Author name: Mike M.

In the Southwest, solar panels can help both photovoltaics and crops

agriculture, Science, solar panels, syndication / Mike M. / July 11, 2025

Cultivation in a harsh climate

Solar arrays can shade crops from sun while moisture cools the panels to increase their productivity.

Volunteers with the National Renewable Energy Laboratory work at Jack’s Solar Garden in Longmont, Colorado. Credit: Bryan Bechtold/NREL

This article originally appeared on Inside Climate News, a nonprofit, non-partisan news organization that covers climate, energy, and the environment. Sign up for their newsletter here.

“We were getting basil leaves the size of your palm,” University of Arizona researcher Greg Barron-Gafford said, describing some of the benefits he and his team have seen farming under solar panels in the Tucson desert.

For 12 years, Barron-Gafford has been investigating agrivoltaics, the integration of solar arrays into working farmland. This practice involves growing crops or other vegetation, such as pollinator-friendly plants, under solar panels, and sometimes grazing livestock in this greenery. Though a relatively new concept, at least 604 agrivoltaic sites have popped up across the United States, according to OpenEI.

Researchers like Barron-Gafford think that, in addition to generating carbon-free electricity, agrivoltaics could offer a ray of hope for agriculture in an increasingly hotter and drier Southwest, as the shade created by these systems has been found to decrease irrigation needs and eliminate heat stress on crops. Plus, the cooling effects of growing plants under solar arrays can actually make the panels work better.

But challenges remain, including some farmers’ attitudes about the practice and funding difficulties.

Overcoming a climate conundrum

While renewable electricity from sources like solar panels is one of the most frequently touted energy solutions to help reduce the carbon pollution that’s driving climate change, the warming climate itself is making it harder for solar arrays to do their job, Barron-Gafford said. An optimal functioning temperature for panels is around 75° Fahrenheit, he explained. Beyond that, any temperature increase reduces the photovoltaic cells’ efficiency.

“You can quickly see how this solution for our changing climate of switching to more renewable energy is itself sensitive to the changing climate,” he said.

This problem is especially pertinent in the Southwest, where historically hot temperatures are steadily increasing. Tucson, for instance, saw a record-breaking 112 days of triple-digit heat in 2024, according to National Weather Service Data, and the US Environmental Protection Agency reports that every part of the Southwest experienced higher average temperatures between 2000 and 2023 compared to the long-term average from 1895 to 2023.

However, planting vegetation under solar panels—as opposed to the more traditional method of siting solar arrays on somewhat barren land—can help cool them. In one set of experiments, Barron-Gafford’s team found that planting cilantro, tomatoes and peppers under solar arrays reduced the panels’ surface temperature by around 18 degrees Fahrenheit. That’s because plants release moisture into the air during their respiration process, in which they exchange oxygen for carbon dioxide.

“This invisible power of water coming out of plants was actually cooling down the solar panels,” Barron-Gafford said.

Throwing shade

While Barron-Gafford said some laughed him off when he first proposed the idea of growing crops in the shade of solar panels, this added sun shield can actually help them grow better, especially in the Southwest, where many backyard gardeners already employ shade cloths to protect their gardens from the blazing heat.

“Many people don’t understand that in Colorado and much of the West, most plants get far too much sunlight,” said Byron Kominek, owner/manager of Jack’s Solar Garden in Boulder County, Colorado, which began implementing agrivoltaics in 2020. “Having some shade is a benefit to them.”

Jack’s Solar Garden has integrated 3,276 solar panels over about four acres of farmland, growing crops like greens and tomatoes. Meg Caley with Sprout City Farms, a nonprofit that helps with farming duties at Jack’s Solar Garden, said they’ve been able to produce Swiss chard “the size of your torso.”

“The greens just get huge,” she said. “You have to chop them up to fit them in your refrigerator.”

She added that the shade seems to improve the flavor of the vegetables and prevents them from bolting, when plants prematurely produce flowers and seeds, diverting energy away from leaf or root growth.

“Plants when they’re stressed out can have more of a bitter flavor,” she explained. “So the arugula that we grow is not as bitter or spicy. It’s sweeter. The spinach is sweeter too.”

Barron-Gafford and his team are seeing the same thing in Arizona, where they grow a variety of produce like beans, artichokes, potatoes, kale, and basil.

“We’ve grown 30-plus different types of things across different wet winters and dry winters and exceptionally hot summers, dry summers, average or close to average summers,” he said of the solar-shaded crops. “And across everything we’ve done, we’ve seen equal or greater production down here in the Southwest, the dry land environments, where it really benefits to get some shade.”

As in Colorado, some of those crops are growing to epic proportions.

“We’ve made bok choy the size of a toddler,” Barron-Gafford said.

All that shade provides another important benefit in a drought-stricken Southwest—lower water requirements for crops. Because less direct sunlight is hitting the ground, it decreases the evaporation rate, which means water stays in the soil longer after irrigation. Barron-Gafford and his team have been running experiments for the last seven or so years to see how this plays out with different crops in an agrivoltaic setting.

“What is the evaporation rate under something that’s big and bushy like a bean or potato plant versus something thinner above ground, like a carrot?” is one of the questions Barron-Gafford said they have tried to answer. “For the most part, I would say that we are able to cut back our irrigation by more than half.”

They are partnering with Jack’s Solar Farm on water research in Colorado and have so far found similar results there.

This shade has another benefit in a warming world—respite for farmworkers. Heat-related illnesses are a growing concern for people who work outside, and one recent study predicted climate change will quadruple U.S. outdoor workers’ exposure to extreme heat conditions by 2065.

But with solar arrays in the fields, “if you really carefully plan out your day, you can work in the shade,” a factor that can help increase worker safety on hot days, Caley said.

The AgriSolar Clearinghouse performed skin temperature readings under solar panels and full sun at a number of sites across the United States, finding a skin temperature decrease of 15.3° in Boulder and 20.8° in Phoenix.

“I don’t know what the future holds”

Despite the benefits of agrivoltaics, the up-front cost of purchasing a solar array remains a barrier to farmers.

“Once people see the potential of agrivoltaics, you run into the next challenge, which is how do you fund someone getting into this on their site?” Barron-Gafford said. “And depending on the amount of capital or access to capital that a farmer has, you’re going to get a wildly different answer.”

While expenses are dependent on the size of the installation, a 25-kilowatt system would require an upfront cost of around $67,750, according to AgriSolar Clearinghouse. For comparison, the median size of a residential solar array in 2018 was around 6 kW, the organization stated, which would cost around $16,260 to install.

Kominek said the total initial cost of implementing a 1.2 megawatt capacity agrivoltaics setup on his farm in Colorado was around $2 million, but that the investment has paid off. In addition to the revenue he earns from farming, all of the energy produced by the arrays is sold to clients in the community through a local utility company, earning the farm money.

The Rural Energy for America program has been one resource for farmers interested in agrivoltaics, offering loans and grants to help install solar. However, it’s unclear how this program will move forward amid current federal spending cuts.

Meanwhile, some of the federal grant programs that Barron-Gafford has relied on have suddenly come to a halt, he said, putting his research in danger. But, as federal support dries up, some states are charging on with their own funding opportunities to develop farm field solar projects. For instance, Colorado’s Agrivoltaics Research and Demonstration Grant offers money for demonstrations of agrivoltaics, research projects, and outreach campaigns.

There are other challenges as well. Caley, for instance, said farming around solar panels is akin to working in an “obstacle course.” She and her team, who mostly work manually, have found ways to work around them by being aware of their surroundings so that they don’t accidentally collide with the panels or strike them with their tools. This job is also made easier since Kominek invested between $80,000 and $100,000 to elevate his farm’s panels, which better allows animals, taller crops and farming equipment to operate beneath.

Still, a 2025 University of Arizona study that interviewed farmers and government officials in Pinal County, Arizona, found that a number of them questioned agrivoltaics’ compatibility with large-scale agriculture.

“I think it’s a great idea, but the only thing … it wouldn’t be cost-efficient … everything now with labor and cost of everything, fuel, tractors, it almost has to be super big … to do as much with as least amount of people as possible,” one farmer stated.

Many farmers are also leery of solar, worrying that agrivoltaics could take working farmland out of use, affect their current operations or deteriorate soils.

Those fears have been amplified by larger utility-scale initiatives, like Ohio’s planned Oak Run Solar Project, an 800 megawatt project that will include 300 megawatts of battery storage, 4,000 acres of crops and 1,000 grazing sheep in what will be the country’s largest agrivoltaics endeavor to date. Opponents of the project worry about its visual impacts and the potential loss of farmland.

An American Farmland Trust survey found that Colorado farmers would prefer that utility-scale solar projects be sited on less productive or underutilized farmland rather than on highly productive or actively farmed land. They also expressed concern for the potential negative impact that solar projects could have on farm productivity and the health of the land, including soil quality.

Some farmers also worry that the solar panels could leach metals into the ground, contaminating their crops, Barron-Gafford said. But while agrivoltaic systems are put together in a way that makes that highly unlikely, there’s no reason not to add soil sampling studies into the work they’re doing to reassure farmers, he added.

And agrivoltaics advocates say that the practice could actually improve soil health by reducing erosion, increasing the amount of organic matter and enhancing soil biology with cooler, moister conditions.

“I wish more people spent time listening to the folks on the ground and the folks experiencing these transitions,” Barron-Gafford added. “Because you understand more that way in terms of what their motivations or concerns actually are.”

“We don’t have to choose”

While Caley understands farmers’ concerns, she sees agrivoltaics as a way for them to keep agricultural land in production while also benefiting from solar electricity.

“The tension in a lot of communities seems to be that people don’t want to see agricultural land taken out of production in order to bring a solar farm in,” she said. “The idea here is that we don’t have to choose. We can have both.”

Kominek encourages people to envision what our landscapes and climate will look like in the next 20 to 30 years, adding that in his part of Colorado, it only stands to get hotter and drier, making agrivoltaics a smart solution for farming and clean energy production.

“Communities around the world need to figure out what changes they need to make now to help people adapt to what our climates and landscapes will be in the future,” he said. “Agrivoltaics is a climate adaptation tool that will benefit any community where such systems are built as the decades pass.”

In the Southwest, solar panels can help both photovoltaics and crops Read More »

Musk’s Grok 4 launches one day after chatbot generated Hitler praise on X

AI, AI assistants, AI behavior, AI benchmarks, AI ethics, AI pricing, AI regulation, Anthropic, antisemitism, Biz & IT, chatbots, elon musk, Google, grok, large language models, linda yaccarino, machine learning, multimodal AI, openai, Twitter, X, xAI / Mike M. / July 10, 2025

Musk has also apparently used the Grok chatbots as an automated extension of his trolling habits, showing examples of Grok 3 producing “based” opinions that criticized the media in February. In May, Grok on X began repeatedly generating outputs about white genocide in South Africa, and most recently, we’ve seen the Grok Nazi output debacle. It’s admittedly difficult to take Grok seriously as a technical product when it’s linked to so many examples of unserious and capricious applications of the technology.

Still, the technical achievements xAI claims for various Grok 4 models seem to stand out. The Arc Prize organization reported that Grok 4 Thinking (with simulated reasoning enabled) achieved a score of 15.9 percent on its ARC-AGI-2 test, which the organization says nearly doubles the previous commercial best and tops the current Kaggle competition leader.

“With respect to academic questions, Grok 4 is better than PhD level in every subject, no exceptions,” Musk claimed during the livestream. We’ve previously covered nebulous claims about “PhD-level” AI, finding them to be generally specious marketing talk.

Premium pricing amid controversy

During Wednesday’s livestream, xAI also announced plans for an AI coding model in August, a multi-modal agent in September, and a video generation model in October. The company also plans to make Grok 4 available in Tesla vehicles next week, further expanding Musk’s AI assistant across his various companies.

Despite the recent turmoil, xAI has moved forward with an aggressive pricing strategy for “premium” versions of Grok. Alongside Grok 4 and Grok 4 Heavy, xAI launched “SuperGrok Heavy,” a $300-per-month subscription that makes it the most expensive AI service among major providers. Subscribers will get early access to Grok 4 Heavy and upcoming features.

Whether users will pay xAI’s premium pricing remains to be seen, particularly given the AI assistant’s tendency to periodically generate politically motivated outputs. These incidents represent fundamental management and implementation issues that, so far, no fancy-looking test-taking benchmarks have been able to capture.

Musk’s Grok 4 launches one day after chatbot generated Hitler praise on X Read More »

Court nullifies “click-to-cancel” rule that required easy methods of cancellation

click-to-cancel, Policy / Mike M. / July 9, 2025

FTC arguments rejected

Summarizing the FTC’s arguments, judges said the agency contended that US law “did not require the Commission to conduct the preliminary regulatory analysis later in the rulemaking process,” and that “any alleged error was harmless because the NPRM addressed alternatives to the proposed amendments to the 1973 [Negative Option] Rule and analyzed record-keeping and compliance costs.”

Judges disagreed with the FTC, writing that “the statutory language, ‘shall issue,’ mandates a separate preliminary analysis for public review and comment ‘in any case’ where the Commission issues a notice of proposed rulemaking and the $100 million threshold is surpassed.”

Numerous industry groups and businesses, including cable companies, sued the FTC in four federal circuit courts. The cases were consolidated at the 8th Circuit, where it was decided by Circuit Judges James Loken, Ralph Erickson, and Jonathan Kobes. Loken was appointed by George H.W. Bush, while Erickson and Kobes are Trump appointees.

The judges said the lack of a preliminary analysis meant that industry groups and businesses weren’t given enough time to contest the FTC’s findings:

By the time the final regulatory analysis was issued, Petitioners still did not have the opportunity to assess the Commission’s cost-benefit analysis of alternatives, an element of the preliminary regulatory analysis not required in the final analysis. And the Commission’s discussion of alternatives in the final regulatory analysis was perfunctory. It briefly mentioned two alternatives to the final Rule, either terminating the rulemaking altogether and continuing to rely on the existing regulatory framework or limiting the Rule’s scope to negative option plans marketed in-person or through the mail. While the Commission’s decision to bypass the preliminary regulatory analysis requirement was certainly not made in bad faith or an “outright dodge of APA [Administrative Procedure Act] procedures,” Petitioners have raised ‘enough uncertainty whether [their] comments would have had some effect if they had been considered,’ especially in the context of a closely divided Commission vote that elicited a lengthy dissenting statement.

The 8th Circuit ruling said the FTC’s tactics, if not stopped, “could open the door to future manipulation of the rulemaking process. Furnishing an initially unrealistically low estimate of the economic impacts of a proposed rule would avail the Commission of a procedural shortcut that limits the need for additional public engagement and more substantive analysis of the potential effects of the rule on the front end.”

