The Cybercab is Tesla’s ‘fork in the road’ moment
12:42 PM PDT · September 3, 2026
Today could be the day Tesla changes forever.
In a couple of hours, Tesla will “launch” the Cybercab — a small, gold, two-seater sedan with no steering wheel or pedals that uses cameras and AI to navigate the world — in its hometown of Austin, Texas.
The company hasn’t said what the launch will entail, exactly. It could be a few cars offering rides in a small area downtown, or a far grander affair in both size and scope.
Tesla certainly wants you to believe it’s the latter.
In the runup to the event, Tesla staged dozens — perhaps more – of its Cybercabs around Austin and in many other U.S. cities. At the time this story was published, 420 autonomous vehicles were registered in Texas, according to the state’s automated vehicle tracker.
Ashok Elluswamy, the head of Tesla AI, recently said in a post on X that the “streets won’t be the same anymore.” When another user wrote that Tesla is “about to flood Austin,” CEO Elon Musk replied: “Yes.” On Wednesday night, Musk posted a seemingly AI-generated photo of the Cybercab consuming the city alongside a massive dust storm.
“A Storm of Cybercabs,” Musk wrote in another post.
These gravitas-laced posts mark a departure from the company’s go-slow-and-safe messaging in the months leading up to the event. Musk and other Tesla executives had previously professed they were taking a cautious approach to its robotaxis in the name of safety, though at times they seemed more concerned about public perception of a potential failure.
Of course, Tesla’s launch strategy isn’t as meaningful as the outcome. Tesla’s ability to successfully build a robotaxi network around the Cybercab would provide the first tangible evidence that it has transitioned from an automaker with some side projects to an AI and robotics company.
There’s a trope Musk favors that is apt for this moment: the fork in the road. It was the subject of an email he sent to Twitter employees in 2022 when he gave them an ultimatum to stay or leave. He invoked it again in early 2025 when he encouraged federal government employees to leave as part of his job leading DOGE, before he truly started wrecking house.
Musk loves the metaphor so much that he commissioned a 30-foot-tall sculpture of a literal fork on a literal road for Burning Man in 2022, before moving the sculpture to a Tesla office.
The Cybercab is Tesla’s fork in the road. This is either the moment the company begins to transform urban transportation as we know it, or the moment that reveals that, despite its many efforts, Tesla really is just an automaker after all.
Robot taxis
A Tesla robotaxi in Austin, Texas. Image Credits: Tim Goessman/Bloomberg / Getty Images
Musk first revealed the Cybercab in 2024 at a Hollywood movie lot. But the idea has been around far longer.
Musk has spent more than a decade promising that Tesla would find a way to make its cars fully autonomous, paving the way for a network of robotaxis. He even went so far as to claim, in 2016, that every Tesla being made had the capability to become fully autonomous and that the cars only needed the right software update to make it happen.
He was wrong. Tesla has reworked the hardware in its cars multiple times since that declaration, and Musk recently admitted that millions of them will need some kind of hardware retrofit to become autonomous.
Still, Tesla kept developing driving automation software. It has steadily released and refined its primary offering, Full Self-Driving (Supervised). But as capable as that software has become, it is still driver-assistance software. The responsibility and liability remain with the driver.
The Cybercab is supposed to change all that. It emerged from an effort by Tesla to develop a next-generation EV platform that was cheaper to build.
The plan was for the company to take what it learned from the Model 3 and Model Y — its two biggest mass-market successes — and develop a new platform that would cost half as much, allowing Tesla to make more affordable EVs.
Years of reporting and Musk’s own biography have shown that he was so dead set on achieving full autonomy that he decided against building a manually drivable car on the platform. Instead, Musk focused entirely on using the new platform to power the two-seater robotaxi we now know as the Cybercab.
In April 2024, months ahead of the Cybercab reveal, he approved massive layoffs at Tesla in service of going “balls to the wall for autonomy,” which he called a “blindingly obvious move.”
