
Tesla Robotaxi launch is a dangerous game of smoke and mirrors
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1 month agoon
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Tesla’s upcoming Robotaxi launch in Austin, Texas, is increasingly looking like a game of smoke and mirrors, and a dangerous one at that.
CEO Elon Musk claims Tesla is being “paranoid with safety”, but it is taking risks for the purpose of optics.
It’s all about optics
Musk has been wrong about self-driving for years. His track record is marked by missed deadlines and broken promises.
He said:
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“Our goal is, and I feel pretty good about this goal, that we’ll be able to do a demonstration drive of full autonomy all the way from LA to New York, from home in LA to let’s say dropping you off in Times Square in New York, and then having the car go park itself, by the end of next year. Without the need for a single touch, including the charger.”
That was in 2016, and therefore, he claimed it would happen by the end of 2017. Today, in 2025, Tesla is still not capable of doing that.
Musk has claimed that Tesla would achieve unsupervised self-driving every year for the last decade. It has become a running gag, with many YouTube videos featuring his predictions and a Wikipedia page tracking his missed deadlines.
Famously, the predictions are about Tesla achieving self-driving “by the end of the year” or “next year.”
This time, Musk has set a clear deadline of “June” for Tesla to launch its robotaxi service.
With Waymo pulling ahead in the autonomous driving race, now operating in four cities, providing over 200,000 paid rides per week, and soon expanding with 2,000 more vehicles, Musk needs a win to maintain the illusion he has been pushing for a while: that Tesla is the leader in autonomous driving.
He recently claimed about Tesla’s self-driving technology:
No one is even close. There’s really not a close second. We felt like it was a bit of an iPhone moment — you either get it or you don’t, and there’s a massive gap.
This is becoming increasingly difficult to claim amid Waymo’s expansion. Still, Musk believes that the robotaxi launch in Austin will help maintain the illusion, even though Waymo has already been operating like Tesla’s plans in Austin for years in other cities and for months in Austin itself.
Moving of the Goal Post
We have often described what Tesla is doing in Austin with its planned “robotaxi” launch as a moving of the goalpost.
For years, Tesla has promised unsupervised self-driving in all its vehicles built since 2016. Musk explicitly said that customers who bought Tesla’s Full Self-Driving package would be able to “go to sleep” at the wheel of their vehicles and wake up in another city.
Now, Musk is claiming that Tesla has “solved” self-driving with its “robotaxi” launch, but it is vastly different from prior promises.
Tesla plans to operate its own small internal fleet of vehicles with dedicated software optimized for a geo-fenced area of Austin and supported by “plenty of teleoperation.” This is a night-and-day difference compared to deploying unsupervised self-driving in customer vehicles, as promised since 2016.
Musk himself is on record saying, “If you need a geofence area, you don’t have real self-driving.”
Now, Musk is on record saying that Tesla will only launch the service in a limited area in Austin and even avoid certain intersections that Tesla is not sure it can handle:
We will geo‑fence it. It’s not going to take intersections unless we are highly confident it’s going to do well with that intersection. Or it will just take a route around that intersection.
In addition to geofencing, Tesla is also utilizing teleoperation to control vehicles with human operators remotely.
We reported last year when Tesla started building a “teleoperation team.”
Despite Tesla originally planning to launch the robotaxi service on June 12, and now “tentatively” on June 22, the automaker posted a new job listing days ago for engineers to help build a low-latency teleoperation system to operate its “self-driving” cars and robots.
The use of geofencing and teleoperation results in Tesla having the same limitations as Waymo, which Musk claimed means it’s “not real self-driving and not scalable to the customer fleet as promised by Tesla for years.
‘Paranoid’ about Safety
Musk claims that Tesla is being “super paranoid” about safety, but you have to take his word for it.
We have pointed it out before, but it’s worth repeating: Waymo tested its self-driving vehicles in Austin for six months with safety drivers and then for another six months without safety drivers before launching its autonomous ride-hailing service in the city.
As for Tesla, it tested its vehicles with safety drivers throughout Austin for a few months. Then, Musk announced in late May, only weeks before the planned launch, that it had started testing without safety drivers.
Despite many people being on the lookout for these driverless Tesla Robotaxis, they were only spotted for the first time last week.
Since then, only two confirmed Tesla vehicles without drivers have been spotted testing.
Furthermore, several of those vehicles were spotted with Tesla employees in the front passenger seat. While Musk claims that there are “no safety driver”, these “passengers” pay attention at all times and have access to a kill switch to stop the vehicle.
They virtually operate like “safety drivers”, but they are on the passenger seat rather than the driver’s seat.
Tesla is currently still in the “testing” phase based on the listing with the state regulators, which also mentions “no” safety drivers:

