Primer, a London-based payment startup, has announced a €86.2 million Series C funding round to expand its AI payments and finance platform. The capital will also support its market development in the US, where the company plans to grow revenue to more than a third of total revenue by 2028, supported by plans to hire up to 50 roles in the region.

Primer is a financial technology company that provides a unified payment infrastructure, enabling companies to optimize and control every step of the payment journey.

The platform offers complete visibility and control across the entire payment operations system, integrating payment orchestration, reconciliation, security, FX, and financial operations services into a single open layer. 

The company was built to solve a persistent problem: businesses having to rely on multiple fragmented providers and tools throughout the payment journey. Currently, the platform covers the merchant’s entire payment lifecycle, recording over 400 data points per transaction and handling more than 95% of customer payment volume on average.

The investment round was led by Sofina and Peak XV Partners, and backed by existing investors, including Balderton, Accel, ICONIQ, Tencent, and Speedinvest. With this new investment, the company has raised a total of €146.6 million, with platform adoption spanning more than 30 countries. 

Primer was founded in 2020 by Gabriel Let Roux (formerly of Braintree) and Paul Anthony (formerly of PayPal), and was initially backed by Balderton and Accel in pre-seed and Series A rounds. Since then, it has secured multiple investment rounds and released new products, such as Fallbacks and Network Tokenization.

In 2025, Primer launched its own proprietary agent, AI Companion, alongside Global Accounts, allowing companies to optimize and control finance across their businesses. 

With the introduction of their AI agent, they aim to solve a new issue specifically created by the rise of AI: data fragmentation across multiple systems, which can ultimately lead to major vulnerability and potentially flawed, widespread decisions.

“In the next few years, every payment decision in a large business will be initiated, optimised, or audited by AI. That shift is already underway. The question is whether the data those systems run on is complete, because when you deploy agents across fragmented data, they don’t just underperform, they make the wrong decision.

That’s why the next era of payments can only be built on complete, contextual intelligence. And that’s what Primer delivers,” said Gabriel Le Roux, CEO and co-founder of Primer.

The company reports that it processes billions of transactions annually for enterprise clients such as Get your guide, Dialpad, Printful, Conforama, Printify, Jackpot.com, and Maisons du Monde, among others. 

This new capital infusion will support the expansion of Primer’s AI-native infrastructure, which they expect will become the next-generation foundation that payments and finance teams will run on.

The plan is to scale its capabilities to run experiments, optimize performance, and operate autonomously to support contextual, AI-driven decision-making.

“We don’t want merchants chasing problems or missing opportunities. With full context across every payment, Primer Companion can act on their behalf, knowing what’s happening, why, and what to do next.” Le Roux added.

Aakash Kapoor, principal at Peak XV, stated: “As payments enter a new architectural era, that depth of context becomes critical for AI agents to make decisions.”

The US represents a massive growth opportunity for the startup, already accounting for roughly one-fifth of Primer’s revenue, with annual recurring revenue (ARR) in the region doubling year-on-year.

Over the next few decades, billions of autonomous, AI-powered robots will work alongside people in factories, perform tedious tasks in warehouses, care for the elderly, assist in unsafe disaster areas, deliver packages and food to our doorsteps, and eventually, help out in our homes. Some will look like us, and many won’t. What is certain is that regardless of form factor, robots will all rely heavily on AI in order to deliver real-world value.

In 2025, total investments in robotics companies reached a record $40.7 billion, accounting for 9 percent of all venture funding. The multibillion dollar question therefore is this: What will it take for AI-powered robots to begin to have a serious economic impact? Many of today’s robotics and AI companies are making bold claims, such as that humanoid robots will soon be coming into our homes, but there’s still a big gap between promise and reality.

The promise of robots that live and work alongside us has been the stuff of science fiction for a very long time. And while many programmers have tried to make that promise a reality, the physical world is just too complicated for traditional computer programs to handle the endless complexity it presents. Thanks to AI, robots are no longer being programmed—instead, they learn to operate in the real world. With enough practice, they can learn to perceive and understand the world around them, reason about that world, and use that reason and understanding to perform tasks that are useful, reliable, and safe.

