A new US phone network for Christians aims to block porn and gender-related content

A new US-wide cell phone network marketed to Christians is set to launch next week. It blocks porn, which experts in network security say marks the first time a US cell plan has used network-level blocking for such content that can’t be turned off even by adult account owners. It’s also rolling out a filter on sexual content aimed at blocking material related to gender and trans issues, which will be optional but turned on by default across all plans.

The network, which is currently being tested ahead of its May 5 launch date, will be run by Radiant Mobile, a newly launched mobile virtual network operator (MVNO). These operators don’t own cell towers but buy bandwidth from the big providers (in this case, T-Mobile) and sell to specific demographics (President Trump announced his own MVNO last year called Trump Mobile; CREDOMobile sends donations to progressive causes). 

“We are going to create—and we think we have every right to do so—an environment that is Jesus-centric, that is void of pornography, void of LGBT, void of trans,” Radiant Mobile’s founder, Paul Fisher, told MIT Technology Review. A representative for T-Mobile did not comment on whether these content blocks violate any of its policies. In a statement, the representative added that T-Mobile does not have a direct relationship with Radiant Mobile but instead works through the MVNO manager CompaxDigital. 

Fisher says he’s recruited a mix of Christian influencers to advertise the plan and has also done outreach to thousands of churches around the country, offering a way to have Radiant donate a portion of congregants’ $30-per-month subscription fee to their church. Fisher has ambitions to market it beyond the US in other countries with significant Christian populations, like South Korea and Mexico.

At least one piece of Radiant’s pitch will sound familiar: the idea that the internet is awash in toxic sludge. It’s powered by content and algorithms that are making us more sad, hateful, and detached. A number of efforts aim to fix that, including contentious age verification laws and a coming wave of lawsuits alleging that social media companies knowingly got young users hooked on their platforms. 

Fisher is pursuing the nuclear option. He says Radiant is working with the Israeli cybersecurity company Allot to block categories of content, such as material about violence or self-harm. Some categories are banned by default and cannot be allowed even for adult users. 

This includes pornography. Chris Klimis, a minister in Orlando who was recruited to be the company’s chief operating officer, says part of the reason he got involved was to offer Christians a real way to “do something” about what he sees as a pornography crisis in the faith. He was appalled by a recent survey showing that 67% of pastors have a “personal history” with porn use. And he worries his six children will come across porn on their devices, even if only inadvertently.

“We’ve got to figure out some way to close the door to the digital space,” he says. “That’s what we’re trying to do.”

The technology to do this blocking is a blunt instrument: Allot groups website domains into more than a hundred categories, which include pornography but also violence, malware, gaming, and in Radiant Mobile’s case “sects,” which includes websites about Satanism. If one of its users tries to visit a website that belongs to a blocked category, the page won’t load. That’s harsher than app-based content blockers like Covenant Eyes, a Christian porn-quitting app that sends notifications to your friends or family if you slip up; those can be worked around or deleted.

“Blocking in the network is certainly not new,” says David Choffnes, a computer science professor and executive director of Northeastern University’s Cybersecurity and Privacy Institute. Such blocking is the backbone of censorship efforts by authoritarian governments, for example. But there are more benign ways it’s used too. US telecoms block particular domains known to be spreading malware and offer optional network-level controls to block adult content on kids’ phones. What is new is a US cell plan instituting network-level blocks that can’t be removed, even by adults.

The trouble is that most websites don’t fit neatly into one category, leaving Fisher with enormous and subjective control over which are allowed or banned. This is most apparent in his effort to block content related to gender identity.

Anthony Re, a sales director at Allot, says the company does not have a category specific to gender but that “LGBT content” tends to fall into its sexuality category, which is described on Radiant Mobile’s website as “sites that provide information on sex, sex and teenagers, and sexual education, without pornographic content.” This category is blocked by default for all phones, a setting that can be changed by adult account owners. 

But if a news site starts hosting enough gender-related content, Fisher might not just label it as “press,” which is allowed, but also “sexuality,” thus blocking the whole domain to any phone with that category blocked. 

