The deal was executed through Oxy Low Carbon Ventures for an undisclosed amount.
Read more Google DeepMind’s latest update to a top Gemini AI model includes a dial to control how much the system “thinks” through a response. The new feature is ostensibly designed to save money for developers, but it also concedes a problem: Reasoning models, the tech world’s new obsession, are prone to overthinking, burning money and energy in the process.
Since 2019, there have been a couple of tried and true ways to make an AI model more powerful. One was to make it bigger by using more training data, and the other was to give it better feedback on what constitutes a good answer. But toward the end of last year, Google DeepMind and other AI companies turned to a third method: reasoning.
“We’ve been really pushing on ‘thinking,’” says Jack Rae, a principal research scientist at DeepMind. Such models, which are built to work through problems logically and spend more time arriving at an answer, rose to prominence earlier this year with the launch of the DeepSeek R1 model. They’re attractive to AI companies because they can make an existing model better by training it to approach a problem pragmatically. That way, the companies can avoid having to build a new model from scratch.
When the AI model dedicates more time (and energy) to a query, it costs more to run. Leaderboards of reasoning models show that one task can cost upwards of $200 to complete. The promise is that this extra time and money help reasoning models do better at handling challenging tasks, like analyzing code or gathering information from lots of documents.
“The more you can iterate over certain hypotheses and thoughts,” says Google DeepMind chief technical officer Koray Kavukcuoglu, the more “it’s going to find the right thing.”
This isn’t true in all cases, though. “The model overthinks,” says Tulsee Doshi, who leads the product team at Gemini, referring specifically to Gemini Flash 2.5, the model released today that includes a slider for developers to dial back how much it thinks. “For simple prompts, the model does think more than it needs to.”
When a model spends longer than necessary on a problem, it makes the model expensive to run for developers and worsens AI’s environmental footprint.
Nathan Habib, an engineer at Hugging Face who has studied the proliferation of such reasoning models, says overthinking is abundant. In the rush to show off smarter AI, companies are reaching for reasoning models like hammers even where there’s no nail in sight, Habib says. Indeed, when OpenAI announced a new model in February, it said it would be the company’s last nonreasoning model.
The performance gain is “undeniable” for certain tasks, Habib says, but not for many others where people normally use AI. Even when reasoning is used for the right problem, things can go awry. Habib showed me an example of a leading reasoning model that was asked to work through an organic chemistry problem. It started out okay, but halfway through its reasoning process the model’s responses started resembling a meltdown: It sputtered “Wait, but …” hundreds of times. It ended up taking far longer than a nonreasoning model would spend on one task. Kate Olszewska, who works on evaluating Gemini models at DeepMind, says Google’s models can also get stuck in loops.
Google’s new “reasoning” dial is one attempt to solve that problem. For now, it’s built not for the consumer version of Gemini but for developers who are making apps. Developers can set a budget for how much computing power the model should spend on a certain problem, the idea being to turn down the dial if the task shouldn’t involve much reasoning at all. Outputs from the model are about six times more expensive to generate when reasoning is turned on.
Another reason for this flexibility is that it’s not yet clear when more reasoning will be required to get a better answer.
“It’s really hard to draw a boundary on, like, what’s the perfect task right now for thinking?” Rae says.
Obvious tasks include coding (developers might paste hundreds of lines of code into the model and then ask for help), or generating expert-level research reports. The dial would be turned way up for these, and developers might find the expense worth it. But more testing and feedback from developers will be needed to find out when medium or low settings are good enough.
Habib says the amount of investment in reasoning models is a sign that the old paradigm for how to make models better is changing. “Scaling laws are being replaced,” he says.
Instead, companies are betting that the best responses will come from longer thinking times rather than bigger models. It’s been clear for several years that AI companies are spending more money on inferencing—when models are actually “pinged” to generate an answer for something—than on training, and this spending will accelerate as reasoning models take off. Inferencing is also responsible for a growing share of emissions.
(While on the subject of models that “reason” or “think”: an AI model cannot perform these acts in the way we normally use such words when talking about humans. I asked Rae why the company uses anthropomorphic language like this. “It’s allowed us to have a simple name,” he says, “and people have an intuitive sense of what it should mean.” Kavukcuoglu says that Google is not trying to mimic any particular human cognitive process in its models.)
