May 9 2026
Musk v. Altman week 2: OpenAI fires back, and Shivon Zilis reveals that Musk tried to poach Sam Altman

In the second week of the landmark trial between Elon Musk and OpenAI, Musk’s motivations for bringing the suit were under scrutiny.

Last week, Musk took the stand, alleging that OpenAI CEO Sam Altman and president Greg Brockman had deceived him into donating $38 million to the company. He claimed that they’d promised to maintain it as a nonprofit dedicated to developing AI for the benefit of humanity, only to later accept billions of dollars of investment from Microsoft and restructure the company to operate a for-profit subsidiary.  

This week, Brockman fired back with his side of the story, arguing that Musk had actually pushed for OpenAI to create a for-profit arm and fought a bitter battle to have “absolute control” over it. OpenAI has argued that Musk is suing because he didn’t get his way and is now trying to undermine a competitor to his own AI company, xAI.

Shivon Zilis, a former OpenAI board member and the mother of four of Musk’s children, also testified, revealing that Musk tried to recruit OpenAI CEO Sam Altman to lead a new AI lab at his electric-car company, Tesla. 

Musk cofounded OpenAI in 2015 with Altman, Brockman, and others but left in 2018. Now, he’s asking the court to remove Altman and Brockman from their roles and to unwind the restructuring OpenAI undertook last year, which converted its for-profit subsidiary into a public benefit corporation. He is also seeking as much as $134 billion in damages from OpenAI and Microsoft, OpenAI’s investor. 

The outcome of the trial could upend OpenAI’s race toward an IPO at a valuation approaching $1 trillion. Meanwhile, xAI, which Musk founded in 2023, is now a division of his rocket company, SpaceX; the combined companies are also expected to go public as early as June, at a target valuation of $1.75 trillion.

On Monday, Brockman walked into the courtroom in a blue suit and tie, holding hands with his wife, Anna Brockman. On the stand, he was serene, even chipper, as he recalled OpenAI’s early days. But he grew agitated under impassioned questioning from Elon Musk’s lawyer, Steven Molo. Altman listened in silence, while Anna Brockman sat behind him, fidgeting. Outside the courthouse, protesters rallying against the AI race sang hymns over the voices of lawyers giving press conferences.

Two days before trial began, according to Brockman, Musk messaged him to ask if he would be interested in settling. When Brockman suggested that both sides drop their claims, Musk texted back: “By the end of this week, you and Sam will be the most hated men in America. If you insist, so it will be.”

Musk stormed out with a Tesla painting

Last week, Musk testified that he’s suing to save OpenAI’s nonprofit mission to develop AI safely, but he said he was open to seeing OpenAI become a capped-profit company with moderate investments from Microsoft. 

This week, Brockman told the jury that Musk was never truly committed to keeping OpenAI a nonprofit. In the summer of 2017, when an AI model that OpenAI built beat the world’s best players in a video game called Dota 2, Musk hosted a gathering at his “Haunted Mansion” near San Francisco. The house was splattered with confetti and cups, Brockman recalled, and the actress Amber Heard, who was Musk’s girlfriend at the time, served whiskey.

“Time to make the next step for OpenAI. This is the triggering event,” Musk wrote in an email—having said weeks earlier that if OpenAI made a major public achievement, it would be “time to create a for-profit,” Brockman told the jury.

Over the next six weeks, Brockman said, Musk and the other cofounders had intense discussions about creating a for-profit entity to raise enough capital to build artificial general intelligence—powerful AI that can compete with humans on most cognitive tasks. Musk wanted to have majority equity in the entity and the right to choose a majority of the board members. He also wanted to be its CEO, said Brockman. 

Brockman testified that in August 2017, he and other cofounders gathered to hash out the terms of the for-profit structure. Ilya Sutskever, OpenAI’s chief scientist at the time, arrived bearing a painting of a Tesla as a “token of goodwill” in return for the actual Teslas Musk had given them days earlier. “It felt a little bit like [Musk] was buttering us up, right,that he wanted us to feel indebted to him,” Brockman told the jury.

When Brockman and Sutskever proposed that they all have equal shares of equity, said Brockman, Musk fell silent and finally said, “I decline.” Musk then stood up and “stormed around the table,” he said. “I actually thought he was going to hit me.” Musk grabbed the painting and walked out. 

Brockman said that afterwards he struggled to decide whether to continue building OpenAI with Musk or break away. “There was a fork in the road,” he said. “Do we accept Elon’s terms? Or do we reject the terms, he quits to create his own, and then we create our own?”

