This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. 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 using network-level controls 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, optional but turned on by default across all plans. The trouble is, many websites don’t fit neatly into one category. That leaves its maverick founder with broad, subjective control over what is allowed or banned. Read the full story.
—James O’Donnell This startup’s new mechanistic interpretability tool lets you debug LLMs The San Francisco–based startup Goodfire has released a new tool, Silico, that lets researchers peer inside an AI model and adjust its parameters during training. It could give users more control over how this technology is built than was once thought possible.
The goal is to make building AI models less like alchemy and more like a science. Using a technique called mechanistic interpretability, Silico maps the neurons and pathways inside a model and lets developers tweak them to reduce unwanted behaviors or steer outputs. By exposing the “knobs and dials,” Goodfire hopes to bring AI training closer to traditional software engineering. Read the full story. —Will Douglas Heaven With mass firing, Trump deals a fresh blow to American science This past week delivered another gut punch for science in the US. This time, the target was the National Science Foundation—a federal agency that funds major research projects to the tune of around $9 billion. On Friday, the 22 scientists overseeing those efforts were all fired. Since 2025, the NSF has faced budget cuts, grant terminations, and mass firings, with staff numbers down sharply and many ambitious projects grinding to a halt. The result is a major shift in how American science is funded and governed. Discover what it means, and what’s next. —Jessica Hamzelou This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here. China’s open-source bet: 10 Things That Matter in AI Right Now Silicon Valley AI companies follow a familiar playbook: keep the models behind an API and charge for access. China’s leading AI labs are playing a different game, releasing “open-weight” models that developers can download, adapt, and run on their own hardware.

That approach went mainstream after DeepSeek open-sourced its R1 model, which matched top US systems at a fraction of the cost. It also won something subtler: goodwill with developers. A growing cohort of Chinese labs is now following the same blueprint. As AI shifts from hype to deployment, open-source models are making the future of AI more multipolar than Silicon Valley expected. Read the full story. —Caiwei Chen China’s open-source bet is one of the 10 Things That Matter in AI Right Now, our list of the biggest ideas, trends, and advances in AI today. We’re unpacking one item from the list each day here in The Download, so stay tuned. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 Elon Musk has admitted that xAI trained Grok on OpenAI models“Distillation” is standard practice in AI, despite being legally dubious. (Wired $)+ The White House has accused Chinese firms using distillation of theft. (BBC)+ American labs are widely assumed to use similar techniques. (TechCrunch) 2 A “de-extinction” startup wants to resurrect a long-lost antelopeColossal Biosciences wants to bring back the bluebuck. (Axios)+ The company is using genomic editing to revive the animal. (Gizmodo)+ It previously claimed to have cloned red wolves. (MIT Technology Review)
3 ​​An OpenAI model outperformed ER doctors at diagnosing patientsBy analyzing health records data and information provided to physicians. (NPR)+ But it still must be proven in real-world clinical trials. (Vox) 4 Scientists are trying to power AI data centers with tiny nuclear reactorsThey could provide a new way to meet AI’s energy demands. (Gizmodo)+ We did the math on AI’s energy footprint. (MIT Technology Review)
5 Spotify has started verifying human artistsA new badge will distinguish them from AI. (The Guardian)+ Spotify has faced criticism for its handling of AI. (BBC) 6 The US is backing a Congolese railway to break China’s grip on critical mineralsThe old railroad is key to the race for critical metals in Africa. (Rest of World)+ The US is also searching for alternative sources. (MIT Technology Review) 7 Huawei is set to overtake Nvidia in China’s AI chip marketIt’s expected to capture the largest market share this year. (FT $) 8 Japan is building cardboard drones for the battlefieldThe flatpack designs are cheap, disposable, and built at scale. (404 Media) 9 The more young people use AI, the more they hate itResearch shows that Gen Z doesn’t trust GenAI. (The Verge) 10 A new organoid can menstruate—and show how tissue repairs itselfIt’s revealing how the uterus can shed without scarring. (Nature)
Quote of the day “I suspect that there are a number of people who do not want to put the future of humanity in Mr Musk’s hands. But we’re not going to get into that.” —Judge Gonzalez Rogers rebukes attempts by Elon Musk’s lawyer to focus on AI’s existential risks as part of his lawsuit against OpenAI, the New York Times reports.  One More Thing TMY350 VIA WIKIMEDIA COMMONS This rare earth metal shows us the future of our planet’s resources The materials we need to power our world are shifting from fossil fuels to energy sources that don’t produce greenhouse gas emissions.
Take neodymium, a rare earth metal used in powerful magnets that power everything from smartphones to wind turbines. Its story reveals many of the challenges we’ll likely face across the supply chain in the coming century and beyond. The question isn’t whether we’ll run out, but how we extract, process, use, and recycle these materials as technology keeps changing. Find out what it reveals about the future of our planet’s resources. —Casey Crownhart We can still have nice things A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line.)+ Here’s a fascinating visual history of exploring the dark side of the Moon.+ This interactive map lets you compare the actual dimensions of our world.+ These five tiny homes are proof you don’t need a massive footprint to live with style.+ Explore the history of “Control Room Green” and why it was the default choice for the Cold War’s highest-stakes environments.Top image credit: Stephanie Arnett/MIT Technology Review | Adobe Stock

