Just over a week into the US and Israel’s war with Iran, Eric Roscher, an Air Force veteran, published a YouTube video on what he describes as the “very real concerns surrounding sleeper cells and terrorist threats” in the US.
The video, titled “Credible DOMESTIC Threat? FBI warns of attack—Drills/Considerations for the Prepared Citizen,” was produced by Roscher’s Florida-based company Barrel and Hatchet, which runs military-style training, sells branded merchandise and tactical gear, and produces online content. In the video, Roscher and his associates advise viewers to carry “extra mags” and “that truck gun,” while keeping “your head on a swivel.” Toward the end of the post, Roscher shows off a tactical vest that’s on sale from one of the video’s sponsors.
The video, which is part of YouTube’s monetization program and has a total of eight ads, has been viewed over 110,000 times. (YouTube did not respond to a request for comment.)
Barrel and Hatchet is not a militia, but the company and Roscher are part of a broader rebranding of the entire militia movement in the US, one that is focused less on showing up at drag queen story hours and more on expensive weapons, manly sweatshirts, and highly curated Instagram grids.
Influencers like Roscher produce slickly edited content that is then shared widely among militia groups on platforms like Instagram, in an effort to promote not only their ideology but also, crucially, links to their online stores and training sessions. In turn, those same militias emulate Roscher by posting their own videos and images of weekend training sessions in the woods, close-ups of their camo gear and rifles, and slo-mo footage of live firing drills. The give-and-take between these groups, and the influencers and military members they seek to emulate, marks a new era of American militias, where gaining followers and earning clout on social media is as important as being able to hit a target from 300 yards.
Roscher and these modern militia groups, with names like River Valley Minutemen and Mountain State Contingency Group, have positioned themselves as emergency response organizations working to help their communities and prepare citizens to “weather the storm”—whatever, or wherever, that may be. They use real-world events like the Iran war and ICE attacks on immigrant communities to spread fear, leveraging that fear to recruit new members.
These influencers are filling a gap in the US militia landscape, which has changed dramatically in recent years. With the Oath Keepers and Proud Boys largely disbanded in the wake of prosecutions over the January 6 attack on the Capitol, these influencers and groups have filled the vacuum, resulting in a decentralized network of local groups and people who support or emulate the previous movement—albeit in smaller, local ways.
“What used to be a national movement, with groups like Oath Keepers and Three Percenters, has really gone back to their local and regional roots,” says Travis McAdam, a senior analyst with the Southern Poverty Law Center (SPLC) who tracks militias and anti-government groups. “A lot of them have really tried to reframe themselves as auxiliary emergency preparedness groups and have done quite a bit to reform their reputation post-January 6, portraying themselves as ‘oh, we’re just here to help the community.’”
This is a new era of militia recruitment and influence—and it’s all happening in social feeds near you.
The Militia Business
Dirty Civilian is a Tennessee-based group of influencers that describes itself as “prepared citizens inspiring and informing capable men to build strong families and resilient communities” in order “to weather the storms ahead.” The group doesn’t specify what those storms are, but in one YouTube video published on Sunday, Dirty Civilian outlined a scenario where a group of vigilantes take it upon themselves to assassinate someone they believe is a pedophile. The Dirty Civilian channel has almost 750,000 subscribers, and the video, which is monetized, racked up over 100,000 views on YouTube in its first 24 hours. Multiple militia groups reposted the video on Instagram.
“It’s almost like a tutorial or something,” one commenter wrote under the video. “Food for thought at least.” Another commenter, using the acronym for minor-attracted person, a term some online communities use to refer to pedophiles, wrote: “A show that could inspire the targeting of MAPs? FANTASTIC.”
“My belief is he wanted to have long-term control”
After OpenAI’s Dota 2 win, discussions for a for-profit arm started in earnest. “Mr. Musk felt very strongly that if we were going to form a for-profit he needed to have total control over it initially,” Altman said. “He only trusted himself to make non-obvious decisions that were going to turn out to be correct.”
Altman testified that he was uncomfortable with Musk’s insistence on control, not just because Musk hadn’t been as involved as everyone else, but because OpenAI existed so no one person would control AGI. And at Y Combinator, the startup incubator where he was president, Altman had seen a lot of control fights; no one wanted to give up power when things were going well. With structures like supervoting shares, founders could retain control forever. Curiously, Altman’s example was not the most famous one (Mark Zuckerberg at Meta); it was Musk and SpaceX. When Altman asked Musk about succession plans for OpenAI, he got a particularly “hair-raising” answer: In the event of Musk’s death, Musk said, “I haven’t thought about it a ton, but maybe control should pass to my children.”
I don’t know about that. But I do know that I saw a 2017 email from Altman to Zilis in which he wrote, “I am worried about control. I don’t think any one person should have control of the world’s first AGI — in fact the whole reason we started OpenAI was so that wouldn’t happen.” He went on to say that he didn’t mind the idea of immediate control and was open to “creative structures” — which I understood to mean that, in order to placate Musk, Altman was willing to give him control up to specific milestones in company development.
“I read a vague, like, a lightweight threat in there”
“My belief is he wanted to have long-term control and that he would’ve had that had we agreed to the structure he wanted,” Altman said on the stand. This sounds basically right. In later video testimony from Sam Teller’s deposition, we heard that Musk no longer invests in anything he doesn’t control. This also fits with Musk’s long-term fixation on making sure he can’t get booted from his own company the way he got booted from PayPal.
Musk also tried to recruit Altman to Tesla. We saw texts between Altman and Teller, in which Teller told Altman that Musk was committed to beefing up Tesla’s AI no matter what, and that he hoped that Altman, Brockman, and Ilya Sutskever would want to join eventually. “I read a vague, like, a lightweight threat in there, that he’s gonna do this inside of Tesla with or without you,” Altman said. But he felt that Tesla was primarily a car company — allowing it to acquire OpenAI would betray OpenAI’s mission.
Later, in Teller’s testimony, we saw texts Teller sent to Zilis at 12:40AM on February 4th, 2018: “I don’t love OpenAI continuing without Elon,” he wrote. “Would rather disable it by recruiting the leaders.”