“The one thing we could not accept was to hand him unilateral, absolute control, potentially, over the AGI,” Brockman told the jury.

What was Brockman thinking?

In his theatrical baritone, Molo argued that Brockman was motivated by greed rather than a commitment to OpenAI’s nonprofit mission to develop AI that benefits humanity. He noted that while Brockman never invested money in the company, he now owns a stake worth close to $30 billion. 

“Solving for the mission has always been my primary motivation,” Brockman said, pushing back on Molo’s characterization of him. “It remains so today.” 

Molo pulled up Brockman’s electronic journal on a screen in the courtroom, trying to show the jury what Brockman was really thinking behind the scenes. In 2017, while negotiating with Musk about the future of OpenAI, Brockman wrote about wanting to become a billionaire: “Financially what will take me to $1B?” 

“Why didn’t you take the $29 billion and donate it to the nonprofit that you had a fiduciary duty to, for the good of humanity?” Molo asked Brockman, raising his voice to dramatize moral indignation. 

Molo then pulled up a journal entry Brockman had written in November 2017, while he was torn over whether to turn OpenAI into a for-profit without Musk: “it’d be wrong to steal the nonprofit from him. to convert to a b-corp without him. that’d be pretty morally bankrupt.” Brockman and Musk had previously considered creating a b-corp, which is a for-profit company that pursues a social mission.

Brockman explained, “I meant it would actually serve the mission, but it’d be hard to look at yourself in the mirror.”

Molo also tried to undermine Brockman’s credibility by revealing that he holds a stake in multiple companies with business ties to OpenAI, including the AI company Cerebras, the cloud provider CoreWeave, and the nuclear fusion startup Helion Energy. Altman has tried to steer OpenAI into deals with companies that he invests in, including Helion and the rocket maker Stoke Space, drawing scrutiny over potential conflicts of interest.

Former OpenAI chief technology officer Mira Murati and former OpenAI board member Helen Toner both appeared in video depositions. They addressed the brief firing of Altman in 2023, saying that they could not trust him because of his alleged history of lying. Murati’s text messages with Altman from that time, which were introduced as evidence, revealed his desperate attempts to understand what was happening and regain control. 

Musk plotted a rival AI lab at Tesla

After Brockman’s two days of testimony, Shivon Zilis, who left OpenAI’s board in 2023, took the stand in a black jacket and black jeans, appearing composed but with a flicker of nerves. OpenAI’s lawyer Sarah Eddy asked her in a deceptively soothing voice whether she acted as a conduit for Musk as he tried to poach OpenAI’s cofounders to work at a new AI lab within Tesla. Eddy argued that Musk is suing OpenAI only to undermine a competitor in the AI race. 

Zilis said she met Musk while working at OpenAI as an informal advisor in 2016, and that they had a “one-off” romantic encounter. In 2017, she joined Tesla and Musk’s brain-implant company, Neuralink. In 2020, she joined OpenAI’s board of directors. She became pregnant with Musk’s children through IVF but did not disclose her ties with Musk to OpenAI until Business Insider reported them in 2022. 

By late 2017, Musk had concluded that OpenAI was unlikely to build AGI and pivoted to building an AI lab at Tesla, according to an email sent to Zilis. 

Eddy pulled up a draft of an FAQ document that Zilis emailed a colleague at Tesla in 2017 about an event the company was organizing at the NeurIPS AI conference: “The purpose of this event is to share that Tesla is building a world leading AI lab(?) which will rival the likes of Google/DeepMind and Facebook AI Research.” 

Zilis told the jury that when Musk was still on OpenAI’s board, he tried to recruit Altman to lead that prospective AI lab. Musk had asked Andrej Karpathy, an OpenAI research scientist he’d recruited to work at Tesla, “to send a list of top OpenAI people to poach,” according to a text message by Zilis. 

“There is little chance of OpenAI being a serious force if I focus on TeslaAI,” Musk texted Zilis in 2018, just before he left OpenAI. Tesla’s AI lab never came to fruition.

Eddy pressed Zilis about whom she was loyal to when she was working for OpenAI and Musk at the same time. “I had an allegiance to the best outcome for AI for humanity,” Zilis told the jury.

What’s going on next week?

Next week, Ilya Sutskever will testify, as will Microsoft CEO Satya Nadella. The lawyers for both Musk and OpenAI will deliver their closing arguments. The jury will begin deliberating the week after and deliver an advisory verdict guiding the judge to decide the case.

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.

