Author name: Beth Washington

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After 50 million miles, Waymos crash a lot less than human drivers

AI, Cars, Features, self driving cars, waymo / /

Waymo has been in dozens of crashes. Most were not Waymo’s fault.

A driverless Waymo in Los Angeles. Credit: P_Wei via Getty

The first ever fatal crash involving a fully driverless vehicle occurred in San Francisco on January 19. The driverless vehicle belonged to Waymo, but the crash was not Waymo’s fault.

Here’s what happened: A Waymo with no driver or passengers stopped for a red light. Another car stopped behind the Waymo. Then, according to Waymo, a human-driven SUV rear-ended the other vehicles at high speed, causing a six-car pileup that killed one person and injured five others. Someone’s dog also died in the crash.

Another major Waymo crash occurred in October in San Francisco. Once again, a driverless Waymo was stopped for a red light. According to Waymo, a vehicle traveling in the opposite direction crossed the double yellow line and crashed into an SUV that was stopped to the Waymo’s left. The force of the impact shoved the SUV into the Waymo. One person was seriously injured.

These two incidents produced worse injuries than any other Waymo crash in the last nine months. But in other respects, they were typical Waymo crashes. Most Waymo crashes involve a Waymo vehicle scrupulously following the rules while a human driver flouts them, speeding, running red lights, careening out of their lanes, and so forth.

Waymo’s service will only grow in the coming months and years. So Waymo will inevitably be involved in more crashes—including some crashes that cause serious injuries and even death.

But as this happens, it’s crucial to keep the denominator in mind. Since 2020, Waymo has reported roughly 60 crashes serious enough to trigger an airbag or cause an injury. But those crashes occurred over more than 50 million miles of driverless operations. If you randomly selected 50 million miles of human driving—that’s roughly 70 lifetimes behind the wheel—you would likely see far more serious crashes than Waymo has experienced to date.

Federal regulations require Waymo to report all significant crashes, whether or not the Waymo vehicle was at fault—indeed, whether or not the Waymo is even moving at the time of the crash. I’ve spent the last few days poring over Waymo’s crash reports from the last nine months. Let’s dig in.

Last September, I analyzed Waymo crashes through June 2024. So this section will focus on crashes between July 2024 and February 2025. During that period, Waymo reported 38 crashes that were serious enough to either cause an (alleged) injury or an airbag deployment.

In my view, only one of these crashes was clearly Waymo’s fault. Waymo may have been responsible for three other crashes—there wasn’t enough information to say for certain. The remaining 34 crashes seemed to be mostly or entirely the fault of others:

  • The two serious crashes I mentioned at the start of this article are among 16 crashes where another vehicle crashed into a stationary Waymo (or caused a multi-car pileup involving a stationary Waymo). This included 10 rear-end crashes, three side-swipe crashes, and three crashes where a vehicle coming from the opposite direction crossed the center line.
  • Another eight crashes involved another car (or in one case a bicycle) rear-ending a moving Waymo.
  • A further five crashes involved another vehicle veering into a Waymo’s right of way. This included a car running a red light, a scooter running a red light, and a car running a stop sign.
  • Three crashes occurred while Waymo was dropping a passenger off. The passenger opened the door and hit a passing car or bicycle. Waymo has a “Safe Exit” program to alert passengers and prevent this kind of crash, but it’s not foolproof.

There were two incidents where it seems like no crash happened at all:

  • In one incident, Waymo says that its vehicle “slowed and moved slightly to the left within its lane, preparing to change lanes due to a stopped truck ahead.” This apparently spooked an SUV driver in the next lane, who jerked the wheel to the left and ran into the opposite curb. Waymo says its vehicle never left its lane or made contact with the SUV.
  • In another incident, a pedestrian walked in front of a stopped Waymo. The Waymo began moving after the pedestrian had passed, but then the pedestrian “turned around and approached the Waymo AV.” According to Waymo, the pedestrian “may have made contact with the driver side of the Waymo AV” and “later claimed to have a minor injury.” Waymo’s report stops just short of calling this pedestrian a liar.

So that’s a total of 34 crashes. I don’t want to make categorical statements about these crashes because in most cases, I only have Waymo’s side of the story. But it doesn’t seem like Waymo was at fault in any of them.

There was one crash where Waymo clearly seemed to be at fault: In December, a Waymo in Los Angeles ran into a plastic crate, pushing it into the path of a scooter in the next lane. The scooterist hit the crate and fell down. Waymo doesn’t know whether the person riding the scooter was injured.

I had trouble judging the final three crashes, all of which involved another vehicle making an unprotected left turn across a Waymo’s lane of travel. In two of these cases, Waymo says its vehicle slammed on the brakes but couldn’t stop in time to avoid a crash. In the third case, the other vehicle hit the Waymo from the side. Waymo’s summaries make it sound like the other car was at fault in all three cases, but I don’t feel like I have enough information to make a definite judgment.

Even if we assume all three of these crashes were Waymo’s fault, that would still mean that a large majority of the 38 serious crashes were not Waymo’s fault. And as we’ll see, Waymo vehicles are involved in many fewer serious crashes than human-driven vehicles.

Another way to evaluate the safety of Waymo vehicles is by comparing their per-mile crash rate to human drivers. Waymo has been regularly publishing data about this over the last couple of years. Its most recent release came last week, when Waymo updated its safety data hub to cover crashes through the end of 2024.

Waymo knows exactly how many times its vehicles have crashed. What’s tricky is figuring out the appropriate human baseline, since human drivers don’t necessarily report every crash. Waymo has tried to address this by estimating human crash rates in its two biggest markets—Phoenix and San Francisco. Waymo’s analysis focused on the 44 million miles Waymo had driven in these cities through December, ignoring its smaller operations in Los Angeles and Austin.

Using human crash data, Waymo estimated that human drivers on the same roads would get into 78 crashes serious enough to trigger an airbag. By comparison, Waymo’s driverless vehicles only got into 13 airbag crashes. That represents an 83 percent reduction in airbag crashes relative to typical human drivers.

This is slightly worse than last September, when Waymo estimated an 84 percent reduction in airbag crashes over Waymo’s first 21 million miles.

Over the same 44 million miles, Waymo estimates that human drivers would get into 190 crashes serious enough to cause an injury. Instead, Waymo only got in 36 injury-causing crashes across San Francisco or Phoenix. That’s an 81 percent reduction in injury-causing crashes.

This is a significant improvement over last September, when Waymo estimated its cars had 73 percent fewer injury-causing crashes over its first 21 million driverless miles.

The above analysis counts all crashes, whether or not Waymo’s technology was at fault. Things look even better for Waymo if we focus on crashes where Waymo was determined to be responsible for a crash.

To assess this, Waymo co-authored a study in December with the insurance giant Swiss Re. It focused on crashes that led to successful insurance claims against Waymo. This data seems particularly credible because third parties, not Waymo, decide when a crash is serious enough to file an insurance claim. And claims adjusters, not Waymo, decide whether to hold Waymo responsible for a crash.

But one downside is that it takes a few months for insurance claims to be filed. So the December report focused on crashes that occurred through July 2024.