Even if reasoning models continue to dominate, Google DeepMind isn’t the only game in town. When the results from DeepSeek began circulating in December and January, it triggered a nearly $1 trillion dip in the stock market because it promised that powerful reasoning models could be had for cheap. The model is referred to as “open weight”—in other words, its internal settings, called weights, are made publicly available, allowing developers to run it on their own rather than paying to access proprietary models from Google or OpenAI. (The term “open source” is reserved for models that disclose the data they were trained on.)
So why use proprietary models from Google when open ones like DeepSeek are performing so well? Kavukcuoglu says that coding, math, and finance are cases where “there’s high expectation from the model to be very accurate, to be very precise, and to be able to understand really complex situations,” and he expects models that deliver on that, open or not, to win out. In DeepMind’s view, this reasoning will be the foundation of future AI models that act on your behalf and solve problems for you.
“Reasoning is the key capability that builds up intelligence,” he says. “The moment the model starts thinking, the agency of the model has started.”
This story was updated to clarify the problem of “overthinking.“
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.
US office that counters foreign disinformation is being eliminated
The only office within the US State Department that monitors foreign disinformation is to be eliminated, according to US Secretary of State Marco Rubio, confirming reporting by MIT Technology Review.
The Counter Foreign Information Manipulation and Interference (R/FIMI) Hub is a small office in the State Department’s Office of Public Diplomacy that tracks and counters foreign disinformation campaigns.
The culling of the office leaves the State Department without a way to actively counter the increasingly sophisticated disinformation campaigns from foreign governments like those of Russia, Iran, and China. Read the full story.
—Eileen Guo
What is vibe coding, exactly?
When OpenAI cofounder Andrej Karpathy excitedly took to X back in February to post about his new hobby, he probably had no idea he was about to coin a phrase that encapsulated an entire movement steadily gaining momentum across the world.
“There’s a new kind of coding I call ‘vibe coding’, where you fully give in to the vibes, embrace exponentials, and forget that the code even exists,” he said. “I’m building a project or webapp, but it’s not really coding—I just see stuff, say stuff, run stuff, and copy paste stuff, and it mostly works.”
If this all sounds very different from poring over lines of code, that’s because Karpathy was talking about a particular style of coding with AI assistance. His words struck a chord among software developers and enthusiastic amateurs alike.
In the months since, his post has sparked think pieces and impassioned debates across the internet. But what exactly is vibe coding? Who does it benefit, and what’s its likely future? Read the full story.
—Rhiannon Williams
This story is the latest for MIT Technology Review Explains, our series untangling the complex, messy world of technology to help you understand what’s coming next. You can read more from the series here.
These four charts sum up the state of AI and energy
You’ve probably read that AI will drive an increase in electricity demand. But how that fits into the context of the current and future grid can feel less clear from the headlines.
A new report from the International Energy Agency digs into the details of energy and AI, and I think it’s worth looking at some of the data to help clear things up. Here are four charts from the report that sum up the crucial points about AI and energy demand.
—Casey Crownhart
This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here.
We need targeted policies, not blunt tariffs, to drive “American energy dominance”
—Addison Killean Stark
President Trump and his appointees have repeatedly stressed the need to establish “American energy dominance.”
But the White House’s profusion of executive orders and aggressive tariffs, along with its determined effort to roll back clean-energy policies, are moving the industry in the wrong direction, creating market chaos and economic uncertainty that are making it harder for both legacy players and emerging companies to invest, grow, and compete. Read the full story.
This story is part of Heat Exchange, MIT Technology Review’s guest opinion series, offering expert commentary on legal, political and regulatory issues related to climate change and clean energy. You can read the rest of the pieces here.
MIT Technology Review Narrated: Will we ever trust robots?
If most robots still need remote human operators to be safe and effective, why should we welcome them into our homes?
This is our latest story to be turned into a MIT Technology Review Narrated podcast, which we’re publishing each week on Spotify and Apple Podcasts. Just navigate to MIT Technology Review Narrated on either platform, and follow us to get all our new content as it’s released.
