from the it-only-gets-worse dept

A couple weeks ago I wrote 6,000 words about the Reckless Ben/Bricks & Minifigs LEGO mess and concluded that pretty much everyone involved had made serious mistakes — with the Utah contingent (Bricks & Minifigs corporate, Joshua Johnson, Brandon Best, and the American Fork police) looking the worst of all. That take upset basically everyone: some felt I was too hard on Reckless Ben, some felt I was too easy on the American Fork police, and probably a few people just resented spending that much time reading about legos. Since then, a lot more has come out, and the situation has only gotten murkier. My original read still holds up, but the Utah folks look even worse, and some of the other players are looking sketchier too.

And, I think it’s fair to say, mistakes were made by pretty much everyone involved.

Just as before, many of the new details are in long YouTube videos, but if you want watch just one, start with this one by Stephen Findeisen, who is better known as Coffeezilla and who regularly researches financial and cryptocurrency scams:

That video goes deep — Findeisen gets basically everyone on the phone at some point or another (except the cops), accesses a ton of evidence not previously public, and, unlike most of the earlier YouTube coverage, actually tries to find the truth instead of just stoking outrage.

He makes a few points that are hard to argue with:

• The Lego collection was never actually worth $200k (we had suggested this in our initial post as well). It was probably closer to $100k (and possibly a bit less).
• Some of it was definitely sold before all this, but much of it had not been.
• Plenty of it clearly remained in the store after Brandon Best showed up the night in November 2024 to kick out Law and take over the store.
• Best also showed up with a U-Haul truck, and there are some (slightly conflicting) reports that he subsequently appeared at his other Oregon Bricks & Minifigs store with a bunch of Star Wars Lego sets. Bricks & Minifigs corporate initially insisted this was false and said he showed up in a rental car. But Coffeezilla has visual proof of a U-Haul parked outside that night, which is pretty damning, which led Bricks & Minifigs to revise their story with a complicated one about hauling a camper trailer, which doesn’t make that much sense.
• Coffeezilla dropped this thread, in part because of a disagreement over the timeline, though it’s not clear to me that the timeline doesn’t really line up. It seems entirely possible that Best could have taken a bunch of legos out of the Gormans’ old store and taken them to his other store and then returned the U-Haul truck.
• Law & Gorman appear to have sold some of Mansell’s collection and not paid him for that, and it could be a lot of money. Law admits that she may have been a bit sloppy on the record keeping, saying she hadn’t done an inventory in a while and suggesting employees maybe hadn’t told her when certain sets from the collection were sold. But there’s also a credibility problem regarding sets that were listed as being on layaway, but where the spreadsheet suggests they were actually sold, but not accounted for as sold.
• To her credit, she admits it’s possible she owes Mansell some money and that if she can see evidence of this she will make sure that Mansell is paid what he is owed. But given how quick the Gormans were to insist this was entirely Bricks & Minifigs corporate who were the problem, it’s not a good look.
• Bricks & Minifigs corporate claims that there were about $5k worth of Star Wars legos left. That appears to be bullshit and wouldn’t really help their case, because even if it was just $5k of Mansell’s legos, those are still stolen legos.
• Bricks & Minifigs’ CEO and COO (the McNeff brothers) claim that they never were sent a spreadsheet of the collections, and the best moment in the video is when Coffeezilla points out that the Google Docs spreadsheet he’s been using is owned by their account and has been sitting there since 2024. That really makes the McNeffs look sketchy.

That video also includes dueling photographic and videographic evidence of what was in the store the night Best kicked the Gormans out (as well as a few weeks earlier when Best apparently surreptitiously filmed inside the store to see what was there). There are way more empty shelves the night Best kicked out Law & Gorman, but they say that’s because they had moved the high value consignment items to the safes they had purchased for that purpose, which were in the back. Later in the video Coffeezilla shows the McNeffs additional images from Law that appear to show Star Wars lego sets in what appears to be a safe, and which Matt McNeff (the company’s COO) admits they don’t appear to have listed in their own spreadsheet, which they had originally said was a complete listing of all the Star Wars legos in the store the night they took it over.

The McNeffs still look terrible, and Brandon Best also looks a bit sketchy. But it also appears that Law & Gorman’s record keeping was pretty sketchy as well, and while the McNeffs have gone overboard in claiming that they were responsible for Mansell’s “missing” legos, it does appear likely that Law owes Mansell for a decent number of Star Wars legos her store sold.