Waymo had completed 25 million driverless miles by July 2024. And by the end of November 2024, Waymo had faced only two potentially successful claims for bodily injury. Both claims are pending, which means they could still be resolved in Waymo’s favor.

One of them was this crash that I described at the beginning of my September article about Waymo’s safety record:

On a Friday evening last November, police chased a silver sedan across the San Francisco Bay Bridge. The fleeing vehicle entered San Francisco and went careening through the city’s crowded streets. At the intersection of 11th and Folsom streets, it sideswiped the fronts of two other vehicles, veered onto a sidewalk, and hit two pedestrians.

According to a local news story, both pedestrians were taken to the hospital, with one suffering major injuries. The driver of the silver sedan was injured, as was a passenger in one of the other vehicles. No one was injured in the third car, a driverless Waymo robotaxi.

It seems unlikely that an insurance adjuster will ultimately hold Waymo responsible for these injuries.

The other pending injury claim doesn’t seem like a slam dunk, either. In that case, another vehicle steered into a bike lane before crashing into a Waymo as it was making a left turn.

But let’s assume that both crashes are judged to be Waymo’s fault. That would still be a strong overall safety record.

Based on insurance industry records, Waymo and Swiss Re estimate that human drivers in San Francisco and Phoenix would generate about 26 successful bodily injury claims over 25 million miles of driving. So even if both of the pending claims against Waymo succeed, two injuries represent a more than 90 percent reduction in successful injury claims relative to typical human drivers.

The reduction in property damage claims is almost as dramatic. Waymo’s vehicles generated nine successful or pending property damage claims over its first 25 million miles. Waymo and Swiss Re estimate that human drivers in the same geographic areas would have generated 78 property damage claims. So Waymo generated 88 percent fewer property damage claims than typical human drivers.

Timothy B. Lee was on staff at Ars Technica from 2017 to 2021. Today he writes Understanding AI, a newsletter that explores how AI works and how it’s changing our world. You can subscribe here.

Photo of Timothy B. Lee

Timothy is a senior reporter covering tech policy and the future of transportation. He lives in Washington DC.

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Gemini 2.5 Pro is here with bigger numbers and great vibes

AI, Artificial Intelligence, Google, Google Gemini / /

Just a few months after releasing its first Gemini 2.0 AI models, Google is upgrading again. The company says the new Gemini 2.5 Pro Experimental is its “most intelligent” model yet, offering a massive context window, multimodality, and reasoning capabilities. Google points to a raft of benchmarks that show the new Gemini clobbering other large language models (LLMs), and our testing seems to back that up—Gemini 2.5 Pro is one of the most impressive generative AI models we’ve seen.

Gemini 2.5, like all Google’s models going forward, has reasoning built in. The AI essentially fact-checks itself along the way to generating an output. We like to call this “simulated reasoning,” as there’s no evidence that this process is akin to human reasoning. However, it can go a long way to improving LLM outputs. Google specifically cites the model’s “agentic” coding capabilities as a beneficiary of this process. Gemini 2.5 Pro Experimental can, for example, generate a full working video game from a single prompt. We’ve tested this, and it works with the publicly available version of the model.

Gemini 2.5 Pro builds a game in one step.

Google says a lot of things about Gemini 2.5 Pro; it’s smarter, it’s context-aware, it thinks—but it’s hard to quantify what constitutes improvement in generative AI bots. There are some clear technical upsides, though. Gemini 2.5 Pro comes with a 1 million token context window, which is common for the big Gemini models but massive compared to competing models like OpenAI GPT or Anthropic Claude. You could feed multiple very long books to Gemini 2.5 Pro in a single prompt, and the output maxes out at 64,000 tokens. That’s the same as Flash 2.0, but it’s still objectively a lot of tokens compared to other LLMs.

Naturally, Google has run Gemini 2.5 Experimental through a battery of benchmarks, in which it scores a bit higher than other AI systems. For example, it squeaks past OpenAI’s o3-mini in GPQA and AIME 2025, which measure how well the AI answers complex questions about science and math, respectively. It also set a new record in the Humanity’s Last Exam benchmark, which consists of 3,000 questions curated by domain experts. Google’s new AI managed a score of 18.8 percent to OpenAI’s 14 percent.

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ESA finally has a commercial launch strategy, but will member states pay?

Commercial space, Europe, european space agency, launch, Science, Space / /

Late this year, European governments will have the opportunity to pay up or shut up.

The European Space Agency is inviting proposals to inject competition into the European launch market, an important step toward fostering a dynamic multiplayer industry officials hope, one day, will mimic that of the United States.

The near-term plan for the European Launcher Challenge is for ESA to select companies for service contracts to transport ESA and other European government payloads to orbit from 2026 through 2030. A second component of the challenge is for companies to perform at least one demonstration of an upgraded launch vehicle by 2028. The competition is open to any European company working in the launch business.

“What we expect is that these companies will make a step in improving and upgrading their capacity with respect to what they’re presently working on,” said Toni Tolker-Nielsen, ESA’s acting director of space transportation. “In terms of economics and physics, it’s better to have a bigger launcher than a smaller launcher in terms of price per kilogram to orbit.”

“The ultimate goal is, we should be establishing privately developed competitive launch services in Europe, which will allow us to procure launch services in open competition,” Tolker-Nielsen said in an interview with Ars.

From one to many?

ESA and other European institutions currently have just one European provider, Arianespace, to award launch contracts for the continent’s scientific, Earth observation, navigation, and military satellites. Arianespace operates the Ariane 6 and Vega C rockets. Vega C operations will soon be taken over by Italian aerospace company Avio. Both rockets were developed with ESA funding.

The launcher challenge is modeled on NASA’s use of commercial contracting methods beginning nearly 20 years ago with the agency’s commercial cargo program, which kickstarted the development of SpaceX’s Dragon and Northrop Grumman’s Cygnus resupply freighters for the International Space Station. NASA later applied the same model to commercial crew, and most recently for commercial lunar landers.

Uncharacteristically for ESA, the agency is taking a hands-off approach for the launcher challenge. One of the few major requirements is that the winners should offer a “European launch service” that flies from European territory, which includes the French-run Guiana Space Center in South America.

Europe’s second Ariane 6 rocket lifted off March 6 with a French military spy satellite. Credit: European Space Agency

“We are trying something different, where they are completely free to organize themselves,” Tolker-Nielsen said. “We are not pushing anything. We are in a complete service-oriented model here. That’s the principal difference between the new approach and the old approach.”

ESA also isn’t setting requirements on launcher performance, reusability, or the exact number of companies it will select in the challenge. But ESA would like to limit the number of challengers “to a minimum” to ensure the agency’s support is meaningful, without spreading its funding too thin, Tolker-Nielsen said.

“For the ESA-developed launchers, which are Ariane 6 and Vega C, we own the launch system,” Tolker-Nielsen said. “We finished the development, and the deliverables were the launch systems that we own at ESA, and we make it available to an operator—Arianespace, and Avio soon for Vega C—to exploit.”