The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 The Trump administration has cancelled lifesaving aid to foreign children
After Elon Musk previously promised to preserve it. (The Atlantic $)
+ DOGE worker Jeremy Lewin, who dismantled USAID, has a new role. (Fortune $)
+ The department attempted to embed its staff in an independent non-profit. (The Guardian)
+ Elon Musk, DOGE, and the Evil Housekeeper Problem. (MIT Technology Review)
2 Astronomers have detected a possible signature of life on a distant planet
It’s the first time the potential for life has been spotted on a habitable planet. (NYT $)
+ Maybe we should be building observatories on the moon. (Ars Technica)
3 OpenAI’s new AI models can reason with images
They’re capable of integrating images directly into their reasoning process. (VentureBeat)
+ But they’re still vulnerable to making mistakes. (Ars Technica)
+ AI reasoning models can cheat to win chess games. (MIT Technology Review)
4 Trump’s new chip crackdown will cost US firms billions
It’s not just Nvidia that’s set to suffer. (WP $)
+ But Jensen Huang isn’t giving up on China altogether. (WSJ $)
+ He’s said the company follows export laws ‘to the letter.’ (CNBC)
5 Elon Musk reportedly used X to search for potential mothers of his children
Sources suggest he has many more children than is publicly known. (WSJ $)
6 Local US cops are being trained as immigration enforcers
Critics say the rollout is ripe for civil rights abuses. (The Markup)
+ ICE is still bound by constitutional limits—for now. (The Conversation)
7 This electronic weapon can fry drone swarms from a distance
The RapidDestroyer uses a high-power radio frequency to take down multiple drones. (FT $)
+ Meet the radio-obsessed civilian shaping Ukraine’s drone defense. (MIT Technology Review)
8 TikTok is attempting to fight back against misinformation
It’s rolling out an X-style community notes feature. (Bloomberg $)
9 A deceased composer’s brain is still making music
Three years after Alvin Lucier’s death, cerebral organoids made from his white blood cells are making sounds. (Popular Mechanics)
+ AI is coming for music, too. (MIT Technology Review)
10 This AI agent can switch personalities
Depending what you need it to do. (Wired $)
Quote of the day
“Yayy, we get one last meal before getting on the electric chair.”
—Jing Levine, who runs a party goods business with her husband that’s heavily reliant on suppliers in China, reacts to Donald Trump’s plans to pause tariffs except for China, the New York Times reports.
The big story
AI means the end of internet search as we’ve known it
We all know what it means, colloquially, to google something. You pop a few words in a search box and in return get a list of blue links to the most relevant results. Fundamentally, it’s just fetching information that’s already out there on the internet and showing it to you, in a structured way.
But all that is up for grabs. We are at a new inflection point.
The biggest change to the way search engines deliver information to us since the 1990s is happening right now. No more keyword searching. Instead, you can ask questions in natural language. And instead of links, you’ll increasingly be met with answers written by generative AI and based on live information from across the internet, delivered the same way.
Not everyone is excited for the change. Publishers are completely freaked out. And people are also worried about what these new LLM-powered results will mean for our fundamental shared reality. Read the full story.
—Mat Honan
We can still have nice things
A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or skeet ’em at me.)
+ Essential viewing: Sweden is broadcasting its beloved moose spring migration for 20 days straight.
+ Fearsome warlord Babur was obsessed with melons, and frankly, I don’t blame him.
+ Great news for squid fans: a colossal squid has been captured on film for the first time! 
+ Who stole my cheese?
As a child of an electronic engineer, I spent a lot of time in our local Radio Shack as a kid. While my dad was locating capacitors and resistors, I was in the toy section. It was there, in 1984, that I discovered the best toy of my childhood: the Armatron robotic arm.
COURTESY OF TAKARA TOMY
Described as a “robot-like arm to aid young masterminds in scientific and laboratory experiments,” it was the rare toy that lived up to the hype printed on the front of the box. This was a legit robotic arm. You could rotate the arm to spin around its base, tilt it up and down, bend it at the “elbow” joint, rotate the “wrist,” and open and close the bright-orange articulated hand in elegant chords of movement, all using only the twistable twin joysticks.
Anyone who played with this toy will also remember the sound it made. Once you slid the power button to the On position, you heard a constant whirring sound of plastic gears turning and twisting. And if you tried to push it past its boundaries, it twitched and protested with a jarring “CLICK … CLICK … CLICK.”
It wasn’t just kids who found the Armatron so special. It was featured on the cover of the November/December 1982 issue of Robotics Age magazine, which noted that the $31.95 toy (about $96 today) had “capabilities usually found only in much more expensive experimental arms.”
JIM GOLDEN
A few years ago I found my Armatron, and when I opened the case to get it working again, I was startled to find that other than the compartment for the pair of D-cell batteries, a switch, and a tiny three-volt DC motor, this thing was totally devoid of any electronic components. It was purely mechanical. Later, I found the patent drawings for the Armatron online and saw how incredibly complex the schematics of the gearbox were. This design was the work of a genius—or a madman.