Ecommerce MGMT 0 Comments
May 8 2026
What’s next for IVF

<div data-chronoton-summary="

  • Helping embryos stick: Even healthy-looking embryos only implant 40–60% of the time. Researchers in Spain are trialing a device that physically injects embryos directly into the uterine lining at the press of a button.

  • AI and robots are taking over the lab: Automated systems can now select sperm, fertilize eggs, and culture embryos without human hands. At least 19 children have already been born through fully automated IVF.

  • Genetic testing is getting complicated: Standard embryo screening helps reduce miscarriage, but newer tests claiming to predict IQ or height are gaining ground in the US—and making many fertility doctors deeply uncomfortable.

  • Gene editing is quietly creeping back: Years after He Jiankui went to prison for editing human embryos, startups are revisiting CRISPR as a way to prevent serious inherited disease—raising hopes, and familiar fears about a slippery slope.

” data-chronoton-post-id=”1136946″ data-chronoton-expand-collapse=”1″ data-chronoton-analytics-enabled=”1″>

MIT Technology Review’s What’s Next series looks across industries, trends, and technologies to give you a first look at the future. You can read the rest of them here.

Forty-eight years ago this July, Louise Joy Brown became the world’s first person born with the help of in vitro fertilization. Millions more IVF babies have entered the world since then. And that’s partly thanks to advances in technology that have made IVF safer and more effective.

But it’s still not perfect. The process can be slow, painful, and expensive—and that’s for the lucky people who are able to access it in the first place. And by at least one measure, IVF success rates have been declining in recent years.

Reproduction is complex, and there’s a lot that embryologists and gynecologists still don’t know and can’t control. They don’t know why many healthy-looking embryos don’t “stick” in the uterus, for example. They don’t always have an explanation for why their patients can’t get pregnant. And they can’t always account for vast differences in IVF success rates between individuals and between fertility clinics.

Scientists are working on all those questions and more. They’re wrestling with complex ethical questions about how new genetic tools will be used to analyze or even alter embryos. Meanwhile, technologies designed to standardize treatment, eliminate human error, boost success rates, and make IVF more accessible are already beginning to usher in a new era for assisted reproduction—one aided by AI and robots.

1. Helping embryos stick

Some of those technologies are being developed at the Carlos Simon Foundation in Valencia, Spain. When I visited in March, researchers gave me a tour of the labs and showed me a device that had been used to keep a human uterus alive outside the body for the first time.

While some members of the team dream of building artificial uteruses that might one day be able to carry a fetus to term, they first want to use such devices to learn more about implantation—the moment at which a fertilized egg makes contact with the lining of the uterus, burrows inside, and essentially “hatches,” triggering the start of a pregnancy.

Despite decades of advances in IVF, that process is still poorly understood. Even healthy-looking embryos stick no more than 40% to 60% of the time.

In IVF techniques used today, clinics can create early-stage embryos and wait until the uterus is deemed most receptive, but once they insert the embryo into the uterus, it’s on its own. Xavier Santamaria, senior clinical scientist at the Carlos Simon Foundation, and his colleagues are trialing a different approach. They’ve developed a device that, at the press of a button, injects the embryo into the uterine lining.

Scientists in Valencia showcase Transfer Direct.

JESS HAMZELOU / MITTR

In a demonstration I watched with a prototype, Santamaria picked up his speculum and turned to face the vaginal opening of his “patient,” which in this case was just a model of the real thing—a plastic bottom with labia, a vagina, a uterus, and ovaries, two short stumps representing what would normally be a pair of legs held in stirrups.

He hunched over and peered inside. “Embryo,” he called. His colleague Maria Pardo, an embryologist, passed him a thin needle containing a mouse embryo she had recently collected from a petri dish.

Santamaria’s device allows for the embryo-containing needle to be connected to a delivery tube. This tube also has a camera, a light, and a sensor that lets the doctor know when the needle reaches the uterine lining. Once it has been fed into the uterus, the gynecologist can see the inside of the organ and direct the tube to the lining.

Scientists in Valencia showcase Transfer Direct.

JESS HAMZELOU / MITTR

“When everything is ready, you just press the button,” Santamaria said as he activated it using a foot pedal, allowing the embryo to be injected. “There it goes.”

The team has just started a trial of the device; so far, fewer than 10 women have undergone the procedure, and none of those have become pregnant. But foundation director Carlos Simon is hopeful, noting that the inventors of IVF had to perform over 160 cycles before Louise Brown was born (between 1969 and 1978, that team performed 457 cycles in 250 people, resulting in only two live births). “The trial is ongoing,” he says.