These ESA-led launcher projects were expensive. The development of Ariane 6 cost European governments more than $4 billion. Ariane 6 is now flying, but none of the up-and-coming European alternatives is operational.

Next steps

It has taken a while to set up the European Launcher Challenge, which won preliminary approval from ESA’s 23 member states at a ministerial-level meeting in 2023. ESA released an “invitation to tender,” soliciting proposals from European launch companies Monday, with submissions due by May 5. This summer, ESA expects to select the top proposals and prepare a funding package for consideration by its member states at the next ministerial meeting in November.

The top factors ESA will consider in this first phase of the challenge are each proposer’s business plan, technical credibility, and financial credibility.

In a statement, ESA said it has allotted up to 169 million euros ($182 million at today’s exchange rates) per challenger. This is significant funding for Europe’s crop of cash-hungry launch startups, each of which has raised no more than a few hundred million euros. But this allotment comes with a catch. ESA’s leaders and the winners of the launch challenge must persuade their home governments to pay up.

Let’s take a moment to compare Europe’s launch industry with that of the United States.

There are multiple viable US commercial launch companies. In the United States, it’s easier to attract venture capital, the government has been a more reliable proponent of commercial spaceflight, and billionaires are part of the launch landscape. SpaceX, led by Elon Musk, dominates the market. Jeff Bezos’s space company, Blue Origin, and United Launch Alliance are also big players with heavy-lift rockets.

Rocket Lab and Firefly Aerospace fly smaller, privately developed launchers. Northrop Grumman’s medium-class launch division is currently in between rockets, although it still occasionally launches small US military satellites on Minotaur rockets derived from decommissioned ICBMs.

Of course, it’s not surprising the sum of US launch companies is higher than in Europe. According to the World Bank, the US economy is about 50 percent larger than the European Union’s. But six American companies with operational orbital rockets, compared to one in Europe today? That is woefully out of proportion.

European officials would like to regain a leading position in the global commercial launch market. With SpaceX’s dominance, that’s a tall hill to climb. At the very least, European politicians don’t want to rely on other countries for access to space. In the last three years, they’ve seen their access to Russian launchers dry up after Russia’s invasion of Ukraine, and after signing a few launch contracts with SpaceX to bridge the gap before the first flight of Ariane 6, they now view the US government and Elon Musk as unreliable partners.

Open your checkbook, please

ESA’s governance structure isn’t favorable for taking quick action. On one hand, ESA member states approve the agency’s budget in multiyear increments, giving its projects a sense of stability over time. However, it takes time to get new projects approved, and ESA’s member states expect to receive benefits—jobs, investment, and infrastructure—commensurate with their spending on European space programs. This policy is known as geographical return, or geo-return.

For example, France has placed a high strategic importance on fielding an independent European launch capability for more than 60 years. The administration of French President Charles de Gaulle made this determination during the Cold War, around the same time he decided France should have a nuclear deterrent fully independent of the United States and NATO.

In order to match this policy, France has been more willing than other European nations to invest in launchers. This means the Ariane rocket family, developed and funded through ESA contracts, has been largely a French enterprise since the first Ariane launch in 1979.

This model is becoming antiquated in the era of commercial spaceflight. Startups across Europe, primarily in France, Germany, the United Kingdom, and Spain, are developing small launchers designed to carry up to 1.5 metric tons of payload to low-Earth orbit. This is too small to directly compete with the Ariane 6 rocket, but eventually, these companies would like to develop larger launchers.

Some European officials, including the former head of the French space agency, blamed geo-return as a reason the Ariane 6 rocket missed its price target.

Toni Tolker-Nielsen, ESA’s acting director of space transportation, speaks at an event in 2021. Credit: ESA/V. Stefanelli

With the European Launcher Challenge, ESA will experiment with a new funding model for the first time. This new “fair contribution” approach will see ESA leadership put forward a plan to its member states at the next big ministerial conference in November. The space agency will ask the countries that benefit most from the winners of the launcher challenge to provide the bulk of the funding for the challengers’ contracts.

So, let’s say Isar Aerospace, which is set to launch its first rocket as soon as this week, is one of the challenge winners. Isar is headquartered in Munich, and its current launch site is in Norway. In this case, expect ESA to ask the governments of Germany and Norway to contribute the most money to pay for Isar’s contract.

MaiaSpace, a French subsidiary of ArianeGroup, the parent company of Arianespace, is also a contender in the launcher challenge. MaiaSpace plans to launch from French Guiana. Therefore, if MaiaSpace gets a contract, France would be on the hook for the lion’s share of the deal’s funding.

Tolker-Nielsen said he anticipates a “number” of the launch challengers will win the backing of their home countries in November, but “maybe not all.”

“So, first there is this criteria that they have to be eligible, and then they have to be funded as well,” he said. “We don’t want to propose funding for companies that we don’t see as credible.”

Assuming the challengers’ contracts get funded, ESA will then work with the European Commission to assign specific satellites to launch on the new commercial rockets.

“The way I look at this is we are not going to choose winners,” Tolker-Nielsen said. “The challenge is not the competition we are doing right now. It is to deliver on the contract. That’s the challenge.”

Photo of Stephen Clark

Stephen Clark is a space reporter at Ars Technica, covering private space companies and the world’s space agencies. Stephen writes about the nexus of technology, science, policy, and business on and off the planet.

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Open Source devs say AI crawlers dominate traffic, forcing blocks on entire countries

AI, Biz & IT, DDoS, machine learning / /

AI bots hungry for data are taking down FOSS sites by accident, but humans are fighting back.

Software developer Xe Iaso reached a breaking point earlier this year when aggressive AI crawler traffic from Amazon overwhelmed their Git repository service, repeatedly causing instability and downtime. Despite configuring standard defensive measures—adjusting robots.txt, blocking known crawler user-agents, and filtering suspicious traffic—Iaso found that AI crawlers continued evading all attempts to stop them, spoofing user-agents and cycling through residential IP addresses as proxies.

Desperate for a solution, Iaso eventually resorted to moving their server behind a VPN and creating “Anubis,” a custom-built proof-of-work challenge system that forces web browsers to solve computational puzzles before accessing the site. “It’s futile to block AI crawler bots because they lie, change their user agent, use residential IP addresses as proxies, and more,” Iaso wrote in a blog post titled “a desperate cry for help.” “I don’t want to have to close off my Gitea server to the public, but I will if I have to.”

Iaso’s story highlights a broader crisis rapidly spreading across the open source community, as what appear to be aggressive AI crawlers increasingly overload community-maintained infrastructure, causing what amounts to persistent distributed denial-of-service (DDoS) attacks on vital public resources. According to a comprehensive recent report from LibreNews, some open source projects now see as much as 97 percent of their traffic originating from AI companies’ bots, dramatically increasing bandwidth costs, service instability, and burdening already stretched-thin maintainers.