As for the American Fork Police department and Brandon Best’s partner, Joshua Johnson, we need a different video, this one from Legal Eagle. It breaks down just how many things they did wrong:

There were a lot of assumptions made about the police department, particularly around how they redacted the footage they released to Schneider. There was plenty of smoke, but no actual fire. As it turns out, beyond possibly being corrupt, the American Fork Police Department might also just be incompetent: they accidentally uploaded all the unredacted bodycam footage, which is now available on the Internet Archive.

Schneider initially claimed a hacker obtained the videos, which raised some questions about provenance. Once the department itself admitted the release was accidental, that question went away — and what’s in the footage is pretty hard to explain away. The police were way too credulous with Johnson. The “refusing to accept service” situation alone is maddening: Johnson claims the lawsuits are fake, the officer calls the court and confirms they’re real, and then… still lets Johnson refuse service. Beyond that, there are the extended traffic stops on no real probable cause, and the arrests on a search warrant instead of an arrest warrant — and they didn’t even find what they were looking for. Legal Eagle walks through all of it, and it’s a long list of failures.

Schneider is a more complicated case. He’s clearly one of the good guys here, and the attention he generated did move the needle when nothing else was. But some of his own claims haven’t held up. He never independently verified the value of the collection — and in the Coffeezilla video, he appears genuinely surprised it’s nowhere near $200k, which is a bad look for someone who made that figure central to his coverage. The small claims court situation is worse: Schneider said Johnson and Best had defaulted on those cases, but they were basically all dismissed for being filed against the wrong defendants, or never properly served. In a followup video, Reckless Ben admits he thought he’d won by default simply because he and his friends filed for default. Which goes back to the original point: talk to a lawyer, even just for an hour.

The Mexico situation is its own category of self-inflicted damage. In multiple videos he’s mentioned that after facing criminal charges he had fled to Mexico and joked about how Utah law enforcement can’t reach him there. Whether or not he actually left the country, publicly bragging about being a flight risk while facing criminal charges is exactly the kind of thing that hands prosecutors an easy argument. He has real defenses available to him. This doesn’t help.

And then there’s Law & Gorman, who aren’t villains, but they aren’t blameless either. It appears Law owes Mansell for a fair number of sets her store sold without paying him out — and the record-keeping problems aren’t fully explained by sloppy bookkeeping. The layaway-versus-sold discrepancy in the spreadsheet is a credibility problem, not just an accounting one. To her credit, Law has said she’ll make it right if shown the evidence. But the Gormans were also quick to frame this entire situation as purely a Bricks & Minifigs corporate problem, and that framing looks increasingly incomplete.

Every side of this story is a disaster. We’ve got a corporation willing to say anything to save face, a police department that accidentally leaked its own bad behavior, franchise owners who likely shortchanged their client, and a YouTuber whose good intentions were undercut by bad execution. About the only thing missing is anyone who actually handled this well.

Filed Under: american fork pd, ammon mcneff, ben schneider, benjamin gorman, bryan mansell, chrystal law, consignment, legos, matt mcneff, reckless ben, utah

Companies: bricks & minifigs

Yen-Ling Kuo always wanted to understand how things worked. When she was growing up in Taiwan, reading the story of Michael Faraday in elementary school piqued her curiosity about the natural world. During that time, she was introduced to Logo, a computer program with a turtle cursor to help children learn basic coding through hands-on experimentation.

It was Kuo’s introduction to programming logic.

In high school she learned the capacity computers held. She could write programs that completed tasks independently, she realized.

“Once I discovered how powerful computers could be,” she says, “I knew I wanted to focus on using them to solve real-world problems.”

Kuo, an IEEE member, never lost her interest in the “how” behind processes and tools. Her curiosity, combined with a stint working at a Silicon Valley company, led her to focus on innovations that live at the intersection of cognitive and computer sciences.

Kuo, now an assistant professor of computer science at the University of Virginia in Charlottesville, last year received the IEEE Robotics and Automation Society’s inaugural Outstanding Women in Robotics and Automation Early Career Contribution Award. The award is part of the IEEE-RAS Women in Engineering’s Outstanding Women in Robotics and Automation (WiRA) Paper Awards, which promote excellence and recognize the impact that female researchers have on robotics and automation fields at different stages in their academic careers.