2. Picking the “best” eggs, sperm, and embryos

One long-running challenge of IVF has been selection. Say you manage to collect 10 eggs from one partner and a decent-looking semen sample from the other. How do you choose which cells to use? The same question comes up once the resulting embryos have been cultured in a dish for a few days: Which should you transfer to the uterus?

Traditionally, these judgments have been made by eye. Embryologists literally pick the ones that look the best in terms of their shape or, in the case of sperm, how they move. But scientists have been working on alternatives. And over the last decade or so, many have turned to genetic testing to hint at which embryos have the best chances of creating a healthy baby.

The most commonly used test is called PGT-A, which stands for preimplantation genetic testing for aneuploidy. Aneuploidy essentially means having an “incorrect” number of chromosomes, and it is thought that embryos with such characteristics are more likely to be lost through miscarriage or potentially develop into babies with genetic conditions.

Once embryologists have created embryos in the lab, they can pinch off a few cells and test them for aneuploidies. The tests are especially beneficial for women over the age of 38, says Alan Penzias, a reproductive endocrinologist at Boston IVF. “You start to see an improvement: more babies and fewer miscarriages,” he says. The tests can shorten the time to pregnancy.

This type of genetic testing is possible thanks to multiple advances in technology—not just in genomics, but also in the ability to keep embryos alive in a dish for five to six days and the technique of freezing embryos while the cells undergo testing and thawing them once the results are in. And it has become hugely popular—some clinics do PGT-A tests on all their embryos.

But PGT-A won’t give you a perfect readout of a future baby’s genetics, says Sonia Gayete-Lafuente, a reproductive endocrinologist at the Center for Human Reproduction in New York City. And some of the abnormalities might be able to self-correct with time. Gayete-Lafuente and her colleagues have transferred some of those “abnormal” embryos into patients’ uteruses and seen them develop into perfectly healthy children, she says.

Other forms of PGT are even more controversial. PGT-P tests are designed to predict an embryo’s chances of developing complex traits that rely on multiple genes, including medical disorders but also physical characteristics like height or cognitive factors like IQ. These tests are new, and they are illegal in some countries, including the UK. But they are gaining ground in the US. Nucleus Genomics—a company that invites customers to “have [their] best baby”—promises to predict traits running the gamut from eye color and intelligence to left-handedness and risk of Alzheimer’s.

When I asked IVF practitioners how they might respond if a patient asked for this service, most dodged the question and told me there’s not enough evidence that any of these tests actually work. They also cautioned that selecting for one trait might inadvertently introduce new risks. None seemed especially keen on the idea of using genetic testing for anything other than preventing serious disease.

3. Speeding things up with AI

Some seemed more excited about the potential for AI. After all, AI tools are generally good at recognizing patterns. Many researchers have attempted to train tools to spot healthy sperm, eggs, and embryos.

And they’ve had some success. A team at Columbia University Medical Center in New York has developed a device that uses AI to examine semen samples from men who have only tiny numbers of healthy sperm. An embryologist might struggle to find a single healthy sperm in such a sample. But the Sperm Tracking and Recovery (STAR) system can analyze over a million microscope images in an hour. It has already been used to create healthy embryos. The team behind the work announced the first pregnancy resulting from the treatment in November last year.

Other teams are using AI tools to advance IVF in more dramatic ways. Around a decade ago, a reproductive endocrinologist named Alejandro Chavez-Badiola began developing an AI tool trained to rank embryos, another to rank eggs, and another to select sperm. He recalls being struck by a realization that these tools were “the brains that have the potential to drive robots in the future,” he says.

4. Using robots to standardize IVF

In the early 2020s, Chavez-Badiola and his colleagues decided to combine technologies and develop an automated system for IVF. In theory, a robotic system loaded up with AI tools could undertake most of the steps required in the IVF process: selecting the eggs and sperm, fertilizing eggs to create embryos, culturing those embryos in a dish, and selecting the “best” one for transfer. Such a system could “do everything in a standard way” without ever getting tired, he says.

Chavez-Badiola, who is now founder and chief medical officer at Conceivable, started building prototypes by motorizing regular IVF equipment and connecting it to computers. He and his colleagues started testing their system with animal cells before eventually moving on to human ones. “We were able to prove that integrating robots to automate different steps in IVF is doable,” he says.