Kevin Fenzi, a member of the Fedora Pagure project’s sysadmin team, reported on his blog that the project had to block all traffic from Brazil after repeated attempts to mitigate bot traffic failed. GNOME GitLab implemented Iaso’s “Anubis” system, requiring browsers to solve computational puzzles before accessing content. GNOME sysadmin Bart Piotrowski shared on Mastodon that only about 3.2 percent of requests (2,690 out of 84,056) passed their challenge system, suggesting the vast majority of traffic was automated. KDE’s GitLab infrastructure was temporarily knocked offline by crawler traffic originating from Alibaba IP ranges, according to LibreNews, citing a KDE Development chat.

While Anubis has proven effective at filtering out bot traffic, it comes with drawbacks for legitimate users. When many people access the same link simultaneously—such as when a GitLab link is shared in a chat room—site visitors can face significant delays. Some mobile users have reported waiting up to two minutes for the proof-of-work challenge to complete, according to the news outlet.

The situation isn’t exactly new. In December, Dennis Schubert, who maintains infrastructure for the Diaspora social network, described the situation as “literally a DDoS on the entire internet” after discovering that AI companies accounted for 70 percent of all web requests to their services.

The costs are both technical and financial. The Read the Docs project reported that blocking AI crawlers immediately decreased their traffic by 75 percent, going from 800GB per day to 200GB per day. This change saved the project approximately $1,500 per month in bandwidth costs, according to their blog post “AI crawlers need to be more respectful.”

A disproportionate burden on open source

The situation has created a tough challenge for open source projects, which rely on public collaboration and typically operate with limited resources compared to commercial entities. Many maintainers have reported that AI crawlers deliberately circumvent standard blocking measures, ignoring robots.txt directives, spoofing user agents, and rotating IP addresses to avoid detection.

As LibreNews reported, Martin Owens from the Inkscape project noted on Mastodon that their problems weren’t just from “the usual Chinese DDoS from last year, but from a pile of companies that started ignoring our spider conf and started spoofing their browser info.” Owens added, “I now have a prodigious block list. If you happen to work for a big company doing AI, you may not get our website anymore.”

On Hacker News, commenters in threads about the LibreNews post last week and a post on Iaso’s battles in January expressed deep frustration with what they view as AI companies’ predatory behavior toward open source infrastructure. While these comments come from forum posts rather than official statements, they represent a common sentiment among developers.

As one Hacker News user put it, AI firms are operating from a position that “goodwill is irrelevant” with their “$100bn pile of capital.” The discussions depict a battle between smaller AI startups that have worked collaboratively with affected projects and larger corporations that have been unresponsive despite allegedly forcing thousands of dollars in bandwidth costs on open source project maintainers.

Beyond consuming bandwidth, the crawlers often hit expensive endpoints, like git blame and log pages, placing additional strain on already limited resources. Drew DeVault, founder of SourceHut, reported on his blog that the crawlers access “every page of every git log, and every commit in your repository,” making the attacks particularly burdensome for code repositories.

The problem extends beyond infrastructure strain. As LibreNews points out, some open source projects began receiving AI-generated bug reports as early as December 2023, first reported by Daniel Stenberg of the Curl project on his blog in a post from January 2024. These reports appear legitimate at first glance but contain fabricated vulnerabilities, wasting valuable developer time.

Who is responsible, and why are they doing this?

AI companies have a history of taking without asking. Before the mainstream breakout of AI image generators and ChatGPT attracted attention to the practice in 2022, the machine learning field regularly compiled datasets with little regard to ownership.

While many AI companies engage in web crawling, the sources suggest varying levels of responsibility and impact. Dennis Schubert’s analysis of Diaspora’s traffic logs showed that approximately one-fourth of its web traffic came from bots with an OpenAI user agent, while Amazon accounted for 15 percent and Anthropic for 4.3 percent.

The crawlers’ behavior suggests different possible motivations. Some may be collecting training data to build or refine large language models, while others could be executing real-time searches when users ask AI assistants for information.

The frequency of these crawls is particularly telling. Schubert observed that AI crawlers “don’t just crawl a page once and then move on. Oh, no, they come back every 6 hours because lol why not.” This pattern suggests ongoing data collection rather than one-time training exercises, potentially indicating that companies are using these crawls to keep their models’ knowledge current.

Some companies appear more aggressive than others. KDE’s sysadmin team reported that crawlers from Alibaba IP ranges were responsible for temporarily knocking their GitLab offline. Meanwhile, Iaso’s troubles came from Amazon’s crawler. A member of KDE’s sysadmin team told LibreNews that Western LLM operators like OpenAI and Anthropic were at least setting proper user agent strings (which theoretically allows websites to block them), while some Chinese AI companies were reportedly more deceptive in their approaches.

It remains unclear why these companies don’t adopt more collaborative approaches and, at a minimum, rate-limit their data harvesting runs so they don’t overwhelm source websites. Amazon, OpenAI, Anthropic, and Meta did not immediately respond to requests for comment, but we will update this piece if they reply.

Tarpits and labyrinths: The growing resistance

In response to these attacks, new defensive tools have emerged to protect websites from unwanted AI crawlers. As Ars reported in January, an anonymous creator identified only as “Aaron” designed a tool called “Nepenthes” to trap crawlers in endless mazes of fake content. Aaron explicitly describes it as “aggressive malware” intended to waste AI companies’ resources and potentially poison their training data.

“Any time one of these crawlers pulls from my tarpit, it’s resources they’ve consumed and will have to pay hard cash for,” Aaron explained to Ars. “It effectively raises their costs. And seeing how none of them have turned a profit yet, that’s a big problem for them.”

On Friday, Cloudflare announced “AI Labyrinth,” a similar but more commercially polished approach. Unlike Nepenthes, which is designed as an offensive weapon against AI companies, Cloudflare positions its tool as a legitimate security feature to protect website owners from unauthorized scraping, as we reported at the time.

“When we detect unauthorized crawling, rather than blocking the request, we will link to a series of AI-generated pages that are convincing enough to entice a crawler to traverse them,” Cloudflare explained in its announcement. The company reported that AI crawlers generate over 50 billion requests to their network daily, accounting for nearly 1 percent of all web traffic they process.

The community is also developing collaborative tools to help protect against these crawlers. The “ai.robots.txt” project offers an open list of web crawlers associated with AI companies and provides premade robots.txt files that implement the Robots Exclusion Protocol, as well as .htaccess files that return error pages when detecting AI crawler requests.

As it currently stands, both the rapid growth of AI-generated content overwhelming online spaces and aggressive web-crawling practices by AI firms threaten the sustainability of essential online resources. The current approach taken by some large AI companies—extracting vast amounts of data from open-source projects without clear consent or compensation—risks severely damaging the very digital ecosystem on which these AI models depend.

Responsible data collection may be achievable if AI firms collaborate directly with the affected communities. However, prominent industry players have shown little incentive to adopt more cooperative practices. Without meaningful regulation or self-restraint by AI firms, the arms race between data-hungry bots and those attempting to defend open source infrastructure seems likely to escalate further, potentially deepening the crisis for the digital ecosystem that underpins the modern Internet.

Photo of Benj Edwards

Benj Edwards is Ars Technica’s Senior AI Reporter and founder of the site’s dedicated AI beat in 2022. He’s also a tech historian with almost two decades of experience. In his free time, he writes and records music, collects vintage computers, and enjoys nature. He lives in Raleigh, NC.