The two of us have worked at the forefront of AI and robotics for the last decade, as a Professor in Robotics at Oregon State University and Co-Founder of Agility Robotics, and as former CEO of the Everyday Robots moonshot at Google X. Our experience deploying AI-powered robots in real-world settings has given us a perspective on where AI can be used to great benefit in complex robotic systems in the near term, and where we are still on the frontier of science fiction. We believe AI will enable an inflection point in robotics advances, but that it will be through the well-engineered application of coordinated systems of different AI tools rather than a single ChatGPT-style breakthrough.

As the excitement around AI is matched only by the uncertainty of what will be possible, here are five hard truths that will define AI in robotics.

1. The YouTube-to-Reality Gap Is Real

For years we have been seeing videos on YouTube with humanoid robots performing amazing moves on everything from a dance floor to an obstacle course. The inside knowledge in robotics is to “never trust a YouTube robot video.” The gap between real robots that can perform real work in unstructured human environments and carefully scripted and edited robot performances remains significant. The latest performance to get a lot of attention was a martial arts show featuring Unitree humanoid robots performing with children at the Chinese 2026 Spring Festival Gala. While impressive, this falls into a long lineage of tightly scripted robotic performances, where everything has been carefully choreographed and planned in advance. The low-level controls, synchronization, and choreography were stunning, yet the Spring Gala robot performance showed a level of autonomy and intelligence much closer to industrial robots building cars in a factory than something that will show up in your living room any time soon.

Seeing these kinds of demos nevertheless raises questions about where robotics really is. If robots can perform kung fu moves and do backflips and dance, why aren’t they also showing up on factory floors yet? And why can’t they do the dishes in my home after dinner? The simple answer is this: Making AI-powered robots capable of performing general tasks in varied human environments is still really hard. While impressive technological feats like those at the Spring Festival may make it look like we could be very close, the use of AI in these demos is only for low-level motor control (to keep the robots from falling over) and therefore is only a small part of the solution for robots to be general purpose in the real, unstructured spaces where we humans live and work.

2. Data Is An Unsolved Challenge

Large Language Models like OpenAI’s ChatGPT and Anthropic’s Claude were initially trained on an internet-scale database of text. The world woke up one day in late 2022 to ChatGPT demonstrating that AI computers could suddenly “speak” to us in prose or verse and about seemingly any topic. LLMs have turned out to generalize well and are now able to take multimodal input (text, images, video) and produce multimodal output. Importantly, the corpus of training data was both enormous and human-generated, which are characteristics that form the gold standard for AI training.

The fastest path to robots as part of everyday life may emerge through a range of robot forms performing increasingly sophisticated applications and employing a range of AI tools.Agility Robotics

Giving AI a body (in the form of a robot) so that it can engage with people in the physical world continues to be a very difficult and broadly unsolved problem. AI models for general-purpose robotics must simultaneously satisfy multiple, often conflicting, physical, geometric, and temporal limitations while operating in unstructured, dynamic environments. In order to generalize, robot models need to be trained on data gathered in a high-dimensional configuration space, where “dimensions” represent text, lighting conditions, degrees of freedom, joint limits, velocities, force, and safety boundaries, just to mention a few. Importantly, this must be good data—it must contain many examples from what amounts to an infinite number of possible configurations in the physical world.

Since there are very few existing sources of data like this, approaches like teleoperation, video analysis, motion capture of humans, and self-exploration in simulation and in the real world are all seen as important ways to collect data. It’s a Herculean task. For example, at Everyday Robots at Google X, we ran 240 million robot instances in our simulator over the course of 2022 to collect training data, mostly to train a trash-sorting model. Similar amounts of data will be needed for every skill, to get to a similar level of capability, which is not yet human level.

3. There Will Be No Single Robot AI

We are far away from a moment where a single AI model might allow general-purpose robots to live and work alongside us.

General-purpose robots can have wheels or legs. They can have one, two, three, or more arms. Some have propellers and can fly, while others may be designed to operate under water. Some will drive on busy roads. The physical world is infinitely varied and complex. And then there are all the people and other animals that will be surrounding the robots. How do you train a model to operate a robot safely and reliably in all of these settings? The simple answer is, You don’t. At least not for quite some time.

We believe the winning AI architecture leading to the next big breakthroughs in general-purpose robotics will be “agentic AI” for robots, which are high-level coordinating models that can reason, plan, use tools, and learn from outcomes to execute complex tasks with limited supervision. Agentic, high-level models running on robots will invoke a system of specialized ones for different types of tasks. We will likely soon see multiple robots collaborating and coordinating with each other through their on-board agentic AI models.

