show, please check out our website at technologyreview.com. This show is available wherever you get your podcasts. If you enjoyed this episode, we hope you’ll take a moment to rate and review us. Business Lab is a production of MIT Technology Review, and this episode was produced by Giro Studios. Thanks for listening. This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. This content was researched, designed, and written entirely by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.

Today, we’re releasing the stable version of Gemini 2.5 Flash-Lite, our fastest and lowest cost ($0.10 input per 1M, $0.40 output per 1M) model in the Gemini 2.5 model family. We built 2.5 Flash-Lite to push the frontier of intelligence per dollar, with native reasoning capabilities that can be optionally toggled on for more demanding use cases. Building on the momentum of 2.5 Pro and 2.5 Flash, this model rounds out our set of 2.5 models that are ready for scaled production use.Our most cost-efficient and fastest 2.5 model yet

Gemini 2.5 Flash-Lite strikes a balance between performance and cost, without compromising on quality, particularly for latency-sensitive tasks like translation and classification.Here’s what makes it stand out:Best in-class speed: Gemini 2.5 Flash-Lite has lower latency than both 2.0 Flash-Lite and 2.0 Flash on a broad sample of prompts.Cost-efficiency: It’s our lowest-cost 2.5 model yet, priced at $0.10 / 1M input tokens and $0.40 output tokens, allowing you to handle large volumes of requests affordably. We have also reduced audio input pricing by 40% from the preview launch.Smart and small: It demonstrates all-around higher quality than 2.0 Flash-Lite across a wide range of benchmarks, including coding, math, science, reasoning, and multimodal understanding.Fully featured: When you build with 2.5 Flash-Lite, you get access to a 1 million-token context window, controllable thinking budgets, and support for native tools like Grounding with Google Search, Code Execution, and URL Context.Gemini 2.5 Flash-Lite in actionSince the launch of 2.5 Flash-Lite, we have already seen some incredibly successful deployments, here are some of our favorites:Satlyt is building a decentralized space computing platform that will transform how satellite data is processed and utilized for real-time summarization of in-orbit telemetry, autonomous task management, and satellite-to-satellite communication parsing. 2.5 Flash-Lite’s speed has enabled a 45% reduction in latency for critical onboard diagnostics and a 30% decrease in power consumption compared to their baseline models.HeyGen uses AI to create avatars for video content and leverages Gemini 2.5 Flash-Lite to automate video planning, analyze and optimize content, and translate videos into over 180 languages. This allows them to provide global, personalized experiences for their users.DocsHound turns product demos into documentation by using Gemini 2.5 Flash-Lite to process long videos and extract thousands of screenshots with low latency. This transforms footage into comprehensive documentation and training data for AI agents much faster than traditional methods.Evertune helps brands understand how they are represented across AI models. Gemini 2.5 Flash-Lite is a game-changer for them, dramatically speeding up analysis and report generation. Its fast performance allows them to quickly scan and synthesize large volumes of model output to provide clients with dynamic, timely insights.You can start using 2.5 Flash-Lite by specifying “gemini-2.5-flash-lite” in your code. If you are using the preview version, you can switch to “gemini-2.5-flash-lite” which is the same underlying model. We plan to remove the preview alias of Flash-Lite on August 25th.Ready to start building? Try the stable version of Gemini 2.5 Flash-Lite now in Google AI Studio and Vertex AI.

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. This startup wants to use beams of energy to drill geothermal wells Geothermal startup Quaise certainly has an unconventional approach when it comes to destroying rocks: it uses a new form of drilling technology to melt holes through them. The company hopes it’s the key to unlocking geothermal energy and making it feasible anywhere.Quaise’s technology could theoretically be used to tap into the Earth’s heat from anywhere on the globe. But some experts caution that reinventing drilling won’t be as simple, or as fast, as Quaise’s leadership hopes. Read the full story. —Casey Crownhart
Five things you need to know about AI right now
—Will Douglas Heaven, senior editor for AI Last month I gave a talk at SXSW London called “Five things you need to know about AI”—my personal picks for the five most important ideas in AI right now.  I aimed the talk at a general audience, and it serves as a quick tour of how I’m thinking about AI in 2025. There’s some fun stuff in there. I even make jokes!  You can now watch the video of my talk, but if you want to see the five I chose right now, here is a quick look at them. This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. Why it’s so hard to make welfare AI fair There are plenty of stories about AI that’s caused harm when deployed in sensitive situations, and in many of those cases, the systems were developed without much concern to what it meant to be fair or how to implement fairness.But the city of Amsterdam spent a lot of time and money to try to create ethical AI—in fact, it followed every recommendation in the responsible AI playbook. But when it deployed it in the real world, it still couldn’t remove biases. So why did Amsterdam fail? And more importantly: Can this ever be done right?Join our editor Amanda Silverman, investigative reporter Eileen Guo and Gabriel Geiger, an investigative reporter from Lighthouse Reports, for a subscriber-only Roundtables conversation at 1pm ET on Wednesday July 30 to explore if algorithms can ever be fair. Register here!

