“These strategic quantum technology investments will build on our domestic industry, creating thousands of high-paying American jobs while advancing American quantum capabilities,” he added.

The move is the latest in a series of attempts by the Trump administration to intervene in the market, offering grants to companies in strategic sectors, such as semiconductors and critical minerals, in exchange for equity stakes.

Last year, the commerce department took a 10 percent stake in Intel, by converting $2.2 billion in grants under the Joe Biden-era Chips Act as well as $8.9 billion in federal grants that had been awarded but not yet paid.

Since then, several companies have received smaller sums, including Vulcan Elements, a little-known rare earths startup with about 30 employees, in which Trump Jr’s venture capital firm has invested.

Notably absent from the list of companies that signed letters of intent with the commerce department on Thursday was IonQ, a leading quantum company that has attracted significant investment from Cerberus, a firm co-founded by Donald Trump’s deputy secretary of war, Stephen Feinberg.

The US quantum announcement comes as other countries such as the UK are increasing their investments in technology and other related fields.

The ability of quantum computers to exploit the unusual properties of matter at the atomic and subatomic levels makes them theoretically capable of performing complex calculations much faster than existing machines.

But significant engineering hurdles remain, such as in making the machines less prone to errors, while companies are still dueling over which technical approaches work best.

The deals unveiled on Thursday are not final, and the administration said it was still soliciting proposals from other advanced tech firms. Intel is facing a shareholder lawsuit over its deal with the US government.

Additional reporting by Alex Rogers.

© 2026 The Financial Times Ltd. All rights reserved. Not to be redistributed, copied, or modified in any way.

Sometimes, AI helps you fine-tune weather forecasts or improve the lives of people with disabilities. Other times, well, it loses a fight with a bottle of peppermint syrup. That’s the situation Starbucks CEO Brian Niccol finds himself in after the coffee chain reportedly told staff that it’s scrapping an AI inventory program after only nine months.

Starbucks rolled out the “Automated Counting” software to its North American stores in September 2025. Developed in partnership with NomadGo, the AI-powered tool was supposed to speed up inventory tracking. Employees (likely fearing that they were holding their replacements) would use mobile devices to scan items on shelves.

The idea was simple: Automate the tedious task of counting milks and syrups, increase accuracy, and optimize the supply chain. Welcome to the AI revolution, baby.

A since-deleted September blog post by CTO Deb Hall Lefevre laid on the hype as thick as the whipped cream on a mocha Frappuccino: “With a quick scan using a handheld tablet, partners can instantly see what’s in stock — ensuring cold foam, oat milk, or caramel drizzle are always available,” it read. “Customers can enjoy beverages their way, every time — and partners spend less time in the backroom and more time crafting and connecting.” (“Partners” is Starbucks’ term for its employees.)

Well, things didn’t quite turn out that way. Reuters reports that the tool frequently mislabeled and miscounted items. It was known to mix up similar milk types or skip them altogether.

The video above, embedded in Starbucks’ September blog post, foreshadowed the tool’s struggles. The clip inadvertently showed the system missing a bottle of peppermint syrup as a worker scanned the shelf. (Did Starbucks deploy a half-baked AI video editor, too?)

So, Starbucks “partners” will now go back to the good ol’ days of manually counting inventory. “Beverage components and milk will now be counted the same way you count other inventory categories in your coffeehouse,” an internal company newsletter, viewed by Reuters, said. Apparently, workers won’t miss it much. “Thanks for discontinuing Automatic Counting!” one employee reportedly wrote in response to the change. “The thought behind it was great, but the execution was proving difficult.”

Waymo has suspended robotaxi service on freeways in San Francisco, Los Angeles, Phoenix, and Miami as it works to improve performance in construction zones, the company confirmed to TechCrunch on Thursday.

Waymo said it’s in the process of integrating “recent technical learnings into our software and expect to resume these routes soon.” Waymo robotaxis are still operating on surface streets in those cities.