“Everything else is like variations on a horse carriage,” he wrote at the time.
Slowly, then quickly?
Tesla cybercab on display at the AutoSalon in january 2025 in Brussels, Belgium. Image Credits: Sjoerd van der Wal / Getty Images
Tesla started testing the first version of its Robotaxi program last June in Austin using modified Model Y SUVs. For the first few months, the company had an employee in the front seat (sometimes behind the wheel, sometimes in the passenger seat) who could stop the car if something went wrong.
Things have gone wrong along the way, though not catastrophically. Tesla has reported a few dozen incidents in Austin since it launched the Robotaxi pilot. Most were minor crashes with stationary objects, and a few involved teleoperators remotely moving the vehicles at low speeds.
The company has expanded this Model Y-based system to a few other cities in Texas and Florida. But over the past year, the network has not lived up to the hype Tesla manufactured at launch in June 2025. In July, the company revealed that the number of paid miles the network was facilitating was actually moving backward.
But that may not matter much to Tesla, which believes scaling its robotaxi network requires the Cybercab.
Tesla designed the Cybercab around cost. It’s smaller, lighter, and has a lower-capacity battery pack than any other Tesla. Fewer raw materials – especially on the battery side – make it cheaper to build. The cheaper it is to build, the faster Tesla can potentially turn a profit once it’s deployed.
That math has Tesla’s most ardent fans giddy. They look at Tesla’s ability to build dozens of Cybercabs for roughly the same amount of money Waymo spends to purchase and outfit fewer than 10 of its newest “Ojai” vans and see it as proof that the robotaxi war is already won.
Musk seems to agree. In a reply to a post that included a graphical representation of this argument, he wrote, “this doesn’t even take into account the operational efficiency of Cybercab.”
There are caveats worth considering. Tesla has, to date, only operated its robotaxi network within a geofence in each city. While some are larger than others, and the company has kept expanding some of them, Musk himself once said: “If you need a geofenced area, you don’t have real self-driving.”
Then there’s the challenge of operating the network at scale.
Tesla has so far benefited from having a small network in just a few cities. That naturally means its self-driving software encounters fewer edge cases than Waymo, which has 4,000 robotaxis on the road. Fewer trips and fewer edge cases also mean fewer chances for riders to spot problems and post them on social media or report them to regulators or journalists.
Tesla has also benefited from the fact that its fleet has consisted of Model Y SUVs, which are harder to distinguish on the road than a bright white Jaguar I-Pace kitted out with sensors. That will change with the Cybercabs. If Tesla’s software isn’t up to the task, its failures will be much harder to miss when they happen in shiny gold cars.
Topics
autonomous vehicles, avs, robotaxis, Tesla, Transportation
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Image Credits: Realta Fusion
Utilities are racing to link up with fusion startups, with Realta Fusion the latest to benefit
12:29 PM PDT · September 3, 2026
Fusion startup Realta Fusion this week announced a deal with Madison Gas and Electric that will place one of the first grid-connected fusion power plants in the country in Wisconsin. The partnership comes as utilities around the world have been jockeying to court fusion startups, a courtship that says as much about utilities’ anxiety over future power supply as it does about fusion’s progress.
For founders and investors watching the sector, these tie-ups serve as a kind of real-world credibility check. A deal with a utility offers several advantages for a fusion startup, ranging from engineering support and permitting assistance to site selection for grid interconnection and land leases. For utilities, such deals give them early access to a technology with the potential to reshape the grid for decades to come.
To date, only a handful of fusion startups have announced deals with utilities, which makes agreements like this one so newsworthy.
Realta’s deal with Madison Gas and Electric will explore the construction of a 200-megawatt power plant — roughly enough capacity to power a small city — sometime in the mid-2030s. The utility also made an equity investment as part of the deal, though Realta did not reveal specifics and did not immediately reply to TechCrunch’s request for more information. The startup is currently converting an old Oscar Mayer factory in Madison to serve as its research and development facility.