The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 America’s grand data center ambitions aren’t being realized A major partnership between SoftBank and OpenAI hasn’t got off to a flying start. (WSJ $)+ The setback hasn’t stopped OpenAI opening its first DC office. (Semafor) 2 OpenAI is partnering with the UK governmentIn a bid to increase its public services’ productivity and to drive economic growth. (BBC)+ It all sounds pretty vague. (Engadget) 3 The battle for AI math supremacy is heating upGoogle and OpenAI went head to head in a math competition—but only one played by the rules. (Axios)+ The International Math Olympiad poses a unique challenge to AI models. (Ars Technica)+ What’s next for AI and math. (MIT Technology Review) 4 Mark Zuckerberg’s secretive Hawaiian compound is getting biggerThe multi-billionaire is sinking millions of dollars into the project. (Wired $) 5 India’s back offices are meeting global demand for AI expertise New ‘capability centers’ could help to improve the country’s technological prospects. (FT $)+ The founder of Infosys believes the future of AI will be more democratic. (Rest of World)+ Inside India’s scramble for AI independence. (MIT Technology Review)
6 A crime-tracking app will share videos with the NYPDPublic safety agencies will have access to footage shared on Citizen. (The Verge)+ AI was supposed to make police bodycams better. What happened? (MIT Technology Review) 7 China has a problem with competition: there’s too much of itIts government is making strides to crack down on price wars within sectors. (NYT $)+ China’s Xiaomi is making waves across the world. (Economist $)
8 The metaverse is a tobacco marketer’s playground 🚬Fed up of legal constraints, they’re already operating in unregulated spaces. (The Guardian)+ Welcome to the oldest part of the metaverse. (MIT Technology Review) 9 How AI is shaking up physicsModels are suggesting outlandish ideas that actually work. (Quanta Magazine) 10 Tesla has opened a diner that resembles a spaceshipIt’s technically a drive-thru that happens to sell Tesla merch. (TechCrunch) Quote of the day  “If you can pick off the individuals for $100 million each and they’re good, it’s actually a bargain.”
—Entrepreneur Laszlo Bock tells Insider why he thinks the eye-watering sums Meta is reportedly offering top AI engineers is money well spent. One more thing The world’s first industrial-scale plant for green steel promises a cleaner futureAs of 2023, nearly 2 billion metric tons of steel were being produced annually, enough to cover Manhattan in a layer more than 13 feet thick.Making this metal produces a huge amount of carbon dioxide. Overall, steelmaking accounts for around 8% of the world’s carbon emissions—one of the largest industrial emitters and far more than such sources as aviation.A handful of groups and companies are now making serious progress toward low- or zero-emission steel. Among them, the Swedish company Stegra stands out. The startup is currently building the first industrial-scale plant in the world to make green steel. But can it deliver on its promises? Read the full story.—Douglas Main

A beam of energy hit the slab of rock, which quickly began to glow. Pieces cracked off, sparks ricocheted, and dust whirled around under a blast of air.  From inside a modified trailer, I peeked through the window as a millimeter-wave drilling rig attached to an unassuming box truck melted a hole into a piece of basalt in less than two minutes. After the test was over, I stepped out of the trailer into the Houston heat. I could see a ring of black, glassy material stamped into the slab fragments, evidence of where the rock had melted.   This rock-melting drilling technology from the geothermal startup Quaise is certainly unconventional. The company hopes it’s the key to unlocking geothermal energy and making it feasible anywhere. Geothermal power tends to work best in those parts of the world that have the right geology and heat close to the surface. Iceland and the western US, for example, are hot spots for this always-available renewable energy source because they have all the necessary ingredients. But by digging deep enough, companies could theoretically tap into the Earth’s heat from anywhere on the globe.