The man behind the arm
I needed to know the story of this toy. I reached out to the manufacturer, Tomy (now known as Takara Tomy), which has been in business in Japan for over 100 years. It put me in touch with Hiroyuki Watanabe, a 69-year-old engineer and toy designer living in Tokyo. He’s retired now, but he worked at Tomy for 49 years, building many classic handheld electronic toys of the ’80s, including Blip, Digital Diamond, Digital Derby, and Missile Strike. Watanabe’s name can be found on 44 patents, and he was involved in bringing between 50 and 60 products to market. Watanabe answered emailed questions via video, and his responses were translated from Japanese.
“I didn’t have a period where I studied engineering professionally. Instead, I enrolled in what Japan would call a technical high school that trains technical engineers, and I actually [entered] the electrical department there,” he told me.
Afterward, he worked at Komatsu Manufacturing—because, he said, he liked bulldozers. But in 1974, he saw that Tomy was hiring, and he wanted to make toys. “I was told that it was the No. 1 toy company in Japan, so I decided [it was worth a look],” he said. “I took a night train from Tohoku to Tokyo to take a job exam, and that’s how I ended up joining the company.”
The inspiration for the Armatron came from a newspaper clipping that Watanabe’s boss brought to him one day. “It showed an image of a [mechanical arm] holding an egg with three fingers. I think we started out thinking, ‘This is where things are heading these days, so let’s make this,’” he recalled.
As the lead of a small team, Watanabe briefly turned his attention to another project, and by the time he returned to the robotic arm, the team had a prototype. But it was quite different from the Armatron’s final form. “The hand stuck out from the main body to the side and could only move about 90 degrees. The control panel also had six movement positions, and they were switched using six switches. I personally didn’t like that,” said Watanabe. So he went back to work.
COURTESY OF TAKARA TOMY
Watanabe’s breakthrough was inspired by the radio-controlled helicopters he operated as a hobby. Holding up a radio remote controller with dual joystick controls, he told me, “This stick operation allows you to perform four movements with two arms, but I thought that if you twist this part, you can use six movements.”
COURTESY OF HIROYUKI WATANABE
“I had always wanted to create a system that could rotate 360 degrees, so I thought about how to make that system work,” he added.
Watanabe stressed that while he is listed as the Armatron’s primary inventor, it was a team effort. A designer created the case, colors, and logo, adding touches to mimic features seen on industrial robots of the time, such as the rubber tubes (which are just for looks).
When the Armatron first came out, in 1981, robotics engineers started contacting Watanabe. “I wasn’t so much hearing from people at toy stores, but rather from researchers at university laboratories, factories, and companies that were making industrial robots,” he said. “They were quite encouraging, and we often talked together.”
The long reach of the robot at Radio Shack
The bold look and function of Armatron made quite an impression on many young kids who would one day have a career in robotics.
One of them was Adam Borrell, a mechanical design engineer who has been building robots for 15 years at Boston Dynamics, including Petman, the YouTube-famous Atlas, and the dog-size quadruped called Spot.
Borrell grew up a few blocks away from a Radio Shack in New York City. “If I was going to the subway station, we would walk right by Radio Shack. I would stop in and play with it and set the timer, do the challenges,” he says. “I know it was a toy, but that was a real robot.” The Armatron was the hook that lured him into Radio Shack and then sparked his lifelong interest in engineering: “I would roll pennies and use them to buy soldering irons and solder at Radio Shack.”
“There’s research to this day using AI to try to figure out optimal ways to grab objects that [a robot] sees in a bin or out in the world.”
Borrell had a fateful reunion with the toy while in grad school for engineering. “One of my office mates had an Armatron at his desk,” he recalls, “and it was broken. We took it apart together, and that was the first time I had seen the guts of it.
“It had this fantastic mechanical gear train to just engage and disengage this one motor in a bunch of different ways. And it was really fascinating that it had done so much—the one little motor. And that sort of got me back thinking about industrial robot arms again.”
Eric Paulos, a professor of electrical engineering and computer science at the University of California, Berkeley, recalls nagging his parents about what an educational gift Armatron would make. Ultimately, he succeeded in his lobbying.
“It was just endless exploration of picking stuff up and moving it around and even just watching it move. It was mesmerizing to me. I felt like I really owned my own little robot,” he recalls. “I cherish this thing. I still have it to this day, and it’s still working.”
PUBLIC DOMAIN
Today, Paulos builds robots and teaches his students how to build their own. He challenges them to solve problems within constraints, such as building with cardboard or Play-Doh; he believes the restrictions facing Watanabe and his team ultimately forced them to be more creative in their engineering.