AI tools are unlocking new and powerful capabilities in robotics, which in turn will enable new solutions and new markets. It’s encouraging to see these new models being made broadly available, some even as open-source solutions. This availability is akin to what happened with the internet: Real progress occurred when it became ubiquitous. We anticipate an inevitable democratization of complex behaviors in robotics with wide access to these AI tools and technologies.

4. Hardware Is Still Very Hard

Robots are complex systems with many parts that all need to work together with great precision. For a robot to be useful and safe, every part of it must be coordinated, from its perception systems, to the computer controlling it, all the way down to its individual actuators.

Actuators—that is, the motors and gears—are a good example of an important part of the robot where what got us here won’t get us there. The actuators used at scale by most industrial robots will not work for robots that will operate in human environments. If these robots accidentally collide with an obstacle, the resulting impacts are harsh, forces are high, and things break. Humans don’t move in this way. We are far more compliant in how we interact with the world, and we’re constantly making contact with our environment and using that contact to help us accomplish things.

Consider the challenge of inserting a key in a lock: Humans typically don’t do this by aligning the key perfectly with the keyhole. Instead, we just feel for the edge of the keyhole and jiggle the key in. Robots need to be able to operate in novel ways to achieve comparable capabilities by using a new class of actuators that are sensitive to force and able to have a compliant interaction with the environment. While these kinds of actuators do exist, they are not yet generally available at scale for robot systems designed to operate around people.

5. Real Value Comes From “Easy” Tasks

There’s a big difference between tasks that look impressive and real-world tasks that provide value. Robotics is a perfect example of Moravec’s paradox, which states that tasks that are hard for humans are easy for computers (like multiplying two big numbers), and tasks easy for humans (like a toddler’s movements) are extremely difficult for computers and robots.

Serving customers is an unforgiving reality check, because customers only care about solving the real problems they have. If we are to deploy AI-based robot solutions, they must outperform the way things are currently done, while demonstrating reliable performance metrics and safety. Agility Robotics’ early work to deploy our humanoid robot Digit in customer locations led to the realization that our first obstacle was safety: Robots that balance and manipulate objects in human spaces bring new types of risk to the workplace. In the first humanoid deployments, physical barriers were necessary, and Agility kicked off a multi-year engineering effort to solve the safety challenge, touching nearly every aspect of robot design and relying heavily on new AI-based approaches to human detection and behavior control.

Everyday Robots at Google deployed robots in 2019 that worked autonomously in office buildings doing chores like cleaning cafe tables and sorting trash. We quickly learned how “messy” and difficult the real world is for a robot. This experience informed the architecture and deployment of our AI systems while also gathering real-world data that could be combined with simulation data for training and improving models.

This focus on creating a product to meet specific customer needs and deploying robots in real-world settings is the only way to inform the structure of the AI tools and infrastructure for near-term utility on a path towards long-term broader capability and generality. There will be no “aha” moment, no silver bullet algorithm, and no volume of data sufficient to produce a general-purpose robot without extensive real-world experience.

AI Robots Are Coming, One Step at a Time

As we look to the future, there is no doubt that the world is bringing AI into the physical world through robots. We are at the beginning of a “Cambrian explosion“ of useful, intelligent machines. We believe AI is not one tool, but a huge frontier of technical approaches that is unlocking new capabilities so powerful, they will define our economy moving forward. This will happen not in one single definitive moment, but as an ongoing set of small and large breakthroughs, where AI-driven robots begin to provide real value in a few tasks, and then a few more, with impacts unfolding across numerous $100 billion-plus markets that will dramatically improve the quality of our lives.

AI + ML

Most users lose access June 18 – unless you’ve got enterprise creds or paid API keys

Pour one out for the Gemini Command Line Interface. Come June 18, the open source development agent will stop serving most users in favor of the new Antigravity CLI, and developers aren’t happy that the replacement is far less open than the old tool.

Google announced the Antigravity CLI at Google I/O this week, billing it as a way for the Chocolate Factory to unify its efforts in developing a command line interface for AI agents. One of the key arguments Google makes in a post about transitioning from Gemini CLI to Antigravity CLI is that the new one has improved support for multi-agent environments, but the company isn’t giving most of its users much of a choice on whether to switch. 