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No cloud needed: Nvidia creates gaming-centric AI chatbot that runs on your GPU

AI, artificial inteligence, gaming, NVIDIA / /

Nvidia has seen its fortunes soar in recent years as its AI-accelerating GPUs have become worth their weight in gold. Most people use their Nvidia GPUs for games, but why not both? Nvidia has a new AI you can run at the same time, having just released its experimental G-Assist AI. It runs locally on your GPU to help you optimize your PC and get the most out of your games. It can do some neat things, but Nvidia isn’t kidding when it says this tool is experimental.

G-Assist is available in the Nvidia desktop app, and it consists of a floating overlay window. After invoking the overlay, you can either type or speak to G-Assist to check system stats or make tweaks to your settings. You can ask basic questions like, “How does DLSS Frame Generation work?” but it also has control over some system-level settings.

By calling up G-Assist, you can get a rundown of how your system is running, including custom data charts created on the fly by G-Assist. You can also ask the AI to tweak your machine, for example, optimizing the settings for a particular game or toggling on or off a setting. G-Assist can even overclock your GPU if you so choose, complete with a graph of expected performance gains.

Nvidia on G-Assist.

Nvidia demoed G-Assist last year with some impressive features tied to the active game. That version of G-Assist could see what you were doing and offer suggestions about how to reach your next objective. The game integration is sadly quite limited in the public version, supporting just a few games, like Ark: Survival Evolved.

There is, however, support for a number of third-party plug-ins that give G-Assist control over Logitech G, Corsair, MSI, and Nanoleaf peripherals. So, for instance, G-Assist could talk to your MSI motherboard to control your thermal profile or ping Logitech G to change your LED settings.

No cloud needed: Nvidia creates gaming-centric AI chatbot that runs on your GPU Read More »

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Napster to become a music-marketing metaverse firm after being sold for $207M

acquisition, napster, streaming, Tech / /

Infinite Reality, a media, ecommerce, and marketing company focused on 3D and AI-powered experiences, has entered an agreement to acquired Napster. That means that the brand originally launched in 1999 as a peer-to-peer (P2P) music file-sharing service is set to be reborn again. This time, new owners are reshaping the brand into one focused on marketing musicians in the metaverse.

Infinite announced today a definitive agreement to buy Napster for $207 million. The Norwalk, Connecticut-based company plans to turn Napster into a “social music platform that prioritizes active fan engagement over passive listening, allowing artists to connect with, own, and monetize the relationship with their fans.” Jon Vlassopulos, who became Napster CEO in 2022, will continue with his role at the brand.

Since 2016, Napster has been operating as a (legal) streaming service. It claims to have over 110 million high-fidelity tracks, with some supporting lossless audio. Napster subscribers can also listen offline and watch music videos. The service currently starts at $11 per month.

Since 2022, Napster has been owned by Web3 and blockchain firms Hivemind and Algorand. Infinite also develops Web3 tech, and CEO John Acunto told CNBC that Algorand’s blockchain background was appealing, as was Napster’s licenses for streaming millions of songs.

To market musicians, Infinite has numerous ideas for helping Napster users interact more with the platform than they do with the current music streaming service. The company shared goals of using Napster to offer “branded 3D virtual spaces where fans can enjoy virtual concerts, social listening parties, and other immersive and community-based experiences” and more “gamification.” Infinite also wants musicians to use Napster as a platform where fans can purchase tickets for performances, physical and virtual merchandise, and “exclusive digital content.” The 6-year-old firm also plans to offer artists abilities to use “AI-powered customer service, sales, and community management agents” and “enhanced analytics dashboards to better understand fan behavior” with Napster.

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We’ve outsourced our confirmation biases to search engines

cognitive bias, confirmation bias, Human behavior, Science, search engines / /

So, the researchers decided to see if they could upend it.

Keeping it general

The simplest way to change the dynamics of this was simply to change the results returned by the search. So, the researchers did a number of experiments where they gave all of the participants the same results, regardless of the search terms they had used. When everybody gets the same results, their opinions after reading them tend to move in the same direction, suggesting that search results can help change people’s opinions.

The researchers also tried giving everyone the results of a broad, neutral search, regardless of the terms they’d entered. This weakened the probability that beliefs would last through the process of formulating and executing a search. In other words, avoiding the sorts of focused, biased search terms allowed some participants to see information that could change their minds.

Despite all the swapping, participants continued to rate the search results relevant. So, providing more general search results even when people were looking for more focused information doesn’t seem to harm people’s perception of the service. In fact, Leung and Urminsky found that the AI version of Bing search would reformulate narrow questions into more general ones.

That said, making this sort of change wouldn’t be without risks. There are a lot of subject areas where a search shouldn’t return a broad range of information—where grabbing a range of ideas would expose people to fringe and false information.

Nevertheless, it can’t hurt to be aware of how we can use search services to reinforce our biases. So, in the words of Leung and Urminsky, “When search engines provide directionally narrow search results in response to users’ directionally narrow search terms, the results will reflect the users’ existing beliefs, instead of promoting belief updating by providing a broad spectrum of related information.”

PNAS, 2025. DOI: 10.1073/pnas.2408175122  (About DOIs).

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As preps continue, it’s looking more likely NASA will fly the Artemis II mission

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NASA’s existing architecture still has a limited shelf life, and the agency will probably have multiple options for transporting astronauts to and from the Moon in the 2030s. A decision on the long-term future of SLS and Orion isn’t expected until the Trump administration’s nominee for NASA administrator, Jared Isaacman, takes office after confirmation by the Senate.

So, what is the plan for SLS?

There are different degrees of cancellation options. The most draconian would be an immediate order to stop work on Artemis II preparations. This is looking less likely than it did a few months ago and would come with its own costs. It would cost untold millions of dollars to disassemble and dispose of parts of Artemis II’s SLS rocket and Orion spacecraft. Canceling multibillion-dollar contracts with Boeing, Northrop Grumman, and Lockheed Martin would put NASA on the hook for significant termination costs.

Of course, these liabilities would be less than the $4.1 billion NASA’s inspector general estimates each of the first four Artemis missions will cost. Most of that money has already been spent for Artemis II, but if NASA spends several billion dollars on each Artemis mission, there won’t be much money left over to do other cool things.

Other options for NASA might be to set a transition point when the Artemis program would move off of the Space Launch System rocket, and perhaps even the Orion spacecraft, and switch to new vehicles.

Looking down on the Space Launch System for Artemis II. Credit: NASA/Frank Michaux

Another possibility, which seems to be low-hanging fruit for Artemis decision-makers, could be to cancel the development of a larger Exploration Upper Stage for the SLS rocket. If there are a finite number of SLS flights on NASA’s schedule, it’s difficult to justify the projected $5.7 billion cost of developing the upgraded Block 1B version of the Space Launch System. There are commercial options available to replace the rocket’s Boeing-built Exploration Upper Stage, as my colleague Eric Berger aptly described in a feature story last year.