When Musk stopped his quarterly donations, OpenAI was operating on a “shoestring” with an “extremely short runway of cash.” OpenAI did have other donors, none of whom have sued it or joined Musk’s suit. (One donor in the exhibit that wasn’t called out to the courtroom was Alameda Research, the firm owned by Sam Bankman-Fried, who is now in prison for fraud and money laundering.) Musk’s resignation from the board meant “people wondered if he was gonna try to take, uh, vengeance out on us or something.” On the other hand, Altman said Musk had “demotivated some of our key researchers” and done “huge damage for a long time to the culture of the organization.” So it sure seems like some people were relieved to be rid of him.
I’ve seen some fairly shoddy lawyering from Musk’s side throughout this trial
We saw a lot of evidence that throughout the time Altman was setting up OpenAI’s for-profit arm, he kept Musk apprised of what was going on, either directly or through Zilis or Teller. At no point did Musk object, and whatever he said publicly about the Microsoft investments, there was plenty of evidence that privately he’d been made aware.
On the cross-examination, we were treated to more than 10 minutes of Steven Molo telling Altman that various and assorted people had called him a liar: Sutskever, Mira Murati, Helen Toner, Tasha McCauley, Daniela and Dario Amodei (former OpenAI employees and founders of Anthropic), employees at Altman’s first startup Loopt, that recent New Yorker article, a book called The Optimist, etc. Molo did score some points by asking Altman about testimony in the trial, which Altman said he wasn’t paying close attention to. Molo acted as though this was inconceivable. Surely someone had informed Altman of what was said?
It was a little funny and also a little tiresome. Altman kept his cool, though, seeming hurt and confused by the focus on whether he was a liar. It was also the most successful part of the cross, which declined in focus precipitously afterward. I’ve seen some fairly shoddy lawyering from Musk’s side throughout this trial, and today was pretty bad. At one point, when Molo was trying to capitalize on Altman being both CEO and on the company’s board, Altman said — truthfully — that CEOs are almost always on the boards of the companies they run.
(At this point in my notes, I had written, “Boy, Molo is not very good at this.”)
The point of this trial isn’t to win — it’s to punish Altman, Brockman, and OpenAI
There was also an unconvincing argument about fundraising in nonprofits, specifically that if Stanford could raise $3 billion a year, OpenAI should have remained a nonprofit. Okay, let’s just think about that for a minute. Stanford has a donor network of thousands of graduates. It’s a school, which has very different capital requirements. It is not competing with any reputable for-profit companies. But leave that all aside and assume that some fundraising genius took over at the OpenAI Foundation: $3 billion is the initial two Microsoft investments combined, and not enough to scale OpenAI to where it is now. If compute is the main bottleneck on building AI models, then Molo’s line of argument suggests OpenAI never would have managed to be successful as a nonprofit alone. He’s making the defense’s case for them.
But the thing is, Molo doesn’t actually have to be good at this job, because the point of this trial isn’t to win — though I’m sure Musk wouldn’t mind a win. The point is to punish Altman, Brockman, and OpenAI. Musk has done that pretty thoroughly — reinforcing in the public’s mind that Altman is a liar and a snake. This morning, I read an exclusive in The Wall Street Journal that assorted Republican AGs and the House Oversight committee wanted to look into Sam Altman’s investments. References to the trial are peppered throughout the article.
So yes, Altman was convincing on the stand. He may even win the suit. But it sure seems like Musk’s vengeance has just begun.
Follow topics and authors from this story to see more like this in your personalized homepage feed and to receive email updates.
“My belief is he wanted to have long-term control”
After OpenAI’s Dota 2 win, discussions for a for-profit arm started in earnest. “Mr. Musk felt very strongly that if we were going to form a for-profit he needed to have total control over it initially,” Altman said. “He only trusted himself to make non-obvious decisions that were going to turn out to be correct.”
Altman testified that he was uncomfortable with Musk’s insistence on control, not just because Musk hadn’t been as involved as everyone else, but because OpenAI existed so no one person would control AGI. And at Y Combinator, the startup incubator where he was president, Altman had seen a lot of control fights; no one wanted to give up power when things were going well. With structures like supervoting shares, founders could retain control forever. Curiously, Altman’s example was not the most famous one (Mark Zuckerberg at Meta); it was Musk and SpaceX. When Altman asked Musk about succession plans for OpenAI, he got a particularly “hair-raising” answer: In the event of Musk’s death, Musk said, “I haven’t thought about it a ton, but maybe control should pass to my children.”
I don’t know about that. But I do know that I saw a 2017 email from Altman to Zilis in which he wrote, “I am worried about control. I don’t think any one person should have control of the world’s first AGI — in fact the whole reason we started OpenAI was so that wouldn’t happen.” He went on to say that he didn’t mind the idea of immediate control and was open to “creative structures” — which I understood to mean that, in order to placate Musk, Altman was willing to give him control up to specific milestones in company development.
“I read a vague, like, a lightweight threat in there”
“My belief is he wanted to have long-term control and that he would’ve had that had we agreed to the structure he wanted,” Altman said on the stand. This sounds basically right. In later video testimony from Sam Teller’s deposition, we heard that Musk no longer invests in anything he doesn’t control. This also fits with Musk’s long-term fixation on making sure he can’t get booted from his own company the way he got booted from PayPal.
Musk also tried to recruit Altman to Tesla. We saw texts between Altman and Teller, in which Teller told Altman that Musk was committed to beefing up Tesla’s AI no matter what, and that he hoped that Altman, Brockman, and Ilya Sutskever would want to join eventually. “I read a vague, like, a lightweight threat in there, that he’s gonna do this inside of Tesla with or without you,” Altman said. But he felt that Tesla was primarily a car company — allowing it to acquire OpenAI would betray OpenAI’s mission.
Later, in Teller’s testimony, we saw texts Teller sent to Zilis at 12:40AM on February 4th, 2018: “I don’t love OpenAI continuing without Elon,” he wrote. “Would rather disable it by recruiting the leaders.”