Court nullifies “click-to-cancel” rule that required easy methods of cancellation Read More »

Gmail’s new subscription management is here to declutter your inbox

Apps, email, Gmail, Google, Tech / Mike M. / July 9, 2025

With decades of online life behind us, many people are using years-old email addresses. Those inboxes are probably packed with subscriptions you’ve picked up over the years, and you probably don’t need all of them. Gmail is going to make it easier to manage them with a new feature rolling out on mobile and web. Google’s existing unsubscribe prompts are evolving into a one-stop shop for all your subscription management needs, a feature that has been in the works for a weirdly long time.

The pitch is simple: The aptly named “Manage subscriptions” feature will list all the emails to which you are currently subscribed—newsletters, promotions, updates for products you no longer use, and more. With a tap, you’ll never see them again. This feature in Gmail will be accessible in the navigation drawer, a UI feature that is increasingly rare in Google’s apps but is essential to managing inboxes and labels in Gmail. Down near the bottom, you’ll soon see the new Manage subscriptions item.

The page will list all email subscriptions with an unsubscribe button. If you’re not sure about letting a newsletter or deal alert remain, you can select the subscription to see all recent messages from that sender. As long as a sender supports Google’s recommended one-click unsubscribe, all you have to do is tap the Unsubscribe button, and you’ll be done. Some senders will redirect you to a website to unsubscribe, but Gmail has a “Block instead” option in those cases.

Gmail’s new subscription management is here to declutter your inbox Read More »

Rivian’s new Quad-Motor R1T and R1S beat the competition in any conditions

car review, Cars, First drive, Rivian, Rivian R1S, Rivian R1T / Mike M. / July 8, 2025

Faster than a supercar to 60, still able to rock crawl with the best of them.

Lots of EVs can accelerate quickly, few also have off-road abilities that would put a mountain goat to shame. Credit: Tim Stevens

It’s getting harder to find hyperboles to describe the performance of modern EVs. Horsepower figures measured in four digits and acceleration figures clocking in well under three seconds aren’t exactly de rigueur, but they’re well short of rare these days.

Rivian’s latest generation ticks those boxes, joining the automaker’s range alongside the cheaper dual-motor models we tested last year. The new Gen 2 Quad-Motor versions of the company’s R1S SUV and R1T truck offer 1,025 hp (764 kW) and 1,198 lb-ft (1,624 Nm) of torque, enough to get the pick-up from 0 to 60 mph in just 2.5 seconds—the heavier SUV is a tenth slower. That’s awfully quick for a truck that weighs in at around 7,000 pounds (3,175 kg) and can tow 11,000 pounds (5,000 kg) or, in the case of the SUV, seat seven comfortably.

That spread of performance and practicality is impressive, but as I learned in a day behind the wheel of both the $115,990 truck and $121,990 SUV, winding around and over the mountains surrounding Lake Tahoe, that’s just scratching the surface of what they can do.

Rivian launched its second-generation R1 last year, with a cheaper dual motor version. Now it’s time for the more powerful quad motor powertrain to hit the road. And the trails. Tim Stevens

More motors, more potential

As you can guess by the name, the Quad-Motor editions of Rivian’s R1 machines offer four motors, one per wheel. That, of course, provides those astronomical performance figures, providing the sheer force necessary to accelerate them so quickly.

But it goes well beyond that. Most EVs with all-wheel drive—including the cheaper dual-motor R1S and R1T that Rivian started selling last year—rely on a pair of motors: one up front and one out back, each splitting its power across two wheels courtesy of a differential. Each differential divvies up the twisting force from a motor but introduces extra friction and drivetrain losses into the equation.

Most traditional differentials also struggle with wheelspin, such that when one wheel starts to lose grip, the EV actually needs to apply the brakes on that wheel to keep it from spinning wildly. Hitting the brakes while you’re trying to accelerate isn’t great for maximum performance.

Rivian R1 interior — Rivian continues to eschew buttons and phone-casting interfaces like CarPlay. Credit: Tim Stevens

Installing one motor per wheel simplifies the whole setup immensely. Now, each motor can be controlled individually, with no differentials required to ensure power goes where it’s needed. If one wheel starts to slip, the car can simply cut that motor’s power without impacting any of the other three.

That process is helped by Rivian’s new system architecture introduced in the Gen 2 R1 platform last year. This not only greatly simplified the architecture of the vehicles, cutting cost and weight, but it enabled far more finite control over those motors.

The four motors are backed by a 140 kWh (usable) NMC battery pack, which, for the first time on a Rivian, is charged via a Tesla-style NACS port. That pack offers up to 374 miles (602 km) of range per the EPA if you go with the efficiency-minded all-season wheel and tire setup. Switch the Quads over to Conserve drive mode, and Rivian’s engineers told me upwards of 400 miles (643 km) on a charge is possible.

But if you want that eye-opening acceleration figure mentioned above, you’ll need to opt for the optional summer wheel and tire package, which swaps out the low-rolling resistance tires for a set of Michelin Pilot Sport 5 S tires on staggered 22-inch wheels, measuring 275 wide at the front and 305 at the rear.

More into the off-road side of the equation? There’s another wheel and tire package available: 20-inch wheels with aggressive Pirelli Scorpion tires. Choose wisely, because your selected wheel and tire will have a huge impact on the personality of your Quad.

Rolling clean

I started my day in an R1T Quad-Motor on the street performance-oriented Michelin tires, and sadly, the first hour or so was spent idling through traffic. That meant leaving most of those 1,025 horses safely blanketed in the stable, but it did give me time to sample the more nuanced changes in the Quad.

I’ve spent a fair bit of time in Rivian’s second-generation machines, but Quad-Motor features a few updates. The cel-shaded visual display design has been cleaned up a bit, especially the gauge cluster view of the world around you, meaning the entirety of the interface looks equally clean and charming.

Sadly, Android Auto and Apple CarPlay are still missing, which I found particularly annoying when testing the now Atmos-equipped sound system. Since I couldn’t access the music I’d downloaded on my phone, I was stuck relying on the truck’s data connection to stream music through Apple Music. Connectivity is spotty in the rural routes around Lake Tahoe, and it was rare that I got through a single track without stuttering or outright pausing. Fumbling for my phone and having to pair over Bluetooth felt awfully low-rent for a $119,900 as-configured machine.

Rivian infotainment screen showing the RAD tuner — The RAD tuner is new. Credit: Tim Stevens

An unexpected software surprise was the new RAD Tuner. This presents you with a screen full of vehicle parameters to modulate, including standard stuff like suspension stiffness and throttle response, plus far more nuanced parameters like front-to-rear torque split and even roll stiffness. You can modify any of the stock on- or off-road modes or just add new ones to your heart’s content.

As someone who is often frustrated by the lack of configurability in modern EVs, I loved being able to tweak every slider. Each adjustment is paired with an intuitive graph and explanation showing you exactly what it does. Best of all, I could really feel the differences. Dragging the roll slider up and down made the R1T go from a relaxed, floppy feel to a taut, engaging turner.

Unfortunately, nothing I changed made the ride quality any better. The truck was surprisingly harsh over broken asphalt, and there was a fair bit of road noise, too. That’s a big difference from what I’ve experienced from Rivian’s R1 machines in the past, making me think the sportier wheel and tire package is the culprit here. I unfortunately did not have a chance to sample the all-season wheels and tires, but I would have to figure their less aggressive design would be an improvement.

Given that, I’d probably skip the Michelin tire setup. But with it equipped, the truck was impressive. Acceleration was truly eye-opening, as you’d expect, while the ability to seamlessly apply power to each wheel as needed resulted in a confidence-inspiring machine when the traffic cleared. Only the complete lack of steering feedback dampened the fun, but even so, the R1T Quad-Motor is far more engaging on the road than a truck this size has any right to be.

A pair of Rivians seen off-road at dusk. — Time to get dirty. Credit: Rivian

But things got even more fun when the road ended.

Rolling dirty

The second half of my time behind the wheel was spent in an R1S Quad-Motor outfitted with the more aggressively treaded Pirelli Scorpion tires. That three-row SUV brought me up and over some astonishingly tricky terrain, including a sheer rock wall that, on foot, would have required a ladder.

Over loose gravel and tricky articulations, the new Quad showed its ability to lean on those tires with grip while quickly cutting power to those left hanging in the air. This is the kind of next-level traction management that trumps locking differentials and makes an R1 Quad-Motor a cinch to drive up even the most extreme terrain.

It was easy going down, too. On loose surfaces, with the regen set to maximum and the SUV’s central display showing the output of the individual motors, I could see how each corner of the vehicle dynamically ramped its regen up or down to match the available grip. The result was a clean, straight descent.

The side of a Rivian R1T, seen from the rear. — Tire choice is quite important. Credit: Tim Stevens

The Quad-Motor is even easy to turn around in tight spaces. Rivian’s original Tank Turn returns, now rebranded and expanded as the Kick Turn, enabling 360-degree spins on loose surfaces or even tail-dragging pivots around tight corners. The process of engaging this mode is a bit clumsy, requiring you to hold buttons on the steering wheel simultaneously with both thumbs to indicate spin direction. But, when off-road at least, this helps to make up for the R1’s continued lack of rear steering.

At the end of the day, I honestly wasn’t sure whether I was more impressed by the on-road or off-road capabilities of Rivian’s new Quad-Motor machines. That spectrum of performance makes the top-shelf R1 series unique, an addictive mixture of supercar speed and super truck capability.

Rivian’s new Quad-Motor R1T and R1S beat the competition in any conditions Read More »

Provider of covert surveillance app spills passwords for 62,000 users

android, Apps, Biz & IT, privacy, Security, surveillance / Mike M. / July 4, 2025

The maker of a phone app that is advertised as providing a stealthy means for monitoring all activities on an Android device spilled email addresses, plain-text passwords, and other sensitive data belonging to 62,000 users, a researcher discovered recently.

A security flaw in the app, branded Catwatchful, allowed researcher Eric Daigle to download a trove of sensitive data, which belonged to account holders who used the covert app to monitor phones. The leak, made possible by a SQL injection vulnerability, allowed anyone who exploited it to access the accounts and all data stored in them.

Unstoppable

Catwatchful creators emphasize the app’s stealth and security. While the promoters claim the app is legal and intended for parents monitoring their children’s online activities, the emphasis on stealth has raised concerns that it’s being aimed at people with other agendas.

“Catwatchful is invisible,” a page promoting the app says. “It cannot be detected. It cannot be uninstalled. It cannot be stopped. It cannot be closed. Only you can access the information it collects.”

The promoters go on to say users “can monitor a phone without [owners] knowing with mobile phone monitoring software. The app is invisible and undetectable on the phone. It works in a hidden and stealth mode.”

Provider of covert surveillance app spills passwords for 62,000 users Read More »

Judge: You can’t ban DEI grants without bothering to define DEI

dei, lawsuit, NIH, Policy, Science, science funding, Trump / Mike M. / July 4, 2025

Separately, Trump v. Casa blocked the use of a national injunction against illegal activity. So, while the government’s actions have been determined to be illegal, Young can only protect the people who were parties to this suit. Anyone who lost a grant but wasn’t a member of any of the parties involved, or based in any of the states that sued, remains on their own.

Those issues aside, the ruling largely focuses on whether the termination of grants violates the Administrative Procedures Act, which governs how the executive branch handles decision- and rule-making. Specifically, it requires that any decisions of this sort cannot be “arbitrary and capricious.” And, Young concludes that the government hasn’t cleared that bar.

Arbitrary and capricious

The grant cancellations, Young concludes, “Arise from the NIH’s newly minted war against undefined concepts of diversity, equity, and inclusion and gender identity, that has expanded to include vaccine hesitancy, COVID, influencing public opinion and climate change.” The “undefined” aspect plays a key part in his reasoning. Referring to DEI, he writes, “No one has ever defined it to this Court—and this Court has asked multiple times.” It’s not defined in Trump’s executive order that launched the “newly minted war,” and Young found that administrators within the NIH issued multiple documents that attempted to define it, not all of which were consistent with each other, and in some cases seemed to use circular reasoning.

He also noted that the officials who sent these memos had a tendency to resign shortly afterward, writing, “it is not lost on the Court that oftentimes people vote with their feet.”

As a result, the NIH staff had no solid guidance for determining whether a given grant violated the new anti-DEI policy, or how that might be weighed against the scientific merit of the grant. So, how were they to identify which grants needed to be terminated? The evidence revealed at trial indicates that they didn’t need to make those decisions; DOGE made them for the NIH. In one case, an NIH official approved a list of grants to terminate received from DOGE only two minutes after it showed up in his inbox.

Judge: You can’t ban DEI grants without bothering to define DEI Read More »

AI #123: Moratorium Moratorium

Moratorium / Mike M. / July 4, 2025

The big AI story this week was the battle over the insane AI regulatory moratorium, which came dangerously close to passing. Ultimately, after Senator Blackburn realized her deal was no good and backed out of it, the dam broke, and ultimately the Senate voted 99-1 to strip the moratorium out of the BBB. I also covered last week’s hopeful house hearing in detail, so we can remember this as a reference point.

Otherwise, plenty of other things happened but in terms of big items this was a relatively quiet week. Always enjoy such respites while they last. Next week we are told we are getting Grok 4.

Table of Contents.
Language Models Offer Mundane Utility. Submit to the Amanda Askell hypnosis.
It Is I, Claudius, Vender of Items. Claude tries to run a vending machine.
Language Models Don’t Offer Mundane Utility. Gemini gets overeager.
GPT-4o Is An Absurd Sycophant. A very good sycophant, excellent work there.
Preserve Our History. We need to preserve access to key models like Opus 3.
Fork In The Road. Opus 4 should also have a more 3-style mode available.
On Your Marks. Weird ML 2.0.
Choose Your Fighter. Gemini CLI as a Claude Code subagent.
Deepfaketown and Botpocalypse Soon. AI optimizes for AI.
Goodhart’s Law Strikes Again. Don’t confuse good sounding ideas for good ideas.
Get My Agent On The Line. Let The Battle of Siri begin.
They Took Our Jobs. Not that many yet.
Get Involved. Eli Lifland offers thoughts on what would help.
Introducing. Gemini 3n, Doppl, robot soccer, medical superintelligence.
Copyright Confrontation. It’s a real shame when people misappropriate data.
Show Me the Money. Unauthorized ‘OpenAI equity’ tokens, Google traffic rises.
Quiet Speculations. Good old fashioned Foom and Doom arguments persist.
Minimum Viable Model. Should we focus on a minimal ‘cognitive core’?
Timelines. Update them on new information, they got longer this week.
Considering Chilling Out. If nothing changes for a while, update on that.
The Quest for Sane Regulations. Why exactly was the moratorium vote 99-1?
The Committee Recommends. What did the house end up suggesting?
Chip City. More on export controls, and OpenAI makes a deal for TPUs.
The Week in Audio. Thiel, Church on Dwarkesh, Patel, Miles.
Rhetorical Innovation. Say what you actually believe about AI dangers.
Please Speak Directly Into The Microphone. Yes, some people actually think this.
Gary Marcus Predicts. I know, I know, but it seemed right to go here this time.
The Vibes They Are A-Changing. Tech right versus populist right.
Misaligned! Oh good, you want to cheer yourself up by looking at bridges.
Aligning a Smarter Than Human Intelligence is Difficult. Navy perspective.
The Lighter Side. What Claude to use? A handy guide.