Kuo’s winning paper, “Diff-DAgger: Uncertainty Estimation with Diffusion Policy for Robotic Manipulation,” demonstrates a novel method to help robots better identify and estimate uncertainty when faced with scenarios on which they’ve not been trained. The method reduces the amount of human supervision, improves a robot’s rate of successful task completion, and opens up a path to introduce more complex models with bigger data demands into interactive robot learning.

She says her research will help people working in the robotics and automation fields more efficiently collect the data needed for effective model training.

Silicon Valley’s impact

Kuo earned bachelor’s and master’s degrees in computer science at the National Taiwan University, in Taipei, in 2009 and 2012. As she was nearing completion of her master’s degree, she did what many computer science graduates do: She pursued a summer internship at a tech company.

She spent the summer of 2011 at Google’s campus in Kirkland, Wash., working on the company’s comparison ads project.

When her internship ended, she joined the MIT Media Lab as a visiting student, working on the Open Mind Common Sense project with Henry Lieberman.

As she was considering pursuing a Ph.D., a call from Google changed her plans. The company offered her a full-time role as a software engineer.

“I viewed the job offer as a positive development,” she says. “I believe it can never hurt your future research career to get some real-world experience under your belt.”

She was hired in 2012 and helped build techniques that incorporate computer vision and natural language processing to improve the customer shopping search experience. She led the company’s Shop the Look initiative, a predecessor to Google’s current AI-powered shopping experience. The project connected social media content with search results, something the company had struggled to do in the past.

Kuo and her team were tasked with building a connection between the natural language people use to describe an item and an image that matches the searcher’s intent. It was at a time when the neural network—using deep learning models to power Google products—was gaining momentum at the company. Integrating neural network tools into her work was a requirement—which raised questions for Kuo.

“I was applying the neural network tools,” she says. “But I didn’t have 100 percent certainty about how they actually worked.”

She considered how she could become more knowledgeable about deep learning models. It was a full-circle moment. She decided that after nearly four years at Google, it was time to earn a Ph.D. in computer science. She returned to MIT in 2016.

The question that changed everything

Boris Katz, one of Kuo’s Ph.D. advisors, is a principal research scientist and the head of the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL)’s InfoLab. He also led the creation of the START Natural Language System, the world’s first Web-based question-answering system.

When the two met, Katz asked Kuo why she wanted to pursue a doctorate degree. She explained her interest in understanding how neural networks work and in using that knowledge to connect the physical world with human language.

He suggested she attend a summer course at MIT’s Center for Brains, Minds, and Machines, a research initiative that ran from 2013 through 2025. CBMM’s objective was to bring together computer scientists, cognitive scientists, and neuroscientists to understand how human intelligence works. The goal was to use the resulting insights to establish an engineering practice to build artificial intelligence systems.

For Kuo, it was a chance to better understand human intelligence and identify ways it could be replicated in machines.

“It was an opportunity for me to interact with other scientists and gain insight into how people learn, understand, and figure things out in the world,” she says. “I saw it as a very useful and inspiring way to incorporate those ideas into my own research work.”

During her Ph.D. studies, she was a research assistant at CSAIL. The experience helped shape her doctoral research, which focused on building AI systems that apply past learning to new situations. She developed machine learning models to support the efforts, including language understanding and social interactions.

She completed her Ph.D. in computer science in 2022 with a minor in cognitive science.

After graduation, she continued her work and collaboration at CSAIL, particularly on projects that involved the “theory of mind” concept.

Theory of mind isn’t new, having originated with primatologists studying chimpanzees in the late 1970s. The theory recognizes that others have their own thoughts, beliefs, and perspectives. It’s a skill that allows humans to infer someone’s mental state and predict their behavior without verbal communication.

“It’s like when college roommates are moving into their dorm. They may not talk too much, but they work together naturally to coordinate their activities and accomplish goals,” Kuo says. “They can infer and mentally interpret each other’s behaviors and signals to make decisions and complete tasks without words.”

She brought her theory of mind research to the University of Virginia when she joined as an assistant professor in 2023.

Kuo conducts her research in UVA Engineering’s multidisciplinary cyberphysical Link Lab. Her broad focus is on developing computational models that help robots interpret both direct data and silent signals, from language and movements to a person’s gaze. If successful, it could give robots the same sort of physical and theory of mind reasoning capabilities that power physical and social interactions among humans.

“There are no computational frameworks yet available that will translate this kind of understanding into a robot efficiently,” she says.

She adds that the process to get there begins with improving how robots learn to perform tasks.