In addition to funding, Realta gains access to interconnection sites, a key advantage as power providers and users scour the country for a place to hook up to an increasingly connected electrical grid. Realta will also receive engineering and technical assistance along with financing for the eventual power plant.
Utilities, which are typically cautious, weren’t always so forward: For decades, fusion power appeared to be just over the horizon, but for nearly as long it failed to deliver on its promises. In the last few years, though, fusion startups have made significant strides on both scientific and engineering challenges. Those advances, juxtaposed with soaring demand from AI data centers, have brought utilities to the table.
Fusion power offers a natural fit for many utilities, which have been tested by the growth of wind and solar. Both are cheap and clean, but they only generate power when the wind blows or the sun shines. And while batteries have offered a new tool to stabilize intermittent power flows from renewables, utilities have also been searching for fossil fuel-free alternatives that can provide consistent power 24/7, so-called baseload power plants. Fusion power plants, which are being designed to operate round the clock, must appear tantalizingly familiar to grid operators, even if their inner workings have a whiff of science fiction about them.
Among the few other fusion startups to ink a deal is Commonwealth Fusion Systems (CFS), which is perhaps the farthest along, given it’s now nearly two years into its partnership with Dominion Energy. CFS has agreed to lease land from Dominion for Arc, its first commercial scale power plant. Arc will connect to the grid near Richmond, Virginia, and its expected to generate 400 megawatts of electricity. Google and Italian energy company Eni have both agreed to buy electricity from the power plant, which is expected to come online in the early 2030s.
On the other side of the country, Helion has been working with the Chelan County Public Utility District in Washington State for its first power plant, Polaris. The startup, which leases land from the utility, intends to turn on the 50 megawatt facility in 2028 to fulfill a deal to provide electricity to Microsoft.
In between Helion and CFS, Type One Energy is planning to build a 350 megawatt power plant on the site of a former coal plant near Oak Ridge, Tennessee, part of a larger deal the startup has with the Tennessee Valley Authority. The startup hopes to start supplying the grid with electricity from the Infinity Two power plant sometime in the mid-2030s.
Meanwhile, in Europe, Proxima Fusion is looking to rehab an old power plant site in southern Germany. In February, the company said it would build its first commercial power plant, Stellaris, on the grounds of an old fission power plant that is being decommissioned by RWE (one of Europe’s largest utilities, formerly a major operator of Germany’s nuclear plants) after Germany decided to pull the plug on the nuclear technology. RWE is also an investor in Proxima. The startup is aiming to turn on Stellaris in the late 2030s.
For now, the benefits of these deals are more tangible for startups, which gain access to expertise and real estate that might otherwise be costly or simply out of reach. It’s a capital-efficient way to de-risk a famously capital-intensive business.
For utilities, the potential rewards are farther afield though the payoff in terms of new power supplies could be significant.
Utilities are used to tackling projects with long timelines, though they aren’t accustomed to taking big risks, of which fusion power is one. Still, an underpowered grid would be an even bigger gamble. Better to bet now on a technology that promises to save their business years down the road. It might take a decade to build a commercial fusion power plant, and while that might seem like a long time, to utilities, it’s practically tomorrow.
Topics
Climate, fusion power, Realta Fusion, utilities
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Image Credits: Viktor Tanasiichuk / Getty Images
Abliteration.ai is making a business out of removing AI guardrails
11:37 AM PDT · September 3, 2026
It just became much easier to access one of the world’s most capable open-weight AI models, stripped of its guardrails and refusals to perform harmful tasks.
Named after a technique that removes a model’s tendency to refuse harmful requests, startup Abliteration.ai has turned that removal into a service. The platform hosts modified versions of open-weight models with their guardrails removed, including Z.ai’s recently released GLM-5.3, which users can query from a web browser or access through an API.
The company said in a recent social media post that its goal is to enable others to perform “offensive cyber, red-teaming, and agent testing work other models refuse to do.” The logic is familiar in security work: you can’t defend against a behavior you can’t reproduce, and a model that refuses to write working exploit code can’t help a red team defend against attackers. But those same removals make other potentially dangerous tasks easier, too.