That’s a difficult task, though. In some places, accessing temperatures high enough to efficiently generate electricity would require drilling miles and miles beneath the surface. Often, that would mean going through very hard rock, like granite. Quaise’s proposed solution is a new mode of drilling that eschews the traditional technique of scraping into rock with a hard drill bit. Instead, the company plans to use a gyrotron, a device that emits high-frequency electromagnetic radiation. Today, the fusion power industry uses gyrotrons to heat plasma to 100 million °C, but Quaise plans to use them to blast, melt, and vaporize rock. This could, in theory, make drilling faster and more economical, allowing for geothermal energy to be accessed anywhere.  
Since Quaise’s founding in 2018, the company has demonstrated that its systems work in the controlled conditions of the laboratory, and it has started trials in a semi-controlled environment, including the backyard of its Houston headquarters. Now these efforts are leaving the lab, and the team is taking gyrotron drilling technology to a quarry to test it in real-world conditions.  Some experts caution that reinventing drilling won’t be as simple, or as fast, as Quaise’s leadership hopes. The startup is also attempting to raise a large funding round this year, at a time when economic uncertainty is slowing investment and the US climate technology industry is in a difficult spot politically because of policies like tariffs and a slowdown in government support. Quaise’s big idea aims to accelerate an old source of renewable energy. This make-or-break moment might determine how far that idea can go.  Blasting through Rough calculations from the geothermal industry suggest that enough energy is stored inside the Earth to meet our energy demands for tens or even hundreds of thousands of years, says Matthew Houde, cofounder and chief of staff at Quaise. After that, other sources like fusion should be available, “assuming we continue going on that long, so to speak,” he quips.  “We want to be able to scale this style of geothermal beyond the locations where we’re able to readily access those temperatures today with conventional drilling,” Houde says. The key, he adds, is simply going deep enough: “If we can scale those depths to 10 to 20 kilometers, then we can enable super-hot geothermal to be worldwide accessible.” Though that’s technically possible, there are few examples of humans drilling close to this depth. One research project that began in 1970 in the former Soviet Union reached just over 12 kilometers, but it took nearly 20 years and was incredibly expensive.  Quaise hopes to speed up drilling and cut its cost, Houde says. The company’s goal is to drill through rock at a rate of between three and five meters per hour of steady operation. One key factor slowing down many operations that drill through hard rocks like granite is nonproductive time. For example, equipment frequently needs to be brought all the way back up to the surface for repairs or to replace drill bits. Quaise’s key to potentially changing that is its gyrotron. The device emits millimeter waves, beams of energy with wavelengths that fall between microwaves and infrared waves. It’s a bit like a laser, but the beam is not visible to the human eye. 

Quaise’s goal is to heat up the target rock, effectively drilling it away. The gyrotron beams waves at a target rock via a waveguide, a hollow metal tube that directs the energy to the right spot. (One of the company’s main technological challenges is to avoid accidentally making plasma, an ionized, superheated state of matter, as it can waste energy and damage key equipment like the waveguide.) Here’s how it works in practice: When Quaise’s rig is drilling a hole, the tip of the waveguide is positioned a foot or so away from the rock it’s targeting. The gyrotron lets out a burst of millimeter waves for about a minute. They travel down the waveguide and hit the target rock, which heats up and then cracks, melts, or even vaporizes. Then the beam stops, and the drill bit at the end of the waveguide is lowered to the surface of the rock, rotating and scraping off broken shards and melted bits of rock as it descends. A steady blast of air carries the debris up to the surface, and the process repeats. The energy in the millimeter waves does the hard work, and the scraping and compressed air help remove the fractured or melted material away. This system is what I saw in action at the company’s Houston headquarters. The drilling rig in the yard is a small setup, something like what a construction company might use to drill micro piles for a foundation or what researchers would use to take geological samples. In total, the gyrotron has a power of 100 kilowatts. A cooling system helps the superconducting magnet in the gyrotron reach the necessary temperature (about -200 °C), and a filtration system catches the debris that sloughs off samples.  CASEY CROWNHART Soon after my visit, this backyard setup was packed up and shipped to central Texas to be used for further field testing in a rock quarry. The company announced in July that it had used that rig to drill a 100-meter-deep hole at that field test site.  Quaise isn’t the first to develop nonmechanical drilling, says Roland Horne, head of the geothermal program at Stanford University. “Burning holes in rocks is impressive. However, that’s not the whole of what’s involved in drilling,” he says. The operation will need to be able to survive the high temperatures and pressures at the bottom of wells as they’re drilled, he says. So far, the company has found success drilling holes into columns of rock inside metal casings, as well as the quarry in its field trials. But there’s a long road between drilling into predictable material in a relatively predictable environment and creating a miles-deep geothermal well.  Rocky roads In April, Quaise fully integrated its second 100-kilowatt gyrotron onto an oil and gas rig owned by the company’s investor and technology partner Nabors. This rig is the sort that would typically be used for training or engineering development, and it’s set up along with a row of other rigs at the Nabors headquarters, just across town from the Quaise lab. At 182 feet high, the top is visible above the office building from the parking lot.