For now, it looks like NASA’s orange behemoth has a little life left in it. All the hardware for the Artemis II mission has arrived at the launch site in Florida.

The Trump administration will release its fiscal-year 2026 budget request in the coming weeks. Maybe then NASA will also have a permanent administrator, and the veil will lift over the White House’s plans for Artemis.

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You can now download the source code that sparked the AI boom

AI, Biz & IT / /

On Thursday, Google and the Computer History Museum (CHM) jointly released the source code for AlexNet, the convolutional neural network (CNN) that many credit with transforming the AI field in 2012 by proving that “deep learning” could achieve things conventional AI techniques could not.

Deep learning, which uses multi-layered neural networks that can learn from data without explicit programming, represented a significant departure from traditional AI approaches that relied on hand-crafted rules and features.

The Python code, now available on CHM’s GitHub page as open source software, offers AI enthusiasts and researchers a glimpse into a key moment of computing history. AlexNet served as a watershed moment in AI because it could accurately identify objects in photographs with unprecedented accuracy—correctly classifying images into one of 1,000 categories like “strawberry,” “school bus,” or “golden retriever” with significantly fewer errors than previous systems.

Like viewing original ENIAC circuitry or plans for Babbage’s Difference Engine, examining the AlexNet code may provide future historians insight into how a relatively simple implementation sparked a technology that has reshaped our world. While deep learning has enabled advances in health care, scientific research, and accessibility tools, it has also facilitated concerning developments like deepfakes, automated surveillance, and the potential for widespread job displacement.

But in 2012, those negative consequences still felt like far-off sci-fi dreams to many. Instead, experts were simply amazed that a computer could finally recognize images with near-human accuracy.

Teaching computers to see

As the CHM explains in its detailed blog post, AlexNet originated from the work of University of Toronto graduate students Alex Krizhevsky and Ilya Sutskever, along with their advisor Geoffrey Hinton. The project proved that deep learning could outperform traditional computer vision methods.

The neural network won the 2012 ImageNet competition by recognizing objects in photos far better than any previous method. Computer vision veteran Yann LeCun, who attended the presentation in Florence, Italy, immediately recognized its importance for the field, reportedly standing up after the presentation and calling AlexNet “an unequivocal turning point in the history of computer vision.” As Ars detailed in November, AlexNet marked the convergence of three critical technologies that would define modern AI.

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More on Various AI Action Plans

Various / /

Last week I covered Anthropic’s relatively strong submission, and OpenAI’s toxic submission. This week I cover several other submissions, and do some follow-up on OpenAI’s entry.

The most prominent remaining lab is Google. Google focuses on AI’s upside. The vibes aren’t great, but they’re not toxic. The key asks for their ‘pro-innovation’ approach are:

  1. Coordinated policy at all levels for transmission, energy and permitting. Yes.

  2. ‘Balanced’ export controls, meaning scale back the restrictions a bit on cloud compute in particular and actually execute properly, but full details TBD, they plan to offer their final asks here by May 15. I’m willing to listen.

  3. ‘Continued’ funding for AI R&D, public-private partnerships. Release government data sets, give startups cash, and bankroll our CBRN-risk research. Ok I guess?

  4. ‘Pro-innovation federal policy frameworks’ that preempt the states, in particular ‘state-level laws that affect frontier models.’ Again, a request for a total free pass.

  5. ‘Balanced’ copyright law meaning full access to anything they want, ‘without impacting rights holders.’ The rights holders don’t see it that way. Google’s wording here opens the possibility of compensation, and doesn’t threaten that we would lose to China if they don’t get their way, so there’s that.

  6. ‘Balanced privacy laws that recognize exemptions for publicly available information will avoid inadvertent conflicts with AI or copyright standards, or other impediments to the development of AI systems.’ They do still want to protect ‘personally identifying data’ and protect it from ‘malicious actors’ (are they here in the room with us right now?) but mostly they want a pass here too.

  7. Expedited review of the validity of AI-related patents upon request. Bad vibes around the way they are selling it, but the core idea seems good, this seems like a case where someone is actually trying to solve real problems. I approve.

  8. ‘Emphasize focused, sector-specific, and risk-based AI governance and standards.’ Et tu, Google? You are going to go with this use-based regulatory nightmare? I would have thought Google would be better than trying to invoke the nightmare of distinct rules for every different application, which does not deal with the real dangers but does cause giant pains in the ass.

  9. A call for ‘workforce development’ programs, which as I noted for OpenAI are usually well-intentioned and almost always massive boondoggles. Incorporating AI into K-12 education is of course vital but don’t make a Federal case out of it.

  10. Federal government adaptation of AI, including in security and cybersecurity. This is necessary and a lot of the details here seem quite good.

  11. ‘Championing market-driven and widely adopted technical standards and security protocols for frontier models, building on the Commerce Department’s leading role with the International Organization for Standardization’ and ‘Working with industry and aligned countries to develop tailored protocols and standards to identify and address potential national security risks of frontier AI systems.’ They are treating a few catastrophic risks (CBRN in particular) as real, although the document neglects to mention anything beyond that. They want clear indications of who is responsible for what and clear standards to meet, which seems fair. They also want full immunity for ‘misuse’ by customers or end users, which seems far less fair when presented in this kind of absolute way. I’m fine with letting users shoot themselves in the foot but this goes well beyond that.

  12. Ensuring American AI has access to foreign markets via trade agreements. Essentially, make sure no one else tries to regulate anything or stop us from dying, either.

This is mostly Ordinary Decent Corporate Lobbying. Some of it is good and benefits from their expertise, some is not so good, some is attempting regulatory capture, same as it ever was.

The problem is that AI poses existential risks and is going to transform our entire way of life even if things go well, and Google is suggesting strategies that don’t take any of that into account at all. So I would say that overall, I am modestly disappointed, but not making any major updates.

It is a tragedy that Google makes very good AI models, then cripples them by being overly restrictive in places where there is no harm, in ways that only hurt Google’s reputation, while being mostly unhelpful around the actually important existential risks. It doesn’t have to be this way, but I see no signs that Demis can steer the ship on these fronts and make things change.

John Pressman has a follow-up thread explaining why he thought OpenAI’s thread exceeded his expectations. I can understand why one could have expected something worse than what we got, and he asks good questions about the relationship between various parts of OpenAI – a classic mistake is not realizing that companies are made of individuals and those individuals are often at cross-purposes. I do think this is the best steelman I’ve seen, so I’ll quote it at length.

John Pressman: It’s more like “well the entire Trump administration seems to be based on vice signaling so”.

Do I like the framing? No. But concretely it basically seems to say “if we want to beat China we should beef up our export controls *on China*, stop signaling to our allies that we plan to subjugate them, and build more datacenters” which is broad strokes Correct?

“We should be working to convince our allies to use AI to advance Western democratic values instead of an authoritarian vision from the CCP” isn’t the worst thing you could say to a group of vice signaling jingoists who basically demand similar from petitioners.

… [hold this thought]

More important than what the OpenAI comment says is what it doesn’t say: How exactly we should be handling “recipe for ruin” type scenarios, let alone rogue superintelligent reinforcement learners. Lehane seems happy to let these leave the narrative.