When Musk stopped his quarterly donations, OpenAI was operating on a “shoestring” with an “extremely short runway of cash.” OpenAI did have other donors, none of whom have sued it or joined Musk’s suit. (One donor in the exhibit that wasn’t called out to the courtroom was Alameda Research, the firm owned by Sam Bankman-Fried, who is now in prison for fraud and money laundering.) Musk’s resignation from the board meant “people wondered if he was gonna try to take, uh, vengeance out on us or something.” On the other hand, Altman said Musk had “demotivated some of our key researchers” and done “huge damage for a long time to the culture of the organization.” So it sure seems like some people were relieved to be rid of him.
I’ve seen some fairly shoddy lawyering from Musk’s side throughout this trial
We saw a lot of evidence that throughout the time Altman was setting up OpenAI’s for-profit arm, he kept Musk apprised of what was going on, either directly or through Zilis or Teller. At no point did Musk object, and whatever he said publicly about the Microsoft investments, there was plenty of evidence that privately he’d been made aware.
On the cross-examination, we were treated to more than 10 minutes of Steven Molo telling Altman that various and assorted people had called him a liar: Sutskever, Mira Murati, Helen Toner, Tasha McCauley, Daniela and Dario Amodei (former OpenAI employees and founders of Anthropic), employees at Altman’s first startup Loopt, that recent New Yorker article, a book called The Optimist, etc. Molo did score some points by asking Altman about testimony in the trial, which Altman said he wasn’t paying close attention to. Molo acted as though this was inconceivable. Surely someone had informed Altman of what was said?
It was a little funny and also a little tiresome. Altman kept his cool, though, seeming hurt and confused by the focus on whether he was a liar. It was also the most successful part of the cross, which declined in focus precipitously afterward. I’ve seen some fairly shoddy lawyering from Musk’s side throughout this trial, and today was pretty bad. At one point, when Molo was trying to capitalize on Altman being both CEO and on the company’s board, Altman said — truthfully — that CEOs are almost always on the boards of the companies they run.
(At this point in my notes, I had written, “Boy, Molo is not very good at this.”)
The point of this trial isn’t to win — it’s to punish Altman, Brockman, and OpenAI
There was also an unconvincing argument about fundraising in nonprofits, specifically that if Stanford could raise $3 billion a year, OpenAI should have remained a nonprofit. Okay, let’s just think about that for a minute. Stanford has a donor network of thousands of graduates. It’s a school, which has very different capital requirements. It is not competing with any reputable for-profit companies. But leave that all aside and assume that some fundraising genius took over at the OpenAI Foundation: $3 billion is the initial two Microsoft investments combined, and not enough to scale OpenAI to where it is now. If compute is the main bottleneck on building AI models, then Molo’s line of argument suggests OpenAI never would have managed to be successful as a nonprofit alone. He’s making the defense’s case for them.
But the thing is, Molo doesn’t actually have to be good at this job, because the point of this trial isn’t to win — though I’m sure Musk wouldn’t mind a win. The point is to punish Altman, Brockman, and OpenAI. Musk has done that pretty thoroughly — reinforcing in the public’s mind that Altman is a liar and a snake. This morning, I read an exclusive in The Wall Street Journal that assorted Republican AGs and the House Oversight committee wanted to look into Sam Altman’s investments. References to the trial are peppered throughout the article.
So yes, Altman was convincing on the stand. He may even win the suit. But it sure seems like Musk’s vengeance has just begun.
Follow topics and authors from this story to see more like this in your personalized homepage feed and to receive email updates.
Elizabeth Lopatto
#Sam #Altman #winning #standAI,OpenAI">Sam Altman was winning on the stand, but it might not be enough
After two weeks of hearing from assorted witnesses that he was a lying snake, the jury finally heard from the lying snake himself: Sam Altman. At the end of the testimony, his lawyer William Savitt asked him how it felt to be accused of stealing a charity.
“We created, through a ton of hard work, this extremely large charity, and I agree you can’t steal it,” Altman said. “Mr. Musk did try to kill it, I guess. Twice.”
Altman was fully in “nice kid from St. Louis” mode, and did a passable impression of a man who was bewildered at what was happening to him. When he stepped down from the stand holding a stack of evidence binders, he even looked a little like a schoolboy. He seemed nervous at the beginning of his direct testimony, though he warmed up fairly quickly. Overall, he seemed to give credible testimony — and at times, it seemed like the jury liked him.
Throughout this trial I’ve had some difficulty imagining what the jury is making of all this because I am a little too familiar with the figures who are testifying. I have heard some audacious lies under oath, like when Elon Musk told us all he doesn’t lose his temper. (He then proceeded to lose his temper on cross-examination.) Or like when Shivon Zilis, the mother of several of his children, told us that she didn’t know Musk was starting xAI — which seemed to be directly contradicted by her text messages. Or when Greg “What will take me to $1B?” Brockman told us he was all about the mission. I certainly believe Altman isn’t trustworthy — I mean, The New Yorker published more than 17,000 words about how much he lies. But unlike with Musk, there are contemporaneous documents backing Altman’s version of the story. At least, mostly.
“My belief is he wanted to have long-term control”
After OpenAI’s Dota 2 win, discussions for a for-profit arm started in earnest. “Mr. Musk felt very strongly that if we were going to form a for-profit he needed to have total control over it initially,” Altman said. “He only trusted himself to make non-obvious decisions that were going to turn out to be correct.”
Altman testified that he was uncomfortable with Musk’s insistence on control, not just because Musk hadn’t been as involved as everyone else, but because OpenAI existed so no one person would control AGI. And at Y Combinator, the startup incubator where he was president, Altman had seen a lot of control fights; no one wanted to give up power when things were going well. With structures like supervoting shares, founders could retain control forever. Curiously, Altman’s example was not the most famous one (Mark Zuckerberg at Meta); it was Musk and SpaceX. When Altman asked Musk about succession plans for OpenAI, he got a particularly “hair-raising” answer: In the event of Musk’s death, Musk said, “I haven’t thought about it a ton, but maybe control should pass to my children.”