When I visited in April, the company was still completing initial tests, using special thermal paper and firing short blasts to test the setup. In May the company tested this integrated rig, drilling a hole four inches in diameter and 30 feet deep. Another test in June reached a depth of 40 feet. These holes were drilled into columns of basalt that had been lowered into the ground as a test material. While the company tests its 100-kilowatt systems at the rig and the quarry, the next step is an even larger system, which features a gyrotron that’s 10 times more powerful. This one-megawatt system will drill larger holes, over eight inches across, and represents the commercial-scale version of the company’s technology. Drilling tests are set to begin with this larger drill in 2026. 
The one-megawatt system actually needs a little over three megawatts of power overall, including the energy needed to run support equipment like cooling systems and the compressor that blows air into the hole, carrying the rock dust back up to the surface. That power demand is similar to what an oil and gas rig requires today.  Quaise is in the process of setting up a pilot plant in Oregon, basically on the side of a volcano, says Trenton Cladouhos, the company’s vice president of geothermal resource development. This project will use conventional drilling, and its main purpose is to show that Quaise can build and run a geothermal plant, Cladouhos says.  The company is building an exploration well this year and plans to begin drilling production wells (those that can eventually be used to generate electricity) in 2026. That pilot project will reach about 20 megawatts of power with the first few wells, operating on rock that’s around 350 °C. The company plans to have it operational as early as 2028. Quaise’s strategy with the Oregon project is to show that it can use super-hot rocks to produce geothermal power efficiently, says CEO Carlos Araque. After it fires up the plant and begins producing electricity, the company can go back in and deepen the holes with millimeter-wave drilling in the future, he adds. A drilling test shows Quaise’s millimeter-wave technology drilling into a piece of granite. Araque says the company already has some customers lined up for the energy it’ll produce, though he declined to name them, saying only that one was a big tech company, and there’s a utility involved as well. But the startup will need more capital to finish this project and complete its testing with the larger, one-megawatt gyrotron. And uncertainty is floating around in climate tech, given the Trump administration’s tariffs and rollback of financial support for climate tech (though geothermal has been relatively unscathed). 
Quaise still has some technical barriers to overcome before it begins building commercial power plants.  One potential hurdle: drilling in different directions. Right now, millimeter-wave drilling can go in a straight line, straight down. Developing a geothermal plant like the one at the Oregon site will likely require what’s called directional drilling, the ability to drill in directions other than vertical. And the company will likely face challenges as it transitions from lab testing to field trials. One key challenge for geothermal technology companies attempting to operate at this depth will be  keeping wells functional for a long time to keep a power plant operating, says Jefferson Tester, a professor at Cornell University and an expert in geothermal energy. Quaise’s technology is very aspirational, Tester says, and it can be difficult for new ideas in geothermal to compete economically. “It’s eventually all about cost,” he says. And companies with ambitious ideas run the risk that their investors will run out of patience before they can develop their technology enough to make it onto the grid. “There’s a lot more to learn—I mean, we’re reinventing drilling,” says Steve Jeske, a project manager at Quaise. “It seems like it shouldn’t work, but it does.”