Smack dab between Australia and South America, the US National Oceanic and Atmospheric Administration (NOAA) research vessel Rainier is currently on a mission to map more than 8,000 square nautical miles of the Pacific seafloor in search of critical mineral deposits. But it isn’t doing it alone; for a month starting this week, it will deploy two oblong neon submersibles as the project’s special agents, sending them nearly 6,000 meters down to hop along the seafloor.  The submersibles, built by the young company Orpheus Ocean, are designed to explore just this environment: a squelchy substrate that teems with life of all kinds, from tiny microbes to worms and snails, along with egg-size “nodules” of metals—such as copper, cobalt, nickel, and manganese—that are crucial for technologies worldwide.   Scientists and companies have long sought to probe the deep sea and bring such treasures to the surface. Orpheus, which spun off from the Woods Hole Oceanographic Institution (WHOI) in 2024, could be well positioned to make those possibilities a lot more economical. The company has designed its vehicles on a simple philosophy: “deep for cheap,” says Jake Russell, Orpheus’s cofounder and CEO, who is a chemist by training. The vehicles cost a couple of hundred thousand dollars each to build, whereas existing options can range from $5 million to $10 million. And unlike most autonomous ocean vehicles, they can push into the seafloor and capture cores of sediment—and the creatures within.  Orpheus’s engineers have been tinkering with their deep-sea designs for years, much of the work taking place at WHOI and in collaboration with NOAA and the National Aeronautics and Space Administration. Its prototype vehicles were rated capable of diving to 11,000 meters—the deepest part of the Mariana Trench. They’ve completed two commercial deployments, but this new expedition marks the submersibles’ biggest test yet: operating over large ranges for multiple weeks and with multiple instruments at play. Using Rainier as their home base on the ocean’s surface, the vehicles will swim out for 10 kilometers at a time, taking one high-resolution image every second and up to eight physical samples from the seafloor apiece.
If all goes well, the test could help establish the vehicles as a tool for government agencies, scientists, and companies that hope to probe the vastly understudied deep sea and the resources it holds. And while they’re not the only option on the market, Orpheus hopes their size and low building cost will soon make them one of the most accessible.  At present, to reach these depths scientists must wait for time on a limited and expensive set of submersibles owned by government agencies and research institutes. That formula lends itself better to capturing snapshots of the deep sea than it does to probing its interconnected ecological and biogeochemical systems. “A lot of this region that we’re surveying … has really never been explored in any kind of detail,” says Russell. “Anything we see is going to be new to NOAA and new to science.”
A sediment specialist The Orpheus subs are classified as autonomous underwater vehicles (AUVs), which operate on a mix of preprogrammed commands and live decision-making and without being tethered to a ship. But unlike traditional AUVs engineered for long-distance, high-speed gliding, these submersibles are short and stout with little legs—better for making soft landings on the seafloor and then pushing into the mud to suck out sediment cores for scientists. When they do land, the submersibles can lift off the surface, thrust a few feet, and settle once more in a “hopping” fashion. Their bodies are made mostly of a buoyant material known as syntactic foam, with the important electronics encased in a thick sphere of glass. The same kind of foam, which is interspersed with hollow microspheres of glass to prevent it from collapsing under high pressures, went to the deep in the vehicle that carried the filmmaker James Cameron to the Mariana Trench in 2012; he even donated leftover material for use in earlier Orpheus prototypes. 