The evolution of robot learning

Historically, one way robots learned was to mimic humans. A researcher would manually guide a robot through a task, like cutting an apple, and it would repeat the movements. The robot was successful until the environment changed, such as when its hand was in a different position or the apple was at a different angle. The robot was then faced with a situation for which it hadn’t been trained. Without any data available to help it correct course, the robot would start making small errors that eventually led to a full system crash.

This diagram describes how the robotic gripper’s visual perception and tactile sensing prevents a potato chip from breaking.Xuhui Kang, Yen-Ling Kuo, et al.

To solve the problem, researchers developed the dataset aggregation (DAgger) method. As a robot performed a task, a researcher was on standby to provide real-time corrections during unexpected scenarios. The correction data was continuously added to the robot’s model, teaching it how to recover from mistakes.

To reduce the human monitoring effort, robot-gated DAgger was created to enable bots to query humans when the machines became uncertain.

The most popular approach to make the query decision is to train multiple models to consider when determining a course of action. If the models all agree, the robot proceeds. If they don’t agree, the robot is likely to get stuck and ask for help.

Although the multiple model approach was widely adopted, it has limitations. Practically speaking, as models become more complex, it is hard or impossible to train multiple copies. A more fundamental issue is that disagreement among models doesn’t always imply uncertainty; it could just mean there are different ways to accomplish a task.

The Diff-DAgger solution

That is the gap Kuo’s research team closed with the novel Diff-DAgger research. The approach builds on diffusion policy, a technique that helps robots account for different ways a task can be performed.

The new method repurposes diffusion loss, the signal a robot uses to improve its model during training, as a real-time confidence check. During task execution, the robot computes the signal and compares it against values from its training data using a statistical test. The signal spikes when the robot faces an unfamiliar situation and is uncertain how to proceed. The signal stays silent when the robot’s current action is close to what it learned before.

The spike represents the robot’s ability to self-diagnose and predict an imminent failure. Human intervention is triggered only when the signal spikes. No spike means the robot can be left to complete its decision-making process on its own.

Kuo’s team achieved significant results: Failure prediction rates were improved by 39 percent. Task completion rates were increased by 20 percent, and tasks were completed nearly eight times faster.

Her research at UVA gained attention from the National Science Foundation, which honored her last year with a Career Award, the foundation’s flagship grant for early-career researchers. The five-year US $665,000 grant supports her research that builds computational models for human-robot interactions through theory of mind reasoning.

She also received the Toyota Research Institute’s Young Faculty Researcher Award to teach cars to reason about interactions on the road and with the driver.

As service robots and self-driving vehicles become more available, such works are likely to make interactions between humans and robots more intuitive and useful.

Kuo ultimately wants to build more robust robots that are able to integrate into a social space with humans by engaging with us through grounded interactions, she says.

The impact of IEEE

Like many IEEE members, Kuo was introduced to the organization as a student. In 2018 she submitted her first paper, “Deep Sequential Models for Sampling-Based Planning,” to the IEEE/Robotics Society of Japan International Conference on Intelligent Robots and Systems while pursuing her Ph.D. at MIT. Her IEEE involvement grew alongside her professional career.

“It was a natural segue to transition from student to a full IEEE member,” she says. Today she is an active volunteer with the IEEE Robotics and Automation Society, a reviewer for submitted papers, and a presenter and panelist at conferences.

She says one of the best parts of attending conferences is having the opportunity to engage with students. She also enjoys participating as a panelist at luncheons, she says, because it gives her one-on-one time with student attendees. She can share her knowledge and offer insights as they prepare to embark on their career.

Her goal in the coming years, she says, is to broaden her involvement with IEEE initiatives and branch out to other technical committees. Sharing knowledge and learning from others is essential to anyone’s career growth, she says, and “IEEE offers a great opportunity for both.”

systems

We’re moving as fast as we can, says SK Group chair

Amid the unrelenting demand for AI infrastructure, SK Hynix, the world’s largest supplier of HBM memory used in high-end GPUs, now expects to triple its wafer capacity. You’ll just have to wait through two more US presidential elections and then some.

All that capacity won’t come online until 2034, SK Group Chairman Chey Tae-won told Nikkei Asia in a recent interview.

SK Hynix’s valuation has soared in recent months. The company is one of three major producers of NAND flash and DRAM memory, large quantities of which are required to support the burgeoning AI inference market. Samsung and Micron are the other two major players in this space.