I mostly agree with *what is there*, I’m not sure I mostly agree with what’s not there so to speak. Even the China stuff is like…yeah fearmongering about DeepSeek is lame, on the other hand it is genuinely the case that the CCP is a scary institution that likes coercing people.

The more interesting thing is that it’s not clear to me what Lehane is saying is even in agreement with the other stated positions/staff consensus of OpenAI. I’d really like to know what’s going on here org chart wise.

Thinking about it further it’s less that I would give OpenAI’s comment a 4/5 (let alone a 5/5), and more like I was expecting a 1/5 or 0/5 and instead read something more like 3/5: Thoroughly mediocre but technically satisfies the prompt. Not exactly a ringing endorsement.

We agree about what is missing. There are two disagreements about what is there.

The potential concrete disagreement is over OpenAI’s concrete asks, which I think are self-interested overreaches in several places. It’s not clear to what extent he sees them as overreaches versus being justified underneath the rhetoric.

The other disagreement is over the vice signaling. He is saying (as I understand it) that the assignment was to vice signal, of course you have to vice signal, so you can’t dock them for vice signaling. And my response is a combination of ‘no, it still counts as vice signaling, you still pay the price and you still don’t do it’ and also ‘maybe you had to do some amount of vice signaling but MY LORD NOT LIKE THAT.’ OpenAI sent a strong, costly and credible vice signal and that is important evidence to notice and also the act of sending it changes them.

By contrast: Google’s submission is what you’d expect from someone who ‘understood the assignment’ and wasn’t trying to be especially virtuous, but was not Obviously Evil. Anthropic’s reaction is someone trying to do better than that while strategically biting their tongue, and of course MIRI’s would be someone politely not doing that.

I think this is related to the statement I skipped over, which was directed at me, and I’ll include my response from the thread, and I want to be clear I think John is doing his best and saying what he actually believes here and I don’t mean to single him out but this is a persistent pattern that I think causes a lot of damage:

John Pressman: Anyway given you think that we’re all going to die basically, it’s not like you get to say “that person over there is very biased but I am a neutral observer”, any adherence to the truth on your part in this situation would be like telling the axe murderer where the victim is.

Zvi Mowshowitz: I don’t know how to engage with your repeated claims that people who believe [X] would obviously then do [Y], no matter the track record of [~Y] and advocacy of [~Y] and explanation of [~Y] and why [Y] would not help with the consequences of [X].

This particular [Y] is lying, but there have been other values of [Y] as well. And, well, seriously, WTF am I supposed to do with that, I don’t know how to send or explain costlier signals than are already being sent.

I don’t really have an ask, I just want to flag how insanely frustrating this is and that it de facto makes it impossible to engage and that’s sad because it’s clear you have unique insights into some things, whereas if I was as you assume I am I wouldn’t have quoted you at all.

I think this actually is related to one of our two disagreements about the OP from OpenAI – you think that vice signaling to those who demand vice signaling is good because it works, and I’m saying no, you still don’t do it, and if you do then that’s still who you are.

The other values of [Y] he has asserted, in other places, have included a wide range of both [thing that would never work and is also pretty horrible] and [preference that John thinks follows from [X] but where we strongly think the opposite and have repeatedly told him and others this and explained why].

And again, I’m laying this out because he’s not alone. I believe he’s doing it in unusually good faith and is mistaken, whereas mostly this class of statement is rolled out as a very disingenuous rhetorical attack.

The short version of why the various non-virtuous [Y] strategies wouldn’t work is:

  1. The FDT or virtue ethics answer. The problems are complicated on all levels. The type of person who would [Y] in pursuit of [~X] can’t even figure out to expect [X] to happen by default, let alone think well enough to figure out what [Z] to pursue (via [Y] or [~Y]), in order to accomplish [~X]. The whole rationality movement was created exactly because if you can’t think well in general and have very high epistemic standards, you can’t think well about AI, either, and you need to do that.

  2. The CDT or utilitarian answer. Even if you knew the [Z] to aim for, this is an iterated, complicated social game, where we need to make what to many key decision makers look like extraordinary claims, and ask for actions to be taken based on chains of logic, without waiting for things to blow up in everyone’s face first and muddling through afterwards, like humanity normally does it. Employing various [Y] to those ends, even a little, let alone on the level of say politicians, will inevitably and predictably backfire. And indeed, in those few cases where someone notably broke this rule, it did massively backfire.

Is it possible that at some point in the future, we will have a one-shot situation actually akin to Kant’s ax murderer, where we know exactly the one thing that matters most and a deceptive path to it, and then have a more interesting question? Indeed do many things come to pass. But that is at least quite a ways off, and my hope is to be the type of person who would still try very hard not to pull that trigger.

The even shorter version is:

  1. The type of person who can think well enough to realize to do it, won’t do it.

  2. Even if you did it anyway, it wouldn’t work, and we realize this.

Here is the other notable defense of OpenAI, which is to notice what John was pointing to, that OpenAI contains multitudes.

Shakeel: I really, really struggle to see how OpenAI’s suggestions to the White House on AI policy are at all compatible with the company recently saying that “our models are on the cusp of being able to meaningfully help novices create known biological threats”.

Just an utterly shameful document. Lots of OpenAI employees still follow me; I’d love to know how you feel about your colleagues telling the government that this is all that needs to be done! (My DMs are open.)

Roon: the document mentions CBRN risk. openai has to do the hard work of actually dealing with the White House and figuring out whatever the hell they’re going to be receptive to

Shakeel: I think you are being way too charitable here — it’s notable that Google and Anthropic both made much more significant suggestions. Based on everything I’ve heard/seen, I think your policy team (Lehane in particular) just have very different views and aims to you!

“maybe the biggest risk is missing out”? Cmon.

Lehane (OpenAI, in charge of the document): Maybe the biggest risk here is actually missing out on the opportunity. There was a pretty significant vibe shift when people became more aware and educated on this technology and what it means.

Roon: yeah that’s possible.

Richard Ngo: honestly I think “different views” is actually a bit too charitable. the default for people who self-select into PR-type work is to optimize for influence without even trying to have consistent object-level beliefs (especially about big “sci-fi” topics like AGI)

You can imagine how the creatives reacted to proposals to invalidate copyright without any sign of compensation.

Chris Morris (Fast Company): A who’s who of musicians, actors, directors, and more have teamed up to sound the alarm as AI leaders including OpenAI and Google argue that they shouldn’t have to pay copyright holders for AI training material.

…

Included among the prominent signatures on the letter were Paul McCartney, Cynthia Erivo, Cate Blanchett, Phoebe Waller-Bridge, Bette Midler, Cate Blanchett, Paul Simon, Ben Stiller, Aubrey Plaza, Ron Howard, Taika Waititi, Ayo Edebiri, Joseph Gordon-Levitt, Janelle Monáe, Rian Johnson, Paul Giamatti, Maggie Gyllenhaal, Alfonso Cuarón, Olivia Wilde, Judd Apatow, Chris Rock, and Mark Ruffalo.