Last month I gave a talk at SXSW London called “Five things you need to know about AI”—my personal picks for the five most important ideas in AI right now.  I aimed the talk at a general audience, and it serves as a quick tour of how I’m thinking about AI in 2025. I’m sharing it here in case you’re interested. I think the talk has something for everyone. There’s some fun stuff in there. I even make jokes! The video is now available (thank you, SXSW London). Below is a quick look at my top five. Let me know if you would have picked different ones! 1. Generative AI is now so good it’s scary. Maybe you think that’s obvious. But I am constantly having to check my assumptions about how fast this technology is progressing—and it’s my job to keep up. A few months ago, my colleague—and your regular Algorithm writer—James O’Donnell shared 10 music tracks with the MIT Technology Review editorial team and challenged us to pick which ones had been produced using generative AI and which had been made by people. Pretty much everybody did worse than chance.What’s happening with music is happening across media, from code to robotics to protein synthesis to video. Just look at what people are doing with new video-generation tools like Google DeepMind’s Veo 3. And this technology is being put into everything.My point here? Whether you think AI is the best thing to happen to us or the worst, do not underestimate it. It’s good, and it’s getting better.
2. Hallucination is a feature, not a bug. Let’s not forget the fails. When AI makes up stuff, we call it hallucination. Think of customer service bots offering nonexistent refunds, lawyers submitting briefs filled with nonexistent cases, or RFK Jr.’s government department publishing a report that cites nonexistent academic papers. You’ll hear a lot of talk that makes hallucination sound like it’s a problem we need to fix. The more accurate way to think about hallucination is that this is exactly what generative AI does—what it’s meant to do—all the time. Generative models are trained to make things up.What’s remarkable is not that they make up nonsense, but that the nonsense they make up so often matches reality. Why does this matter? First, we need to be aware of what this technology can and can’t do. But also: Don’t hold out for a future version that doesn’t hallucinate. 3. AI is power hungry and getting hungrier. You’ve probably heard that AI is power hungry. But a lot of that reputation comes from the amount of electricity it takes to train these giant models, though giant models only get trained every so often.What’s changed is that these models are now being used by hundreds of millions of people every day. And while using a model takes far less energy than training one, the energy costs ramp up massively with those kinds of user numbers. ChatGPT, for example, has 400 million weekly users. That makes it the fifth-most-visited website in the world, just after Instagram and ahead of X. Other chatbots are catching up. So it’s no surprise that tech companies are racing to build new data centers in the desert and revamp power grids.The truth is we’ve been in the dark about exactly how much energy it takes to fuel this boom because none of the major companies building this technology have shared much information about it. That’s starting to change, however. Several of my colleagues spent months working with researchers to crunch the numbers for some open source versions of this tech. (Do check out what they found.)
4. Nobody knows exactly how large language models work. Sure, we know how to build them. We know how to make them work really well—see no. 1 on this list.But how they do what they do is still an unsolved mystery. It’s like these things have arrived from outer space and scientists are poking and prodding them from the outside to figure out what they really are.It’s incredible to think that never before has a mass-market technology used by billions of people been so little understood.Why does that matter? Well, until we understand them better we won’t know exactly what they can and can’t do. We won’t know how to control their behavior. We won’t fully understand hallucinations. 5. AGI doesn’t mean anything. Not long ago, talk of AGI was fringe, and mainstream researchers were embarrassed to bring it up. But as AI has got better and far more lucrative, serious people are happy to insist they’re about to create it. Whatever it is.AGI—or artificial general intelligence—has come to mean something like: AI that can match the performance of humans on a wide range of cognitive tasks.But what does that mean? How do we measure performance? Which humans? How wide a range of tasks? And performance on cognitive tasks is just another way of saying intelligence—so the definition is circular anyway.Essentially, when people refer to AGI they now tend to just mean AI, but better than what we have today.There’s this absolute faith in the progress of AI. It’s gotten better in the past, so it will continue to get better. But there is zero evidence that this will actually play out. So where does that leave us? We are building machines that are getting very good at mimicking some of the things people do, but the technology still has serious flaws. And we’re only just figuring out how it actually works. Here’s how I think about AI: We have built machines with humanlike behavior, but we haven’t shrugged off the habit of imagining a humanlike mind behind them. This leads to exaggerated assumptions about what AI can do and plays into the wider culture wars between techno-optimists and techno-skeptics.It’s right to be amazed by this technology. It’s also right to be skeptical of many of the things said about it. It’s still very early days, and it’s all up for grabs. This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.