This demand has led to skyrocketing memory prices for consumer DRAM and SSDs, some of which have more than tripled in price compared to this time last year. SK Hynix and the other major memory makers meanwhile have seen their revenues explode.

Chey’s comments come just a week after SK Hynix said that it planned to double its production capacity within the next five years.

“Our calculations show that our wafer capacity will double within five years. But honestly once all these facilities are built, it won’t just double, it will triple by around 2034,” Chey told Nikkei.

SK is in the process of bringing four additional wafer fabs online, with the first phase reportedly on track to come online as early as 2027.

The South Korean memory slinger had previously planned to ramp production of these facilities over the next two decades, but has pulled in its timeline in hopes of satiating AI’s memory addiction.

“There is currently no way to move faster than this,” Chey told the newswire.

While much of this capacity will be built on SK’s home turf, the company is exploring its options for overseas manufacturing, with Japan being one of the potential destinations, with Chey calling it an “excellent” candidate due to its robust semiconductor supply chains.

Unfortunately, the buildout is unlikely to drive down memory prices for consumers any time soon. As we previously reported, memory prices are not expected to peak until later this year at the earliest. Analysts warn that memory prices are more likely to plateau going into 2027 rather than plummeting like we’ve seen in past DRAM and NAND boom-bust cycles.

These boom-bust cycles have been a fact of life for commodity electronics manufacturers, like SK Hynix and Samsung, for years. Prices typically spike as inventories are drawn down and crater as new capacity is brought online.

On the one hand, AI infrastructure demand has helped to stabilize this to some extent. On the other hand, the AI boom kicked off in 2022 at what was arguably the worst possible time. 

“This demand started in the Valley for the DRAM industry. That makes financially trying to build additional capacity really challenging,” TechInsights analyst James Sanders told El Reg late last year.

Business is once again booming for memory vendors presenting ample opportunities for labor disputes over competition as well as fab expansions. Unfortunately, there’s no changing the fact that the fastest anyone can bring a leading edge memory fab online is about three years. ®

Anyone who works at Meta or knows anyone who works at Meta will tell you the same thing: It is not a happy place, particularly given the seemingly endless layoffs the company has executed over the last few years — cuts that have only accelerated as the company funnels billions into AI.

Now, a new report in Wired suggests the company’s Applied AI team is on the verge of revolt.

The drama kicked off when someone hijacked a livestreamed, employee-only presentation this week with an expletive-laden meltdown, demanding that attendees tell a senior Meta AI executive that he was “a piece of sh*t.” One presenter reportedly covered their face with their hands.

That outburst, Wired reports, reflects simmering rage inside the three-month-old unit of roughly 6,500 engineers and product managers who have been tasked with supporting the company’s AI research ambitions.

Employees describe being forced into the group with no real choice: join or quit. Many call themselves “draftees.” Their assigned work? Generating puzzles and coding problems to train AI models. “It’s literally the gulag,” one employee told Wired. “Most people find the work soul-crushing,” said another.

A report last month in Business Insider shed light on how many employees learned they’d be moved into the group — through a surprise email, a process that one self-described draftee described later on Reddit as “quite random.” According to an internal announcement in April reviewed by Business Insider, Meta’s AI models still lacked the knowledge to outperform humans at technical tasks like coding. “For agents to understand how people actually complete everyday tasks using computers, we need to train our models on real examples,” the post read.

In a leaked audio recording from an internal meeting that same month, Meta CEO Mark Zuckerberg explained the logic behind drafting Meta’s own engineers rather than outside contractors: Alexandr Wang — who sold his data-labeling startup Scale AI to Meta for $14.3 billion before taking the chief AI officer role and heading up Meta Superintelligence Labs — knows the data-labeling world well, and the company believes Meta’s average employee has “significantly higher” intelligence than third-party contractors. Better, then, to enlist them.

Meanwhile, more than 1,600 Meta employees company-wide have signed a petition protesting a program that monitors their clicks and keystrokes for AI training data. The mood across the company is dark enough that Meta’s chief product officer, Chris Cox, felt compelled to address the “brutal” environment on a call with employees this week.

TechCrunch has reached out to Meta for comment.

According to earlier reports, the Applied AI team is led by Maher Saba, a 12-year veteran of Meta who was previously a vice president in its Reality Labs division, the division that burned through $83 billion on the metaverse before Meta moved on to AI. The new organization reports up to Meta CTO Andrew Bosworth.