I don’t know about that. But I do know that I saw a 2017 email from Altman to Zilis in which he wrote, “I am worried about control. I don’t think any one person should have control of the world’s first AGI — in fact the whole reason we started OpenAI was so that wouldn’t happen.” He went on to say that he didn’t mind the idea of immediate control and was open to “creative structures” — which I understood to mean that, in order to placate Musk, Altman was willing to give him control up to specific milestones in company development.
“I read a vague, like, a lightweight threat in there”
“My belief is he wanted to have long-term control and that he would’ve had that had we agreed to the structure he wanted,” Altman said on the stand. This sounds basically right. In later video testimony from Sam Teller’s deposition, we heard that Musk no longer invests in anything he doesn’t control. This also fits with Musk’s long-term fixation on making sure he can’t get booted from his own company the way he got booted from PayPal.
Musk also tried to recruit Altman to Tesla. We saw texts between Altman and Teller, in which Teller told Altman that Musk was committed to beefing up Tesla’s AI no matter what, and that he hoped that Altman, Brockman, and Ilya Sutskever would want to join eventually. “I read a vague, like, a lightweight threat in there, that he’s gonna do this inside of Tesla with or without you,” Altman said. But he felt that Tesla was primarily a car company — allowing it to acquire OpenAI would betray OpenAI’s mission.
Later, in Teller’s testimony, we saw texts Teller sent to Zilis at 12:40AM on February 4th, 2018: “I don’t love OpenAI continuing without Elon,” he wrote. “Would rather disable it by recruiting the leaders.”
When Musk stopped his quarterly donations, OpenAI was operating on a “shoestring” with an “extremely short runway of cash.” OpenAI did have other donors, none of whom have sued it or joined Musk’s suit. (One donor in the exhibit that wasn’t called out to the courtroom was Alameda Research, the firm owned by Sam Bankman-Fried, who is now in prison for fraud and money laundering.) Musk’s resignation from the board meant “people wondered if he was gonna try to take, uh, vengeance out on us or something.” On the other hand, Altman said Musk had “demotivated some of our key researchers” and done “huge damage for a long time to the culture of the organization.” So it sure seems like some people were relieved to be rid of him.
I’ve seen some fairly shoddy lawyering from Musk’s side throughout this trial
We saw a lot of evidence that throughout the time Altman was setting up OpenAI’s for-profit arm, he kept Musk apprised of what was going on, either directly or through Zilis or Teller. At no point did Musk object, and whatever he said publicly about the Microsoft investments, there was plenty of evidence that privately he’d been made aware.
On the cross-examination, we were treated to more than 10 minutes of Steven Molo telling Altman that various and assorted people had called him a liar: Sutskever, Mira Murati, Helen Toner, Tasha McCauley, Daniela and Dario Amodei (former OpenAI employees and founders of Anthropic), employees at Altman’s first startup Loopt, that recent New Yorker article, a book called The Optimist, etc. Molo did score some points by asking Altman about testimony in the trial, which Altman said he wasn’t paying close attention to. Molo acted as though this was inconceivable. Surely someone had informed Altman of what was said?
It was a little funny and also a little tiresome. Altman kept his cool, though, seeming hurt and confused by the focus on whether he was a liar. It was also the most successful part of the cross, which declined in focus precipitously afterward. I’ve seen some fairly shoddy lawyering from Musk’s side throughout this trial, and today was pretty bad. At one point, when Molo was trying to capitalize on Altman being both CEO and on the company’s board, Altman said — truthfully — that CEOs are almost always on the boards of the companies they run.
(At this point in my notes, I had written, “Boy, Molo is not very good at this.”)
The point of this trial isn’t to win — it’s to punish Altman, Brockman, and OpenAI
There was also an unconvincing argument about fundraising in nonprofits, specifically that if Stanford could raise $3 billion a year, OpenAI should have remained a nonprofit. Okay, let’s just think about that for a minute. Stanford has a donor network of thousands of graduates. It’s a school, which has very different capital requirements. It is not competing with any reputable for-profit companies. But leave that all aside and assume that some fundraising genius took over at the OpenAI Foundation: $3 billion is the initial two Microsoft investments combined, and not enough to scale OpenAI to where it is now. If compute is the main bottleneck on building AI models, then Molo’s line of argument suggests OpenAI never would have managed to be successful as a nonprofit alone. He’s making the defense’s case for them.
But the thing is, Molo doesn’t actually have to be good at this job, because the point of this trial isn’t to win — though I’m sure Musk wouldn’t mind a win. The point is to punish Altman, Brockman, and OpenAI. Musk has done that pretty thoroughly — reinforcing in the public’s mind that Altman is a liar and a snake. This morning, I read an exclusive in The Wall Street Journal that assorted Republican AGs and the House Oversight committee wanted to look into Sam Altman’s investments. References to the trial are peppered throughout the article.
So yes, Altman was convincing on the stand. He may even win the suit. But it sure seems like Musk’s vengeance has just begun.
Follow topics and authors from this story to see more like this in your personalized homepage feed and to receive email updates.
Elizabeth Lopatto
#Sam #Altman #winning #standAI,OpenAI
Early-stage venture firm A* on Tuesday announced a $450 million Fund III. The firm takes a generalist approach, backing companies across categories including AI applications, fintech, healthcare, and security. The average check size for this fund will be between $3 million and $5 million, with the aim to back at least 30 startups. The capital will be deployed over the next two to three years, as with the firm’s previous funds. Limited partners include nonprofits, foundations, and endowments; Carnegie Mellon University is among the publicly named backers.
A*, founded in 2020 and run by Kevin Hartz and Bennet Siegel, previously raised a $315 million Fund II in 2024 and a $300 million Fund I in 2021. Hartz is a serial entrepreneur best known for co-founding Xoom, the international money-transfer service PayPal later acquired for $1.1 billion in 2015, and Eventbrite, the event-ticketing platform that went public in 2018. Siegel came up through Boston Consulting Group and Altamont Capital Partners before spending four years as a partner at Coatue Management.