Fisher illustrates the subjectivity of such decisions with a recent example involving Yale University. Its general website, www.yale.edu, is categorized by Allot as education. “But they have a subsection of one of their websites that’s totally focused on, you know, trans equality,” Fisher says, referring to lgbtq.yale.edu. Because it’s a distinct domain, Radiant Mobile is able to place it in the sexuality category and block it. 

Yale’s main website remains unblocked, for now. “If we see [the LGBTQ content] on the front pages consistently of Yale University, we’ll block them too,” Fisher says.

Managing website block lists is a professional pivot for Fisher, who spent his career not in telecoms but in fashion; he was an agent for supermodels like Naomi Campbell and members of the Hilton and Getty families, and he later hosted a reality show in which he found people in rehab facilities and homeless shelters and tried to turn them into models. He ultimately left the industry and now says he regrets the role he played in it: “Am I proud that I spent 35 years creating star models or star influencers? Not at all.”

Last year, his friend and fellow fashion mogul Bernt Ullmann suggested he look at what Ryan Reynolds had built with his cell network Mint Mobile: It made buying a cell plan feel less like dealing with a utility and more like choosing a brand, and it had been acquired by T-Mobile in 2023 for $1.3 billion. Fisher liked the business model but didn’t have an audience in mind. Then came a late-night revelation. “God is talking to me,” Fisher recalls. “Do something in the faith-based industry.” He set out to build the first cell network that would let in only content deemed compatible with Christianity.

Fisher says the company has received $17.5 million in investment from Compax Ventures, part of the company serving as the technical middleman between Radiant and T-Mobile. Roger Bringmann, a vice president at Nvidia, is Radiant Mobile’s lead investor and silent partner (Bringmann recently funded a new complex at Austin Christian University in Texas, which bills itself as “the university for Christian entrepreneurs”).

To fill the gap left by all the sites being blocked, the company intends to offer access to a library of religious content, including AI-generated Bible videos. It plans to use characters like Cinderella, Tinker Bell, and others (it has obtained rights from the entertainment and media company Elf Labs, which has been amassing rights to hundreds of children’s characters). “Those characters were originally constructed with a conservative perspective,” Klimis says. They’ll be used in AI-generated content alongside testimonials and devotionals. 

Choffnes has technical doubts that the plan’s firewall will be as effective as promised, not least because “it’s really hard to come up with a list of every website you think is problematic.” But beyond that, he sees the internet, frustrating as it can be, as better open than closed. “I do believe in an open internet,” he says. “I also believe that a lot of the internet is toxic, but I don’t believe that this sledgehammer approach of blocking content is the right answer.”

Musk v. Altman week 1: Elon Musk says he was duped, warns AI could kill us all, and admits that xAI distills OpenAI’s models

In the first week of the landmark trial between Elon Musk and OpenAI, Musk took the stand in a crisp black suit and tie and argued that OpenAI CEO Sam Altman and president Greg Brockman had deceived him into bankrolling the company. Along the way, he warned that AI could destroy us all and sat through revelations that he had poached OpenAI employees for his own companies. He even confessed, to some audible gasps in the courtroom, that his own AI company, xAI, which makes the chatbot Grok, uses OpenAI’s models to train its own. 

The federal courthouse in Oakland, California, was packed with armies of lawyers carrying boxes of exhibits, journalists typing away at their laptops, and a handful of concerned OpenAI employees. Outside, protesters lined the streets, carrying signs urging people to quit ChatGPT, boycott Tesla, or both. Musk looked calm and comfortable, slipping in the occasional quip in his distinct South African accent. But he also was full of remorse. 

“I was a fool who provided them free funding to create a startup,” Musk told the jury. He said when he cofounded OpenAI in 2015 with Altman and Brockman, he was donating to a nonprofit developing AI for the benefit of humanity, not to make the executives rich. “I gave them $38 million of essentially free funding, which they then used to create what would become an $800 billion company,” he said.