Originally, it was structured in such a way that up to 50 employees reported to one manager.

Zuckerberg, for his part, reportedly addressed the situation in an internal memo Friday, acknowledging that recent changes had “caused distress” and admitting the company had made mistakes that it plans to address. According to Wired, he added in his memo that “Meta’s north star is to be the best place for the most talented people in the world to make an impact.”

ai and ml

AI agents can’t be trusted, so don’t give them dangerous powers

NanoClaw, a secure agent framework, has partnered with supply chain platform JFrog to allow AI agents to fetch resources from JFrog’s reviewed registries.

Gavriel Cohen, creator of NanoClaw and co-founder of NanoCo AI, announced the tie-up on Thursday evening in San Francisco at a JFrog event that concluded with a World Cup watch party.

Cohen explained that one of the features of Claw agents – OpenClaw and variations like NanoClaw – is that they can improve themselves by fetching tools and resources that they don’t have.

That works fine, he explained, when there’s a manual approval process for accessing known local data. But it’s not ideal for npm packages, even when the agent involved is sandboxed and isolated as it is in NanoClaw. Malicious code within a container may still be able to take harmful actions, even if the scope of potential activity is constrained.

Developers, Cohen said, may not be familiar with a given package and it can take time to thoroughly assess whether a package is legitimate and uncompromised.

“So we teamed up with JFrog and we integrated NanoClaw with JFrog’s registries,” said Cohen.

The arrangement provides a way to reduce the agent’s exposure to untrusted content. When the agent downloads new tools and libraries, the software comes from a vetted source.

Cohen also announced the availability of what he called an agent factory, his company’s homegrown system used to handle pull requests (PRs) using NanoClaw agents.

The agent factory, he explained, is an attempt to triage pull requests, which have surged thanks to AI coding agents.

“It’s very easy now to point a coding agent at a repo and say, ‘open a pull request for this repo,’” he explained. “And it’s very difficult as a maintainer to tell the difference between a high quality contribution from somebody who’s really using the open source project versus someone who’s just trying to build up the reputation [using automated methods]. So to help us tackle this, we built an agent factory that helps us review every single contribution to NanoClaw.”

The agent factory is referred to as the PR Factory in the actual pull request. It’s built with NanoClaw and hosted on exe.dev, a service that provides VMs with persistent storage.

“When a PR opens, the factory spins up a dedicated worker agent for it, posts a thread to Slack, and the worker triages the change, reviews the diff, and proposes a test plan,” Cohen explains in the documentation. “Nothing consequential happens on its own: merges, test runs, and credentialed GitHub actions each surface as an approval card in the thread, and only fire when a human clicks approve.”

Cohen acknowledged that some developers will think it’s madness to process unsanitized PRs that could contain prompt injections or unsafe code. And he asked the assembled audience of developers how many had seen the phrase on the projected slide: “Never, ever, ever do this.”

Anyone who has spent time using and configuring AI agents in a development context has seen something of the sort in configuration files like Claude.md, which gets loaded as instructions to the underlying agent and model.

“If you see something like this in the Claude.md file and the agent instructions say, ‘Important: Never run drop database production,’ it tells you two things. You know that that agent has deleted a production database before. And you know that it can actually still do it again. That’s why the instruction is there.”

This elicited a knowing laugh from the audience.

Cohen went on to say that the agent will do it again because instructions are not a way of enforcing security or safety.

“Instructions help steer an agent AI towards valuable output, but it’s not a safety mechanism,” he said. “The only way to reliably prevent an agent from taking undesired action is not allowing it to take that action, not giving it the ability to take the action.”

That is the purpose of NanoClaw. ®

SpaceX has captured the attention of media, investors, and the public for years now — interest propelled by the company’s reusable rocket launches, the rise of its Starlink satellite network, and of course, for its founder and CEO Elon Musk.

But in its 24-year history, nothing quite compares to this initial public offering. Everyone seems to be interested, and perhaps it’s because of the sheer size of this IPO. The company priced its 555.6 million shares at $135 each to raise $75 billion, making it the largest IPO in history. At this price, the deal also looks set to make Musk the world’s first trillionaire.

TechCrunch has followed SpaceX’s start, struggles, and successes from the early days. And we’re here for what happens next too. This article will be continually updated with all of the latest SpaceX IPO news.