“On June 18, 2026, Gemini CLI and Gemini Code Assist IDE extensions will stop serving requests for Google AI Pro and Ultra, as well as those using it free of charge using Gemini Code Assist for individuals,” the Gemini CLI team wrote in their announcement of the transition. The change also affects Gemini Code Assist for GitHub, which won’t allow new installations beginning June 18, and will stop serving requests in the following weeks. 

Enterprises appear to be getting a pass, however, with Google noting that those using Gemini CLI or its IDE extensions through a Gemini Code Assist Standard or Enterprise license won’t see any changes in their access, nor will Gemini Code Assist for GitHub users accessing the tools through their enterprise Google Cloud accounts. 

“Gemini CLI will remain accessible via paid Gemini and Gemini Enterprise Agent Platform API keys,” Google explained. For everyone else, sorry: It’s Antigravity CLI or bust, but don’t expect the same experience.

“There won’t be 1:1 feature parity right out of the gate” between Gemini CLI and Antigravity CLI, Google added. Agent skills, hooks, subagents, and extensions are all being supported by Antigravity CLI at launch. But other stuff may take time to arrive, if it does at all.

Pray we don’t alter the deal any further

Take a look at the Gemini CLI GitHub page, and you’ll find all the code that made it possible – it is an open-source project, after all. Surf over to the Antigravity CLI GitHub page and all you’ll see is a change log, readme, and a GIF file demonstrating the tool’s appearance. 

That’s right: Antigravity CLI isn’t open source – at least not from what Google has published so far – and it took developers no time at all to notice.

Gemini CLI Lead Product Manager Dmitry Lyalin took to GitHub to make an announcement detailing some additional info about the forced CLI tool migration, and the comments are rife with people frustrated by the move. No small portion of the vitriol is targeted at apparent usage limits, with multiple people reporting they’d hit their weekly quota with just a couple of requests. 

The issues page for Antigravity CLI similarly has numerous posts asking Google to look into usage limits. Other posts accuse Google of using open-source contributions to improve a new closed-source product and generally express frustration with Google for killing yet another thing customers relied on. 

At the same time, Lyalin teased developers by telling them that, no, Gemini CLI isn’t truly gone if you’re willing to pay top dollar for it. 

“The project remains available to the community as an Apache 2.0 licensed repository with no changes,” Lyalin noted in his GitHub post. “You will continue to see us work on GitHub as we keep Gemini CLI updated with latest model releases, bugs and security fixes for our enterprise customers.”

Now please open your wallets if you want access to this open-source product. 

Google didn’t respond to questions for this story. ®

Normally, when you think of a radio transmitter, you want the strongest signal and range. But if your radio operator is secretly operating as a spy, broadcasting their position isn’t a feature; it is a liability. This fact didn’t escape World War II radio designers.

In late 1942, the British realized they needed a way for Special Operation Executive agents, resistance members, and other friendly forces to communicate with an aircraft without attracting undue attention. Two engineers from the Royal Corps of Signals developed a pair of transceivers — the S-Phone — operating around 380 MHz just for this purpose. Frequencies this high were unusual at the time, which further deterred enemy detection.

The output power was below 200 mW, and the ground equipment consisted of a dipole strapped to the operator. No transistors, so with rechargable batteries, the rig weighed about fifteen pounds and reused some parts of a paratrooper radio, Wireless Set Number 37. The other side of the connection was installed in an airplane.

Close Air Support

The low power and directional antenna meant that it was nearly impossible to pick up any signal on the ground if you were more than a mile away. The airplane that the operator was facing, on the other hand, could pick up the voice signal up to 30 miles away. Unfortunately, they also had to be under 10,000 feet, exposing the plane to enemy fire.

The highly directional gear could give the pilot a clue that he was closing on the target, and when the signal suddenly went out, it indicated that the aircraft was directly overhead the transmitter.

The Special Operation Executive had a lot of cool gear, and you can learn more about their gadgets and methods in the 1943 film “School for Danger” that you can see below. Look for the S-Phone at around the 7-minute mark. Interestingly, the two main characters are actual Special Operation Executive agents who actually did the things that are fictionalized in the movie.

The CryptoMuseum has a scan of the S-Phone manual. There are many interesting tidbits there. For example, the set came with a lamp that could show if the transmitter was working. These radios used early NiCad batteries. The manual goes to great lengths to explain that you should not try adding sulpheric acid to the batteries.