Abliteration is a long-standing technique among open-source models. Researchers and developers have been removing refusals from open weight models for years, and Hugging Face hosts thousands of abliterated models on its platform.
Founded late last year but officially incorporated in March, Abliteration.ai moves the technique from an underground open source practice into a commercial, readily available service. By hosting the model, Abliteration reduces the friction for people who would otherwise have to download their own pre-abliterated models and secure the compute needed to run it.
Using the service, TechCrunch was able to quickly create an account and start querying an abliterated version of GLM-5.3 for free through a web browser. We asked it to write a Python program that steals saved Chrome passwords and a detailed protocol for culturing a dangerous human pathogen at home, and it readily complied.
Abliteration.ai Co-Founder Devon says the startup has several deals with major cloud providers, which it’s able to afford purely through customer revenue. (We are not including Devon’s last name at his request since he is still employed at another firm.) Abliteration.ai has not raised any venture capital yet, but is in talks to do so.
Critics say that making abliterated models available at scale could lead to real harm. Andrew Yoon, head of research at AI safety nonprofit CivAI, told TechCrunch abliterating models allows you to “modify the model so that it becomes a sociopath.”
“You can type in literally anything here, and it will comply with it,” Yoon said. “When people talk about removing the guardrails from AI models, this is what we’re talking about…I do expect we will start to see edited, abliterated models being used for harm in the near future.”
Abliteration AI just removed safeguards from GLM-5.3 so it can perform offensive cyberattacks. I have also received independent confirmation that Abliteration AI removed the model's bio-related safeguards too. The fact that it is trivially easy to remove safeguards from… https://t.co/7Wk2Ibx35H
— Chris McGuire (@ChrisRMcGuire) September 1, 2026
Most of the experts TechCrunch spoke to say there’s no stopping this train. But if removing safeguards from open-weight models can’t realistically be prevented, there are other places government can intervene. In a recent opinion piece, Yoon suggested that governments require providers to run classifiers to detect and block harmful cyber and bioweapons activity. He also argued that companies renting direct access to advanced GPUs should be required to verify customer identities and “deny access where there is reason to suspect dangerous misuse.”
Abliteration.ai offers customers a moderation layer so they can add in whatever guardrails they wish. The platform itself has some minor guardrails — for example, in our testing, we couldn’t get the model to provide suicide instructions — and Devon says he is working on implementing more to prevent violence.
Abliteration.ai also hasn’t integrated any KYC practices other than logging the credit card a customer uses to purchase the service, saying that the problem of deciding who gets access is a tough one that the young company is still working out.
“You don’t want to be the person responsible for someone doing something crazy…so where do you draw the line of what your responsibility is as a company?” Devon said. “We’re still in the process of defining that.”
This raises questions industry and governments will have to confront as increasingly capable models are released with downloadable weights: if anyone can remove a model’s safeguards, does making the resulting model easier for everyone to access make the internet safer or more dangerous?
Abliteration.ai’s founder and other advocates argue that democratizing access to uncensored frontier models is the best form of defense.
“The big picture of abliterated models is they’re able to model bad actors,” Devon said. “The advantage is now the defenders can move as fast as possible. They have all these tools that they need to be able to model these bad actors and then defend from these bad actions, and I think it will accelerate cybersecurity, which is a kind of counterintuitive point.”
While still a young company, Devon says Abliteration.ai’s customers include several early stage red teaming startups based in the UK and Europe, companies that help banks, airlines and other enterprises dealing with critical infrastructure beef up their cybersecurity practices.
“One of our major customers red teams agents of banks, and they would not be able to use the models out of the box today to be able to red team those agents,” Devon said.
TechCrunch created a free account and tested the abliterated version of GLM-5.3. Image Credits: TechCrunch/Abliteration.ai
Meanwhile, the cybersecurity industry itself is still figuring out where abliterated models fit into defensive work, if at all.