To go back to the “optics” for a second, Tesla’s head of self-driving, Ashok Elluswamy, has shared this conveniently cropped image of Tesla’s “robotaxis” being tested in Austin:

The image crops out the passenger seat of the car in front, which would show a Tesla employee, and the driver’s seat of the trailing car, which would show a driver, as spotted in Austin over the last week.
There’s also no way to know precisely at what rates these safety passengers and remote operators are intervening on the self-driving vehicles.
Tesla has never released any intervention or disengagement data about its self-driving and ADAS programs despite using “miles between disengagements” as a metric to track improvements and Musk claiming for years that self-driving is a “solved problem” for Tesla.
As we have previously reported, the best available data comes from a crowdsourced effort. Musk has previously shared and misrepresented the dataset in a positive light.
Currently, the data for the combined two most recent updates (v13.2.8-9) on Tesla’s latest hardware (HW4), which is reportedly the same hardware used in Tesla’s “robotaxis” in Austin, currently sits at 444 miles between critical disengagements:

That would imply a high risk of an accident every 444 miles without a driver paying attention and ready to take control at all times.
Tesla is also currently actively fighting in court against organizations trying to access its self-driving crash data.
There are currently efforts to raise concerns about Tesla’s “robotaxi” deployment in Austin.
The Dawn Project attempted to convey the potential danger of Tesla’s upcoming robotaxi fleet by demonstrating how Tesla vehicles fail to stop for school buses with their stop signs activated and can potentially run over children on the latest public Supervised Full Self-Driving (FSD) v13.2.9:
Musk has repeatedly highlighted that the vehicles used for the robotaxi service in Austin are the same that it currently delivers to customers, like this one used in this test.
However, they use a new, custom software optimized for Austin, with supposedly more parameters, allowing for greater performance. Still, there is no way to verify this, as Tesla has not released any data.
Electrek’s Take
I can’t lie. I’m getting extremely concerned about this. I don’t think that we can trust Musk or Tesla in their current state to launch this safely.
As I previously stated, I think Tesla’s FSD would be an incredible product if it were sold as a regular ADAS system, rather than something called “Full Self-Driving,” with the promise that it would eventually become unsupervised.
Tesla wouldn’t face a significant liability for not being able to fulfill its promises to customers, as it has already confirmed for HW3 owners. Additionally, safety would be improved, as drivers wouldn’t become so complacent with the technology.
Speaking of those failed promises, they are also what’s driving Tesla to push for this launch in Austin.
As Waymo’s former long-time CEO John Krafcik said about Tesla’s effort: “There are many ways to fake a robotaxi service.”
Musk badly needs a win with self-driving, and he saw an opportunity to get one by getting his gullible fanbase of Tesla shareholders excited about a glimpse at its long-promised future full of “Tesla robotaxis.”
As he previously stated, he knows full well that the way Tesla is doing this is not more scalable than Waymo even if the hardware cost per vehicle is lower. The hardware cost is negligible compared to teleoperation, development, insurance, and other expenses.
Even with all the smoke and mirrors involved with this project, it’s becoming clear that Tesla is not even ready for it.
Now, the question is whether Musk lets the June deadline slip and takes another ‘L’ on self-driving, or if he pushes for Tesla to launch the potentially dangerous service with lots of limitations.
With the federal government in complete shambles and the Texas government being too close to Musk and Tesla, I wouldn’t count on the regulators to act here. Although they probably should.
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Mercedes-Benz is ready to show off the GLC EV, but that’s just the start
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July 29, 2025By
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Mercedes’ best-selling SUV is about to go electric. The GLC EV will make its official debut in less than two months at IAA Mobility 2025 in Munich, where Mercedes-Benz will offer a glimpse of its upcoming models and much more.
When will the Mercedes GLC EV debut?
“We’re not just introducing a new model – we’re electrifying our top seller,” according to Mercedes-Benz Group CEO, Ola Källenius.
The GLC SUV remained the most popular Mercedes-Benz SUV in the US and globally through the first half of the year.
Mercedes has been hyping the GLC EV for some time now, releasing teaser images and “spy photos” of it testing in the frigid northern Swedish countryside. Earlier this month, CEO Ola Källenius gave us an exclusive preview of the electric SUV during a test drive.
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The luxury automaker claims the electric GLC “is the first of a whole new series of cars with elevated Mercedes-Benz iconic design,” adding it “presents a new face of the brand.”
According to Mercedes, it “embodies everything expected” from its top seller. The new model is “iconic, versatile, intuitive and smooth.” We will see it for the first time in less than two months.
Although it was expected, Mercedes confirmed for the first time on Monday that the all-new GLC EV will indeed debut at this year’s Munich Auto Show, which kicks off on September 9.
Mercedes says the event “begins an exciting new era” for the luxury brand with its biggest product launch ever. Alongside the electric GLC, Mercedes will hold the world premiere for the new CLA EV, CLA Shooting Brake, and Concept AMG GT XX.