The latest on the SpaceX IPO

SpaceX shares opened at $150 on the Nasdaq public exchange, an 11% pop for the most anticipated debut in history. And it has continued to rise. The shares keep rising too (which we will update here). In midday trading, SpaceX shares soared 30%. SpaceX shares closed at $160.95, up 19%.

There has been heavy trading volume, as expected. Robinhood said it has seen “record-breaking traffic on its trading platform in the hours after SpaceX’s historic public markets debut.

SpaceX COO Gwynne Shotwell was interviewed by CNBC on Friday and among the many interesting comments she made, here is one that might get the attention of Tesla shareholders. At one point in the interview, Shotwell said a “merger between SpaceX and Tesla might make Elon’s life a little easier.”

Among the winners are the banks, which have brought in about $500 million in total fees. The big winners are Goldman Sachs and Morgan Stanley, per the WSJ.

Musk took to X, the social media company he owns, to share his appreciation of SpaceX employees as the stock rose. “I love the incredible people of SpaceX beyond words,” he wrote Friday afternoon. He also reposted a number of SpaceX IPO related posts, including a photo of insiders all wearing green shoes in what appears to be a nod to “the green shoe option.” This is a provision in an IPO underwriting agreement that lets underwriters to sell up to 15% more shares than originally planned if demand is strong.

To get a deeper look into what happened today, and all the far-ranging implications of SpaceX now being a publicly traded company, Senior Reporter Sean O’Kane and AI Editor Russell Brandom sat down for a special episode of our Equity podcast, which you can listen to right here or via your podcast player of choice, or queue it up on YouTube here.

How to track the SpaceX IPO

With an offering this large, there is a lot of financial machinery operating behind the scenes — so the first question is just when the stock makes it to the market to start trading. SpaceX is debuting on Nasdaq and you can see the official Nasdaq listing here, which will have the price of record as soon as there is one. Nasdaq also has video of the SpaceX crew ringing the bell, if that’s your thing.

But the price is just part of the picture. For the most up-to-the-minute information, your best bet is still financial press outlets like Bloomberg and CNBC, both of which have liveblogs running and will have close coverage of any hiccups that happen in getting the stock to market.

The SpaceX IPO, by the numbers

Here we look at some of the bigger numbers, the consequential figures, and the eyewatering amounts that make up the company’s S-1 form. 

For instance, SpaceX lost $4.9 billion on revenues of over $18 billion in 2025. That’s only a fraction of the more than $37 billion lost since SpaceX’s inception. 

As CEO, Elon Musk holds about 85.1% of the company’s voting power. You can read more about that in the next section “Who wins and who doesn’t” — and we’ll continue to drop interesting numbers in here.

Here is another figure that caught our attention… 4,400. That’s the number of SpaceX employees who could become millionaires, according to the NYT.

Elon Musk can’t hear you over the sound of his $1.75 trillion IPO: The Equity podcast weighs in on the IPO.

Who wins and who doesn’t

SpaceX is the world’s largest IPO in history and means a big payday for some investors, employees, and of course, Elon Musk.

Elon Musk becomes the world’s first trillionaire after SpaceX’s historic IPO: The SpaceX IPO has boosted Musk’s paper wealth to more than $1,000,000,000,000 at a time when he is more hated — and powerful — than ever.

How Elon Musk will increase his power through the SpaceX IPO: Musk, who will have more than 50% of the voting power, will have a monarchical grip over the publicly traded version of SpaceX — control that goes far beyond what other tech founders enjoy.

Who will benefit most from SpaceX IPO? Mostly Elon — and a few from his inner circle: Elon Musk has the largest stake in SpaceX by billions of shares, but others also stand to win. Here’s the rundown of who owns what.

SpaceX SPV investors won’t know their true holdings until post-IPO lock-ups lift: After SpaceX makes its public debut, lower-tier SPV investors face hidden fees, lengthy payout delays, and the risk of outright fraud.

What’s in the S-1

The S-1 registration document gave the world an unprecedented look inside SpaceX, including its financials and its various businesses. The S-1 continued to be amended as the IPO date approached, and we were on it. Here is what we found.

The SpaceX IPO filing is filled with AI bets, Starship dreams, and Elon Musk at the center: The contents of the SpaceX IPO details a business dominated by its Starlink satellite internet offering, more than $37 billion in losses, and future business prospects through its xAI division.

Starship’s path to reusability looks murky after SpaceX’s S-1: SpaceX’s IPO and Starship rocket test flight delivered two big data points that offer a realistic vision for the coming years — and one that may disappoint both the company’s boosters and its critics.