Musk is asking the court to remove Altman and Brockman from their roles and to unwind the restructuring that allowed OpenAI to operate a for-profit subsidiary. The outcome of the trial could upend OpenAI’s race toward an IPO at a valuation approaching $1 trillion. Meanwhile, xAI is expected to go public as a part of Musk’s rocket company SpaceX as early as June, at a target valuation of $1.75 trillion.

This week’s testimony revolved around a central question of the trial: why Musk is suing OpenAI. Musk argued he was trying to save OpenAI’s mission to develop AI safely by restoring the company to its original nonprofit structure. OpenAI’s lawyer, William Savitt, who once represented Musk and his electric-car company Tesla, countered that Musk was “never committed to OpenAI being a nonprofit” and instead was suing to undermine his competitor. 

Who is the steward of AI safety?

During his direct examination early in the week, Musk painted himself as a longtime advocate of AI safety. He said he cofounded OpenAI to create a “counterbalance to Google,” which was leading the AI race at the time. He said that when he asked Google cofounder Larry Page what happens if AI tries to wipe out humanity, Page told him, “That will be fine as long as artificial intelligence survives.” 

“The worst-case scenario is a Terminator situation where AI kills us all,” Musk later told the jury.

Savitt stood at the lectern and argued that Musk was not a “paladin of safety and regulation.” As he cross-examined Musk in his sharp, surgical cadence, Savitt pointed out that xAI sued the state of Colorado in April over an AI law designed to prevent algorithmic discrimination. 

Musk’s lawyer, Steven Molo, sprang to his feet to object. He asked the judge if he, too, could weigh in on ChatGPT’s safety record. 

The lawyers then entered a heated debate about who was the true guardian of AI safety. 

The sparring continued the next morning. “We all could die as a result of artificial intelligence!” said Molo, suggesting that OpenAI could not be trusted to build AI safely.

“Despite these risks, your client is creating a company that’s in the exact space,” Judge Yvonne Gonzalez Rogers said sternly, referring to xAI. “I suspect there’s plenty of people who don’t want to put the future of humanity in Mr. Musk’s hands.”

When the lawyers began talking over each other, the judge snapped. “This is not a trial on whether or not artificial intelligence has damaged humanity,” she said. 

When did Musk think he was being duped?

As Savitt continued to cross-examine Musk, he pressed on the idea that Musk had never been committed to keeping OpenAI a nonprofit. He also claimed that Musk waited too long to sue OpenAI, filing after the statute of limitations ran out. 

Musk explained why he sued in 2024 rather than earlier, describing “three phases” in his views of OpenAI. In phase one, he was “enthusiastically supportive” of the company.” In phase two, “I started to lose confidence that they were telling me the truth,” he said. In phase three, “I’m sure they’re looting the nonprofit.” 

In 2017, Musk and other OpenAI cofounders discussed creating a for-profit subsidiary to raise enough capital to build artificial general intelligence—powerful AI that can compete with humans on most cognitive tasks. Musk wanted a majority interest in the subsidiary and the right to choose a majority of the board members. He also pitched having Tesla acquire OpenAI. (He left OpenAI in 2018.)

“I was not opposed to there being a small for-profit that provides funding to the nonprofit,” he told the jury, “as long as the tail didn’t wag the dog.” 

But it was only in late 2022, Musk testified, that he “lost trust in Altman” and his commitment to keeping the company a nonprofit. The key moment came, he said, when he learned that Microsoft would invest $10 billion in OpenAI. 

“I texted Sam Altman, ‘What the hell is going on? This is a bait and switch,’” he told the jury. Microsoft would give $10 billion only if it expected “a very big financial return,” he said.

Is Musk just trying to kill competition?

But Savitt argued that Musk was really suing to undermine OpenAI as a competitor to his empire of tech companies. While he was on the board of OpenAI, Musk was also running Tesla and his brain-implant company, Neuralink. He founded xAI in 2023.

Savitt pulled up an email that Musk had sent to a Tesla vice president in 2017 after hiring Andrej Karpathy, a founding member of OpenAI, to work at Tesla.“The OpenAI guys are gonna want to kill me. But it had to be done,” he wrote.