The decision to pull robotaxis off of freeways follows Waymo’s decision to pause operations in Atlanta and San Antonio, Texas to address problems with flooding in those cities. The company announced a software recall last week that was supposed to help its fleet avoid flooded areas in San Antonio, in which service has been halted for weeks, while it worked on a more permanent fix. At least one robotaxi was spotted getting stuck in Atlanta this week, causing Waymo to suspend operations there, too.

These service interruptions come as Waymo is pushing to expand to a number of new cities around the globe this year, with the goal of offering as many as one million paid rides per week at the end of 2026. Waymo is also currently testing its new Zeekr-built robotaxi, which it calls Ojai, and is expected to start offering rides in that vehicle in the coming months.

Waymo started offering highway rides in late 2025. Putting its robotaxis on these higher-speed roads has been crucial to its expansion in large metro areas, as it helps the company better connect riders to local airports, and reduces ride times by skipping surface streets.

In the Bay Area in particular, freeway travel has helped Waymo dramatically cut trip times across the peninsula that previously took anywhere from 45 minutes to over an hour.

Waymo didn’t cite a specific incident behind its decision to suspend freeway driving this week. But the company’s robotaxis have been spotted struggling with highway construction zones. On May 19, X user @Elliot_slade posted a video claiming that his Waymo ride “blasted through cones” and claimed the vehicle was “chased” by police.

Getting concert tickets has always felt like a losing battle. You show up at the right time, refresh the page endlessly, and still walk away empty-handed. Scalpers and bots snap up the best seats before real fans even get a look in.

This is why Spotify has announced Reserved, a new feature that holds two concert tickets for an artist’s most dedicated fans before tickets go on sale to the general public.

How does Spotify decide who qualifies for Reserved?

Spotify will identify superfans based on real engagement signals like streams, shares, and other activity on the platform. The system will also actively monitor accounts to make sure offers go to real fans instead of bots.

If you qualify, you will receive an email and an in-app notification with a window of roughly a day to complete your purchase through a ticketing partner. There are no added fees from Spotify on top of the ticket price.

However, availability will vary by artist, tour, and location, so make sure that your preferred location is enabled in Spotify’s Live Events Feed and your notifications are switched on.

Most importantly, there will always be far more superfans than available seats on any given tour, so qualifying for Reserved does not guarantee you will get an offer every time.

When will Spotify Reserved be available?

Reserved kicks off this summer with select newly announced tours and will expand to more shows of all sizes over time, including some of the most in-demand concerts on the road.

For now, it is only available to Premium subscribers aged 18 and above in the US, with more countries coming later. Your location also plays a role, so if a tour is not coming anywhere near you, do not expect a Reserved offer for that artist.

Meanwhile, Spotify has also revealed plans to let Premium users create AI-powered covers and remixes of their favourite songs for an additional fee.

This sponsored article is brought to you by Wetour Robotics.

A field technician on a wind turbine, harness clipped, both hands on a wrench, needs to send a command to the diagnostic device hanging at her belt. A logistics worker on a loading dock, gloves on, eyes on the pallet, needs to redirect a connected lift. A person using an assistive mobility device on a crowded street wants to nudge it forward without taking out a phone or speaking aloud. None of these moments call for a smarter robot. They call for a smarter way to be heard by the machines that already exist.

The industry has been building from one side

The past three years of Physical AI have been a story of remarkable progress on the robot side of the loop. Companies like Boston Dynamics, Figure, and Unitree have advanced actuators, locomotion, and dexterity to a level that would have seemed implausible a decade ago. Google DeepMind’s Gemini Robotics has redefined what vision-language-action models can do in unstructured settings. The trajectory of the hardware and the foundation models is real, and it is accelerating.

But there is another side to this loop, and it has been treated as a solved problem for too long. The interface between humans and machines has defaulted, for 40 years, to three input modalities: screens, buttons, and voice. Each of those assumes the user can stop, look down, and translate intent into structured commands. That assumption breaks the moment the work moves into a real environment. On a turbine. On a dock. On a sidewalk. In any setting where hands are occupied, eyes are committed, or speaking is impractical, the conventional interface stack quietly fails.