The firm has also drawn attention for backing unusually young founders, even as the practice has become more common since. Hartz told TechCrunch last fall that close to 20% of the firm’s current portfolio involve teenage entrepreneurs. Among others of its investments, it has backed the fintech company Ramp and the AI firm Mercor.
This story was updated to clarify the name of the firm.
Early-stage venture firm A* on Tuesday announced a $450 million Fund III. The firm takes a generalist approach, backing companies across categories including AI applications, fintech, healthcare, and security. The average check size for this fund will be between $3 million and $5 million, with the aim to back at least 30 startups. The capital will be deployed over the next two to three years, as with the firm’s previous funds. Limited partners include nonprofits, foundations, and endowments; Carnegie Mellon University is among the publicly named backers.
A*, founded in 2020 and run by Kevin Hartz and Bennet Siegel, previously raised a $315 million Fund II in 2024 and a $300 million Fund I in 2021. Hartz is a serial entrepreneur best known for co-founding Xoom, the international money-transfer service PayPal later acquired for $1.1 billion in 2015, and Eventbrite, the event-ticketing platform that went public in 2018. Siegel came up through Boston Consulting Group and Altamont Capital Partners before spending four years as a partner at Coatue Management.
The firm has also drawn attention for backing unusually young founders, even as the practice has become more common since. Hartz told TechCrunch last fall that close to 20% of the firm’s current portfolio involve teenage entrepreneurs. Among others of its investments, it has backed the fintech company Ramp and the AI firm Mercor.
This story was updated to clarify the name of the firm.
#Kevin #Hartzs #closed #fund #million #TechCrunchA* Capital,Kevin Hartz,Startups">Kevin Hartz’s A* just closed its third fund with $450 million | TechCrunch
Early-stage venture firm A* on Tuesday announced a $450 million Fund III. The firm takes a generalist approach, backing companies across categories including AI applications, fintech, healthcare, and security. The average check size for this fund will be between $3 million and $5 million, with the aim to back at least 30 startups. The capital will be deployed over the next two to three years, as with the firm’s previous funds. Limited partners include nonprofits, foundations, and endowments; Carnegie Mellon University is among the publicly named backers.
A*, founded in 2020 and run by Kevin Hartz and Bennet Siegel, previously raised a $315 million Fund II in 2024 and a $300 million Fund I in 2021. Hartz is a serial entrepreneur best known for co-founding Xoom, the international money-transfer service PayPal later acquired for $1.1 billion in 2015, and Eventbrite, the event-ticketing platform that went public in 2018. Siegel came up through Boston Consulting Group and Altamont Capital Partners before spending four years as a partner at Coatue Management.
The firm has also drawn attention for backing unusually young founders, even as the practice has become more common since. Hartz told TechCrunch last fall that close to 20% of the firm’s current portfolio involve teenage entrepreneurs. Among others of its investments, it has backed the fintech company Ramp and the AI firm Mercor.
This story was updated to clarify the name of the firm.
Counter-Strike 2 is a great demonstration of the finest economic systems of games created so far. In particular, such an economic system is represented by virtual trading of items worth more than $8 billion. Indeed, one should bear in mind that this is not a typo – this figure really represents the cost of items worth $8 billion. It may be added that this amount is larger than GDP in many countries, despite not having any effect on the gameplay. So, how did some in-game items attain such a multi-billion dollar economy and what fuels it? We will explain.
How a Digital Skin Gets Its Price Tag
Every skin in CS2 has a set of properties that determine its value, and understanding them is the first step to making sense of this economy.
Rarity tier is the most obvious one. Skins are categorized from Consumer Grade (white, the most common) all the way up to Covert (red, the rarest non-knife items) and Contraband (the ultra-rare category with only one item — the M4A4 Howl). Knives and gloves sit in their own Extraordinary tier, which is part of why they command such premium prices.
Then there’s float value — a number between 0.00 and 1.00 that determines a skin’s visual condition. A float of 0.01 means the skin looks virtually brand new (Factory New), while 0.85 means it’s scratched up and Battle-Scarred. Two AK-47 Redlines might look similar at a glance, but a 0.01 float Factory New will sell for significantly more than a 0.15 Minimal Wear.
And finally, there are pattern-based factors. Certain skins like Case Hardened and Fade have pattern indexes that produce unique visual results. A Case Hardened AK-47 with a full blue gem pattern can sell for tens of thousands of dollars, while the same skin with a standard pattern might go for $40. Doppler knives have distinct phases, each with its own pricing tier. Even sticker placements matter — a skin with rare Katowice 2014 stickers in the right positions can multiply the base price several times over.
The Marketplace Ecosystem
Here’s where things get interesting from a tech perspective. Unlike most games where you buy skins from a single in-game store, CS2 has an entire ecosystem of competing marketplaces.
Steam Community Market is Valve’s own platform and the default option for most players. It’s integrated directly into the Steam client, making it convenient, but it comes with a 15% transaction fee and locks your earnings in Steam Wallet — you can’t cash out to real money.
It resulted in the creation of an extensive array of third-party marketplaces, which include websites like Skinport, DMarket, CSFloat, Buff163, and countless other options. These websites allow users to exchange skins for real money, using various means of payment, such as PayPal payments, bank transfers, and cryptocurrency transactions. The fee structures on these websites vary considerably, ranging from zero percent up to 10 percent and beyond.
This price fragmentation is exactly why analytics and comparison tools have become essential for anyone who takes CS2 trading seriously. Experienced traders routinely check CS2 prices across multiple platforms before making a move, because the price gap between the cheapest listing and the most expensive one for the same skin can easily be 15-30%.
Market Cap Tracking — Like Crypto, But For Skins
One of the more fascinating developments in the CS2 economy has been the adoption of financial tracking concepts borrowed from traditional and crypto markets.
The total CS2 market capitalization — the combined estimated value of every tradeable item in the ecosystem — is tracked in real time, much like how CoinMarketCap tracks cryptocurrency values.At the end of 2025, the peak market capitalization of CS2 was more than $6 billion; however, the market capitalization dropped by roughly 30% in a single move when Valve made an update (to be discussed later).