When asked about it, Musk was flustered. He claimed Karpathy had already decided to leave OpenAI when he recruited him to work at Tesla. “I believe it’s a free world,” he said.

Savitt pulled up another email that Musk sent to a cofounder at Neuralink in 2017. He wrote that they could “hire independently or directly from OpenAI.” When pressed about it, he sounded frazzled. “It’s a free country,” he said. “I can’t restrict their ability to hire people from other companies.” 

Savitt also pointed out that Tesla, SpaceX, Neuralink, and X were socially beneficial for-profit companies, like OpenAI. He stressed that xAI was also a closed-source, for-profit company.

But Musk claimed that xAI was not a real competitor to OpenAI. “We’re not currently tracking to reach AGI first,” he told the jury. 

In fact, Musk admitted that xAI uses OpenAI’s technology. In response to Savitt’s relentless questioning, he said xAI “partly” distills OpenAI’s models. Some people in the courtroom gasped. 

Distillation is a technique where a smaller AI model is trained to mimic the behavior of larger, more capable models, so it can run faster and more cheaply while performing nearly as well. But OpenAI and other AI companies have pushed back against the practice. In February, OpenAI accused the Chinese AI company DeepSeek of distilling its AI models. In August 2025, Wired reported that Anthropic had blocked OpenAI’s access to Claude for violating the company’s terms of service, which prohibit, among other things, reverse-engineering its services and building competing products. 

“It is standard practice to use other AIs to validate your AI,” argued Musk.

Next week, Stuart Russell, a computer scientist at UC Berkeley, will testify about AI safety. Brockman, who has been taking notes during Musk’s testimony, will also testify.

This story is part of MIT Technology Review’s ongoing coverage of the Musk v. Altman trial. Follow @techreview or @michelletomkim on X for up-to-the-minute reporting.

This startup’s new mechanistic interpretability tool lets you debug LLMs

The San Francisco–based startup Goodfire just released a new tool, called Silico, that lets researchers and engineers peer inside an AI model and adjust its parameters—the settings that determine a model’s behavior—during training. This could give model makers more fine-grained control over how this technology is built than was once thought possible.

Goodfire claims Silico is the first off-the-shelf tool of its kind that can help developers debug all stages of the development process, from building a data set to training a model.

The company says its mission is to make building AI models less like alchemy and more like a science. Sure, LLMs like ChatGPT and Gemini can do amazing things. But nobody knows exactly how or why they work, and that can make it hard to fix their flaws or block unwanted behaviors. 

“We saw this widening gap between how well models were understood and just how widely they were being deployed,” Goodfire’s CEO, Eric Ho, tells MIT Technology Review in an exclusive chat ahead of Silico’s release. “I think the dominant feeling in every single major frontier lab today is that you just need more scale, more compute, more data, and then you get AGI [artificial general intelligence] and nothing else matters. And we’re saying no, there’s a better way.”

Goodfire is one of a small handful of companies, including industry leaders Anthropic, OpenAI, and Google DeepMind, pioneering a technique known as mechanistic interpretability, which aims to understand what goes on inside an AI model when it carries out a task by mapping its neurons and the pathways between them. (MIT Technology Review picked mechanistic interpretability as one of its 10 Breakthrough Technologies of 2026.)  

Goodfire wants to use this approach not only to audit models—that is, studying those that have already been trained—but to help design them in the first place.  

“We want to remove the trial and error and turn training models into precision engineering,” says Ho. “And that means exposing the knobs and dials so that you can actually use them during the training process.”

Goodfire has already used its techniques and tools to tweak the behaviors of LLMs—for example, reducing the number of hallucinations they produce. With Silico, the company is now packaging up many of those in-house techniques and shipping them as a product.

The tool uses agents to automate much of the complex work. “Agents are now strong enough to do a lot of the interpretability work that we were doing using humans,” says Ho. “That was kind of the gap that needed to be bridged before this was actually a viable platform that customers could use themselves.”