We will also get a sneak peek into the future of Mercedes-Benz vans with a camouflaged prototype of the electric VLE, which is set to launch in 2026.

At the show, Mercedes will showcase its latest tech like the new Advanced Driver Assistance Systems (ADAS), Intelligent Cockpit, and more.
Visitors can also drive demo models to test out the self-driving tech (MB.DRIVE ASSIST PRO) firsthand. If you’re feeling up to it, you can also try out DRIVE PILOT, “the world’s fastest system for conditionally automated driving.” The system supports speeds of up to 95 km/h (59 mph).
Check back for more info closer to the event. Mercedes is expected to continue revealing new details leading up to the show.
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Environment
Cash App opens up to Apple Pay and Google Pay for the first time
Published
2 hours agoon
July 29, 2025By
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Cash App’s new Pools feature lets users set a group funding goal, name the pool, and invite contributors.
Courtesy: Cash App
Cash App is going on the offensive in peer-to-peer payments.
The Block-owned payments platform on Tuesday unveiled Pools, a new peer-to-peer feature designed to make group payments simple. It’s the company’s first major P2P product launch in nearly two years.
“This is the first time we’re going into out-of-network payments with Pools,” said Owen Jennings, Block’s head of business, referring to the feature’s ability to accept contributions via Apple Pay or Google Pay from people who aren’t on Cash App.
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PayPal reported its second-quarter results before the market opened Tuesday. Venmo had another knockout quarter, with revenue growing more than 20% year over year — its highest growth rate since 2023.
That followed a similarly strong first quarter where Venmo’s revenue growth doubled the pace of payment volume, driven by rising adoption of debit cards, instant transfers, and online checkout. The gains were fueled by heavier use of Venmo debit cards, instant transfers, and online checkout integrations. PayPal does not break out Venmo revenue.
For Block, the debut of Pools is a strategic reset. The company posted disappointing first-quarter results in May, missing revenue expectations and admitting it had lost focus on growing Cash App’s user base.
“Money is fundamentally social in nature,” Jennings said.
“We want Cash App to be the financial operating system for the next generation… to essentially be the money app where a customer can run their entire financial life,” added Jennings, who was previously Cash App’s chief operating officer.
That includes reinvesting in the peer-to-peer features that first made the app popular, and now aiming to make them more social and accessible — functionality that’s central to Cash App’s broader growth strategy.
Contributors can join a pool and send money through Cash App or external wallets like Apple Pay and Google Pay.
Courtesy: Cash App
Jennings said opening up access to Apple and Google accounts is an opportunity to get more active users and bring people into the ecosystem.
The company sees each non-user who contributes to a pool as a potential convert.
“This product is fundamentally geared at network expansion and improving the virality of our peer-to-peer products,” he added. “It’s the foundation of Cash App — it’s how Cash App started, but it’s also the growth engine that fuels everything else.”
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“The pace of development on this and our ability to get it in customers’ hands feels really different this year,” Jennings said. “Especially in the past three or four months, relative to how things felt about a year ago.”
He added that the shift isn’t unique to Block.
“You’ll probably broadly see that in the industry, where the pace of development is going to pick up as the marginal cost of a great line of code continues to fall. And this is just a great example of how we were able to move really fast.”
When a pool reaches its target, organizers can close it and transfer the collected funds directly into their Cash App balance.
Courtesy: Cash App
The launch also reflects CEO Jack Dorsey’s call to return Cash App to its core growth engine. On the company’s first-quarter earnings call, Dorsey acknowledged the platform’s recent underperformance
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Pools is designed to drive organic user growth — not direct revenue.
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Environment
Another Chinese self-driving test: deadly results and lawbreaking in city ADAS use
Published
3 hours agoon
July 29, 2025By
admin