…

“It is clear that Google . . . and OpenAI . . . are arguing for a special government exemption so they can freely exploit America’s creative and knowledge industries, despite their substantial revenues and available funds.”

No surprises there. If anything, that was unexpectedly polite.

I would perhaps be slightly concerned about pissing off the people most responsible for the world’s creative content (and especially Aubrey Plaza), but hey. That’s just me.

I’ve definitely been curious where these folks would land. Could have gone either way.

I am once again disappointed to see the framing as Americans versus authoritarians, although in a calm and sane fashion. They do call for investment in ‘reliability and security’ but only because they recognize, and on the basis of, the fact that reliability and security are (necessary for) capability. Which is fine to the extent it gets the job done, I suppose. But the complete failure to consider existential or catastrophic risks, other than authoritarianism, is deeply disappointing.

They offer six areas of focus.

  1. Making it easier to build AI data centers and associated energy infrastructure. Essentially everyone agrees on this, it’s a question of execution, they offer details.

  2. Supporting American open-source AI leadership. They open this section with ‘some models… will need to be kept secure from adversaries.’ So there’s that, in theory we could all be on the same page on this, if more of the advocates of open models could also stop being anarchists and face physical reality. The IFP argument for why it must be America that ‘dominates open source AI’ is the danger of backdoors, but yes it is rather impossible to get an enduring ‘lead’ in open models because all your open models are, well, open. They admit this is rather tricky.

    1. The first basic policy suggestion here is to help American open models git gud via reliability, but how is that something the government can help with?

    2. They throw out the idea of prizes for American open models, but again I notice I am puzzled by how exactly this would supposedly work out.

    3. They want to host American open models on NAIRR, so essentially offering subsidized compute to the ‘little guy’? I pretty much roll my eyes, but shrug.

  3. Launch R&D moonshots to solve AI reliability and security. I strongly agree that it would be good if we could indeed do this in even a modestly reasonable way, as in a fraction of the money turns into useful marginal spending. Ambitious investments in hardware security, a moonshot for AI-driven formally verified software and a ‘grand challenge’ for interpretability, would be highly welcome, as would a pilot for a highly secure data center. Of course, the AI labs are massively underinvesting in this even purely from a selfish perspective.

  4. Build state capacity to evaluate the national security capabilities and implications of US and adversary models. This is important. I think their recommendation on AISI is making a tactical error. It is emphasizing the dangers of AISI following things like the ‘risk management framework’ and thus playing into the hands of those who would dismantle AISI, which I know is not what they want. AISI is already focused on what IFP is referring to as ‘security risks’ combined with potential existential dangers, and emphasizing that is what is most important. AISI is under threat mostly because MAGA people, and Cruz in particular, are under the impression that it is something that it is not.

  5. Attracting and retaining superstar AI talent. Absolutely. They mention EB-1A, EB-2 and O-3, which I hadn’t considered. Such asks are tricky because obviously we should be allowing as much high skill immigration as we can across the board, especially from our rivals, except you’re pitching the Trump Administration.

  6. Improving export control policies and enforcement capacity. They suggest making export exceptions for chips with proper security features that guard against smuggling and misuse. Sounds great to me if implemented well. And they also want to control high-performance inference chips and properly fund BIS, again I don’t have any problem with that.

Going item by item, I don’t agree with everything and think there are some tactical mistakes, but that’s a pretty good list. I see what IFP is presumably trying to do, to sneak useful-for-existential-risk proposals in because they would be good ideas anyway, without mentioning the additional benefits. I totally get that, and my own write-up did a bunch in this direction too, so I get it even if I think they took it too far.

This was a frustrating exercise for everyone writing suggestions. Everyone had to balance between saying what needs to be said, versus saying it in a way that would cause the administration to listen.

How everyone responded to that challenge tells you a lot about who they are.

Discussion about this post

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CEO of AI ad-tech firm pledging “world free of fraud” sentenced for fraud

ad fraud, ad tech, AI fraud, crime, fraud, IPO, kubient, Security, Tech / /

In May 2024, the website of ad-tech firm Kubient touted that the company was “a perfect blend” of ad veterans and developers, “committed to solving the growing problem of fraud” in digital ads. Like many corporate sites, it also linked old blog posts from its home page, including a May 2022 post on “How to create a world free of fraud: Kubient’s secret sauce.”

These days, Kubient’s website cannot be reached, the team is no more, and CEO Paul Roberts is due to serve one year and one day in prison, having pled guilty Thursday to creating his own small world of fraud. Roberts, according to federal prosecutors, schemed to create $1.3 million in fraudulent revenue statements to bolster Kubient’s initial public offering (IPO) and significantly oversold “KAI,” Kubient’s artificial intelligence tool.

The core of the case is an I-pay-you, you-pay-me gambit that Roberts initiated with an unnamed “Company-1,” according to prosecutors. Kubient and this firm would each bill the other for nearly identical amounts, with Kubient purportedly deploying KAI to find instances of ad fraud in the other company’s ad spend.

Roberts, prosecutors said, “directed Kubient employees to generate fake KAI reports based on made-up metrics and no underlying data at all.” These fake reports helped sell the story to independent auditors and book the synthetic revenue in financial statements, according to Roberts’ indictment.

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How the language of job postings can attract rule-bending narcissists

hiring, narcississm, psychology, Science, Social science, syndication / /

Why it matters

Companies write job postings carefully in hopes of attracting the ideal candidate. However, they may unknowingly attract and select narcissistic candidates whose goals and ethics might not align with a company’s values or long-term success. Research shows that narcissistic employees are more likely to behave unethically, potentially leading to legal consequences.

While narcissistic traits can lead to negative outcomes, we aren’t saying that companies should avoid attracting narcissistic applicants altogether. Consider a company hiring a salesperson. A firm can benefit from a salesperson who is persuasive, who “thinks outside the box,” and who is “results-oriented.” In contrast, a company hiring an accountant or compliance officer would likely benefit from someone who “thinks methodically” and “communicates in a straightforward and accurate manner.”

Bending the rules is of particular concern in accounting. A significant amount of research examines how accounting managers sometimes bend rules or massage the numbers to achieve earnings targets. This “earnings management” can misrepresent the company’s true financial position.

In fact, my co-author Nick Seybert is currently working on a paper whose data suggests rule-bender language in accounting job postings predicts rule-bending in financial reporting.

Our current findings shed light on the importance of carefully crafting job posting language. Recruiting professionals may instinctively use rule-bender language to try to attract someone who seems like a good fit. If companies are concerned about hiring narcissists, they may want to clearly communicate their ethical values and needs while crafting a job posting, or avoid rule-bender language entirely.

What still isn’t known

While we find that professional recruiters are using language that attracts narcissists, it is unclear whether this is intentional.

Additionally, we are unsure what really drives rule-bending in a company. Rule-bending could happen due to attracting and hiring more narcissistic candidates, or it could be because of a company’s culture—or a combination of both.

The Research Brief is a short take on interesting academic work.

Jonathan Gay is Assistant Professor of Accountancy at the University of Mississippi.

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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