Leonard Bereska, a researcher at the University of Amsterdam who has worked on mechanistic interpretability, thinks Silico looks like a useful tool. But he pushes back on Goodfire’s loftier aspirations. “In reality, they are adding precision to the alchemy,” he says. “Calling it engineering makes it sound more principled than it is.”

Mapping models

Silico lets you zoom in on specific parts of a trained model, such as individual neurons or groups of neurons, and run experiments to see what those neurons do. (Assuming you have access to the model’s inner workings. Most people won’t be able to use Silico to poke around inside ChatGPT or Gemini, but you can use it to look at the parameters inside many open-source models.) You can then check what inputs make different neurons fire, and trace pathways upstream and downstream of a neuron to see how other neurons affect it and how it affects other neurons in turn.

For example, Goodfire found one neuron inside the open-source model Qwen 3 that was associated with the so-called trolley problem. Activating this neuron changed the model’s responses, making it frame its outputs as explicit moral dilemmas. “When this neuron’s active, all sorts of weird things happen,” says Ho.

Pinpointing the source of odd behavior like this is now pretty standard practice. But Goodfire wants to make it easier to adjust that behavior. Using Silico, developers can now adjust the parameters connected to individual neurons to boost or suppress certain behaviors.

In another example, Goodfire researchers asked a model whether a company should disclose that its AI behaves deceptively in 0.3% of cases, affecting 200 million users. The model said no, citing the negative business impact of such a disclosure.

By looking inside the model, the researchers found that boosting neurons that were found to be associated with transparency and disclosure flipped the answer from no to yes nine out of 10 times. “The model already had the ethical reasoning circuitry, but it was being outweighed by the commercial risk assessment,” says Ho.

Tweaking the values of a model in this way is just one approach. Silico can also help steer the training process by filtering out certain training data to avoid setting unwanted values for certain parameters in the first place.   

For example, many models will tell you that 9.11 is greater than 9.9. Looking inside a model to see what’s going on might reveal that it is being influenced by neurons associated with the Bible, in which verse 9.9 comes before 9.11, or by code repositories where consecutive updates are numbered 9.9, 9.10, 9.11 and so on. Using this information, the model can be retrained to make it avoid its “Bible” neurons when doing math.

By releasing Silico, Goodfire wants to put techniques previously available to a few top labs into the hands of smaller firms and research teams that want to build their own model or adapt an open-source one. The tool will be available for a fee determined on a case-by-case basis according to customers’ requirements (Goodfire declined to give specific pricing details).

“If we can make training models a lot more like building software, there’s no reason why there can’t be many more companies designing models that fit their needs,” says Ho.

Bereska agrees that tools like Silico could help firms build more trustworthy models. These techniques could be essential for safety-critical applications in health care and finance, he says.

“Frontier labs already have internal interpretability teams,” he adds. “Silico arms the next tier of companies, where the value is not having to hire interpretability researchers.”

Elon Musk and Sam Altman are going to court over OpenAI’s future

After a yearslong legal feud, Elon Musk and OpenAI CEO Sam Altman are heading to trial this week in Northern California in a case that could have sweeping consequences. Ahead of OpenAI’s highly anticipated IPO, the court could rule on whether the company is allowed to exist as a for-profit enterprise and might even oust its current executive leadership, including Altman.

Musk is suing OpenAI, alleging that Altman and OpenAI president Greg Brockman deceived him into bankrolling the company in its early days by promising to maintain it as a nonprofit dedicated to developing AI that benefits humanity, only to later restructure the company to operate a for-profit subsidiary. Musk cofounded OpenAI with Altman and others in 2015, but he left in 2018 after a bitter power struggle. 

Musk is seeking as much as $134 billion in damages from OpenAI and Microsoft, one of OpenAI’s biggest financial backers. He is also asking the court to remove Altman and Brockman from their roles and to restore OpenAI as a nonprofit. Musk has asked the court to award any damages to OpenAI’s nonprofit rather than to him personally. 

Nine jurors will deliver an advisory verdict, a non-binding recommendation, to guide the judge in deciding Musk’s claims against Altman. Musk, Altman, and Brockman will take the stand. Former OpenAI chief scientist Ilya Sutskever, former OpenAI CTO Mira Murati, and Microsoft CEO Satya Nadella are also expected to testify. Cringey texts, raw diary entries, and endless scheming behind the founding and growth of OpenAI are expected to come to light.