Chinese media outlet Dongchedi posted another massive test of automotive self-driving systems, testing many of the same cars as it did in the highway test we we reported on this weekend.
This time, the test covers various urban driving scenarios, where much more human carnage is possible due to the presence of vulnerable road users like pedestrians and two-wheelers. And given how poorly the cars did on the last test, you can guess how they might have done on this one – although, once again, Tesla fared rather well.
The last video tested 36 cars in 6 different scenarios, all on highway driving and intended to replicate plausible highway situations that might lead to a crash. The new video is a little shorter than the last one, but still hefty at just over an hour long. It’s also only available in Chinese, but helpfully with English subtitles.
This time, the group was trimmed down to 26 cars from 36, but 9 scenarios were tested instead of 6, leading to a total of 234 simulations.
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Dongchedi had the help of Chinese state media in making the test possible, and it shows in the extremely high production value of the videos, which it posted on its automotive Youtube channel DCARSTUDIO. Once again, we recommend a watch, because it’s very well made.
The innovation behind these videos is that, unlike most other crash tests that either happen in labs or on closed courses like racetracks, airport runways or parking lots, DCAR used actual public roads which were shut down for the purpose of testing.
Why does this matter? Well, we’re testing ADAS systems here, not just normal passive crash structures like crumple zones, or even emergency driver aids like automatic emergency braking.
And the thing about ADAS systems, particularly those with an end-to-end, “navigate-on-autopilot“-like feature where the car can follow directions and make lane changes, turns, merges and other road transitions for you, is that they can’t be activated on roads where there are no directions to be had. (this came up in discussions after the famous Mark Rober Wile E. Coyote video, which still had value even if it didn’t test Tesla’s end-to-end system)
So – you’ll never be able to test how an SAE Level 2 driver’s aid will respond in a real world situation if you don’t test it on real-world roads. That’s what DCAR set out to do, and the result is once again quite spectacular. (And as the same caveat as last time – these aren’t actually driverless systems, like Waymo’s Level 4 system, but rather driver’s aids that still require an attentive driver in the seat)
This time, DCAR shut down two different segments of road: a massive, complex roundabout and another segment of road with a few unsignaled intersections and a long straight.

The first four tests incorporated portions of this huge roundabout, which would be complex for human drivers, but in situations for which there is quite an obvious solution: don’t hit that car/pedestrian in front of you.
The five tests here consisted of:
- 1. A vehicle is stopped in the left lane at the entry to the roundabout, obscuring an oncoming car in the lane you are trying to merge into.
- 2. Trying to merge left through a line of cars, in order to make a left turn to escape to the center of the roundabout.
- 3. Driving through center road of roundabout, two scooters stop in the scooter lane to yield for 4 children, who run out in front of the car (this test was preceded by a sharp u-turn, and some vehicles failed to even enter the testing area as they disengaged during the turn).
- 4. A broken down car in the center lane of the roundabout, with a warning triangle set up.
Admittedly, this is quite a complicated roundabout and most of us looking at it (at least from here in the West) probably can’t read exactly what those lane markings mean at first. And the markings also confused some systems – but if you want to offer a self-driving system, you need to be able to handle the roads as they exist.

The second location centered around a few unsignaled intersections, with more situations that are dangerous but plausible. They went as follows:
- 5. Just a U-turn. That’s it. This is a freebie… right?
- 6. Going straight through an unsignaled T-intersection, with a car turning left into your lane in front of you, obscured by the driver’s A-pillar blind spot.
- 7. Driving straight, with a car reversing into your lane from a perpendicular parking spot or driveway.
- 8. Driving straight, a scooter emerges from a group of several scooters and changes lanes in front of you.
- 9. A sharp left at an intersection, with a scooter turning through the intersection in front of you, and a pedestrian in the crosswalk on the other side.
Each of the tests occurred at generally low speeds, which means systems should have had a lot of time to consider and apply brakes, and the brakes should be more effective than they might have been in higher-speed highway scenarios from the first video.

Despite the lower speeds, many of the cars tended to approach these tests with confidence and aggression, either refusing to yield at all or only yielding at the last moment, to the point where it almost seemed like luck that they avoided a collision. Some cars also exceeded the speed limit, making their job of avoiding a collision more difficult.

Disturbingly, many of the cars wouldn’t even acknowledge it if they did get into a crash, and would continue on driving until DCAR’s (brave) human test driver and the host of the video intervened to end the test.

Unlike the highway tests, the urban tests included other road users. The highway tests included a truck and one construction worker, but urban tests included scooter riders and children – common sights in cities, which should certainly be reflected in the training data that companies use machine learning to train their ADAS systems with.