Joan-Eleanor

Where the British had the Special Operation Executive, the United States had the Office of Strategic Services. Working at RCA laboratories, OSS engineers along with [Al Gross W8PAL] who would become a pioneer in the development of walkie-talkies, pagers, and cordless telephones, designed the Joan-Elanor, named after the engineer’s wife and a WAC member.

Joan was the field tranceiver, technically SSTC-502, while Eleanor, SSTR-6, was mounted in the aircraft. Joan weighed less than four pounds, using a super-regenerative dual triode that doubled as the transmit oscillator. Originally, the radio was set for 250 MHz, but when it was found that the Germans had the ability to receive at that frequency, they pushed Joan-Eleanor to 260 MHz.

The radio had a range of about 20 miles and, unlike the S-Phone, the aircraft could fly overhead at 30,000 feet. It also took ordinary batteries, so you didn’t need a charger as the S-Phone did.

The system recorded transmissions on a wire recorder in the aircraft. The intent was that agents behind enemy lines could secretly transmit intelligence reports to aircraft flying what appeared to be routine reconnaissance flights.

The radio gear was usually jammed in the rear of the host aircraft, usually a DeHavilland Mosquito, along with an operator aft of the bomb bay. The operator entered the position through a side hatch and remained there the entire flight. You can see an OSS film about the system, which was classified until 1976, in the video below.

Tech

These radios had a few things in common. Both used frequencies that were uncommon at the time, making it less likely the enemy could overhear or even detect conversations. This made it less risky to speak “in the clear” so agents didn’t need incriminating code books and cumbersome encoding and decoding steps.

Similarly, both systems used voice, meaning that agents didn’t need to learn Morse code. They probably needed a little training to use the equipment, but that was far easier than expecting a resistance fighter to study Morse code for weeks.

While the S-Phone depended on directionality, Joan seemed content to rely on being high in frequency. Both had to be lightweight, easy to conceal, and quick to set up and take down.

The Joan radio was critical for agents going behind enemy lines. They’d be brought to an airbase in a car with blacked-out windows to prevent them from knowing where they were leaving from. They’d be given forged papers, an entrenching tool, local money in a belt, a pistol, a food package, a silk map, and, of course, a Joan radio.

We wonder if any Joan radios were captured during the war? A lot of wartime high-tech was highly protected, and we’re sure the agents were instructed on how to destroy the radios. Spies were also famous for using suitcase or even shoe radios.

• 52% of customers had success negotiating with Vodafone, just 37% with BT
• Incentives come in the form of discounts, credits and free upgrades
• Sometimes it’s better to switch than persistently chase discounts

New research has claimed some of Britain’s biggest broadband providers might be more open to negotiation than others when customers try to reduce their monthly bills.

The survey from comparison site Go.Compare found Vodafone emerged as the easiest broadband provider to haggle with – more than half (52%) of customers said they were able to successfully negotiate a better price.

Staff are encouraged to work from home amid the layoffs

Meta is laying off around 8,000 employees globally, as part of a restructuring aimed at improving efficiency and reducing costs as the company invests heavily in artificial intelligence (AI), Bloomberg reported.

The tech giant sent out emails in Singapore at 4AM local time on Wednesday (May 20) to employees who were being laid off. Workers in the United Kingdom, the United States and elsewhere are expected to be notified early morning on the same day in their respective time zones.

In the meantime, staff are being encouraged to work from home while the company proceeds with its layoffs.

This round of cuts targets Meta’s engineering and product teams in particular, and more layoffs could follow later in 2026, people familiar with the plans said, asking not to be named as the information isn’t public.

Just two days prior, Meta announced in an internal memo circulated that some 7,000 workers have been reassigned to newly formed teams that are focused on AI initiatives, including products and agents.

The company had just under 80,000 employees at the end of Mar—before reassignments and layoffs—and has committed well over US$100 billion (S$128 billion) to AI capital expenditures in 2026.

“We’re now at the stage where many orgs can operate with a flatter structure with smaller teams of pods/cohorts that can move faster and with more ownership,” Meta’s head of people Janelle Gale said in the memo, which was reviewed by Bloomberg News. “We believe this will make us more productive and make the work more rewarding.”

Chief executive Mark Zuckerberg has made AI Meta’s top priority, committing all resources to keeping pace with rivals like Alphabet’s Google and OpenAI. That focus is reshaping the workforce and operations.