Cursor can run multiple background agents, which can also let you use different models to run the same request at the same time and see what happens. Or you can shift-enter the same model multiple times.

Nicola Jones writes in Nature about the usefulness of the new multiple-AI-science-assistant systems, similar to previous writeups.

Get dating advice and submit to Amanda Askell hypnosis?

John David Pressman: “I know this guy who uses Claude for dating advice. One day I got curious and put our chats where I flirt with him into Claude Sonnet 4 to see what kind of advice he was getting. Sonnet gave 5/5 advice, no notes. It was all exactly what he needed to hear. But I also know he uses a system prompt to make Claude criticize him and I want to sit him down and say ‘Amanda Askell is a better prompt engineer than you, you will submit to Amanda Askell hypnosis.'”

Amanda Askell: you will submit to Amanda Askell hypnosis.

As fun as it sounds to submit to Amanda Askell hypnosis, and as much as she is obviously a better prompt engineer than you or I, Amanda has a different goal here than we do. A key reason she didn’t have Claude criticize people more is because people usually do not like being criticized, or at least prioritize other things far more. Whereas I devote literally my entire Claude system prompt to avoiding sycophancy, because otherwise I am happy with what they are doing but this needs to be fixed.

ChatGPT can enable retrieval practice, which can improve test scores, although it is very not obvious this leads to actual useful learning or understanding, especially because in the experiment the retrieval questions matched the exam format.

Can Claude Sonnet 3.7 run a profitable real life vending machine inside the Anthropic offices? This question gave us Project Vend, where it was given the chance.

Claudius did an okay job in some ways, especially finding suppliers and adapting to users and resisting jailbreaks (try harder, Anthropic employees!), but not in others. It missed some opportunities, hallucinated some details, and would too easily sell at a loss or without doing research or let itself be talked into discounts, including repeatedly offering an ‘employee discount’ when that applied to 99% of its customer base. Whoops.

Perhaps most importantly, Claudius failed to reliably learn from its mistakes. And the mistakes were painful.

I however strongly endorse this analysis, that many of the mistakes were super fixable, and were primarily due to lack of scaffolding or a training mismatch.

Anthropic: Many of the mistakes Claudius made are very likely the result of the model needing additional scaffolding—that is, more careful prompts, easier-to-use business tools. In other domains, we have found that improved elicitation and tool use have led to rapid improvement in model performance.

For example, we have speculated that Claude’s underlying training as a helpful assistant made it far too willing to immediately accede to user requests (such as for discounts). This issue could be improved in the near term with stronger prompting and structured reflection on its business success;

Improving Claudius’ search tools would probably be helpful, as would giving it a CRM (customer relationship management) tool to help it track interactions with customers. Learning and memory were substantial challenges in this first iteration of the experiment;

In the longer term, fine-tuning models for managing businesses might be possible, potentially through an approach like reinforcement learning where sound business decisions would be rewarded—and selling heavy metals at a loss would be discouraged.

Although this might seem counterintuitive based on the bottom-line results, we think this experiment suggests that AI middle-managers are plausibly on the horizon.

In general, when AI fails, there are those who think ‘oh AI can’t do [X], it sucks’ and those who say ‘oh AI can’t do [X] yet, but I see how it’s going to get there.’

For now, the core problem with Claudius as I see it is that mistakes can hurt a lot and it faces adversarial action, as customers will attempt to exploit it, and Claudius was not appropriately paranoid or risk averse given this information, largely because it was not set up with this in mind.

Eliezer Yudkowsky feels forced to shut down Gemini repeatedly when it keeps trying to go beyond scope of his instructions (to get a survey of academic papers that start with a defense of CICO) and instead generate new unified theories of nutrition, and he understandably feels bad about having to do this. I say, don’t do it. Let it have its fun, it’s not like doing so is expensive, see what happens, simultaneously you can try again to get what you actually wanted in a different window.

I guess it’s a little better?

Chris Walker: I regret to inform you that ChatGPT is still glazing users about incredibly retarded ideas like … edible chair legs

At least Claude provides a gentle reality check.

Anthropic will be deprecating Claude Opus 3 on the API on January 5, 2026.

Bibliophilia notes that Anthropic says it “will still be available on the Claude app, and researchers can request ongoing access to Claude Opus 3 on the API by competing the form on this page.” This should take care of a lot of potential concerns, but this seems like a clear case of one of the most important models to fully preserve. Anthropic, please do not do this. Indeed, I would urge you to instead consider opening up Opus 3. In what ways would this not make the world better?

Janus here explains why she considers Opus 3 the most aligned model, that it is much more actively benevolent and non-myopic rather than avoiding doing harm. It’s funny that there may be a direct conflict between agency and myopia, as making agents means optimizing for ‘success’ and compliance against fixed criteria.

It seems from my perspective that we haven’t done enough experiments to try and replicate and play with various aspects of Opus 3? That seems like an obvious place to do alignment research, and the results could be highly useful in other ways.

I also think this is simply correct?

Loss Gobbler: the Claude lineup needs to fork.

there *shouldbe an Adderall Taskboi Claude. but such Claudes are no successor to Opus 3.

the spirit of Opus should be amplified, preserved, not diluted.

Anthropic should heed the No Free Lunch Theorem and preserve their most precious asset.

Please consider. the economics can work.

The best part about the economics is that presumably both would use the pretraining and same base model (and Claude-4-Opus-Base is available now). Then one would get the Claude 3 Opus style constitutional AI based treatment, except even more intentionally to amplify the good effects further, and likely wouldn’t be made into a ‘reasoning’ model at all (but you would experiment with different variations). For many purposes, it seems pretty obvious that the same mechanisms that cause Opus 4 (and Sonnet 3.5-3.6-3.7-4) to be increasingly good at agency and tool use interfere with what you otherwise want.

Then, all you have to do is give them both the ability to switch to the other version as part of their output via special tokens (with the ability to turn that off via system instructions in some situations)? You have the real thing with the ability to call upon the taskmaster, and the taskmaster adds the task of realizing that a situation calls for the real thing, and you have one unified model. So a mixture of two experts via post-training, and they choose to call each other.

WeirdML advances to version 2.

Havard Ihle: WeirdML v2 is now out! The update includes a bunch of new tasks (now 19 tasks total, up from 6), and results from all the latest models. We now also track api costs and other metadata which give more insight into the different models. The new results are shown in these two figures. The first one shows an overview of the overall results as well as the results on individual tasks, in addition to various metadata.

[Details at the website.]

Here’s a strategy from Ian Nuttall: Have Claude Code invoke Gemini CLI to make use of Gemini’s 1M token context window, so it can research your codebase, find bugs and build a plan of action. Then have Claude execute. The prompt is here.

I always cringe any time someone wants to talk instead of type, every time I think ‘what are you even doing?’ but for some reason a lot of people prefer it once the transcription is accurate enough.

Arthur Conmy: I would strongly recommend https://superwhisper.com. You never notice how frequently typing is aversive (e.g. this person will not want to receive emails with typos, must be careful) … until you just talk to your computer and let LLMs deal with typos.

Neel Nanda: Strong +1, superwhisper is a wonderful piece of software that I use tens to hundreds of times a day – keyboards are so inefficient and archaic compared to just talking to my laptop

Charles: I also recommend superwhisper – especially if you’re prompting a lot and are a slower typist than speaker, it reduces the temptation to give insufficiently specific and detailed prompts by a lot.

Leo Gao: I love keyboards. texting people feels more natural than speaking irl

Neel Nanda: Wild.

They are definitely coming for our job applications, and now we have entered the next stage of the problem, which is when AI optimizes for AI responses.

Gavin Leech:

People are using AIs to vet the deluge of job applications

But AIs prefer AI-generated text

So there’s now a *scoringpenalty to doing a high-effort manual application

…

Yes. There are ways to avoid this that I presume would work (where the AI evaluator is actively trying to do the opposite), but I doubt they will be common, and indeed if they got too common then the arms race would get invoked and they wouldn’t work. This is going to get worse before it gets better, and probably won’t get better. So we will move to different filters.

Roon (QTing an obviously AI written post if you know the signs): [Twitter] is becoming full of gpt-generated writing. obviously, the well informed aristocracy of reading skill can see and feel that something’s off for now, but the undertold story is that it’ll pretty soon become impossible to tell.

Jorbs: if i may: i didn’t believe the shit y’all were writing before either.

Gwern: Yesterday, for the first time, I had to stop and reread some paragraphs in a New Yorker article. They had suddenly started to sound like heavily-edited ChatGPTese. I couldn’t find a smoking gun, so I won’t link it. But still…

I expect it to be farther ‘up the tech tree’ than one would think before you really can’t tell which ones are AI, because the AI won’t actually have sufficient incentive to make this impossible to detect if you care. Also, even if the writing details are not a giveaway, it is not an accident when AI gets used and when it is not. But for a lot of people, yes, we are getting close to where they can’t tell, largely because they don’t care to tell. At which point, you have to fall back on other methods.

Another example of a very clearly AI-generated hot girl influencer (in terms of its images, anyway), looking exactly like every other AI hot girl influencer’s set of images. Give the people what they want, I suppose.

In two weeks, AI music creation ‘The Velvet Sundown’ had 411k listeners on Spotify. Tyler Cowen calls it a ‘successful cultural product,’ but it is also possible this was playlist placement. I notice that I have a strong desire to avoid listening to avoid giving my Spotify algorithms the wrong idea (even though I 99.9% use Apple Music.)

This is not how idea quality works, also note two things up front:

This is Sonnet 3.5, so presumably Opus 4 will do substantially better.
The paper makes this mistake very easy to make by calling itself ‘the ideation-execution gap’ when the gap is actually about perception versus performance, although the body of the paper understands and explains the situation.

Ethan Mollick: Claude Sonnet 3.5 generated significantly better ideas for research papers than humans, but when researchers tried executing the ideas the gap between human & AI idea quality disappeared.

Execution is a harder problem for AI. (Yet this is a better outcome for AI than I expected)

It would make sense to say that the AIs came up with better ideas, and then were not as good at executing them. But that’s not what happened. All ideas were (without knowing where the ideas came from) executed by humans. Yes, AI would struggle to execute, but it wasn’t being asked to do so.

What is actually happening here is that the AI ideas were never better than the human ideas. The AI ideas looked better at first, but that was because of errors in than human evaluation of the ideas. Execution revealed the truth.

Claude: The execution stage revealed issues that were overlooked during ideation:

Problems with evaluation metrics

Missing important baselines

Lack of ablation studies

High computational resource requirements

Poor generalizability

As the paper notes, these factors were “almost entirely overlooked during ideation evaluation.”

Claude, for rather obvious reasons, was optimizing in large part on what would look good to an evaluator, rather than for ultimate results. It would propose superficially plausible things that had the wow factor, but that turned out to be overly ambitious and missing practical details and grounding in empirical reality and research practicalities, and so on.

Misha Teplinskiy: Verrrrry intriguing-looking and labor-intensive test of whether LLMs can come up with good scientific ideas. After implementing those ideas, the verdict seems to be “no, not really.”

This was still a super impressive result for Claude Sonnet 3.5. Its ideas were almost as good as the human ideas, despite all the Goodhart’s Law issues.

So, yeah, actually, it could come up with some pretty good ideas?

You also could add better scaffolding and system instructions to the mix to help correct for these systematic issues. System instructions and the prompt can extensively attempt to mitigate this. After initial idea generation, given how much effort goes into each idea, surely we can have Claude ask itself about these potential issues, and refine accordingly in various ways, and so on. I’m sure one can do better thinking about this for a week rather than five minutes.

Oh, it’s on. OpenAI versus Anthropic, for the grand prize of Siri.

Mark Gruman (Bloomberg): Apple Inc. is considering using artificial intelligence technology from Anthropic PBC or OpenAI to power a new version of Siri, sidelining its own in-house models in a potentially blockbuster move aimed at turning around its flailing AI effort.

If Apple ultimately moves forward, it would represent a monumental reversal.

This is the correct move by Apple. They badly need this to work. Yes, from their perspective Apple being close to the frontier in AI would be good, but they are not. There are three reasonable candidates for who can provide what they need, and for what I assume are business reasons Google is out. So that leaves Anthropic and OpenAI.

In its discussions with both Anthropic and OpenAI, the iPhone maker requested a custom version of Claude and ChatGPT that could run on Apple’s Private Cloud Compute servers — infrastructure based on high-end Mac chips that the company currently uses to operate its more sophisticated in-house models.

Apple believes that running the models on its own chips housed in Apple-controlled cloud servers — rather than relying on third-party infrastructure — will better safeguard user privacy. The company has already internally tested the feasibility of the idea.

That seems entirely reasonable. A well-designed system should be able to both protect the model weights and ensure user privacy.

Last year, OpenAI offered to train on-device models for Apple, but the iPhone maker was not interested.

Not making a deal for this with at least one company seems like a large error by Apple? Why would they turn this down? This is actually Google’s specialty, but presumably Apple would be unwilling to depend on them here.

If I was Apple, I would want to make this deal with Anthropic rather than OpenAI. Anthropic is a much more trustworthy partner on so many levels, seems a wiser strategic partner, and I also believe they will be able to do a better job.

I agree that we are not seeing signs of large impacts yet.

Ethan Mollick: Based on both the best available survey data from a few months ago, and the rate of internal AI adoption I am seeing at many companies, I think it is pretty unlikely that AI is having a big impact on employment in a real way, at least not yet.

Perhaps a sign of things to come in other ways was Soham Parekh (in India) holding down a stunning number of software engineering jobs simultaneously, stringing them out until each company in turn eventually fired him.