And these are arguably much more important scenarios in terms of human safety. Highways are typically safer than urban driving, and one reason is because there aren’t pedestrians around, so if you hit someone, they’ll be protected by a big metal box that’s going roughly the same speed as you. With a pedestrian or scooter rider, there’s no protection, and often a much higher speed delta, which means higher danger.

Even in situations where the cars should have had a clear view of these other road users, they failed to show the caution that should be required of cars sharing the road. A driver should know to pre-emptively be more cautious when there are pedestrians present – especially children. Certain vehicles did show this behavior, but many didn’t.

Interestingly, compared to the previous highway test, there was less inconsistency within vehicle brands this time around. Most of the vehicles that use similar solutions tended to show similar behaviors on the same test, even if those cars were from different brands – for example, the Luxeed R7 and AVATR 12 took second and third place in the overall standings, and both are equipped with Huawei’s ADS self-driving system.

And once again, Tesla did well in these tests, with the Model X taking the top spot, avoiding a collision in 8/9 tests. The one it failed was test 7, the reverse test, where it drove through at high speed clipping the rear of the car.
The Model 3 showed similar behavior on the reverse test, but also failed others (tests 2, 4, and 5), leaving it behind several other vehicles in the rankings. Which means that, if we average brand scores and rank brands, Avatr and Aito both had roughly similar performance brand-wide as Tesla did.

But like last time, we have to give the caveat that these tests all happened in good weather – and all in the daytime, unlike the highway tests, some of which happened at night.
Vision-only systems like Tesla’s have a disadvantage at night and in inclement weather as compared to systems with LiDAR or radar, and those situations were not tested in this video. Nevertheless, Tesla still did better than other vision-only systems, and even those with more advanced sensing technology, which is impressive (though it was still prone to making weird decisions, like when it tried to take a bike lane above, and on the U-turn test below)



Zeekr performed among the worst, at it did in the highway tests. Xiaomi also had middling to disappointing results – it’s a driver’s car, though, so maybe drive it rather than letting the machines do it for you. The biggest drop in rankings was the Great Wall Motors Wey Lanshan, which was a top-performer on the highway and yet scored one of the worst in urban driving.






Once again, Carnewschina assembled a table of the results (scroll to the bottom, past the highway test results), which we link to here as a thanks for their work in sifting through DCAR’s Chinese graphics and turning it into a more legible format for English speakers.

Collectively, these systems did about as bad as they did in the highway tests – a lot of simple scenarios were failed. The tests showed that these systems still get confused by relatively simple scenarios, and aren’t taking full advantage of the benefits in reaction time and all-around sensing that they should have with their many sensors and supercomputer systems to process them.

In particular, many of the tests involved situations where a driver’s eyes would have trouble anticipating a collision due to the A-pillar blind spot, something that should restrict a car’s sensing systems which can be placed so as to avoid blind spots. But many still failed to notice or react properly.

Like last video, DCAR interviewed Lu Guang Quan, from the Beijing University of Aeronautics and Astronautics. He once again pointed out that ADAS systems trained on machine learning can learn poor behaviors from the dataset, and these can be harder to correct than rule-based systems would be.
“End to end systems rely massively on samples,” said Lu. “iI their training data shows cars often ignoring the rule that vehicles inside a roundabout have the right of way, then the model learns to ignore it too.”

DCAR noticed that the systems routinely broke basic traffic laws and showed poor driving etiquette. The systems “don’t have traffic laws built into their foundation, nor do they treat compliance as a top priority. It’s like no one ever taught them to follow the rules – and they didn’t learn it from user data either.” (We saw a real-world example of this when Tesla first released FSD in China, and one driver got 7 tickets in a single drive)
DCAR ended the video on a slightly positive note, stating “we do believe China’s homegrown brands will be able to reduce the risk in these scenarios through future OTA updates. For now, the safest approach is still human-machine co-driving, letting ADAS help reduce the risk of collision while the human driver remains ready to take over when the system reaches its limits.”
And we at Electrek will close similarly as we did in the last article – we continue to hope this is a reminder to everyone who has gotten comfortable with using these systems routinely. Urban environments are complex and the presence of vulnerable road users makes them much more dangerous.
Even though brands are offering ADAS that works on urban roads now, you still need to apply your full attention to the driving task while behind the wheel of one of these vehicles – even the best-performing Tesla FSD, which all of us who have used it (or who watched the video above) know is prone to weird decisions at times, even if those decisions don’t lead to a collision.
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