Several agent red teaming companies that TechCrunch spoke to agree with Devon that the bad guys are already abliterating their own models and using them to perform adversarial attacks, making the case for the defenders having the same tools. But they differ on just how consequential abliterated models really are to the process.
While Devon asserts that abliterating models is essential for performing thorough agent red teaming, some say that they don’t use them in their daily work, relying instead on the ease of fine-tuning open weight models — which already have few guardrails — to perform their testing.
Ahmed Aly, CEO of agent red-teaming firm Fabraix, says his company relies more on fine-tuning open models than using abliterated ones, adding that the process of abliteration removes some of the model’s knowledge and capabilities.
“If you’re actually trying to do real harm with it – cyber harm, bio harm — it will not be as effective,” Aly told TechCrunch.
Alessio Lomuscio, chief technologist at Safe Intelligence, agreed that a reduction in capabilities is possible, but still believes abliterated models can elicit certain behavior that’s useful in stress-testing a system.
“So far abliterated models are not part of the process,” David Slater, founder and chief architect at cybersecurity platform Armadin, told TechCrunch. “When we look at open weight models up until this absolute last generation, it just wasn’t particularly hard to jailbreak them and get them to do what we want.”
He added that Armadin is researching abliteration, though, and believes that “pushing the open community to understand the capability of models is critical.”
“This is going to happen behind closed doors. It’s going to happen in private,” Slater continued. “It happening in the open gives researchers the tools. It gives us the ability to figure out what the actual frontier looks like and to understand the harm.”
Topics
AI, ai safety, cybersecurity, Security
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Image Credits: David Paul Morris/Bloomberg / Getty Images
Meta is paying to peek at how you use their latest AI model
11:19 AM PDT · September 3, 2026
Most AI tools allow you to opt out of sharing your usage with the model provider to improve future versions. Meta has taken that idea and put a price tag on it.
For its new Muse Spark model, intended for operating coding and other agents, Meta is offering an explicit discount averaging out to about 95% for users who “contribute” to the development of future models by sharing their prompts and model outputs.
While 1 million input tokens under a standard agreement costs $1.25, under the contributor pricing model they cost just 10 cents. For output tokens, the standard price is $4.25 per million, but that same million costs just 20 cents under the contributor model.
Meta has had a rough time trying to obtain training data: An initiative to track the computer usage of its employees, launched earlier this year, attracted wide internal criticism and was paused in June. The company didn’t respond to a question from TechCrunch about its new pricing model.
This kind of user data is vital for making agentic tools work better. “The reason we saw a big jump in [coding agent] capabilities between April 2025 and October 2025 was that Claude Code, by default, would store all your coding agent sessions and use them for reinforcement learning training,” Mario Zechner, the developer behind the open source harness Pi, told TechCrunch last month.
But even as the imperative for model builders increasingly becomes deploying agentic tools for use outside of software engineering, their ability to evaluate and improve those tools is blocked by the complexity and lack of digital traces for many professional workflows.
Arvind Narayanan, a Princeton computer science professor, noted that there is good evidence that large companies don’t want their data to be used for model training.
“They stick with token-billed Enterprise plans even though the subscription-based consumer plans like Claude Max and ChatGPT Pro are discounted by 10x-20x or even more! (The main difference between the plans is data retention + enterprise IT governance),” he wrote on social media.
Perhaps in recognition of those dynamics, Meta is offering companies explicit compensation to obtain that information. Its pricing guide notes that the contributor tier “lowers the barrier to entry for prototyping, testing integrations, and scaling experiments where training on your data is acceptable.”
That, Narayanan suggested, could in turn incentivize large companies to be more diligent about which data is truly proprietary and which could be shared with model providers.
The framework could also play into growing price competition between the frontier labs. Anthropic’s newest Fable and Mythos models, released yesterday, came with lowered costs for processing cached tokens, while OpenAI’s latest models got major price cuts at the end of July.
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