The device is now being used to prepare sperm and eggs and create embryos. At least 19 children have been born following the automated IVF. It is early days, but Chavez-Badiola is hoping that future iterations of the machine could each process thousands of IVF cycles in a year, potentially making the procedure more affordable and accessible.

Many in the field are excited about the potential for automated devices like Conceivable’s. “This is all time saved for the embryologists,” says Laura Rienzi, a clinical embryologist and scientific director of the IVIRMA network of fertility centers in Italy. She also hopes it will help standardize IVF treatments. “Automation [will allow for] every patient to be treated in the same way in every single lab in the world,” she says.

5. Controversial edits are on the table

There’s a catch, however: All these technologies rely on the availability of at least some healthy sperm, eggs, and embryos at the outset. Embryologists and IVF patients have to work with what they’ve got. And sometimes, what they’ve got won’t result in a healthy baby. 

That’s why some scientists are proposing a controversial idea: using gene-editing technologies like CRISPR to tinker with the genome of an IVF embryo before it is implanted. The biophysicist He Jiankui infamously took this approach to create embryos that resulted in the births of three children in the late 2010s. He was widely condemned by the scientific community and ultimately spent three years in a Chinese prison. 

His former romantic partner Cathy Tie, who now leads startup Origin Genomics, is pursuing the technology as a potential way to prevent serious disease in children. At a recent event held at the Hastings Center for Bioethics, Tie made the case for using embryo editing to prevent diseases like cystic fibrosis, Huntington’s, and sickle-cell.

It won’t be straightforward from a technical, legal, or ethical perspective. Diseases that are known to be caused by single-gene mutations are good first candidates, but as the Center for Human Reproduction’s Gayete-Lafuente points out, most diseases are much more complicated than that. “I wish we could understand the genetic basis of every disease to be able to prevent it,” she says. So far, we can’t. Besides, most diseases can be influenced by our diets, behaviors, and environments as well as our genes.

As things stand, no one knows if editing a human embryo to eliminate the risk of one disease might increase a future child’s risk of some other disorder. And some scientists worry that such edits might be a slippery slope to genetic enhancement or eugenics.

Rienzi hopes that the technology might be developed in a safe way with regulatory oversight, and only for a specific list of diseases. “It has to be within a legal context,” she says. “But to me, it’s a dream.”

In the meantime, the field looks set to keep transforming with the development of new technologies that are already creating healthy babies. Watch this space. 

Ecommerce MGMT 0 Comments
May 8 2026
The balcony solar boom is coming to the US

Dozens of US states are considering legislation to allow people to install plug-in solar systems, often called balcony solar. These small arrays require little to no setup and could help cut emissions and power bills.

Balcony solar is already popular in Europe, and proponents say that the systems could make solar power more accessible for more people in the US, including renters. As popularity rises, though, some experts caution that there are safety concerns with how balcony solar would work with existing electrical equipment in homes.

Let’s talk about what balcony solar is, why it’s unique, and how new testing requirements could affect our progress toward deploying the technology in the US.

Plug-in solar systems are designed to be simple to install, often requiring no electrician or specialized worker at all. They’re small, and many can be plugged into existing outlets.

People across Germany have installed over a million balcony solar systems. They generally measure up to roughly two square meters or about 20 square feet, and can generate up to 800 watts—enough to power a standard microwave.

Now the plug-in solar wave is coming to the US. Many Americans have already installed DIY balcony solar without the permission of their utilities—it’s something of a regulatory gray area. In late 2025, Utah became the first state to explicitly allow people to install and use balcony solar systems. Over two dozen other states are now considering similar legislation.

Generally, utilities require users to sign an interconnection agreement before they can plug in large arrays of solar panels that generate power for the grid. There can be fees and permits, and it all amounts to an expensive and lengthy process.

Utah’s law ditched the interconnection requirement for panels that have a low power cap and that are certified by a national testing facility. (Legislation under consideration in other states, including New York, includes the same requirements.) The thinking is that since the panels produce very little power, which would be used to meet a home’s own energy demand and probably not get sent back to the grid, the same requirements shouldn’t apply. 

As for that certification piece, in January the national testing and certification lab UL Solutions released UL 3700, a testing protocol to certify balcony solar systems and ensure that they’re safe. 

There are three main safety considerations to address for these plug-in solar systems, says Joseph Bablo, manager of principal engineering, energy, and industrial automation at UL Solutions. First, there’s the possibility of overloading a circuit. Generally, electrical circuits have circuit breakers, which can trip and interrupt current if necessary. But if there’s a solar panel adding extra power to a circuit, a traditional breaker might not be able to respond to overload. Over time, overloaded circuits can damage equipment or even start a fire. 