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At less than two meters in length and under 600 pounds (270 kilograms), Russell says the Orpheus robots are the smallest—and correspondingly the least expensive—ocean vehicles on the market capable of descending to 6,000 meters. They’re designed to populate future fleets of robotic explorers. The approach stems from a fundamental challenge, says Victoria Orphan, a geobiologist at the California Institute of Technology, who has previously worked with an Orpheus vehicle on a science campaign: “Anytime you do things in the deep ocean, you always run this risk, when you put something over the side [of a ship], that it might not come back.” With existing fleets of large, expensive vessels operated by groups like NOAA, WHOI, and the Monterey Bay Aquarium Research Institute (MBARI), losing a vehicle can be disastrous, not least because scientists must already compete for their limited time. In the spring of 2024, Orphan and her colleagues put an Orpheus sub through its paces during an expedition to study deep-sea methane seeps off the coast of Alaska’s Aleutian Islands. They hoped to use the vehicle to create maps of the area before the team sent down a human-crewed submersible called Alvin to study specific areas—and the microorganisms and animals that live there—in more detail.  But as with any sort of new type of technology, “there’s always growing pains,” recalls Orphan. Frigid temperatures and steep topography added unseen challenges, and it took the full three weeks for the sub to get high-resolution photographs of the seeps.  The setback didn’t dull Orphan’s excitement about the potential of these machines. “There’s a lot of real, unknown science right at that interface between the sediment and the ocean surface,” she says. “The Orpheus-type class of instrument, with the right kinds of sensors and samplers, could be a very enabling tool.” Russell envisions pairing the vehicles with specially designed payloads that can sense the heat of chemical seeps and detect plumes of sediment, DNA shed from ocean life-forms, or the magnetic tug of buried cables. 

The vehicles are the “the best of both worlds,” says Andrew Sweetman, a deep-sea ecologist at the Scottish Association for Marine Science, who has not worked with Orpheus. While they can roam large areas like an AUV, they can also carry out precise sampling maneuvers like a remotely operated vehicle (ROV), a robot connected to a ship via cables that fulfills real-time human commands. In addition to the low price tag, says Sweetman, the small size of the vessels means they don’t require a large research vessel to ferry them out to sea. That might make exploration more accessible for smaller or poorer countries without such ships, he says: “It will, in a way, help democratize deep-sea science.” He imagines using the sediment cores the submersibles gather to probe how seafloor-dwelling animals cycle nutrients—a crucial element of the ocean’s role as a carbon sink.  The mining push  As much as smaller, cheaper ocean vehicles have caught scientists’ eye, they have also piqued the interest of companies. Russell says inquiries come in weekly from businesses involved in deep-sea mining, defense, offshore wind, telecommunication, and oil and gas. He notes that Orpheus is merely a “service provider,” helping collect data where needed but not making decisions about how to use the seafloor. And he says that better data—such as information on the shape of the seafloor, the sediment quality, and the presence of life—also “raises the bars” that governments and regulators are only beginning to set. But many scientists are far from eager about the growing push for seabed mining, which an executive order from President Donald Trump stoked further last week by mandating that the US government rapidly develop mineral exploration and processing. And earlier last month, the administration announced the creation of a new government office: the Marine Minerals Administration.  A view of an Orpheus vehicle from below.ORPHEUS OCEAN Given the current dearth of information on the deep sea, says Sweetman, “I think the push for deep-sea mining is happening way too fast.” And deep-sea communities are “probably the most stable environment on our planet,” adds Orphan. “The organisms that live there are really not adapted to a lot of disturbance, and it takes a really, really long time for them to recover, if at all.” One mining method that governments and companies propose involves a machine that essentially operates like a giant bulldozer, trawling the seafloor, sucking up a trail of material, and leaving scar marks and sediment plumes in its wake. Brett Hobson, an ocean engineer at MBARI, says that Orpheus-like technology might enable companies to “take samples in a more surgical way, instead of just grossly scooping everything up off the seafloor and filtering through it.” Hobson, who has run MBARI’s work on ocean vehicles for decades, also notes that Orpheus submersibles won’t be the only option available. Companies and government agencies—including those in Norway, France, Japan, China, and the UK—are developing similar deep-sea vehicles, he says: “What we really need [as] a society is just more of these systems out there.”  As Orpheus’s neon vehicles plunge into the Pacific over the next few weeks, their readiness for future scientific and resource surveys should become clearer. Each time they dive, they will get a little bit more data—“just the smallest of postage stamps of our planet,” says Orphan. “There’s still so much to learn.”