Such advanced monitoring is essential for the user to see whether the general market is expanding or contracting. If there is an increase in the market cap, then demand and investments are likely increasing; otherwise, a sharp drop may indicate a Valve update, season, or a major event in the global gaming economy.
The data-driven platform collects information from over 20 marketplaces and provides dashboards that contain trend analysis, volumes, and price movements that could have been taken directly from a professional stock trading platform. The economy of CS2 has reached such a degree of development that the very concept of “gaming” becomes irrelevant.
Trade-Up Contracts: The Economy’s Built-In Upgrade Path
Valve didn’t just build a marketplace — they built game mechanics directly into the economic system. However, the most crucial part is the Trade-Up Contracts where the user gets a skin from the next level collection using ten skins from the current level collection.
Even though this concept seems quite simple, it requires rather complex mathematical calculations. Namely, the output skin’s type is dependent on the input collections’ types, whereas its float is calculated according to the average float of all input skins scaled to the output collection’s range. Thus, an advanced player may affect the probability of getting a certain skin via inputs manipulation.
To explain, if seven skins belong to one collection while three skins are from another, the output skin will most probably originate from the first collection. If the first collection contains a $500 skin at the next tier and the second contains a $30 skin, you can engineer a heavily weighted gamble in your favor.
But here’s the catch — the math only works if you actually run the numbers. The inputs might cost $80 in total, but if the expected value of the output is only $60, you’re making a bad bet regardless of the potential upside. That’s why experienced traders simulate their contracts using a CS2 trade-up calculator before committing any skins. These tools predict every possible outcome with exact probabilities, float projections, and expected profit or loss.
The trade-up system was further shaken in October 2025 when Valve added the ability to trade up Covert skins into knives and gloves — something that was previously impossible. Players could suddenly turn five Covert skins worth roughly $5-10 each into knives that were previously selling for $1,000+. The result? Knife prices crashed overnight, the total market cap dropped by hundreds of millions, and the entire pricing hierarchy had to readjust.
The Tech Infrastructure Behind It All
All that lies beneath all these graphs and calculations is quite a bit of technology. Real-time data feeds, APIs, and aggregators pull pricing information from several different marketplaces simultaneously.
Automated trading, monitoring services, portfolio management tools, and other such applications are developed by third parties using the marketplace APIs. Some platforms offer their own developer APIs with endpoints for price recommendations, market analytics, and cross-platform price comparison — essentially creating the financial infrastructure layer that the CS2 economy needed to operate at scale.
Steam itself provides API access for inventory data, market listings, and transaction history, which third-party services use to power everything from inventory valuation tools to automated trading systems.
The sophistication has reached a point where the CS2 economy has its own version of Bloomberg terminals — dashboards that track market-wide trends, individual item price histories, trading volumes, liquidity scores, and even volatility metrics. Professional traders monitor these tools the same way a Wall Street analyst watches stock tickers.
Why It Matters Beyond Gaming
The CS2 skin economy isn’t just a curiosity — it’s a case study in how digital ownership, market dynamics, and community-driven value creation work at scale.
This is what some of the main points which can be derived from this are. Firstly, scarcity defines value in all instances. It has been illustrated in the CS2 skins case study, in which it is clear that it does not matter whether items are tangible or useful in order for them to have economic value.
Second, platform decisions have outsized economic impact. Valve’s single update in October 2025 erased over a billion dollars in virtual item value. No other company has that kind of direct influence over a player-driven economy of this scale.
And third, the line between gaming economies and financial markets is dissolving. When your hobby comes with real-time price tracking, market cap analytics, trade-up calculators, and cross-platform arbitrage opportunities, you’re not just playing a game anymore. You’re participating in a micro-economy that happens to live inside one.
Whether you’re a casual CS2 player who’s never sold a skin or a veteran trader running profit calculations on every drop, the scale and sophistication of what’s been built here is worth paying attention to. An $8 billion economy that runs on cosmetic pixels, community trust, and a few really good APIs — that’s the kind of thing you only find in gaming.
Counter-Strike 2 is a great demonstration of the finest economic systems of games created so far. In particular, such an economic system is represented by virtual trading of items worth more than $8 billion. Indeed, one should bear in mind that this is not a typo – this figure really represents the cost of items worth $8 billion. It may be added that this amount is larger than GDP in many countries, despite not having any effect on the gameplay. So, how did some in-game items attain such a multi-billion dollar economy and what fuels it? We will explain.
How a Digital Skin Gets Its Price Tag
Every skin in CS2 has a set of properties that determine its value, and understanding them is the first step to making sense of this economy.
Rarity tier is the most obvious one. Skins are categorized from Consumer Grade (white, the most common) all the way up to Covert (red, the rarest non-knife items) and Contraband (the ultra-rare category with only one item — the M4A4 Howl). Knives and gloves sit in their own Extraordinary tier, which is part of why they command such premium prices.
Then there’s float value — a number between 0.00 and 1.00 that determines a skin’s visual condition. A float of 0.01 means the skin looks virtually brand new (Factory New), while 0.85 means it’s scratched up and Battle-Scarred. Two AK-47 Redlines might look similar at a glance, but a 0.01 float Factory New will sell for significantly more than a 0.15 Minimal Wear.
And finally, there are pattern-based factors. Certain skins like Case Hardened and Fade have pattern indexes that produce unique visual results. A Case Hardened AK-47 with a full blue gem pattern can sell for tens of thousands of dollars, while the same skin with a standard pattern might go for $40. Doppler knives have distinct phases, each with its own pricing tier. Even sticker placements matter — a skin with rare Katowice 2014 stickers in the right positions can multiply the base price several times over.
The Marketplace Ecosystem
Here’s where things get interesting from a tech perspective. Unlike most games where you buy skins from a single in-game store, CS2 has an entire ecosystem of competing marketplaces.
Steam Community Market is Valve’s own platform and the default option for most players. It’s integrated directly into the Steam client, making it convenient, but it comes with a 15% transaction fee and locks your earnings in Steam Wallet — you can’t cash out to real money.