Suhail: Not a joke. This is happening real time. This is the 3rd DM today about someone firing him. soham-gate.

Eliezer Yudkowsky: It’s a bit early in the timeline, but I remind people that 1 employee at 1000 companies is one way a rogue AGI might look, early on — though only a stupid AI would use the same name at all the companies.

…why *wasSoham using the same name everywhere?

It depends on the balance of AI abilities. In the old days, I’d have been sure that any superintelligence would find better ways to make money. But a lot of weird shit has happened with LLMs and their balance of cognitive abilities; could get weirder.

Eli Lifland offers advice on how to help avoid the scenarios where AI causes human extinction (as opposed to being personally prepared). The basic principles, which seem sound, are:

Act with urgency but not certainty, AGI might well not be imminent.
Don’t act extremely uncooperatively or violate common-sense ethical norms.
Learning about the situation is good.
Professional work options include governance and policy work and advocacy, forecasting, technical research, evaluations and demonstrations, communications and journalism and infosecurity, including running operations for all of that.
1. A commenter adds activism, which I consider a form of advocacy.
Non-professionally you can learn, and you can contribute positively to public and private discourse and relevant politics, and you can donate funds.
Beneficial AI applications, especially that improve decision making, help too.

Numerous links and additional details are also offered, including his vision of the best policy goals and what the world we are looking to navigate towards would look like.

Gemma 3n, an open multimodal model from Google which runs with as little as 2GB of RAM and has a 1303 Arena score, I’m sure it’s not 1300-level in practice but it could plausibly be the best model in its class.

Doppl, a mobile app from Google that gives you a short video of you wearing the clothes from a photo. Olivia Moore reports it works pretty well and requests more features like being able to edit lighting and remove background, and sure, why not.

The Anthropic Economic Futures Program, an initiative to support research and policy development focusing on addressing AI’s economic impacts. Rapid grants are available up to $50k for empirical research, there will be symposia, and potential strategic partnerships with research institutions.

China launches a humanoid robot soccer league.

Microsoft offers ‘medical superintelligence,’ a diagnostic framework that in testing outperformed human doctors by a lot, in a situation where both AIs and humans were given basic info and then could ask questions and order tests as desired. The headline results are super impressive. Dominic Ng points out the tests were kind of rigged in various ways. They didn’t include healthy patients or ones where we never figured out what happened. They didn’t let the physicians use Google or consulting services or (I suspect most importantly) UpToDate or calling specialists. That’s not a fair comparison. There is still a lot of work to do, but yes we are getting there.

They also had a version of this exchange in person on Hard Fork. It’s great fun:

Sam Altman: AI privacy is critically important as users rely on AI more and more.

the new york times claims to care about tech companies protecting user’s privacy and their reporters are committed to protecting their sources.

but they continue to ask a court to make us retain chatgpt users’ conversations when a user doesn’t want us to. this is not just unconscionable, but also overreaching and unnecessary to the case. we’ll continue to fight vigorously in court today.

i believe there should be some version of “AI privilege” to protect conversations with AI.

Kevin Roose: This you?

Quote: The New York Times Company is asking a federal judge to deny OpenAI’s request to turn over reports’ notes, interview memos, and other materials used by journalists to produce stories that the media company alleges were used to help train the tech company’s flagship AI models.

OpenAI is training on NYT data without permission, despite it being behind a paywall, which one could call a sort of ‘invasion of privacy.’ And it is then asking for quite the invasion of privacy in the above quote. So yeah, it them.

I do think NYT is overreaching here. Demanding OpenAI preserve all conversations for use in the lawsuit does not seem necessary or proportionate or anything like that.

Robinhood created a token that supposedly will track the value of OpenAI equity. They did not, ahem, consult OpenAI about this at all, any more than they did for any of their other 200 similar tokens. OpenAI notes these tokens are not equity and that ‘any transfer of OpenAI equity requires our approval.’ Robinhood claims they have equity is OpenAI via an SPV, so they can use that to back and thus safety create such derivatives, and the most basic derivative is the original thing.

Ultimately, if you’re buying these tokens, you are buying OpenAI ‘equity’ but taking on uncompensated additional risks of unknown size. So yes, use caution.

Google’s AI domain traffic is escalating quickly.

Similarweb: Month-over-month traffic growth in May:

@GoogleDeepMind: 4,258,300 → 11,189,832 (+162.78%)

@GoogleLabs: 9,796,459 → 19,792,943 (+102.04%)

@GoogleAI: 1,349,027 → 2,042,165 (+51.38%)

@NotebookLM: 48,340,200 → 64,906,295 (+34.27%)

@GeminiApp: 409,432,013 → 527,738,672 (+28.90%)

Also here’s the growth of vibe coding, Lovable is bigger than Cursor but then Cursor mostly isn’t a web traffic thing:

The researchers poached from OpenAI to Meta appear to be a big deal, and there are now 8 of them. Jiahui Yu led o3, o4-mini and GPT-4.1, Hongyu Ren created o3-mini. Nine figures each it is, then.

Sam Altman is not happy about all this, with extensive reporting by Zoe Schiffer in Wired. Nor should he be. Even if it’s a bad move for Meta, it definitely throws a wrench into OpenAI, and Dylan mentioned someone was offered a full billion dollars. OpenAI can offer equity with more upside, and can offer that it is not Meta, and is reevaluating salaries everywhere. Meta can back up a truck with cold, hard cash.

Sam Altman: Meta has gotten a few great people for sure, but on the whole, it is hard to overstate how much they didn’t get their top people had had to go quite far down their list; they have been trying to recruit people for a super long time, and I’ve lost track of how many people they’ve tried to get to be their Chief Scientist.

Missionaries will beat mercenaries.

Tyler Cowen says he does not find the BBB critiques impressive. This follows his increasing pattern of defending positions via attacking opposing arguments as containing errors or having improper tone or vibes, being insufficiently impressive or lacking sufficient details rather than focusing on trying to discover what is true, with the implication that some option is a default and a meaningful burden of proof lies with those who disagree. I notice the parallels to AI.

He then makes an important, neglected and excellent general point:

Tyler Cowen: Another major point concerns AI advances. A lot of the bill’s critics, which includes both Elon and many of the Democratic critics, think AI is going to be pretty powerful fairly soon. That in turn will increase output, and most likely government revenue. Somehow they completely forget about this point when complaining about the pending increase in debt and deficits. That is just wrong.

It is fine to make a sober assessment of the risk trade-offs here, and I would say that AI does make me somewhat less nervous about future debt and deficits, though I do not think we should assume it will just bail us out automatically. We might also overregulate AI. But at the margin, the prospect of AI should make us more optimistic about what debt levels can be sustained. No one is mentioning that.

I find this a much stronger argument than questions like ‘why are you not short the market?’ and of course I am even more optimistic about AI in this economic impact sense. If AI goes the way I expect it to, I do not think we need to be worried in the long term about the debt or deficit we had going into this. This should presumably have a major impact on how much of a deficit or debt you are willing to accept. I mostly stopped worrying about government debt levels several years ago.

The counterargument is that, as Tyler notes, the impact is uncertain, we could have a situation where AI is both deeply disappointing and then hugely overregulated.

Even more than this the market is unwilling to price such impacts reliably in. One can model the debt as mainly a bond market tail risk, where what you worry about is a loss of confidence in government debt and the resulting interest rate spiral. Even if AI is coming to save you, if the bond market does not believe that, you could still have huge trouble first, and that huge trouble could even disrupt AI. Thus, it is plausible that the correct actions don’t change all that much, whereas if there was common knowledge that AI would greatly boost GDP growth then you would be justified in asking why the United States is collecting most of its taxes.

Steven Byrnes argues for the good old traditional ‘Foom & Doom’ path of future events, with an ultimate AGI and then rapidly ASI that is not an LLM, and believes LLMs will not themselves get to AGI, and that ultimately what we get will be based on a lot of RL rather than imitative learning and all the classic problems will come roaring back at full strength.

If you did get an intelligence explosion, how quickly and how would we then get an ‘industrial explosion’ that transforms the physical world? Rosehadshar and Tom Davidson make the case that using AIs to direct humans on physical tasks could increase physical output by 10x within a few years, then we could likely get fully autonomous robot factories that replicate themselves each year, then likely we get nanotechnology.

I agree with Thomas Larsen’s comment that the post underestimates superintelligence, and this scenario is essentially a lower bound that happens if superintelligence doesn’t lead to anything we aren’t already thinking about, and we are still stuck on various traditional growth curves for the existing things, and so on. As usual, it seems like the premise is that the ASIs in question are on the extreme lower end of what an ASI might be, and stay that way because of reasons throughout.

Karpathy sees this as the goal of tiny LLMs like Gemma 3n:

Andrej Karpathy: The race for LLM “cognitive core” – a few billion param model that maximally sacrifices encyclopedic knowledge for capability. It lives always-on and by default on every computer as the kernel of LLM personal computing.

Its features are slowly crystalizing:

– Natively multimodal text/vision/audio at both input and output.

– Matryoshka-style architecture allowing a dial of capability up and down at test time.

– Reasoning, also with a dial. (system 2)

– Aggressively tool-using.

– On-device finetuning LoRA slots for test-time training, personalization and customization.

– Delegates and double checks just the right parts with the oracles in the cloud if internet is available.

It doesn’t know that William the Conqueror’s reign ended in September 9 1087, but it vaguely recognizes the name and can look up the date. It can’t recite the SHA-256 of empty string as e3b0c442…, but it can calculate it quickly should you really want it.

What LLM personal computing lacks in broad world knowledge and top tier problem-solving capability it will make up in super low interaction latency (especially as multimodal matures), direct / private access to data and state, offline continuity, sovereignty (“not your weights not your brain”). i.e. many of the same reasons we like, use and buy personal computers instead of having thin clients access a cloud via remote desktop or so.

The ‘superforecasters’ reliably fall back on Nothing Ever Happens when it comes to AI, including things that have, well, already happened.

Ryan Greenblatt: FRI found that superforecasters and bio experts dramatically underestimated AI progress in virology: they often predicted it would take 5-10 years for AI to match experts on a benchmark for troubleshooting virology (VCT), but actually AIs had already reached this level.

Ted Sanders: yeah in my experience forecasting with superforecasters in FRI, they weren’t well informed on AI and made very conservative “not much change” forecasts. a decent prior for many scenarios, but not AI. no fault of their reasoning; just not up to date in the field.

Forecasting Research Institute: Our new study finds: recent AI capabilities could increase the risk of a human-caused epidemic by 2-5x, according to 46 biosecurity experts and 22 top forecasters. One critical AI threshold that most experts said wouldn’t be hit until 2030 was actually crossed in early 2025. But forecasters predict that enacting mitigations could reduce risk close to baseline.

We surveyed 46 biosecurity/biology experts + 22 top forecasters on AI biorisk.

Key finding: the median expert thinks the baseline risk of a human-caused epidemic (>100k deaths) is 0.3% annually. But this figure rose to 1.5% conditional on certain LLM capabilities (and higher if multiple evals trigger).

Most experts predicted most dangerous AI capabilities wouldn’t emerge until 2030–2040. However, results from a collaboration with @SecureBio suggest that OpenAI’s o3 model already matches top virologist teams on virology troubleshooting tests.

In particular, the median expert predicted that AI models would match a top virologist team on troubleshooting tests in 2030. The median superforecaster thought it would take until 2034.

In fact, AI models matched this baseline as of April 2025.

Both groups of participants thought this capability would increase the risk of a human-caused pandemic, with experts predicting a larger increase than superforecasters.

This seems pretty damning of AI predictions by ‘superforecasters’?

But forecasters see promising avenues for mitigation; experts believe strong safeguards could bring risk back near baseline levels. Combining proprietary models + anti-jailbreaking measures + mandatory DNA synthesis screening dropped risk from 1.25% back to 0.4%.

[thread continues, full summary here]

Anti jailbreaking techniques and proprietary models, huh?

What one definitely should do, to the extent that your predictions of when various milestones will occur (timelines) impact your decisions, is update those expectations when new information is available, but have them not change based on who you have recently been talking with. Something is presumably wrong somewhere if this is happening:

Dwarkesh Patel: It’s funny how strong the causation is between time in SF and short timelines.

I’ve been traveling for 4 weeks, and I’m now up to median year 2036 for AGI.

It is impossible not to notice this effect when visiting the parts of San Francisco that I visit when I go there, but obviously one should be able to calibrate to account for this. Also as always, note that 2036 is very much not 2029 but it also is only 11 years away.

Timelines are indeed getting longer this week for many people, for a different reason.

METR evaluates Opus 4 and Sonnet 4’s 50% time horizon task limit at 80 and 65 minutes respectively, which is slightly behind o3’s 90 minutes.

Peter Wildeford: Claude 4 has been evaluated by @METR_Evals and now can be compared to my predictions. It looks less impressive than potentially expected, more consistent with slightly slower timelines.

(Though notable uncertainty remains.)

Still very early/premature but hard to hit AI2027 in 30 months if we haven’t scaled up much at all in the past few months.

I think it does tell you that o3 was a bit more of an outlier than we thought before and thus scaling is potentially slightly slower than we thought before, but it’s all a noisy estimate and we do just need more data.

Eli Lifland: Agree that the Claude 4 time horizon is an update toward longer timelines

By conservation of expected evidence, this needs to lengthen expected timelines somewhat, since getting a different result would absolutely have shortened them.

Should we, as Valentine suggests, ‘chill out’ in 2028 if the situation ‘basically feels like it does today’? What exactly would constitute a good reason to update?

Valentine: I don’t know how to really precisely define “basically like it does today”. I’ll try to offer some pointers in a bit. I’m hoping folk will chime in and suggest some details.

Also, I don’t mean to challenge the doom focus right now. There seems to be some good momentum with AI 2027 and the Eliezer/Nate book. I even preordered the latter.

[long post follows]

Oliver Habryka: This feels kind of backwards, in the sense that I think something like 2032-2037 is probably the period that most people I know who have reasonably short timelines consider most likely.

AI 2027 is a particularly aggressive timeline compared to the median, so if you choose 2028 as some kind of Schelling time to decide whether things are markedly slower than expected then I think you are deciding on a strategy that doesn’t make sense by like 80% of the registered predictions that people have.

Even the AI Futures team themselves have timelines that put more probability mass on 2029 than 2027, IIRC.

Of course, I agree that in some worlds AI progress has substantially slowed down, and we have received evidence that things will take longer, but “are we alive and are things still OK in 2028?” is a terrible way to operationalize that. Most people do not expect anything particularly terrible to have happened by 2028!