Spatial Intent Fusion is the simultaneous processing of three streams of human-centered information, namely spatial position, visual context, and gestural intent: Your body is the interface.

The bottleneck on the human side of the loop is becoming as important as the one on the machine side. And solving it requires a different question. Not how do we make the robot more capable, but how do we let the human participate in the computing system as naturally as the robot already does.

Wetour Robotics’ bet: put the human back into the computing loop

Wetour Robotics is betting that the next architectural leap in Physical AI is not about making the robot more capable. It is about making the human a first-class node in the computing network, with the same kind of low-latency, high-fidelity participation that connected devices already enjoy.

Wetour Robotics’ engineers frame the problem this way: a wristband that recognizes a gesture is not enough. A camera that recognizes a scene is not enough. The information a human carries about what they are about to do is distributed across multiple channels, including where their body is in space, what their eyes are attending to, and what their muscles are preparing to do, and any single channel observed in isolation is ambiguous. Reconstructing intent reliably means fusing those channels at the operating system level, with latency low enough that the loop feels closed rather than mediated.

This approach has a name. Wetour Robotics calls it Spatial Intent Fusion: the simultaneous processing of three streams of human-centered information, namely spatial position, visual context, and gestural intent, fused into a single real-time command for any connected physical device. It is the technical implementation behind a simpler positioning statement the company uses externally: your body is the interface.

Orchestra is a portable intelligent hub running the operating system that handles sensor fusion, intent inference, command translation, and safety arbitration. The reference compute platform is NVIDIA Jetson Orin Nano Super, which provides enough on-device inference capacity to keep the entire control loop at the edge, with no cloud dependency on the critical path. Wetour Robotics

The architecture: three layers, four engines, one loop

Orchestra is not a single device but a layered platform, designed from the start to be sensor-flexible and actuator-agnostic. The architecture decomposes into three perception layers and four coordination engines.

Orchestra itself is the local compute and orchestration core: a portable intelligent hub running the operating system that handles sensor fusion, intent inference, command translation, and safety arbitration. The reference compute platform is NVIDIA Jetson Orin Nano Super, which provides enough on-device inference capacity to keep the entire control loop at the edge, with no cloud dependency on the critical path. Edge inference is non-negotiable for this application. Full-chain latency from biosignal acquisition to actuator command is held under 100 milliseconds, the envelope inside which closed-loop control feels natural rather than laggy.

VisionLink handles visual and spatial perception. Cameras feed into vision models that identify objects, estimate distances, and track environmental context. VisionLink is designed not as a passive recognition layer but as a real-time command generator: its outputs feed directly into Orchestra OS to be fused with biosignal data.

Conductor is the biosignal pipeline. It ingests raw surface electromyographic (sEMG) data from a wrist-worn device, classifies temporal patterns into discrete gestures or continuous control signals, and outputs actuator commands. The technically interesting property of sEMG for this use case is that the signal precedes visible motion. Motor unit action potentials appear at the skin surface roughly 50 to 80 milliseconds before a finger completes the corresponding gesture. Wetour Robotics calls this property pre-motion intent sensing, and it is what allows Orchestra to anticipate user intent rather than react to it.

On top of the three perception layers, Orchestra OS runs four coordination engines. The Perception Engine ingests and normalizes raw sensor streams. The Intent Engine performs Spatial Intent Fusion across modalities, resolving what the user is trying to do given where they are, what they are looking at, and what their hand is signaling. The Orchestration Engine translates intent into device-specific command sequences for any connected actuator. The Safety Engine arbitrates conflicting commands, enforces operational envelopes, and gates execution against runtime safety conditions.

Wetour Robotics

The trade-offs we’re honest about

No system that bridges the human body and the digital world is finished. Three engineering challenges remain open, and the company addresses each with a deliberate trade-off rather than a claim of having fully solved it.