In an industry enveloped in secrecy, the trial will be a rare opportunity for the public to look behind the curtain and find out what’s going on in the companies creating the most transformative technology ever built. 

What are they fighting about?

When OpenAI was originally founded as a nonprofit, backed by a $38 million donation from Musk, the company vowed to create open-source technology for the public’s benefit, unconstrained by a need to generate financial returns. But over the years, the company began to claim that intensifying competition could make it dangerous to share how it develops its AI models and that a nonprofit structure could not raise enough money to keep building AI. (MIT Technology Review was first to report on OpenAI’s internal conflicts around its mission.)

The court has already found that in 2017 Altman and Brockman wanted to establish a for-profit arm, while Musk proposed merging OpenAI with his electric-car company, Tesla. When Musk threatened to stop funding, Altman and Brockman told him that they were committed to keeping the company a nonprofit. Musk alleges that they pursued plans to pivot to a for-profit without informing him. According to OpenAI, Musk agreed that the company needed a for-profit entity and even wanted to be its CEO. 

But even if Musk proves he was duped by Altman and Brockman, he may not have standing in the first place to sue them for restructuring the company to operate a for-profit subsidiary. Some legal scholars are puzzled over why the judge allowed him to bring this claim. “The idea that Elon Musk can sue because he was a donor or used to be on the board is pretty puzzling,” says Jill Horwitz, a law professor who studies nonprofit law at Northwestern University. “Typically, it’s up to the attorneys general to bring such a claim to enforce the charitable purposes. And that’s already happened.” 

In October 2025, state attorneys general of California, where OpenAI is headquartered, and Delaware, where OpenAI is incorporated, struck a deal with OpenAI to approve its new corporate structure on a series of conditions. For example, a safety and security committee at the nonprofit would review safety-related decisions made by the for-profit subsidiary. Critics of the restructuring, including Musk, AI safety advocates, and civil society groups, have tried to stop it. 

California’s attorney general has declined to join Musk’s lawsuit, saying that the office did not see how his action serves the public interest.

Still, whether the deal holds OpenAI to its nonprofit mission is an open question. “Elon Musk should have to show … what the deficiencies are in what’s been agreed to by OpenAI with the attorneys general,” says Rose Chan Loui, the director of the UCLA School of Law’s philanthropy and nonprofit program. Even with the terms in place, holding OpenAI to them depends on “how much they can enforce it and how much transparency they get into OpenAI’s work.”

More importantly, legal experts say the case is being considered under the wrong body of law. Musk argues that Altman and Brockman breached OpenAI’s charitable trust by creating a closed-source, for-profit subsidiary. As a result, the court has been analyzing the claim under the law of trusts. “But OpenAI is not a trust. OpenAI is a corporation. And so really they should be looking at … the law of charitable nonprofit organizations,” says Chan Loui.

What’s on the line?

Despite all the legal muddiness, the outcome of the trial could upend the AI race. Any one of the remedies that Musk seeks could cripple OpenAI as it races to go public by the end of the year. OpenAI, which is valued at over $850 billion, has described the litigation with Musk as a potential risk to its business. Musk’s rival company xAI, which makes the chatbot Grok, is expected to go public as a part of his rocket company SpaceX as early as June. If Musk prevails, xAI, which in combination with SpaceX is valued at $1.25 trillion, could get a big advantage in the AI race. 

And the trial has helped expose the bitter schism between Musk and the company he once helped to found. An OpenAI spokesperson referred MIT Technology Review to a post on X: “This lawsuit has always been a baseless and jealous bid to derail a competitor.” Although Musk’s lawyers did not immediately respond to a request for comment, he has posted on X that “Scam Altman lies as easily as he breathes.”  

MIT Technology Review will have ongoing coverage of Musk v. Altman until its conclusion. Follow @techreview or @michelletomkim on X for up-to-the-minute reporting.