It resulted in the creation of an extensive array of third-party marketplaces, which include websites like Skinport, DMarket, CSFloat, Buff163, and countless other options. These websites allow users to exchange skins for real money, using various means of payment, such as PayPal payments, bank transfers, and cryptocurrency transactions. The fee structures on these websites vary considerably, ranging from zero percent up to 10 percent and beyond.
This price fragmentation is exactly why analytics and comparison tools have become essential for anyone who takes CS2 trading seriously. Experienced traders routinely check CS2 prices across multiple platforms before making a move, because the price gap between the cheapest listing and the most expensive one for the same skin can easily be 15-30%.
Market Cap Tracking — Like Crypto, But For Skins
One of the more fascinating developments in the CS2 economy has been the adoption of financial tracking concepts borrowed from traditional and crypto markets.
The total CS2 market capitalization — the combined estimated value of every tradeable item in the ecosystem — is tracked in real time, much like how CoinMarketCap tracks cryptocurrency values.At the end of 2025, the peak market capitalization of CS2 was more than $6 billion; however, the market capitalization dropped by roughly 30% in a single move when Valve made an update (to be discussed later).
Such advanced monitoring is essential for the user to see whether the general market is expanding or contracting. If there is an increase in the market cap, then demand and investments are likely increasing; otherwise, a sharp drop may indicate a Valve update, season, or a major event in the global gaming economy.
The data-driven platform collects information from over 20 marketplaces and provides dashboards that contain trend analysis, volumes, and price movements that could have been taken directly from a professional stock trading platform. The economy of CS2 has reached such a degree of development that the very concept of “gaming” becomes irrelevant.
Trade-Up Contracts: The Economy’s Built-In Upgrade Path
Valve didn’t just build a marketplace — they built game mechanics directly into the economic system. However, the most crucial part is the Trade-Up Contracts where the user gets a skin from the next level collection using ten skins from the current level collection.
Even though this concept seems quite simple, it requires rather complex mathematical calculations. Namely, the output skin’s type is dependent on the input collections’ types, whereas its float is calculated according to the average float of all input skins scaled to the output collection’s range. Thus, an advanced player may affect the probability of getting a certain skin via inputs manipulation.
To explain, if seven skins belong to one collection while three skins are from another, the output skin will most probably originate from the first collection. If the first collection contains a $500 skin at the next tier and the second contains a $30 skin, you can engineer a heavily weighted gamble in your favor.
But here’s the catch — the math only works if you actually run the numbers. The inputs might cost $80 in total, but if the expected value of the output is only $60, you’re making a bad bet regardless of the potential upside. That’s why experienced traders simulate their contracts using a CS2 trade-up calculator before committing any skins. These tools predict every possible outcome with exact probabilities, float projections, and expected profit or loss.
The trade-up system was further shaken in October 2025 when Valve added the ability to trade up Covert skins into knives and gloves — something that was previously impossible. Players could suddenly turn five Covert skins worth roughly $5-10 each into knives that were previously selling for $1,000+. The result? Knife prices crashed overnight, the total market cap dropped by hundreds of millions, and the entire pricing hierarchy had to readjust.
The Tech Infrastructure Behind It All
All that lies beneath all these graphs and calculations is quite a bit of technology. Real-time data feeds, APIs, and aggregators pull pricing information from several different marketplaces simultaneously.
Automated trading, monitoring services, portfolio management tools, and other such applications are developed by third parties using the marketplace APIs. Some platforms offer their own developer APIs with endpoints for price recommendations, market analytics, and cross-platform price comparison — essentially creating the financial infrastructure layer that the CS2 economy needed to operate at scale.
Steam itself provides API access for inventory data, market listings, and transaction history, which third-party services use to power everything from inventory valuation tools to automated trading systems.
The sophistication has reached a point where the CS2 economy has its own version of Bloomberg terminals — dashboards that track market-wide trends, individual item price histories, trading volumes, liquidity scores, and even volatility metrics. Professional traders monitor these tools the same way a Wall Street analyst watches stock tickers.
Why It Matters Beyond Gaming
The CS2 skin economy isn’t just a curiosity — it’s a case study in how digital ownership, market dynamics, and community-driven value creation work at scale.
This is what some of the main points which can be derived from this are. Firstly, scarcity defines value in all instances. It has been illustrated in the CS2 skins case study, in which it is clear that it does not matter whether items are tangible or useful in order for them to have economic value.
Second, platform decisions have outsized economic impact. Valve’s single update in October 2025 erased over a billion dollars in virtual item value. No other company has that kind of direct influence over a player-driven economy of this scale.
And third, the line between gaming economies and financial markets is dissolving. When your hobby comes with real-time price tracking, market cap analytics, trade-up calculators, and cross-platform arbitrage opportunities, you’re not just playing a game anymore. You’re participating in a micro-economy that happens to live inside one.
Whether you’re a casual CS2 player who’s never sold a skin or a veteran trader running profit calculations on every drop, the scale and sophistication of what’s been built here is worth paying attention to. An $8 billion economy that runs on cosmetic pixels, community trust, and a few really good APIs — that’s the kind of thing you only find in gaming.
In addition to being a very popular first-person shooter game, Counter-Strike 2 is a great demonstration of the finest economic systems of games created so far. In particular, such an economic system is represented by virtual trading of items worth more than $8 billion. Indeed, one should bear in mind that this is not a typo – this figure really represents the cost of items worth $8 billion. It may be added that this amount is larger than GDP in many countries, despite not having any effect on the gameplay. So, how did some in-game items attain such a multi-billion dollar economy and what fuels it? We will explain.
How a Digital Skin Gets Its Price Tag
Every skin in CS2 has a set of properties that determine its value, and understanding them is the first step to making sense of this economy.
Rarity tier is the most obvious one. Skins are categorized from Consumer Grade (white, the most common) all the way up to Covert (red, the rarest non-knife items) and Contraband (the ultra-rare category with only one item — the M4A4 Howl). Knives and gloves sit in their own Extraordinary tier, which is part of why they command such premium prices.
Then there’s float value — a number between 0.00 and 1.00 that determines a skin’s visual condition. A float of 0.01 means the skin looks virtually brand new (Factory New), while 0.85 means it’s scratched up and Battle-Scarred. Two AK-47 Redlines might look similar at a glance, but a 0.01 float Factory New will sell for significantly more than a 0.15 Minimal Wear.