The company has weathered waves of layoffs in recent years as Zuckerberg pushes for efficiency. He has urged engineers to use AI agents for coding and other tasks, outlined plans to track employee devices to improve the technology, and even built his own AI-powered assistant to handle some CEO duties—like soliciting employee feedback.

The changes have left Meta employees frustrated and anxious. More than a thousand signed a petition to Zuckerberg and other leaders demanding the company stop collecting device data—including keystrokes, mouse movements, and screen content—to train AI. Others have taken to social media to vent about how layoff threats have hit morale and work.

Meta’s aggressive AI spending has rattled investors, who question whether the investment will pay off. While Meta has framed the layoffs as a way to “offset” major AI costs, Evercore analysts estimate the cuts will save only about US$3 billion.

That is a fraction of Meta’s projected 2026 capital expenditures—up to US$145 billion—and the hundreds of billions more it anticipates spending on AI infrastructure before the decade’s end.

• Read other articles we’ve written on tech giants here.

Feature Image Credit: Shutterstock

His client is a New York cop who was injured during a private security gig at Madison Square Garden. He sued the Garden on behalf of the cop.

Now John Scola, a lawyer well known for representing local police officers, is banned from the high-profile arena and several others owned by the famously controlling James Dolan.

For years, Dolan openly excluded entire law firms from his venues if a single attorney was in any sort of legal dispute with the Garden; those bans would then be enforced by Dolan’s increasingly sophisticated facial recognition system. What wasn’t entirely clear was whether Madison Square Garden was continuing to grow its legal blacklist. A letter to Scola, dated April 30 and reviewed by WIRED, suggested this practice continues. “Any tickets to MSG Venues,” the letter reads, “are hereby revoked.”

The ban also highlights the fissures in the multilayered relationship between New York City’s public servants and its most iconic arena. As WIRED reported last month, MSG security functionally acted as a second, unsanctioned surveillance force in midtown Manhattan—without the New York Police Department’s formal permission. (NYC mayor Zohran Mamdani called this expansion beyond the Garden’s walls “deeply troubling,” and promised further investigation.)

Dolan says that the biometric surveillance system is in place to stop dangerous actors from entering his properties—”if you’re a terrorist, [the list] will say that’s a terrorist,” he once told the local Fox affiliate—but the NYPD hasn’t shared facial recognition or any other kind of data with the Garden. The Garden did, however, add a New York police officer’s photo to the many, many others in its facial recognition database, as WIRED reported. “New Yorkers should be able to go to a game or a concert without their rights being violated,” New York attorney general Letitia James told the Pablo Torre Finds Out podcast in a statement. “My office is closely reviewing the latest reporting on Madison Square Garden surveillance tactics.”

On the other hand, the Garden does hire NYPD officers, through the city’s paid detail program, to augment its own security forces. That’s what happened in February of 2025, when a lightweight boxing match was being held at MSG’s then-named Hulu Theater. The audience was likely to be large and “requir[e] active crowd control,” according to the lawsuit, so the Garden brass figured they’d need eight off-duty cops to help. “Despite that determination,” the suit claims, “only two officers were actually present.” One of them was seven-year NYPD veteran John Przybyszewski.

At some point, an incident erupted near ringside.The rapper Lil Tjay seemed to spit in the face of a Garden security staffer who appeared to be trying to keep him from getting closer to the ring. Videos from the night show a chaotic scene. Lil Tjay’s bodyguards and entourage joined in the scuffle. According to the lawsuit, Przybyszewski claims he was knocked to the ground, pinned beneath several people.

Przybyszewski claims that when he got up, he was “in severe pain,” and was sent to the hospital in an ambulance. According to the lawsuit, “diagnostic imaging revealed significant cervical and lumbar spine injuries,” some of them “permanent.”

Przybyszewski blamed both the rapper and Garden officials. He sued Lil Tjay and Madison Square Garden. For a lawyer, he tapped Scola, who frequently represents NYPD officers in disputes with their bosses and the city. Scola filed his suit in February of this year. “Defendants made conscious operational decisions that placed Plaintiff directly in harm’s way. Those decisions caused his injuries,” the lawsuit claims.

Vehicle-specific integrations, Immersive Navigation and Android-inspired widgets are small but helpful additions.

Immersive Navigation looks great

Igor Bonifacic for Engadget

Android-like widgets come to Android Auto

Igor Bonifacic for Engadget