It’s not very hard to draw connections between the Armatron—an impossibly analog robot—and highly advanced machines that are today learning to move in incredible new ways, powered by AI advancements like computer vision and reinforcement learning.
Paulos sees parallels between the problems he tackled as a kid with his Armatron and those that researchers are still trying to deal with today: “What happens when you pick things up and they’re too heavy, but you can sort of pick it up if you approach it from different angles? Or how do you grip things? There’s research to this day using AI to try to figure out optimal ways to grab objects that [a robot] sees in a bin or out in the world.”
While AI may be taking over the world of robotics, the field still requires engineers—builders and tinkerers who can problem-solve in the physical world.
featuring the Armatron for $31.95.
COURTESY OF RADIOSHACKCATALOGS.COM
The Armatron encouraged kids to explore these analog mechanics, a reminder that not all breakthroughs happen on a computer screen. And that hands-on curiosity hasn’t faded. Today, a new generation of fans are rediscovering the Armatron through online communities and DIY modifications. Dozens of Armatron videos are on YouTube, including one where the arm has been modified to run on steam power.
“I’m very happy to see people who love mechanisms are amazed,” Watanabe told me. “I’m really happy that there are still people out there who love our products in this way.”
Jon Keegan writes about technology and AI and publishes Beautiful Public Data, a curated collection of government data sets (beautifulpublicdata.com).
While it’s rare to look at the news without finding some headline related to AI and energy, a lot of us are stuck waving our hands when it comes to what it all means.
Sure, you’ve probably read that AI will drive an increase in electricity demand. But how that fits into the context of the current and future grid can feel less clear from the headlines. That’s true even for people working in the field.
A new report from the International Energy Agency digs into the details of energy and AI, and I think it’s worth looking at some of the data to help clear things up. Here are four charts from the report that sum up the crucial points about AI and energy demand.
1. AI is power hungry, and the world will need to ramp up electricity supply to meet demand.
This point is the most obvious, but it bears repeating: AI is exploding, and it’s going to lead to higher energy demand from data centers. “AI has gone from an academic pursuit to an industry with trillions of dollars at stake,” as the IEA report’s executive summary puts it.
Data centers used less than 300 terawatt-hours of electricity in 2020. That could increase to nearly 1,000 terawatt-hours in the next five years, which is more than Japan’s total electricity consumption today.
Today, the US has about 45% of the world’s data center capacity, followed by China. Those two countries will continue to represent the overwhelming majority of capacity through 2035.
2. The electricity needed to power data centers will largely come from fossil fuels like coal and natural gas in the near term, but nuclear and renewables could play a key role, especially after 2030.
The IEA report is relatively optimistic on the potential for renewables to power data centers, projecting that nearly half of global growth by 2035 will be met with renewables like wind and solar. (In Europe, the IEA projects, renewables will meet 85% of new demand.)
In the near term, though, natural gas and coal will also expand. An additional 175 terawatt-hours from gas will help meet demand in the next decade, largely in the US, according to the IEA’s projections. Another report, published this week by the energy consultancy BloombergNEF, suggests that fossil fuels will play an even larger role than the IEA projects, accounting for two-thirds of additional electricity generation between now and 2035.
Nuclear energy, a favorite of big tech companies looking to power operations without generating massive emissions, could start to make a dent after 2030, according to the IEA data.
3. Data centers are just a small piece of expected electricity demand growth this decade.
We should be talking more about appliances, industry, and EVs when we talk about energy! Electricity demand is on the rise from a whole host of sources: Electric vehicles, air-conditioning, and appliances will each drive more electricity demand than data centers between now and the end of the decade. In total, data centers make up a little over 8% of electricity demand expected between now and 2030.
There are interesting regional effects here, though. Growing economies will see more demand from the likes of air-conditioning than from data centers. On the other hand, the US has seen relatively flat electricity demand from consumers and industry for years, so newly rising demand from high-performance computing will make up a larger chunk.
4. Data centers tend to be clustered together and close to population centers, making them a unique challenge for the power grid.
The grid is no stranger to facilities that use huge amounts of energy: Cement plants, aluminum smelters, and coal mines all pull a lot of power in one place. However, data centers are a unique sort of beast.
First, they tend to be closely clustered together. Globally, data centers make up about 1.5% of total electricity demand. However, in Ireland, that number is 20%, and in Virginia, it’s 25%. That trend looks likely to continue, too: Half of data centers under development in the US are in preexisting clusters.