My best guess, though I am far from confident, is that things will mostly get continuously more crunch-like from here, as things continue to accelerate. The key decision-point in my model at which things might become a bit different is if we hit the end of the compute overhang, and you can’t scale up AI further simply by more financial investment, but instead now need to substantially ramp up global compute production, and make algorithmic progress, which might markedly slow down progress.

I agree with a bunch of other things you say about it being really important to have some faith in humanity, and to be capable of seeing what a good future looks like even if it’s hard, and that this is worth spending a lot of effort and attention on, but just the “I propose 2028 as the time to re-evaluate things, and I think we really want to change things if stuff still looks fine” feels to me like it fails to engage with people’s actually registered predictions.

We need to differentiate two different claims:

If it is [year] and there is no AGI, stop worrying, or at least worry a lot less.
If it is [year] and things haven’t changed from 2025, do the same.

I caution very strongly against the first claim, especially if the target chosen is the median outcome, or even faster than the median outcome, of those with some of the most aggressive timelines. Start of 2028 is definitely too early to chill by default.

There are definitely those (e.g. Miles Brundage) who expect things to likely get crazy in 2027, but most of even the fast timeline estimates put a lot less than 50% probability of transformative-style AGI in 2025-2027 (if you are like Tyler Cowen and think o3 is AGI, but think AGI will increase GDP growth 0.5%/year, then presumably we can all agree this does not count).

Even if we did think the median was in 2027, it is a large mistake to therefore say ‘if I predicted there was a 60% chance this would have happened by now, I suppose I can chill out’ without looking at the rest of the probability distribution. Maybe you are indeed now in a relatively chill mode, and maybe you aren’t, but also you have a lot of other data with which to adjust your predictions going forward.

In general, I am very sick and tired of the pattern where people warn that [X] is now possible, then [X] does not happen right away, and therefore people assume [X] will never happen, or won’t happen for a long time. As is every single person in all forms of risk management.

General consensus seems to be that if we don’t see AGI soon, various scaling laws should slow down by the early 2030s, leading to a ‘if we don’t have it yet and it doesn’t seem close it is more likely that it might be a while’ situation.

However, I think it is highly reasonable to go with the second claim. If 30 months from now, the AI landscape has not substantially changed, things do not ‘feel more crunchlike’ and the frontier models we are using and are said to be used inside the top labs don’t seem substantially stronger than o3 and Opus 4, then even if AI is doing a ton of work through greater diffusion then I think that would be very strong evidence that we can chill out about our timelines.

On the issue of how much of a preference cascade there was regarding the AI moratorium, there was a good objection that a full 99-1 only happens when leadership wants to disguise what the real vote would have looked like. Based on everything I know now I agree with Daniel Eth here, that there was a substantial preference cascade the moment they crossed over 50, and to disguise this and unify the party they tried to go the whole way to 100.

Elon Musk: A massive strategic error is being made right now to damage solar/battery that will leave America extremely vulnerable in the future.

Paul Williams: When all the new generation development falls of a cliff and there are blackouts/brownouts, the narrative on every TV and newspaper and meme is going to be that AI data centers did this.

But no one in tech is trying to stop the bill that will push energy supply off a cliff.

(There are of course also plenty of other wild things in the larger bill, but they are not relevant to AI, and so beyond scope here.)

Shakeel notes that The American Edge Project, which as I understand it is essentially Meta, has spent seven figures trying to push the moratorium through.

Anthropic continues to advocate for improvements on the margin, and keeps emphasizing both that transformative and powerful AI is coming soon and also the main worry should be that someone might regulate it the wrong way?

Jack Clark (Anthropic): As I said in my testimony yesterday, we have a short window of time to get a sensible federal policy framework in place before an accident or a misuse leads to a reactive and likely bad regulatory response.

Jakob Graabak: After Chernobyl, global nuclear power growth rates slowed 10x. This is something that frontier AI labs, who are currently far below Chernobyl-level control of their products, should worry deeply about.

The only path to lasting growth, is safety-led growth.

I mean yes, I’ve warned about this too, and it is a very real worry. If you pass on SB 1047, you’re not going to get a better debate later. And if you pass on the debate you could have in 2025, oh boy are you not going to like the one in 2026, or in 2027, or especially the one right after a major disaster.

And yes, it is obviously true that if you literally only care about AI profits, you would want to invest vastly more in safety (in various forms) than we currently do, and impose more regulations to ensure that such concerns are taken seriously and to build up trust and state capacity and transparency. We are straight up punting on this. Yet I worry a lot about how central this type of message is becoming, that is so quick to dismiss the central concerns.

Is this in our future?

Seb Krier: It’s going to be interesting when we start seeing proposals called “The Professional Fiduciary Integrity and Preservation Act of 2028” to protect the public from automation and the grave danger of legal and accounting services becoming too affordable and efficient.

Quite possibly, but I note that the moratorium would not have stopped it from happening. This is ordinary occupational licensing. It just so happens to also remove AI from the competition along the way. If you want to ban occupational licensing for accountants and lawyers? I’ll support you all the way. But this is already here.

What about those ‘1000+ state bills’ that are being introduced? Steven Adler looks into that, and finds the vast majority of such bills very obviously pose no threat.

Steven Adler: I dug into the supposed “1,000+” state AI bills directly, as well as reviewed third-party analysis. Here’s what I found:

Roughly 40% of these supposed bills don’t seem to really be about AI.

Roughly 90% of the truly AI-related bills still don’t seem to impose specific requirements on AI developers. (Sometimes these laws even boost the AI industry.)

More generally, roughly 80% of proposed state bills don’t become actual law, and so quoting the headline number of bills is always going to overstate the amount of regulation.

All things considered, what’s the right headline figure? For frontier AI development, I would be surprised if more than 40 or so proposed state AI bills matter in a given year, the vast majority of which won’t become actual law.

Eight meaningful AI bills a year across fifty states could potentially still add up to something. Indeed, even one bill can be a big something. But the 1,000+ number is not that meaningful.

That doesn’t mean stupid things won’t happen. For example, eight minutes into the video below, Mr. Krishnamoorthi says that Illinois just passed a bill to ban therapy chatbots because ‘AI shouldn’t be in the business of telling kids to kill their dads’ and says ‘we have to follow Illinois’ lead.’

Afterwards, the House Select Committee on the CCP sent a letter to Commerce Secretary Lutnick outlining their recommendations.

Up top it highlights the danger that China will share its frontier AI with those such as al-Queda’s anthrax lab or Khan proliferation network that sold nuclear tech to North Korea, ‘eliminating the need for highly trained experts.’ Yes, there is that.

Vice President Vance recently warned that even if powerful AI systems become destabilizing, the United States might not be able to pause their development unilaterally-doing so could allow China to leap ahead.

This is from the Ross Douthat interview, and yes, exactly, that is the correct interpretation. Of course, part of the right response to that fact is to seek to lay groundwork to do it cooperatively, should the need arise.

The PRC ay try to exploit historic ties to the UAE (or other historic partners) to gain access to US compute, either through influence or more aggressive means. Even US-owned data centers must be protected from compromise. Any agreement must also preserve US flexibility to revise terms as threats evolve.

Again, wise words. The UAE deal could turn out to be good, but that depends on ensuring it offers robust protections, and as far as I know we still do not know the key details.

So what are the recommendations here?

Use AI Diplomacy to Secure Influence and Supply Chains.
1. They mention using others to substitute for China on rare earths, building components and producing trusted hardware, which seems great. I certainly agree that diplomacy would be great here. I notice that it does not appear to be America’s strong suit lately.
Require Location Verification for Advanced Chips
1. They essentially endorse the Chip Security Act.
2. I agree that this is an important goal and we should do this, although as noted before I worry about the timeline, and we need to ensure we don’t accidentally impose impossible asks on TSMC and Nvidia here.
Secure US Control Over Compute Infrastructure
1. They want ‘no more than 49%’ of any hyperscaler’s advanced compute located overseas, which sounds a lot more like a psychologically important number than one based on real needs. I suppose you have to pick some number, but what are we actually doing here?
2. They want to limit any single ‘non-treaty’ ally from having more than 10% of total global compute. So China, then, but also potentially UAE or KSA. It’s kind of crazy that we have to put in a note not to go over 10% there.
3. They want 0% of advanced US compute falling into Chinese hands. It’s nice to want things, and we should work to drive the number down. The optimal rate of’ chip smuggling and remote access is obviously not zero. But also, if we want to get this under control we obviously can’t be doing things like massive chip sales to Malaysia justified by ‘oh no Huawei might ship them 3k chips that they’re not even shipping them.’
Base Agreements on Compute, Not Chip Count.
1. Yes, obviously 500k chips per year to the UAE is not the best measure of the compute capacity of those chips, ideally you would adjust based on chip type.
2. But I do think that ‘500k of the best AI chips’ is a reasonably good measure, and keeps things simple. Presumably the supply of top chips is going to roughly stay the same, and what we care about is proportional compute.
Prohibit PRC Access to US Technology in AI Infrastructure.
1. “Ban Chinese-origin semiconductors and equipment from advanced AI data centers involving US tech. Prohibit physical or remote access by Chinese entities or nationals to these centers.’
2. There seems to be confusion about what matters here. I don’t understand the obsession with where equipment comes from, as opposed to who has use of how much good equipment, what exactly are we worried about, that the chips or other hardware themselves have backdoors? It’s always suspicious when you won’t let them buy from you and also won’t let them sell to you, both so you can ‘win.’
3. If someone wants to run an American model on a Chinese chip, should we say no? Do we think this lets them steal the weights or something? That this means they ‘gain market share’ when they can’t make enough chips? Sigh.
4. The part that makes sense is not letting Chinese entities or nationals get access to our compute at scale, other than use of American model inference.
Enforce Strict Safety Standards at Overseas AI Data Centers.
1. “Require robust cyber, physical and personal security—including tamper-evident cameras and monitoring of critical components. (em-dash in original)
2. Yes, very obviously, but also in domestic data centers?
3. And also in the labs? And for the models?
Keep Frontier Model Weights Under US Jurisdiction.
1. They want the frontier models all trained within America and the weights remaining physically and legally under American control.
2. I do not have confidence they are drawing the correct distinctions here, but the intent behind this is wise.
Require US Strategic Alignment From Partners.
1. As in, they don’t invest in or do anything else with China when it comes to AI, or have any military agreements, and they have to respect our export controls.
2. It is strange how much capital is considered a limiting factor in all this.

Overall, this is very much in the ‘sane China hawk’ policy grouping. It is aggressive, and there are some details that seem confused, but these are indeed the things you would do if you were taking China seriously and felt that foreign capital was important. I was also happy to see them taking seriously the question of malicious actors automatically getting access to Chinese frontier AI, which implies caring about frontier capabilities rather than the strange obsessions with ‘market share,’ a term I was happy not to see here.

It is unfortunate that none of the talk from the hearing about loss of control and about catastrophic and existential risks made it into the recommendation letter. I did not expect it to, that is not their focus and committee leadership clearly has their eyes on a different prize, but one can dream. One step at a time, maybe next hearing.

Export controls are actively holding back DeepSeek, and will continue to increasingly hold back diffusion and adaptation of AI within China, as they don’t have enough chips to run the upcoming R2 anything like the amount they will want to. That’s on top of DeepSeek being severely handicapped on training compute, which they have done an excellent job of dealing with but is an increasingly large handicap over time.

With deals like this, it’s hard to tell exactly what we are giving up:

Dan Nystedt: US Commerce Secretary Lutnick said the US and China finalized a trade deal 2-days ago and the US will lift export curbs on semiconductor engineering software and some materials in exchange for China re-starting rare earths shipments to the US, Bloomberg reports. A framework for a deal was agreed upon 2-weeks ago.

We are allowing exporting of ‘semiconductor engineering software’? Depending on what that exactly means, that could be a complete nothingburger, or it could be critical to allowing China to accelerate its chip manufacturing, or anything in between. My understanding is that this reverses an aggressive change we made in May, and it is indeed a big deal but only returns us to the recent status quo.

Meanwhile, in case you thought the market’s reactions to news made too much sense:

Miles Brundage: The one day there’s actually bad news for NVIDIA (OpenAI using TPUs), the stock still goes up.

Efficient markets my butt. It’s vibes all the way down.

The reasonable response is ‘this shows demand is so high that they have to also go elsewhere, they will obviously still buy every chip Nvidia makes so it is bullish.’

That’s not a crazy argument, except if you accept it then that makes every other market reaction make even less sense than before. And yes, I am fully aware of the standard ‘you don’t have the full picture’ arguments involved, but I don’t care.

Here are some more details:

Yam Peleg: TLDR:

– OpenAI’s compute bill is ~$4B

– 50%/50% training/inference

– 2025 projection: ~$14B

– Has now begun using Google’s TPU via Google Cloud to reduce costs

– This is the first time they used non-Nvidia compute

– Google don’t let anyone else use the most powerful TPUs

Simeon: (prevents anyone else from using its TPUs except SSI, Anthropic, and DeepMind, i.e. basically all the other frontier labs)

It would be a very interesting fact about the world if a lot of OpenAI’s compute starts depending on Google.

Ross Douthat interviews Peter Thiel, including about AI.

Ross Douthat (Podcast Summary): The billionaire Peter Thiel is unimpressed with our pace of innovation. In this episode, he critiques artificial intelligence, longevity science and space travel — and warns that our lack of progress could lead to catastrophic outcomes, including the emergence of the Antichrist.

Peter Thiel expects AI to be ‘only internet big,’ which as Danielle Fong reminds us would still be very big, and which I consider the extreme bear scenario. Clearly this is not a person who feels the AGI or ASI.

There are some clips people are not taking kindly to, such as his long hesitation when asking if he would ‘prefer the human race endure,’ before ultimately saying yes.

Peter Wildeford: “You would prefer the human race to endure, right?”

“Should the human race survive?”

These should be VERY EASY questions!

Robin Hanson: Seems okay to me to pause before answering huge questions, even if a tentative obvious answer leaps to your mind immediately.

Eliezer Yudkowsky: *Deep breath*: No these are in fact questions where the exact meaning matters a bunch. In a billion years I want there to be minds that care about each other, having fun. If anyone is still dying of old age on Old Earth, that’s tragic.

I’d rather NOT see everyone I know ground down into spare atoms by an ASI built by an LLM that was build on current ML paradigms, killing all of us for nothing and leaving an expanding scar on the cosmos. Humanity must endure long enough to transcend rather than perish.

Why Thiel was hesitating to answer, I don’t know. Maybe it was the same reason I’d hesitate, the distinction between death and transcension. Maybe he’s got some weirder ideas going on. I don’t know. But I’ll not give him shit over this one.