This past week delivered another gut punch for science in the US. This time, the target was the National Science Foundation—a federal agency that funds major research projects to the tune of around $9 billion. The foundation’s efforts were overseen by a board of 22 prominent scientists. On Friday last week, they were all fired. The NSF has been without a director since April 2025, when former director Sethuraman Panchanathan stepped down in the wake of DOGE-led funding cuts and mass firings. Trump’s nominee for the role is Jim O’Neill, an investor and longevity enthusiast who does not have a science background. It’s hard to predict exactly how things will shake out for science. But it’s not looking great. The NSF was established in 1950 to “promote the progress of science,” among other goals. It has served as a major source of support for research and education since then. In 2024, the agency spent $9.39 billion—a substantial figure but only 0.1% of all federal spending.
Key decisions about how that money is spent have been made by the National Science Board. Each of the scientists who made up the board until last week was appointed by a US president to serve, at least initially, a six-year term. Those members were responsible for establishing NSF policies, authorizing major expenditures and providing oversight, says Keivan Stassun, a physicist and astronomer at Vanderbilt University who was appointed to the board in late 2022. A few years ago, the board was responsible for establishing a new “directorate” within the agency to channel funding to “technology, innovations and partnerships,” for example. The board also authorized funding for the US Extremely Large Telescope Program.
“It’s a relatively small group with a tremendous amount of responsibility and authority,” says Stassun. He viewed his appointment as “a tremendous honor.” Then, last Friday, the email landed in his inbox. “It said: On behalf of President Trump, this letter is to notify you that your position as a member of the National Science Board is terminated effective immediately. Thank you for your service,” says Stassun. “It was deeply disappointing.” Still, Stassun wasn’t surprised, given the administration’s actions across federal science agencies over the past year. Since Donald Trump took office at the start of 2025, the NSF—along with many other federal agencies—has frozen, unfrozen, and terminated grants. “The board was not involved in any of those [terminations],” says Stassun. Members had no say in the firing of agency staff either, he says. Staff numbers are currently down 40%, he adds. In a 2026 budget request, the Trump administration sought to cut the NSF’s budget by around 57%. Last summer, NSF staffers wrote a letter of dissent arguing that such substantial cuts would “cripple American science.” The proposed cuts would have hit biological sciences, engineering, and STEM education particularly hard. Those cuts were rejected by Congress earlier this year. But grant terminations and firings are essentially allowing them to take effect regardless, says Stassun. “The funds that the White House has been dispersing to the agency … have been far less than what Congress intended,” he says. Many ambitious research projects are grinding to a halt as a result. “The Extremely Large Telescope Program appears to be dead in the water for now,” says Stassun. And the NSF arm dedicated to science education “has effectively zeroed out,” he says. But not all of them. While the administration’s 2027 budget request states that NSF will “close out” its directorate for social, behavioral, and economic sciences, it describes AI and quantum information science as key “frontier initiatives.” Biotechnology is described as a “focal point.” 

When asked for comment, the NSF directed MIT Technology Review to the White House press office. The White House did not respond directly to questions about the firing of NSB members and said in a statement, “The National Science Foundation’s work continues uninterrupted.” Jim O’Neill, Trump’s current candidate for the position of NSF director, is certainly interested in biotechnology. Specifically, when I spoke to O’Neill in February, he told me that he supposes he is a Vitalist—a hardcore supporter of efforts to extend human longevity who believes that death is wrong. O’Neill was deputy secretary of the Department of Health and Human Services and acting director of the Centers for Disease Control and Prevention until a leadership shakeup a couple of months ago. But he isn’t a scientist. And that has some scientists worried. He has yet to be confirmed by the Senate for the role. In the meantime, the administration’s efforts are having a real impact on research. “We [NSB members] tried to stand for a continued investment in science, engineering, and technology, and in science education broadly,” says Stassun. “The administration will now be able to operate the agency the way that [it wants to, with] no governance body in the way.”

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.”

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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.”