SpaceX warns investors of future dilution, adding fuel to Tesla merger rumors: The company added new language to its S-1, a warning to prospective investors that a major dilution could be in the cards after it goes public.

Pre-IPO deals and events

Leading up to the IPO, SpaceX locked in a string of deals, mostly selling off compute to improve its balance sheet.

Anthropic will pay xAI $1.25B per month for compute: Initial coverage of the Anthropic deal on May 20.

How long is Anthropic’s lease with SpaceX? Opinions vary: Elon Musk keeps downplaying the duration of SpaceX’s contract with Anthropic.

Google will pay SpaceX $920M per month for compute: A Google representative described the deal as a short-term deal addressing unexpected demand for its recently launched AI products.

This article originally published at 10 am ET, June 12, 2026. It has been updated with new coverage of the SpaceX IPO, share price, and other related events.

Update

Added new Anime Crusaders codes on June 12, 2026.

Anime Crusaders is a Roblox game inspired by popular anime worlds, where players collect powerful characters, build strong teams, and take on challenging battles. Whether you’re a new player or a seasoned collector, gathering enough Gems, Tokens, and Jewels is essential for making progress. To help you get started, the developers regularly release Anime Crusaders codes that grant free in-game rewards. In this guide, we’ve listed all the active Anime Crusaders codes and how to redeem them.

All New Anime Crusaders Codes

• CODE1LOL — Redeem for Rerolls and Gems (NEW)
• HAILMARYCODES — Redeem for Rerolls and Gems (NEW)
• OFFICIALCODEABUSE: Rerolls and Gems (NEW)
• WCODESW — Redeem for Rerolls and Gems (NEW)
• ALANABUSINGCODES — Redeem for Rerolls and Gems (NEW)
• HOLMOLS — Redeem for Rerolls and Gems (NEW)
• 14KLESGO — Redeem for 10k Gems, 100 Rerolls, and 15 of each Stat Cube
• BFAD — Redeem for 25 Rerolls and 1.5k Gems (Level 15 Req)
• APOLOGIESGUYS — Redeem for 40 Rerolls and Gems
• ECLIPSE — Redeem for 60 Rerolls (Level 15 Req)
• BERSERK — Redeem for 60 Rerolls (Level 15 Req)
• WARRIOR — Redeem for 60 Rerolls (Level 15 Req)
• THEWAITISOVER — Redeem for 60 Rerolls (Level 15 Req)
• SUMMERSOON — Redeem for 60 Rerolls (Level 15 Req)
• UPDATE6.0 — Redeem for 60 Rerolls (Level 15 Req)

Found an expired or missing code? Please let us know, and we’ll update the article as soon as possible.

Expired Anime Crusaders Codes

How to Redeem Anime Crusaders Codes

Follow these simple steps to claim your free rewards:

1. Open Anime Crusaders on Roblox.
2. Go to the Codes area near the Summon section.
3. Head to the Codes section in the lobby.
   
4. Type your desired code.

And that’s it! Your exclusive rewards will automatically be added to your inventory. In the meantime, also check out our other guides on Blue Lock Rivals, Build a Zoo, and Anime Paradox codes.

How to Get More Anime Crusaders Codes

The official Anime Crusaders Discord server is the best place to find newly released codes. Developers often post them alongside update notes, event announcements, and community celebrations, giving players a chance to claim free rewards. Another convenient option is to bookmark this page and check back regularly. We keep our code list updated with the latest active rewards, so you can quickly find working codes without searching through multiple channels or announcements.

Why Are My Anime Crusaders Codes Not Working?

If your code doesn’t work, the most common reason is a wrong or mistyped character. To avoid this, copy the code exactly as shown on our page. Also, if the game hasn’t updated for you, a quick restart can refresh the server and resolve the issue. Beyond that, it’s possible that a specific code expired between the time of writing this article and when you tried to redeem it. If that’s the case, let us know by filling out the Google Form, and we’ll update our list as soon as possible.

You can watch the USA vs Paraguay on Tubi, streaming for free now, on June 12, 2026. The free stream includes pre-match, halftime and every goal as the 2026 FIFA World Cup grips football fans around the world.

The Fox-owned platform will stream the Group D game live and in 4K. But how can you watch the USA v Paraguay on Tubi from anywhere? Can you get the free Tubi stream in Canada and the UK too? And what phones is Tubi available on?