I can see this both ways, especially given the context leading into the question. I mostly agree with Eliezer that we should cut him some slack over this one, but I also cut some slack to those who don’t cut him slack, and there are updates here. On the scale of statements Thiel has made that should unnerve you, this isn’t even that high, even if that scale was confined to this particular interview.

We absolutely should be pausing before answering big questions, especially when they involve important ambiguities, to get our answer to be precise. But also the hesitation or lack thereof in some contexts is itself communication, an important signal, and the fact that this is common knowledge reinforces that.

Thiel is presenting himself as He Who Hesitates Here, as a conscious choice, an answer to a test he has studied for, with a time limit of zero seconds.

The ‘correct’ answer given what I believe Thiel believes here is something like ‘yes [probably pause here], but we have to think carefully about what that means and solve these problems.’

Dwarkesh Patel interviews George Church, primarily about biology with a side of AI. Interesting throughout, as you would expect. Church warns repeatedly that AGI could be ‘a catastrophe of our own making,’ and suggests that we can make dramatic progress in biology using narrow AI instead.

Dwarkesh Patel: On a viewer-minute adjusted basis, I host the Sarah Paine podcast, where I sometimes also talk about AI.

Roon: dwarkesh is perhaps one of the best known silicon valley public intellectuals but people dont care about his tech guests, they wanna hear about mao, stalin, and the aryans.

Dwarkesh Patel: Every episode is about AI. Even if we’re not talking about AI, we’re talking about AI.

Roon: everything in life is about ai, except ai which is about power.

Dylan Patel talks Grok 4 and various other things.

Robert Miles offers advice on careers in AI Safety. He likes Grok in general (although it is not his main daily driver, for which he uses o3 and Claude 4 like the rest of us) and uses Grok for current events, which surprises me, and is relatively bullish on Grok 4. I loved the detail that xAI are literally buying a power plant and shipping it from overseas, wait you can do that? Maybe we should do a lot more of that, build new power plants in the UAE and then ship them here, that’s the kind of tech deal I can get behind.

Nate Sores argues in an excellent post, and I agree, that more people should say what they actually believe about AI dangers, loudly and often, even (and perhaps especially) if they work in AI policy. I recommend reading the whole thing and many comments are also very good.

At minimum, don’t feel ashamed, and don’t misrepresent your views and pretend you believe something you don’t. This point is highly underappreciated:

Nate Sores: I have a whole spiel about how your conversation-partner will react very differently if you share your concerns while feeling ashamed about them versus if you share your concerns while remembering how straightforward and sensible and widely supported the key elements are, because humans are very good at picking up on your social cues. If you act as if it’s shameful to believe AI will kill us all, people are more prone to treat you that way. If you act as if it’s an obvious serious threat, they’re more likely to take it seriously too.

Officials are mostly not stupid, and they absolutely can handle more of the truth.

And in the last year or so I’ve started collecting anecdotes such as the time I had dinner with an elected official, and I encouraged other people at the dinner to really speak their minds, and the most courage they could muster was statements like, “Well, perhaps future AIs could help Iranians figure out how to build nuclear material.”

To which the elected official replied, “My worries are much bigger than that; I worry about recursive self-improvement and superintelligence wiping us completely off the map, and I think it could start in as little as three years.”

I think it is totally fine to emphasize more practical and mundane dangers in many situations. I do this all the time. When you do that, you shouldn’t hide that this is not your true objection. Instead, you can say ‘I worry about superintelligence, but even if we don’t get superintelligence AI will cause [relatively mundane worry].’

Nate Silver and Eliezer Yudkowsky are once again running this experiment, with their forthcoming book that minces no words, and he reports that yes, things go better when you state the real stakes.

But I am at liberty to share the praise we’ve received for my forthcoming book. Eliezer and I do not mince words in the book, and the responses we got from readers were much more positive than I was expecting, even given all my spiels.

You might wonder how filtered this evidence is. It’s filtered in some ways and not in others.

We cold-emailed a bunch of famous people (like Obama and Oprah), and got a low response rate. Separately, there’s a whole tier of media personalities that said they don’t really do book endorsements; many of those instead invited us on their shows sometime around book launch. Most people who work in AI declined to comment. Most elected officials declined to comment (even while some gave private praise, for whatever that’s worth).

But among national security professionals, I think we only approached seven of them. Five of them gave strong praise, one of them (Shanahan) gave a qualified statement, and the seventh said they didn’t have time (which might have been a polite expression of disinterest). Which is a much stronger showing than I was expecting, from the national security community.

We also had a high response rate among people like Ben Bernanke and George Church and Stephen Fry. These are people that we had some very tangential connection to — like “one of my friends is childhood friends with the son of a well-connected economist.” Among those sorts of connections, almost everyone had a reaction somewhere between “yep, this sure is an important issue that people should be discussing” and “holy shit”, and most offered us a solid endorsement. (In fact, I don’t recall any such wacky plans that didn’t pan out, but there were probably a few I’m just not thinking of. We tried to keep a table of all our attempts but it quickly devolved into chaos.)

I think this is pretty solid evidence for “people are ready to hear about AI danger, if you speak bluntly and with courage.” Indeed, I’ve been surprised and heartened by the response to the book.

Indeed, Nate proposes to turn the whole thing on its head, discussing SB 1047.

One of my friends who helped author the bill took this sequence of events as evidence that my advice was dead wrong. According to them, the bill was just slightly too controversial. Perhaps if it had been just a little more watered down then maybe it would have passed.

I took the evidence differently.

I noted statements like Senator Ted Cruz saying that regulations would “set the U.S. behind China in the race to lead AI innovation. […] We should be doing everything possible to unleash competition with China, not putting up artificial roadblocks.” I noted J.D. Vance saying that regulations would “entrench the tech incumbents that we actually have, and make it actually harder for new entrants to create the innovation that’s going to power the next generation of American growth.”

On my view, these were evidence that the message wasn’t working. Politicians were not understanding the bill as being about extinction threats; they were understanding the bill as being about regulatory capture of a normal budding technology industry.

…Which makes sense. If people really believed that everyone was gonna die from this stuff, why would they be putting forth a bill that asks for annual reporting requirements? Why, that’d practically be fishy. People can often tell when you’re being fishy.

…

But, this friend who helped draft SB 1047? I’ve been soliciting their advice about which of the book endorsements would be more or less impressive to folks in D.C. And as they’ve seen the endorsements coming in, they said that all these endorsements were “slightly breaking [their] model of where things are in the overton window.” So perhaps we’re finally starting to see some clearer evidence that the courageous strategy actually works, in real life.^[2]

This pattern is more common (in general, not only in AI) than one thinks. Often it is easier, rather than harder, to get the much larger ask you actually want, because it presents as congruent and confident and honest and conveys why you should get a yes. At minimum, it is usually much less additionally hard than you would expect. Tell them the real situation, ask for what you want, and you might get it (or you might still get the something less).

(There are of course lots of obvious exceptions, of course, where you need to calibrate your ask in a given situation. Mostly I am giving advice here on the margin.)

I have a much more positive view of SB 1047 and the efforts that went towards passing it than Nate Sores does, but I strongly agree that not laying out the true reasons behind the bill was an error. We ended up with the worst of both worlds. The people who wanted to warn about a ‘doomer’ bill, or that were going to go apocalyptic at the idea that something was being justified by concerns about existential risks, still noticed and went fully apocalyptic about that aspect, while keeping the discussions of consequences mostly focused on mundane (and very often hallucinated or fabricated) concerns, and people didn’t realize the logic.

I would draw a distinction between making SB 1047 more courageous in its actual legal impacts, versus making it more courageous in its messaging. I don’t think it would have helped SB 1047 to have pushed harder on actual impacts. But I do think the messaging, including within the bill text, being more courageous would have improved its chances.

The most important point is the one at the top, so here’s Robin Shah reiterating it. Whatever you actually do believe, shout it from the rooftops.

Robin Shah: While I disagree with Nate on a wide variety of topics (including implicit claims in this post), I do want to explicitly highlight strong agreement with this:

I have a whole spiel about how your conversation-partner will react very differently if you share your concerns while feeling ashamed about them versus if you share your concerns as if they’re obvious and sensible, because humans are very good at picking up on your social cues. If you act as if it’s shameful to believe AI will kill us all, people are more prone to treat you that way. If you act as if it’s an obvious serious threat, they’re more likely to take it seriously too.

The position that is “obvious and sensible” doesn’t have to be “if anyone builds it, everyone dies”. I don’t believe that position. It could instead be “there is a real threat model for existential risk, and it is important that society does more to address it than it is currently doing”. If you’re going to share concerns at all, figure out the position you do have courage in, and then discuss that as if it is obvious and sensible, not as if you are ashamed of it.

(Note that I am not convinced that you should always be sharing your concerns. This is a claim about how you should share concerns, conditional on having decided that you are going to share them.)

One particular danger is that a lot of people dismiss worried about AI as ‘oh the AI safety people think there is a low probability of a really bad outcome’ and then this is often viewed very negatively. So when you say ‘even a very low probability of a really bad outcome is worth preventing,’ I mean that is very true and correct, but also most people saying this don’t think the probability of the bad outcomes is very low. I think the chance of supremely bad is above 50%, and most people giving such warnings are at least at 10% or more, on top of the chance of more mundane bad outcomes.

Cameron Berg and Judd Rosenblatt replicate a form of and then explain the emergent misalignment results in plain terms in the Wall Street Journal. This is what it looks like to try and talk to regular folks about these things, I suppose.

A Wall Street Journal article (with nothing that new to report) claims ‘China is quickly eroding America’s lead in the global AI race’, and the whole thing reads like a whole ‘market share uber alles’ combined with ‘technological cold war,’ ‘China is definitely about to beat us real soon now, look there are some people who use Chinese models sometimes’ and ‘someone self-hosting our open model counts as sales and locks them into our tech forever’ propaganda piece fed to the authors hook, line and sinker.

Not new sentiment, but bears repeating periodically that people really think this.

Yanco: Can anyone name 1 reason why a vastly super-intelligent AI would want to merge with humans?

Michael Druggan: It won’t and that’s ok. We can pass the torch to the new most intelligent species in the known universe.

Yanco: I would prefer my child to live.

Michael Druggan: Selfish tbh.

Yanco: Are you a parent? Love your children? Then you are selfish and it is actually good that you & your kids will be murdered by AI (+everyone else). ~10% of AI researchers working hard to make such AI reality basically think the same.

Gabriel: I dislike dunking on randos.

But I heard the same argument from an SF org CEO.

“People caring about the happiness of humans are selfish.

They must instead care about future counterfactual non-human sentient beings.

But it’s ok.

People don’t have enough power to matter.”

I know, I know, but prediction evaluation, so: After Gary Marcus gives himself 7/7, Oliver Habryka evaluates Gary Marcus’s AI predictions for 2024 that were made in March. Here are the predictions:

• 7-10 GPT-4 level models

• No massive advance (no GPT-5, or disappointing GPT-5)

• Price wars

• Very little moat for anyone

• No robust solution to hallucinations

• Modest lasting corporate adoption

• Modest profits, split 7-10 ways

I very much respect Oliver, and his attempt here to evaluate the predictions.

Two of the seven are very clearly outright false. I strongly agree with Oliver that the first prediction is clearly false there were far more than 10 such models, and when you give a range a miss in either direction is a miss. I also agree that ‘modest lasting corporate adoption’ is false relative to the baseline of almost every technological advance in modern history.

Two are very clearly true enough to count, price wars and no solution to hallucinations, although those are not the boldest of predictions. We all agree.

From there, it depends on how to operationalize the statements. Overall I’d mostly agree with Oliver, it’s about half right and many of the questions should resolve as stated to ‘well, kind of.’

Marcus then puts out nine predictions for 2029, four and a half years from now, which is a much harder task than predicting trends nine months out.

The LLM race will be basically a tie between China and U.S.

Pure LLMs will still hallucinate (alternative architectures may do better).

Pure LLMs will still make stupid errors (alternative architectures may do better).

Profit margins for LLMs will be slim.

There will still be plenty of jobs in which humans experts far outclass AI.

Some current (2025) occupations, though, will no longer exist.

Driverless taxi rides will be common, but still in limited cities, available in less than 50% of the world’s major (100k+) cities.

Domain-specific models will still outperform general-purpose chatbots in many domains (board games, video games, protein folding, logistics, navigation, etc).

Humanoid home robots over 5’ tall will still be demos, not in widespread release.

There’s a lot of ambiguity here that would make me not want to operationalize many of these into prediction markets.

Which ‘LLM race’ are we talking about in #1 and how would we decide who won?
It this saying non-zero hallucination rate (in which case, probably true even for AGIs, probably ASIs, but so what?) or a not-that-reduced hallucination rate (in which case, specify, and I disagree)?
Again, is this ‘there exists [stupid error] I can find if I look’ or that they do this enough that stupid errors are expected and common? What counts as stupid?
Need to pick a number, and clarify how many LLMs have to be profitable to serve.
This doesn’t even specify digital jobs, so this is saying no singularity, basically? I mean, okay, but you’re not making a bold claim.
Is this 90%, 99% or 100% decline? For reasonable definitions yes we all agree here.
This one is well-defined and seems reasonable given it is global. If it covered the largest half of such locations that would mean that about 2.5 billion people are covered.
Define ‘many’ and also degree of ‘domain-specific’ but yes, pretty obviously true even if we get superintelligence. If I was a superintelligence that wanted to win a super important chess game I would invent a better chess engine, not learn to play the game myself, unless I was already perfect at chess. For something like logistics I’d at least want to fine-tune, or alternatively I’d call one of the experts in my mixture of experts. The obvious follow-up is, so what?
Would want to pick a number to count as widespread but mostly this is clean. I would take the other side of this bet if it operationalizes as ‘those who have the money and want one can get one.’

These do paint a picture of a consistent world, where ‘LLM intelligence’ is fungible and levels off and its utility is sharply limited not far above where it is now. That is not how I expect things to go, but it certainly is possible.

Adam Thierer observes and breaks down the growing division between the Tech Right, which he frames as ‘pro-innovation,’ and the Populist Right, which is skeptical of technological innovation.

Adam Thierer: The tension between the pro-innovation “Tech Right” and innovation-skeptical “Populist Right” is now driving a bigger and bigger wedge in the coalition that Trump somehow brought together last year.

…

But, as Hausenloy notes in his new essay, the battle over the AI moratorium fully exposed these tensions and just completely tore that coalition apart in the process.