Second, these small systems are typically installed on the outside of homes, and outdoor power outlets generally have ground fault circuit interruption (GFCI). Basically, if an outlet or its surroundings are wet, it can shut down to prevent electric shock. Many GFCI systems may not work if there’s power going back into an outlet from a solar panel.

Finally, there’s touch safety: If a plug gets disconnected from the wall, the blades of the plug may still have power running through them for a short time. If a panel is getting sunlight, those blades could be energized for longer than is typical.

The new UL Solutions testing framework aims to address these concerns. One of the key recommendations is that plug-in solar panels should use a special outlet that’s designed specifically for them. The safety measures included in that connection, and within a panel, would ensure that the panels are safe.

The need for a special outlet means that currently, people who want to plug in a solar panel array would probably need to have an electrician come and update their wiring in order to comply with the protocol, Bablo says. “I know they want to say ‘No electrician, no permits’—we’re not there.”

Today, anyone can buy products like solar panels and inverters, some of which carry their own component UL certifications, and string them together. (Inverters are covered under UL 1741, for example.)

But the gold standard is to have an entire system that meets the safety requirements, and that means adhering to the new standard, Bablo says. As of early May, there aren’t any plug-in solar systems that have been fully certified by UL Solutions. And Bablo said he couldn’t share information about what, if any, are in the pipeline.  

Even with the new certification requirements, Bablo still thinks plug-in solar still has the potential to help more people access the technology. “There’s a way for it to work, but we want it to work safely,” he says.

This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here. 

Ecommerce MGMT 0 Comments
May 6 2026
A blueprint for using AI to strengthen democracy

Every few centuries, changes in how information moves reshape how societies govern themselves. The printing press spread vernacular literacy, helping give rise to the Reformation and, eventually, representative government. The telegraph made it possible to administer vast nations like the US, accelerating the growth of the modern bureaucratic state. Broadcast media created shared national audiences, which in turn fueled mass democracy.

We are now in the early stages of another such shift. Faster than many realize, AI is becoming the primary interface through which we form beliefs and participate in democratic self-governance. If left unchecked, this shift could further strain America’s already fragile institutions. But it could also help address long-standing problems, like lagging civic engagement and deepening polarization. What happens next depends on design choices that are already being made, whether we know it or not.

Start with what might be called the epistemic layer—how we come to know things. People are increasingly relying on AI to know what is true, what is happening, and whom to trust. Search is already substantially AI-mediated. The next generation of AI assistants will synthesize information, frame it, and present it with authority. For a growing number of people, asking an AI will become the default way to form views on a candidate, a policy, or a public figure. Whoever controls what these models say therefore has increasing influence over what people believe. 

Technology has always shaped the way citizens interact with information. But a new problem will soon arise in the form of personal AI agents, which can change not only how people receive information but how they act on it. These systems will conduct research, draft communications, highlight causes, and lobby on a user’s behalf. They will inform decisions such as how to vote on a ballot measure, which organizations are worth supporting, or how to respond to a government notice. They will, in a meaningful sense, begin to mediate the relationship between individuals and the institutions that govern them.

We’ve already seen with social media what happens when algorithms optimize for engagement over understanding. Platforms do not need to have an explicit political agenda to produce polarization and radicalization. An agent that knows your preferences and your anxieties—one shaped to keep you engaged—poses the same risks. And in this case the risks may be even more difficult to detect, because an agent presents itself as your advocate. It speaks for you, acts on your behalf, and may earn trust precisely through that intimacy.

Now zoom out to the collective. AI agents and humans could soon participate in the same forums, where it may be impossible to tell them apart. Even if every individual AI agent were well-designed and aligned with its user’s interests, the interactions of millions of agents could produce outcomes that no individual wanted or chose. For example, research shows that agents displaying no individual bias can still generate collective biases at scale. And setting aside what agents do to each other, there is what they do for their users. A public sphere in which everyone has a personalized agent attuned to their existing views is not, in aggregate, a public sphere at all. It is a collection of private worlds, each internally coherent but collectively inhospitable to the kind of shared deliberation that democracy requires.

Taken together, these three transformations—in how we know, how we act, and how we engage in collective governance—amount to a fundamental change in the texture of citizenship. In the near future, people will form their political views through AI filters, exercise their civic agency through AI agents, and participate in institutions and public discussions that are themselves shaped by the interactions of millions of such agents.