Baseline stability of sEMG under motion. In a stationary user, continuous gesture recognition from sEMG is reliable. Once the user is walking, climbing, or otherwise moving, motion artifacts and electrode drift degrade the signal in ways that are difficult to fully compensate for. Rather than overpromise on continuous control in dynamic settings, Orchestra defaults to a smaller set of robust discrete gestures in complex operating environments, and reserves continuous control modes for contexts where the signal-to-noise ratio supports them.

Miniaturization of edge AI compute. Running the Orchestra control loop entirely at the edge requires real on-device inference, which has historically meant trading off between compute capacity, battery life, and form factor. Wetour Robotics’ approach has been a compact carrier board paired with a thermal design and a battery module sized for all-day wearability. The result is a hub that travels with the user rather than tethering them to a desk, and that performs the full perception-to-actuation loop without offloading to the cloud.

Heterogeneity of third-party device protocols. The actuator side of the loop is a fragmented landscape. Different manufacturers expose different command interfaces, different communication stacks, and different safety conventions, and a Physical AI operating system has to integrate with all of them. Wetour Robotics uses an AI-agent layer to negotiate connection and protocol translation adaptively, so that Orchestra OS can ingest data from a wide range of devices, run them through neural network models that infer human intent, and emit the right command on the right protocol for the device on the other end.

Why this matters, and why it helps the rest of the field

The history of computing is a history of interface revolutions. Command lines gave way to graphical user interfaces, which gave way to touch, which gave way to voice. Each transition expanded who could participate in the system and what they could do with it. The next transition is not about a new screen or a new microphone. It is about treating the human body itself as a participant in the computing network, capable of contributing intent at the same speed and fidelity that any other connected node can.

The history of computing is a history of interface revolutions. The next transition is not about a new screen or a new microphone — it is about treating the human body itself as a participant in the computing network.

This path is not a competitor to the work being done on humanoid robots, foundation models for embodied AI, and dexterous manipulation. It is the missing complement to that work. The hardest open problem for humanoid systems is the data: every natural interaction between a human and the physical world is a potential training signal, and most of those interactions are currently invisible to any computing system. As more humans become first-class nodes in the loop, those interactions become observable, structured, and ultimately useful for training the next generation of embodied AI, including the humanoid robots being developed today.

In other words: putting the human back into the computing loop is not just about better interfaces for individual users. It is about generating the kind of grounded, in-the-wild human-machine interaction data that the broader Physical AI ecosystem will need to keep advancing. The robot side and the human side of the loop are not two competing futures. They are two halves of the same one.

That is what Wetour Robotics means when it says: Your body is the interface.

Learn more at wetourrobotics.com.

Bungie will deliver a final update to wrap things up.

Storage furniture is too often drab and functional, intended to blend into the background of your home. Mustard Made is an exception. The company’s storage lockers come in a variety of loud colors that announce themselves in a room.

Mustard Made makes some of our favorite storage solutions here on the WIRED Reviews team, especially when it comes to outfitting our overflowing home offices. Julian Chokkattu from the WIRED Gear team takes Zoom meetings from a space full of high-end office chairs and brand-new gadgets, and yet my residual image of his home office will always be the stylish yellow Mustard Made lockers behind him. My colleague Louryn Strampe gave the brand’s smaller dual-shelf low-down locker an 8/10 review.

Mustard Made is having a rare sale from today through the end of May, with 20 percent off eight colors from its collection. Among the sale colors is that yellow favored by Julian, a really soft and lovely sage, a noble navy blue, and a medium pink they call “berry,” shown below. (Unfortunately, the vibrant poppy color I like and just introduced into my bedroom is not on sale.)

Photograph: Louryn Strampe

Mustard Made only runs a handful of sales a year, and you probably won’t see a 20 percent discount again until Black Friday.

I am testing the Midi, a midsized locker that sits almost the same height as my dresser and comes with adjustable shelves and locking doors. Transparently, I have long been a fan of locker-style storage furniture and have had an actual gym locker, a row of three of the now-discontinued tall version of the Ikea PS cabinet, and a current Ikea PS cabinet that used to hold my TV and now holds a 3D printer. There’s no question that the Mustard Made Midi is the prettiest of the lot.

Photograph: Martin Cizmar