Data centers also tend to be closer to urban areas than other energy-intensive facilities like factories and mines.
Since data centers are close both to each other and to communities, they could have significant impacts on the regions where they’re situated, whether by bringing on more fossil fuels close to urban centers or by adding strain to the local grid. Or both.
Overall, AI and data centers more broadly are going to be a major driving force for electricity demand. It’s not the whole story, but it’s a unique part of our energy picture to continue watching moving forward.
This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here.
On Thanksgiving weekend of 2013, Jeff Bezos, then Amazon’s CEO, took to 60 Minutes to make a stunning announcement: Amazon was a few years away from deploying drones that would deliver packages to homes in less than 30 minutes.
It lent urgency to a problem that Parimal Kopardekar, director of the NASA Aeronautics Research Institute, had begun thinking about earlier that year.
“How do you manage and accommodate large-scale drone operations without overloading the air traffic control system?” Kopardekar, who goes by PK, recalls wondering. Busy managing all airplane takeoffs and landings, air traffic controllers clearly wouldn’t have the capacity to oversee the fleets of package-delivering drones Amazon was promising.
The solution PK devised, which subsequently grew into a collaboration between federal agencies, researchers, and industry, is a system called unmanned-aircraft-system traffic management, or UTM. Instead of verbally communicating with air traffic controllers, drone operators using UTM share their intended flight paths with each other via a cloud-based network.
This highly scalable approach may finally open the skies to a host of commercial drone applications that have yet to materialize. Amazon Prime Air launched in 2022 but was put on hold after crashes at a testing facility, for example. On any given day, only 8,500 or so unmanned aircraft fly in US airspace, the vast majority of which are used for recreational purposes rather than for services like search and rescue missions, real estate inspections, video surveillance, or farmland surveys.
One obstacle to wider use has been concern over possible midair drone-to-drone collisions. (Drones are typically restricted to airspace below 400 feet and their access to airports is limited, which significantly lowers the risk of drone-airplane collisions.) Under Federal Aviation Administration regulations, drones generally cannot fly beyond an operator’s visual line of sight, limiting flights to about a third of a mile. This prevents most collisions but also most use cases, such as delivering medication to a patient’s doorstep or dispatching a police drone to an active crime scene so first responders can better prepare before arriving.
Now, though, drone operators are increasingly incorporating UTM into their flights. The system uses path planning algorithms, like those that run in Google Maps, to chart a course that considers not only weather and obstacles like buildings and trees but the flight paths of nearby drones. It’ll automatically reroute a flight before takeoff if another drone has reserved the same volume of airspace at the same time, making the new flight trajectory visible to subsequent pilots. Drones can then fly autonomously to and from their destination, and no air traffic controller is required.
Over the past decade, NASA and industry have demonstrated to the FAA through a series of tests that drones can safely maneuver around each other by adhering to UTM. And last summer, the agency gave the go-ahead for multiple drone delivery companies using UTM to begin flying simultaneously in the same airspace above Dallas—a first in US aviation history. Drone operators without in-house UTM capabilities have also begun licensing UTM services from FAA-approved third-party providers.
UTM only works if all participants abide by the same rules and agree to share data, and it’s enabled a level of collaboration unusual for companies competing to gain a foothold in a young, hot field, notes Peter Sachs, head of airspace integration strategy at Zipline, a drone delivery company based in South San Francisco that’s approved to use UTM.
“We all agree that we need to collaborate on the practical, behind-the-scenes nuts and bolts to make sure that this preflight deconfliction for drones works really well,” Sachs says. (“Strategic deconfliction” is the technical term for processes that minimize drone-drone collisions.) Zipline and the drone delivery companies Wing, Flytrex, and DroneUp all operate in the Dallas area and are racing to expand to more cities, yet they disclose where they’re flying to one another in the interest of keeping the airspace conflict-free.
Greater adoption of UTM may be on the way. The FAA is expected to soon release a new rule called Part 108 that may allow operators to fly beyond visual line of sight if, among other requirements, they have some UTM capability, eliminating the need for the difficult-to-obtain waiver the agency currently requires for these flights. To safely manage this additional drone traffic, drone companies will have to continue working together to keep their aircraft out of each other’s way.
Yaakov Zinberg is a writer based in Cambridge, Massachusetts.
Zuckerberg says that time spent using Meta’s apps has declined since the arrival of TikTok.
Another update for X’s AI chatbot.
Facebook’s change in approach to political content appears to be driving more referrals to publisher sites.
Some pointers for your YouTube strategy.