Today’s democracy is not ready for this. Our institutions were designed for a world in which power was exercised visibly, information traveled slowly enough to be contested, and reality felt more shared, if imperfectly. All of this was already fraying long before generative AI arrived. And yet this need not be a story of decline. Avoiding that outcome requires us to design for something better.

On the informational layer, AI companies must ramp up existing efforts to ensure that models’ outputs are truthful. They should also explore some promising early findings that AI models can help reduce polarization. A recent field evaluation of AI-generated fact checks on X found that people with a variety of political viewpoints deemed AI-written notes more helpful than human-written ones. The paper is yet to be peer-reviewed, but that is a potentially revolutionary finding: AI-assisted fact-checking may be able to achieve the kind of cross-partisan credibility that has eluded most manual human efforts. Greater understanding of and transparency about how models make these assertions and prioritize sources in the process could help build further public trust.

On the agentic layer, we need ways to evaluate whether AI agents faithfully represent their users. An agent must never have an agenda of its own or misrepresent its user’s views—a technically daunting requirement in domains where users may have not explicitly stated any preferences. But faithful representation also cannot become an accessory to motivated reasoning. An agent that refuses to present uncomfortable information, that shields its user from ever questioning prior beliefs or fails to adjust to a change of heart, is not acting in the person’s best interest.

Finally, on the institutional level, policymakers should hurry to harness AI’s potential to make governance more responsive and legitimate. Several states and localities are already using AI-mediated platforms to conduct democratic deliberation at scale, building on research showing that AI mediators can help citizens find common ground. As agents become increasingly common participants in public input processes—and there is already evidence that bots are skewing those processes—identity verification for both humans and their agentic proxies must be built in from the start.

What is needed is a new generation of democratic infrastructure, technological and institutional, built for the world that is actually here. Failing to design for democratic outcomes, in a domain this consequential, means designing for something else. And the history of unaccountable power does not leave much room for optimism about what that something else tends to be.

Andrew Sorota and Josh Hendler lead work on AI and democracy at the Office of Eric Schmidt.

Ecommerce MGMT 0 Comments
May 5 2026
Week one of the Musk v. Altman trial: What it was like in the room

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.

Two of the most powerful people in AI—Sam Altman and Elon Musk—began their face-off in court in Oakland, California, last week. Musk is suing OpenAI, alleging that the millions he spent to fund it around a decade ago were meant for a nonprofit, not a corporation, and that the company has reneged on that mission since. 

The stakes are high—even a partial win for Musk could set OpenAI back as it reportedly plans to go public this year. But most of the attention comes from the spectacle of a feud on X now playing out in federal court. “Cringey texts, raw diary entries, and endless scheming behind the founding and growth of OpenAI are expected to come to light,” my colleague Michelle Kim wrote before it began. And the trial unfolds as the cultural backlash against AI swells; some of the signs held by protesters outside the courthouse suggest that to a significant number of people, whatever the outcome of Musk v. Altman, we all lose.  

Most of us have had to observe the trial from afar, but Michelle, who also happens to be a lawyer, has been in court each day. I caught up with her to learn what’s unfolded thus far and what might come next.

Can you give us the overview of what this case is actually about? What exactly is being decided, and who is favored right now?

Elon Musk is arguing that Sam Altman and OpenAI president Greg Brockman have breached the company’s charitable trust by effectively converting OpenAI into a for-profit company. Musk alleges that is not what they promised him in the company’s early days. He has asked for several remedies, like a crazy amount of damages and removing Sam Altman. But the main remedy he wants is unwinding OpenAI’s restructuring. [In October 2025 OpenAI struck deals with the attorneys general of California and Delaware that would essentially allow its nonprofit portion to have less day-to-day control of OpenAI. It’s a compromise from what OpenAI originally proposed, but Musk still wants to stop it.] 

OpenAI argues that Elon Musk actually agreed to have the company operate a for-profit arm, because he knew building AI is very expensive. So it’s about proving what Musk knew, what he didn’t know, and whether he really was deceived by Altman and Brockman.

There’s a big debate about when exactly Musk found out about this alleged misconduct. Musk founded OpenAI with Altman and Brockman in 2015, and he brought the suit in 2024. There’s a statute of limitations for charitable trust claims; you need to have brought a claim within three to four years after you find out about the alleged misconduct. So Musk tries to paint a picture that back in the day he was a little suspicious, but that it was really only in 2022 that he realized OpenAI was no longer committed to its original charitable mission, and that he had been scammed. It’s only the first week of trial, but I’m not sure Musk has proved this to the judge and jury.