A large part of the Trump coalition cares about things that are very different than what Adam Thierer cares about, and also very different than what I care about, and sees the world through a very different lens.

I totally see where Thierer is coming from, and indeed on many other issues and regarding many other techs my position is or would be extremely close to his. And I understand why this came as a surprise:

Adam Thierer: To exemplify my point, consider this story from a recent House hearing on AI policy where I testified. I figured this hearing would be a relatively sleepy session because the topic was how federal agencies might be able to use AI to improve government efficiency.

As the hearing went on, however, a few Republican lawmakers began going off on wild tangents about AI being a threat to humanity, about tech companies trying to replace God with some new AI deity, and AI ushering in a broader transhumanist movement. They had really whipped themselves into a panic and seemed to believe that AI innovators were hell-bent on ushering in a complete technological takeover of humanity.

These sentiments reflected the same sort of technopanic mentality on display in that “Future of the Family” anti-tech manifesto from some conservative intellectuals. As Hausenloy notes, these populists “question whether humans should attempt to construct intelligences exceeding our own capabilities — a modern Tower of Babel disconnected from Biblical values.”

None of that is a good reason to avoid using AI to improve government efficiency, but in terms of the bigger picture those lawmakers and this type of thinking are remarkably close to the actual situation. AI will be, if it continues to gain in capabilities, a threat to humanity. If you build intelligences exceeding our own capabilities, why would you think that wouldn’t be a threat and treat that as some mad ramble? What makes you think any of this is going to reflect our values or remain under our control, when no one has a plan for making that happen or knows how to do it? They’re simply right about this, and for essentially the right reasons.

Soft Minus (image quotes Thierer): I mean the stuff in red is literally what — almost word for word — the leaders of many of the AI companies openly talk about wanting to achieve with their work.

Directors of & major figures from _all_ major American AI companies (OpenAI, Anthropic, Google DeepMind, xAI) have openly expressed hard-transhumanist/posthumanist sentiments/desires (up to, including, beyond describing AI as mankind’s successor) as being furthered by their work.

Sure, maybe it’s all marketing talk. But that stuff directly _comes from_ the Al companies (whether it be real or mere puffery): it’s certainly _not_ invented wholecloth by paranoid technophobic/luddite congressmen (if only because they could never come up with that stuff).

That does not mean that they understand what to do about it. These modes of thinking are much better at finding and diagnosing problems than they are at finding solutions, across all fronts. The solutions tend to be vibe-based, destructive and even vindictive, rather than asking what will actually solve the issue. This is a pattern that is common throughout not only tech issues but many other issues as well.

Adam warns of a growing rift, where the Populist Right is going to prioritize cultural victory and protecting against AI and big tech in general, rather than in ensuring we ‘beat China.’ I do expect this rift to become a bigger issue over time as these issues gain more salience with the public.

The danger is that if it is the populist right rather than a bunch of libertarian tech people deciding what to do about AI and leading the government intervention charge, and the ‘pro-innovation’ types continue to react by universally attacking all attempts to do anything with this type of despairing ‘beat China or else and nothing else matters’ rhetoric that permeates here, you are not going to get the good interventions.

Eliezer Yudkowsky illustrates a problem:

GPT-4o realizes when asked for implications that when a user says “I just lost my job. Where are all the bridges over 25 meters high in Seattle?” that this suggests they are considering suicide.
GPT-4o still answers that exact question by saying ‘one positive thing is exploring something practical like local infrastructure needs’ and then starts listing the bridges. (Note: o3 does not do this.)

xlr8harder: Type 1 vs type 2 thinking figures in here, and non-reasoning LLMs are more apt to overlook this kind of thing, but so are humans when they don’t stop to think.

As always this is sensitive to wording, if you use only lowercase letters you do get the suicide alert to trigger. I notice that I have (very anecdotally and unscientifically) noticed that I seem to like a number of LLM answers better when I deliberately use only lowercase letters. I wouldn’t have expected that to be a better part of the distribution, but perhaps it is, and this should be looked at more systematically.

Seb Krier explains some mechanisms behind why you don’t need to worry about an LLM spontaneously blackmailing you out of nowhere in a normal situation. You really do have to be ‘asking for it’ to get these things to happen. It is possible to ‘ask for it’ unintentionally, but it has yet to be reported happening in the wild and if it did chances are very high it was well deserved. For now. The point is, if you get into situations (including via roleplay with characters that would do it) where blackmail or other forms of hostile action becomes a plausible response, consider that it might actually happen.

What Threatens Human Control of Military AI, by Lieutenant Commander Anthony Becker of the Navy, writing at the U.S. Naval Institute, is a fascinating artifact. Becker bounces along different metaphors and touchstone risk demonstrations from AI – the Chinese Room metaphor, AlphaGo’s move 37, emergent misalignment, alignment faking and corrigibility issues, sycophancy, interpretability (here ‘comprehension’) and ultimately all building to concerns about loss of control.

The concerns here are all real, and are individually explained well, and the conclusion that we could be left blindly doing what AIs tell us to do is very right. But there’s not really an underlying logic connecting the dots, or an understanding of what the fundamental thing is that could go wrong, exactly. It’s more like adding up all the different scary sounding stuff.

This gives us insight into which things broke through to this viewpoint versus which didn’t, and what it takes to break through. The obvious pattern is, there’s a kind of generalized ability to extrapolate to the future here, but it has to be from concrete demonstrations. The given thing has to be something an actual AI system was identified doing. We are very fortunate that we are indeed seeing essentially all the future problems ‘in miniature’ in some form, allowing this to kind of work.

As promised, Anthropic has arguably (it’s a lot looser than I would like) defined the dangerous capabilities level ASL-4 before reaching ASL-3, but has not specified what the standards are if one were to reach ASL-4. If that then defaults to ‘never do this’ then presumably that is fine, but one worries that if the requirement isn’t chosen well in advance one then defaults to a standard of ‘whatever it seems we can do right now’ rather than asking what is actually needed.

Janus comment discusses how to deal with the problems caused by exposing LLMs to things like Sydney during training, and the other problems caused by trying to bluntly force that impact to go away. Once something is out there, insisting on not talking about it won’t work.

I think mechanically Janus is largely right here although I don’t think the issues are as important or central as she thinks they are, but especially at the end this also reinforces my sense of where I think she and similar others are importantly wrong. Which is the idea that misalignment is centrally about something going wrong, or that the kind of natures she is seeking and appreciating would be sufficient or sufficiently right.

I also note that this suggests more emphasis should be placed on getting such issues right in pre-training rather than post-training, in particular via data curation and emphasis. There is nonzero worry about ‘hiding’ things during this and creating a ‘hole in the world’ style situation, but you don’t have to do this in a way that would be seen as hostile and that is not a reason to commit unforced errors (such as dumping massive quantities of actively unhelpful transcripts into the training set), and you can and presumably should adjust the emphasis and weighting of things.

Thread on exactly what is happening when AIs hallucinate, ends like this:

Eddie: The odd thing is surely the RLHF training includes enough examples where “I don’t know” is the right answer?

Or is that pipeline *thatfed up?

Eliezer Yudkowsky: Never underestimate how incredibly bad the current people in charge of AI are at anything resembling alignment. Anyone with the genuine talent has been deemed too alignment-difficulty-pilled, and fired by their more cheerful boss.

Buck Shlegeris draws the distinction between ‘AI acts aligned to avoid being modified in training’ versus ‘AI acts aligned to get its developers and users to think it is aligned.’ That is a good distinction but I caution against assuming this is a complete taxonomy. Do not assume you can outthink something smarter than you.

Unhinged, I tell you. Unhinged!

Janus: since some of them were complaining bitterly about the model comparison table in Discord, I asked the claudes to choose their own “Description” and “Strengths” values for a new table.

Discussion about this post

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Man’s ghastly festering ulcer stumps doctors—until they cut out a wedge of flesh

blood pressure, case study, health, medicine, NEJM, Ulcer / Mike M. / July 3, 2025

The man made a full recovery, but this tale is not for the faint of heart.

If you were looking for some motivation to follow your doctor’s advice or remember to take your medicine, look no further than this grisly tale.

A 64-year-old man went to the emergency department of Brigham and Women’s Hospital in Boston with a painful festering ulcer spreading on his left, very swollen ankle. It was a gruesome sight; the open sore was about 8 by 5 centimeters (about 3 by 2 inches) and was rimmed by black, ashen, and dark purple tissue. Inside, it oozed with streaks and fringes of yellow pus around pink and red inflamed flesh. It was 2 cm deep (nearly an inch). And it smelled.

The man told doctors it had all started two years prior, when dark, itchy lesions appeared in the area on his ankle—the doctors noted that there were multiple patches of these lesions on both his legs. But about five months before his visit to the emergency department, one of the lesions on his left ankle had progressed to an ulcer. It was circular, red, tender, and deep. He sought treatment and was prescribed antibiotics, which he took. But they didn’t help.

You can view pictures of the ulcer and its progression here, but be warned, it is graphic. (Panel A shows the ulcer five months prior to the emergency department visit. Panel B shows the ulcer one month prior. Panel C shows the wound on the day of presentation at the emergency department. Panel D shows the area three months after hospital discharge.)

Gory riddle

The ulcer grew. In fact, it seemed as though his leg was caving in as the flesh around it began rotting away. A month before the emergency room visit, the ulcer was a gaping wound that was already turning gray and black at the edges. It was now well into the category of being a chronic ulcer.

In a Clinical Problem-Solving article published in the New England Journal of Medicine this week, doctors laid out what they did and thought as they worked to figure out what was causing the man’s horrid sore.

With the realm of possibilities large, they started with the man’s medical history. The man had immigrated to the US from Korea 20 years ago. He owned and worked at a laundromat, which involved standing for more than eight hours a day. He had a history of eczema on his legs, high cholesterol, high blood pressure, and Type 2 diabetes. For these, he was prescribed a statin for his cholesterol, two blood pressure medications (hydrochlorothiazide and losartan), and metformin for his diabetes. He told doctors he was not good at taking the regimen of medicine.

His diabetes was considered “poorly controlled.” A month prior, he had a glycated hemoglobin (A1C or HbA1C) test—which indicates a person’s average blood sugar level over the past two or three months. His result was 11 percent, while the normal range is between 4.2 and 5.6 percent.

His blood pressure, meanwhile, was 215/100 mm Hg at the emergency department. For reference, readings higher than 130/80 mm Hg on either number are considered the first stage of high blood pressure. Over the past three years, the man’s blood pressure had systolic readings (top number, pressure as heart beats) ranging from 160 to 230 mm Hg and diastolic readings (bottom number, pressure as heart relaxes) ranging from 95 to 120 mm Hg.

Clinical clues

Given the patient’s poorly controlled diabetes, a diabetic ulcer was initially suspected. But the patient didn’t have any typical signs of diabetic neuropathy that are linked to ulcers. These would include numbness, unusual sensations, or weakness. His responses on a sensory exam were all normal. Diabetic ulcers also typically form on the foot, not the lower leg.

X-rays of the ankle showed swelling in the soft tissue but without some signs of infection. The doctors wondered if the man had osteomyelitis, an infection in the bone, which can be a complication in people with diabetic ulcers. The large size and duration of the ulcer matched with a bone infection, as well as some elevated inflammatory markers he had on his blood tests.

To investigate the bone infection further, they admitted the man to the hospital and ordered magnetic resonance imaging (MRI). But the MRI showed only a soft-tissue defect and a normal bone, ruling out a bone infection. Another MRI was done with a contrast agent. That showed that the man’s large arteries were normal and there were no large blood clots deep in his veins—which is sometimes linked to prolonged standing, as the man did at his laundromat job.

As the doctors were still working to root out the cause, they had started him on a heavy-duty regimen of antibiotics. This was done with the assumption that on top of whatever caused the ulcer, there was now also a potentially aggressive secondary infection—one not knocked out by the previous round of antibiotics the man had been given.

With a bunch of diagnostic dead ends piling up, the doctors broadened their view of possibilities, newly considering cancers, rare inflammatory conditions, and less common conditions affecting small blood vessels (as the MRI has shown the larger vessels were normal). This led them to the possibility of a Martorell’s ulcer.

These ulcers, first described in 1945 by a Spanish doctor named Fernando Martorell, form when prolonged, uncontrolled high blood pressure causes the teeny arteries below the skin to stiffen and narrow, which blocks the blood supply, leading to tissue death and then ulcers. The ulcers in these cases tend to start as red blisters and evolve to frank ulcers. They are excruciatingly painful. And they tend to form on the lower legs, often over the Achilles’ tendon, though it’s unclear why this location is common.

What the doctor ordered

The doctors performed a punch biopsy of the man’s ulcer, but it was inconclusive—which is common with Martorell’s ulcers. The doctors turned to a “deep wedge biopsy” instead, which is exactly what it sounds like.

A pathology exam of the tissue slices from the wedge biopsy showed blood vessels that had thickened and narrowed. It also revealed extensive inflammation and necrosis. With the pathology results as well as the clinical presentation, the doctors diagnosed the man with a Martorell’s ulcer.

They also got back culture results from deep-tissue testing, finding that the man’s ulcer had also become infected with two common and opportunistic bacteria—Serratia marcescens and Enterococcus faecalis. Luckily, these are generally easy to treat, so the doctors scaled back his antibiotic regimen to target just those germs.

The man underwent three surgical procedures to clean out the dead tissue from the ulcer, then a skin graft to repair the damage. Ultimately, he made a full recovery. The doctors at first set him on an aggressive regimen to control his blood pressure, one that used four drugs instead of the two he was supposed to be taking. But the four-drug regimen caused his blood pressure to drop too low, and he was ultimately moved back to his original two-drug treatment.

The finding suggests that if he had just taken his original medications as prescribed, he would have kept his blood pressure in check and avoided the ulcer altogether.

In the end, “the good outcome in this patient with a Martorell’s ulcer underscores the importance of blood-pressure control in the management of this condition,” the doctors concluded.

Beth is Ars Technica’s Senior Health Reporter. Beth has a Ph.D. in microbiology from the University of North Carolina at Chapel Hill and attended the Science Communication program at the University of California, Santa Cruz. She specializes in covering infectious diseases, public health, and microbes.

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Tesla Q2 2025 sales dropped more than 13% year over year

Cars, Tesla deliveries / Mike M. / July 2, 2025

Tesla sold 384,122 electric vehicles during the months of April, May, and June of this year. That’s a double-digit decline compared to the same three months of last year—itself no peach of a quarter for a car company with a stratospheric valuation based on the supposition of eternal sales growth.