And finally, there are pattern-based factors. Certain skins like Case Hardened and Fade have pattern indexes that produce unique visual results. A Case Hardened AK-47 with a full blue gem pattern can sell for tens of thousands of dollars, while the same skin with a standard pattern might go for $40. Doppler knives have distinct phases, each with its own pricing tier. Even sticker placements matter — a skin with rare Katowice 2014 stickers in the right positions can multiply the base price several times over.
The Marketplace Ecosystem
Here’s where things get interesting from a tech perspective. Unlike most games where you buy skins from a single in-game store, CS2 has an entire ecosystem of competing marketplaces.
Steam Community Market is Valve’s own platform and the default option for most players. It’s integrated directly into the Steam client, making it convenient, but it comes with a 15% transaction fee and locks your earnings in Steam Wallet — you can’t cash out to real money.
It resulted in the creation of an extensive array of third-party marketplaces, which include websites like Skinport, DMarket, CSFloat, Buff163, and countless other options. These websites allow users to exchange skins for real money, using various means of payment, such as PayPal payments, bank transfers, and cryptocurrency transactions. The fee structures on these websites vary considerably, ranging from zero percent up to 10 percent and beyond.
This price fragmentation is exactly why analytics and comparison tools have become essential for anyone who takes CS2 trading seriously. Experienced traders routinely check CS2 prices across multiple platforms before making a move, because the price gap between the cheapest listing and the most expensive one for the same skin can easily be 15-30%.
Market Cap Tracking — Like Crypto, But For Skins
One of the more fascinating developments in the CS2 economy has been the adoption of financial tracking concepts borrowed from traditional and crypto markets.
The total CS2 market capitalization — the combined estimated value of every tradeable item in the ecosystem — is tracked in real time, much like how CoinMarketCap tracks cryptocurrency values.At the end of 2025, the peak market capitalization of CS2 was more than $6 billion; however, the market capitalization dropped by roughly 30% in a single move when Valve made an update (to be discussed later).
Such advanced monitoring is essential for the user to see whether the general market is expanding or contracting. If there is an increase in the market cap, then demand and investments are likely increasing; otherwise, a sharp drop may indicate a Valve update, season, or a major event in the global gaming economy.
The data-driven platform collects information from over 20 marketplaces and provides dashboards that contain trend analysis, volumes, and price movements that could have been taken directly from a professional stock trading platform. The economy of CS2 has reached such a degree of development that the very concept of “gaming” becomes irrelevant.
Trade-Up Contracts: The Economy’s Built-In Upgrade Path
Valve didn’t just build a marketplace — they built game mechanics directly into the economic system. However, the most crucial part is the Trade-Up Contracts where the user gets a skin from the next level collection using ten skins from the current level collection.
Even though this concept seems quite simple, it requires rather complex mathematical calculations. Namely, the output skin’s type is dependent on the input collections’ types, whereas its float is calculated according to the average float of all input skins scaled to the output collection’s range. Thus, an advanced player may affect the probability of getting a certain skin via inputs manipulation.
To explain, if seven skins belong to one collection while three skins are from another, the output skin will most probably originate from the first collection. If the first collection contains a $500 skin at the next tier and the second contains a $30 skin, you can engineer a heavily weighted gamble in your favor.
But here’s the catch — the math only works if you actually run the numbers. The inputs might cost $80 in total, but if the expected value of the output is only $60, you’re making a bad bet regardless of the potential upside. That’s why experienced traders simulate their contracts using a CS2 trade-up calculator before committing any skins. These tools predict every possible outcome with exact probabilities, float projections, and expected profit or loss.
The trade-up system was further shaken in October 2025 when Valve added the ability to trade up Covert skins into knives and gloves — something that was previously impossible. Players could suddenly turn five Covert skins worth roughly $5-10 each into knives that were previously selling for $1,000+. The result? Knife prices crashed overnight, the total market cap dropped by hundreds of millions, and the entire pricing hierarchy had to readjust.
The Tech Infrastructure Behind It All
All that lies beneath all these graphs and calculations is quite a bit of technology. Real-time data feeds, APIs, and aggregators pull pricing information from several different marketplaces simultaneously.
Automated trading, monitoring services, portfolio management tools, and other such applications are developed by third parties using the marketplace APIs. Some platforms offer their own developer APIs with endpoints for price recommendations, market analytics, and cross-platform price comparison — essentially creating the financial infrastructure layer that the CS2 economy needed to operate at scale.
Steam itself provides API access for inventory data, market listings, and transaction history, which third-party services use to power everything from inventory valuation tools to automated trading systems.
The sophistication has reached a point where the CS2 economy has its own version of Bloomberg terminals — dashboards that track market-wide trends, individual item price histories, trading volumes, liquidity scores, and even volatility metrics. Professional traders monitor these tools the same way a Wall Street analyst watches stock tickers.
Why It Matters Beyond Gaming
The CS2 skin economy isn’t just a curiosity — it’s a case study in how digital ownership, market dynamics, and community-driven value creation work at scale.
This is what some of the main points which can be derived from this are. Firstly, scarcity defines value in all instances. It has been illustrated in the CS2 skins case study, in which it is clear that it does not matter whether items are tangible or useful in order for them to have economic value.
Second, platform decisions have outsized economic impact. Valve’s single update in October 2025 erased over a billion dollars in virtual item value. No other company has that kind of direct influence over a player-driven economy of this scale.
And third, the line between gaming economies and financial markets is dissolving. When your hobby comes with real-time price tracking, market cap analytics, trade-up calculators, and cross-platform arbitrage opportunities, you’re not just playing a game anymore. You’re participating in a micro-economy that happens to live inside one.
Whether you’re a casual CS2 player who’s never sold a skin or a veteran trader running profit calculations on every drop, the scale and sophistication of what’s been built here is worth paying attention to. An $8 billion economy that runs on cosmetic pixels, community trust, and a few really good APIs — that’s the kind of thing you only find in gaming.
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