What were some standout moments thus far?

At one point one of Elon Musk’s lawyers said, “We could all die as a result of AI.” I think a lot of the people in the room were really shaken by this comment, and the judge told Musk’s lawyer: You talk about all these safety risks that OpenAI has when building AI, but Musk is also creating a company that’s in the same exact space. She basically said, I’m sure there’s plenty of people who also don’t want to put the future of humanity in Elon Musk’s hands. 

And then the lawyers just kept going on and on about the catastrophic risks of AI and whether Elon Musk or OpenAI was in the better position to steward AI safety. And the judge sort of snapped. She said very sternly that this trial was not about whether or not artificial intelligence has damaged humanity. And I thought that was a really striking standout moment of the trial that pointed at how even though it is technically just about whether Elon Musk was really deceived by OpenAI, it’s also become a huge discussion about AI safety and some of the practices that the labs are engaging in when building AI. 

Can you give us a look behind the curtain at how getting into this trial works?

There are tons of reporters. This is a very high-profile suit, so I have to wake up around 4:30 a.m. and show up to the Oakland courthouse at 6 a.m. sharp to get in line. And on some days, even 6 a.m. doesn’t get you into the courtroom. There are lots of photographers in front of the courthouse, especially on days when you know Musk or Altman and Brockman are present. And there’s also some concerned citizens who want to watch the trial. I usually have to wait, like, two hours in line to get in to be one of the 30 people who claim the unreserved seats in the courtroom. 

What has it felt like to see Elon Musk testify? How would you describe his demeanor?

He shows up in a crisp black suit. He can be this inflammatory person on X, but in the courtroom, he is calm, cool, collected, and looks very comfortable. He has been in a lot of lawsuits. He knows how to talk to the jury and how to present himself in front of them and the judge. He’s also cracking jokes with his lawyer and even the opposing party’s lawyer and the judge. 

And he can be witty. There was this one moment when OpenAI’s lawyer was asking Musk a question and sort of fed him an answer. And Musk said “That’s not a leading question, that’s a leading answer.” The judge intervened and said, “You’re not a lawyer, Elon.” And then he was like, “Well, I did take Law 101.”

That said, he does get flustered and uncomfortable when OpenAI’s lawyer asks tough, piercing questions. Which he’s been doing.

What are the biggest things we’ve learned that weren’t clear in the earlier phases of this case?

On the fourth day of the trial, Musk admitted during cross-examination that xAI distills OpenAI’s models to train its own models, which was shocking. Musk followed up by saying that this is standard practice among all the labs now and that xAI wasn’t doing anything beyond what others were already doing. But a lot of the journalists started typing away at their laptops as soon as Musk made this comment. 

I also learned that there’s just so much scheming among Big Tech executives. You know about it vaguely, but to hear firsthand accounts and read their emails and text messages is fascinating. 

For example, there was a text message between Musk and Mark Zuckerberg of Meta, where they’re kind of teaming up to stop OpenAI’s restructuring. They’re even trying to make a bid to buy all the assets of OpenAI’s nonprofit. The level of scheming that goes on among these executives is mind-blowing.

What happens next?

OpenAI’s president, Greg Brockman, who was meticulously taking notes during some of Elon Musk’s testimony, is expected to testify next week. And Stuart Russell, a computer scientist at UC Berkeley, will testify about AI safety. I’m expecting that to open the floodgates to this crazy discussion about who can be trusted to build AI. 

A bunch of other high-profile people are expected to testify, like former OpenAI chief scientist Ilya Sutskever, former CTO Mira Murati, and Microsoft CEO Satya Nadella. 

The trial is supposed to last around three weeks. The nine jurors will deliver an advisory verdict that guides the judge on how to decide Musk’s claims against OpenAI. The judge doesn’t have to listen to the jury and can decide however she wants. If she decides OpenAI is liable, then she’ll decide what sort of remedies are appropriate. 

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.

Ecommerce MGMT 0 Comments
May 2 2026
Trump’s mass firing just dealt another 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. 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.”

Ecommerce MGMT 0 Comments
May 2 2026
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.”

Ecommerce MGMT 0 Comments
May 2 2026
Inexpensive seafloor-hopping submersibles could stoke deep-sea science—and mining

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. 

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. 

the Orpheus from below with flare from its two lower lights
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.”

Ecommerce MGMT 0 Comments
May 2 2026
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.

Ecommerce MGMT 0 Comments
May 1 2026
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.”

Ecommerce MGMT 0 Comments