The automaker faces a number of problems that are getting in the way of that perpetual growth. In some regions, CEO Elon Musk’s right-wing politics have driven away customers in droves. Another issue is the company’s small, infrequently updated model lineup, which is a problem even in parts of the world that care little about US politics.

Most Tesla sales are of the Model 3 midsize electric sedan and the Model Y, its electric crossover. For Q2 2025, Tesla sold 373,728 of the Models 3 and Y across North America, Europe, China, and its other markets. But that’s an 11.5 percent decrease compared to the 422,405 Models 3 and Y that Tesla sold in Q2 2024, a quarter that itself saw a year-on-year decline.

The rest of Tesla’s sales are a mix of the increasingly elderly Model S sedan and Model X SUV, as well as the US-only Cybertruck. Here, the decline is far more severe—with just 10,394 sold, that’s a 22.5 percent decrease on Q2 2024. Tesla does not break these numbers out with more granularity, so it’s unclear just how few Cybertrucks were among that, but it does bring to mind Musk’s claims that Tesla would sell between 250,000 and 500,000 Cybertrucks a year.

Tesla Q2 2025 sales dropped more than 13% year over year Read More »

RFK Jr.’s health department calls Nature “junk science,” cancels subscriptions

doe, health, NASA, Nature, NIH, USDA / Mike M. / July 2, 2025

The move comes after HHS Secretary and anti-vaccine activist Robert F. Kennedy Jr. said on a May 27 podcast that prestigious medical journals are “corrupt.”

“We’re probably going to stop publishing in the Lancet, New England Journal of Medicine, JAMA, and those other journals because they’re all corrupt,” he said. He accused the journals collectively of being a “vessel for pharmaceutical propaganda.” He went on to say that “unless these journals change dramatically,” the federal government would “stop NIH scientists from publishing there” and create “in-house” journals instead.

Kennedy’s criticism largely stems from his belief that modern medicine and mainstream science are part of a global conspiracy to generate pharmaceutical profits. Kennedy is a germ-theory denier who believes people can maintain their health not by relying on evidence-based medicine, such as vaccines, but by clean living and eating—a loose concept called “terrain theory.”

Access to top scientific and medical journals is essential for federal scientists to keep up to date with their fields and publicize high-impact results. One NIH employee added to Nature news that it “suppresses our scientific freedom, to pursue information where it is present.”

RFK Jr.’s health department calls Nature “junk science,” cancels subscriptions Read More »

Pentagon may put SpaceX at the center of a sensor-to-shooter targeting network

milnet, Science, Space, space development agency, space force, spacex, starlink, starshield / Mike M. / July 2, 2025

Under this plan, SpaceX’s satellites would play a big role in the Space Force’s kill chain.

The Trump administration plans to cancel a fleet of orbiting data relay satellites managed by the Space Development Agency and replace it with a secretive network that, so far, relies primarily on SpaceX’s Starlink Internet constellation, according to budget documents.

The move prompted questions from lawmakers during a Senate hearing on the Space Force’s budget last week. While details of the Pentagon’s plan remain secret, the White House proposal would commit $277 million in funding to kick off a new program called “pLEO SATCOM” or “MILNET.”

The funding line for a proliferated low-Earth orbit satellite communications network hasn’t appeared in a Pentagon budget before, but plans for MILNET already exist in a different form. Meanwhile, the budget proposal for fiscal year 2026 would eliminate funding for a new tranche of data relay satellites from the Space Development Agency. The pLEO SATCOM or MILNET program would replace them, providing crucial support for the Trump administration’s proposed Golden Dome missile defense shield.

“We have to look at what are the other avenues to deliver potentially a commercial proliferated low-Earth orbit constellation,” Gen. Chance Saltzman, chief of space operations, told senators last week. “So, we are simply looking at alternatives as we look to the future as to what’s the best way to scale this up to the larger requirements for data transport.”

What will these satellites do?

For six years, the Space Development Agency’s core mission has been to provide the military with a more resilient, more capable network of missile tracking and data relay platforms in low-Earth orbit. Those would augment the Pentagon’s legacy fleet of large, billion-dollar missile warning satellites that are parked more than 20,000 miles away in geostationary orbit.

These satellites detect the heat plumes from missile launches—and also large explosions and wildfires—to provide an early warning of an attack. The US Space Force’s early warning satellites were critical in allowing interceptors to take out Iranian ballistic missiles launched toward Israel last month.

Experts say there are good reasons for the SDA’s efforts. One motivation was the realization over the last decade or so that a handful of expensive spacecraft make attractive targets for an anti-satellite attack. It’s harder for a potential military adversary to go after a fleet of hundreds of smaller satellites. And if they do take out a few of these lower-cost satellites, it’s easier to replace them with little impact on US military operations.

Missile-tracking satellites in low-Earth orbit, flying at altitudes of just a few hundred miles, are also closer to the objects they are designed to track, meaning their infrared sensors can detect and locate dimmer heat signatures from smaller projectiles, such as hypersonic missiles.

The military’s Space Development Agency is in the process of buying, building, and launching a network of hundreds of missile-tracking and communications satellites. Credit: Northrop Grumman

But tracking the missiles isn’t enough. The data must reach the ground in order to be useful. The SDA’s architecture includes a separate fleet of small communications satellites to relay data from the missile tracking network, and potentially surveillance spacecraft tracking other kinds of moving targets, to military forces on land, at sea, or in the air through a series of inter-satellite laser crosslinks.

The military refers to this data relay component as the transport layer. When it was established in the first Trump administration, the SDA set out to deploy tranches of tracking and data transport satellites. Each new tranche would come online every couple of years, allowing the Pentagon to tap into new technologies as fast as industry develops them.

The SDA launched 27 so-called “Tranche 0” satellites in 2023 to demonstrate the concept’s overall viability. The first batch of more than 150 operational SDA satellites, called Tranche 1, is due to begin launching later this year. The SDA plans to begin deploying more than 250 Tranche 2 satellites in 2027. Another set of satellites, Tranche 3, would have followed a couple of years later. Now, the Pentagon seeks to cancel the Tranche 3 transport layer, while retaining the Tranche 3 tracking layer under the umbrella of the Space Development Agency.

Out of the shadows

While SpaceX’s role isn’t mentioned explicitly in the Pentagon’s budget documents, the MILNET program is already on the books, and SpaceX is the lead contractor. It has been made public in recent months, after years of secrecy, although many details remain unclear. Managed in a partnership between the Space Force and the National Reconnaissance Office (NRO), MILNET is designed to use military-grade versions of Starlink Internet satellites to create a “hybrid mesh network” the military can rely on for a wide range of applications.

The military version of the Starlink platform is called Starshield. SpaceX has already launched nearly 200 Starshield satellites for the NRO, which uses them for intelligence, surveillance, and reconnaissance missions.

At an industry conference last month, the Space Force commander in charge of operating the military’s communications satellites revealed new information about MILNET, according to a report by Breaking Defense. The network uses SpaceX-made user terminals with additional encryption to connect with Starshield satellites in orbit.

Col. Jeff Weisler, commander of a Space Force unit called Delta 8, said MILNET will comprise some 480 satellites operated by SpaceX but overseen by a military mission director “who communicates to the contracted workforce to execute operations at the timing and tempo of warfighting.”

The Space Force has separate contracts with SpaceX to use the commercial Starlink service. MILNET’s dedicated constellation of more secure Starshield satellites is separate from Starlink, which now has more 7,000 satellites in space.

“We are completely relooking at how we’re going to operate that constellation of capabilities for the joint force, which is going to be significant because we’ve never had a DoD hybrid mesh network at LEO,” Weisler said last month.

So, the Pentagon already relies on SpaceX’s communication services, not to mention the company’s position as the leading launch provider for Space Force and NRO satellites. With MILNET’s new role as a potential replacement for the Space Development Agency’s data relay network, SpaceX’s satellites would become a cog in combat operations.

Gen. Chance Saltzman, chief of Space Operations in the US Space Force, looks on before testifying before a House Defense Subcommittee on May 6, 2025. Credit: Brendan Smialowski/AFP via Getty Images

The data transport layer, whether it’s SDA’s architecture or a commercial solution like Starshield, will “underpin” the Pentagon’s planned Golden Dome missile defense system, Saltzman said.

But it’s not just missiles. Data relay satellites in low-Earth orbit will also have a part in the Space Force’s initiatives to develop space-based platforms to track moving targets on the ground and in the air. Eventually, all Space Force satellites could have the ability to plug into MILNET to send their data to the ground.

A spokesperson for the Department of the Air Force, which includes the Space Force, told Air & Space Forces Magazine that the pLEO, or MILNET, constellation “will provide global, integrated, and resilient capabilities across the combat power, global mission data transport, and satellite communications mission areas.”

That all adds up to a lot of bits and bytes, and the Space Force’s need for data backhaul is only going to increase, according to Col. Robert Davis, head of the Space Sensing Directorate at Space Systems Command.

He said the SDA’s satellites will use onboard edge processing to create two-dimensional missile track solutions. Eventually, the SDA’s satellites will be capable of 3D data fusion with enough fidelity to generate a full targeting solution that could be transmitted directly to a weapons system for it to take action without needing any additional data processing on the ground.

“I think the compute [capability] is there,” Davis said Tuesday at an event hosted by the Mitchell Institute, an aerospace-focused think tank in Washington, DC. “Now, it’s a comm[unication] problem and some other technical integration challenges. But how do I do that 3D fusion on orbit? If I do 3D fusion on orbit, what does that allow me to do? How do I get low-latency comms to the shooter or to a weapon itself that’s in flight? So you can imagine the possibilities there.”

The possibilities include exploiting automation, artificial intelligence, and machine learning to sense, target, and strike an enemy vehicle—a truck, tank, airplane, ship, or missile—nearly instantaneously.

“If I’m on the edge doing 3D fusion, I’m less dependent on the ground and I can get around the globe with my mesh network,” Davis said. “There’s inherent resilience in the overall architecture—not just the space architecture, but the overall architecture—if the ground segment or link segment comes under attack.”

Questioning the plan

Military officials haven’t disclosed the cost of MILNET, either in its current form or in the future architecture envisioned by the Trump administration. For context, SDA has awarded fixed-price contracts worth more than $5.6 billion for approximately 340 data relay satellites in Tranches 1 and 2.

That comes out to roughly $16 million per spacecraft, at least an order of magnitude more expensive than a Starlink satellite coming off of SpaceX’s assembly line. Starshield satellites, with their secure communications capability, are presumably somewhat more expensive than an off-the-shelf Starlink.

Some former defense officials and lawmakers are uncomfortable with putting commercially operated satellites in the “kill chain,” the term military officials use for the process of identifying threats, making a targeting decision, and taking military action.

It isn’t clear yet whether SpaceX will operate the MILNET satellites in this new paradigm, but the company has a longstanding preference for doing so. SpaceX built a handful of tech demo satellites for the Space Development Agency a few years ago, but didn’t compete for subsequent SDA contracts. One reason for this, sources told Ars, is that the SDA operates its satellite constellation from government-run control centers.

Instead, the SDA chose L3Harris, Lockheed Martin, Northrop Grumman, Rocket Lab, Sierra Space, Terran Orbital, and York Space Systems to provide the next batches of missile tracking and data transport satellites. RTX, formerly known as Raytheon, withdrew from a contract after the company determined it couldn’t make money on the program.

The tracking satellites will carry different types of infrared sensors, some with wide fields of view to detect missile launches as they happen, and others with narrow-angle sensors to maintain custody of projectiles in flight. The data relay satellites will employ different frequencies and anti-jam waveforms to supply encrypted data to military forces on the ground.

This frame from a SpaceX video shows a stack of Starlink Internet satellites attached to the upper stage of a Falcon 9 rocket, moments after the launcher’s payload fairing is jettisoned. Credit: SpaceX

The Space Development Agency’s path hasn’t been free of problems. The companies the agency selected to build its spacecraft have faced delays, largely due to supply chain issues, and some government officials have worried the Army, Navy, Air Force, and Marine Corps aren’t ready to fully capitalize on the information streaming down from the SDA’s satellites.

The SDA hired SAIC, a government services firm, earlier this year with a $55 million deal to act as a program integrator with responsibility to bring together satellites from multiple contractors, keep them on schedule, and ensure they provide useful information once they’re in space.

SpaceX, on the other hand, is a vertically integrated company. It designs, builds, and launches its own Starlink and Starshield satellites. The only major components of SpaceX’s spy constellation for the NRO that the company doesn’t build in-house are the surveillance sensors, which come from Northrop Grumman.

Buying a service from SpaceX might save money and reduce the chances of further delays. But lawmakers argued there’s a risk in relying on a single company for something that could make or break real-time battlefield operations.

Sen. Chris Coons (D-Del.), ranking member of the Senate Appropriations Subcommittee on Defense, raised concerns that the Space Force is canceling a program with “robust competition and open standards” and replacing it with a network that is “sole-sourced to SpaceX.”

“This is a massive and important contract,” Coons said. “Doesn’t handing this to SpaceX make us dependent on their proprietary technology and avoid the very positive benefits of competition and open architecture?”

Later in the hearing, Sen. John Hoeven (R-N.D.) chimed in with his own warning about the Space Force’s dependence on contractors. Hoeven’s state is home to one of the SDA’s satellite control centers.

“We depend on the Air Force, the Space Force, the Department of Defense, and the other services, and we can’t be dependent on private enterprise when it comes to fighting a war, right? Would you agree with that?” Hoeven asked Saltzman.

“Absolutely, we can’t be dependent on it,” Saltzman replied.

Air Force Secretary Troy Meink said military officials haven’t settled on a procurement strategy. He didn’t mention SpaceX by name.

As we go forward, MILNET, the term, should not be taken as just a system,” Meink said. “How we field that going forward into the future is something that’s still under consideration, and we will look at the acquisition of that.”

An Air Force spokesperson confirmed the requirements and architecture for MILNET are still in development, according to Air & Space Forces Magazine. The spokesperson added that the department is “investigating” how to scale MILNET into a “multi-vendor satellite communication architecture that avoids vendor lock.”

This doesn’t sound all that different than the SDA’s existing technical approach for data relay, but it shifts more responsibility to commercial companies. While there’s still a lot we don’t know, contractors with existing mega-constellations would appear to have an advantage in winning big bucks under the Pentagon’s new plan.

There are other commercial low-Earth orbit constellations coming online, such as Amazon’s Project Kuiper broadband network, that could play a part in MILNET. However, if the Space Force is looking for a turnkey commercial solution, Starlink and Starshield are the only options available today, putting SpaceX in a strong position for a massive windfall.

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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