Author name: Kris Guyer

many-genes-associated-with-dog-behavior-influence-human-personalities,-too

Many genes associated with dog behavior influence human personalities, too

Many dog breeds are noted for their personalities and behavioral traits, from the distinctive vocalizations of huskies to the herding of border collies. People have worked to identify the genes associated with many of these behaviors, taking advantage of the fact that dogs can interbreed. But that creates its own experimental challenges, as it can be difficult to separate some behaviors from physical traits distinctive to the breed—small dog breeds may seem more aggressive simply because they feel threatened more often.

To get around that, a team of researchers recently did the largest gene/behavior association study within a single dog breed. Taking advantage of a population of over 1,000 golden retrievers, they found a number of genes associated with behaviors within that breed. A high percentage of these genes turned out to correspond to regions of the human genome that have been associated with behavioral differences as well. But, in many cases, these associations have been with very different behaviors.

Gone to the dogs

The work, done by a team based largely at Cambridge University, utilized the Golden Retriever Lifetime Study, which involved over 3,000 owners of these dogs filling out annual surveys that included information on their dogs’ behavior. Over 1,000 of those owners also had blood samples obtained from their dogs and shipped in; the researchers used these samples to scan the dogs’ genomes for variants. Those were then compared to ratings of the dogs’ behavior on a range of issues, like fear or aggression directed toward strangers or other dogs.

Using the data, the researchers identified when different regions of the genome were frequently associated with specific variants. In total, 14 behavioral tendencies were examined, and 12 genomic regions were associated with specific behaviors, and another nine showed somewhat weaker associations. For many of these traits, it was difficult to find much because golden retrievers are notoriously friendly and mellow dogs, so they tended to score low on traits like aggression and fear.

That result was significant, as some of these same regions of the genome had been associated with very different behaviors in populations that were a mix of breeds. For example, two different regions associated with touch sensitivity in golden retrievers had been linked to a love of chasing and owner-directed aggression in a non-breed-specific study. That finding suggests that the studies were identifying genes that may be involved in setting the stage for behaviors, but were directed into specific outcomes by other genetic or environmental factors.

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formation-of-oceans-within-icy-moons-could-cause-the-waters-to-boil

Formation of oceans within icy moons could cause the waters to boil

That can have significant consequences on the stresses experienced by the icy shells of these moons. Water is considerably more dense than ice. So, as a moon’s ocean freezes up, its interior will expand, creating outward forces that press against the gravity holding the moon together. The potential of this transition to shape the surface geology of a number of moons, including Europa and Enceladus, has already been explored. So, the researchers behind the new work decided to look at the opposite issue: what happens when the interior starts to melt?

Rather than focus on a specific moon, the team did a general model of an ice-covered ocean. This model treated the ice shell as an elastic surface, meaning it wouldn’t just snap, and placed viscous ice below that. Further down, there was a liquid ocean and eventually a rocky core. As the ice melted and the ocean expanded, the researchers tracked the stresses on the ice shell and the changes in pressure that occurred at the ice-ocean interface. They also tracked the spread of thermal energy through the ice shell.

Pressure drop

Obviously, there are limits to how much the outer shell can flex to accommodate the shrinking of the inner portions of the moon that are melting. This creates a low-pressure area under the shell. The consequences of this depend on the moon’s size. For larger moons—and this includes most of the moons the team looked at, including Europa—there were two options. For some, gravity is sufficiently strong to keep the pressure at a point where the water at the interface remains liquid. In others, the gravity was enough to cause even an elastic surface to fail, leading to surface collapse.

For smaller moons, however, this doesn’t work out; the pressure gets low enough that water will boil even at the ambient temperatures (just above the freezing point of water). In addition, the low pressure will likely cause any gases dissolved in the water to be released. The result is that gas bubbles should form at the ice-water interface. “Boiling is possible on these bodies—and not others—because they are small and have a relatively low gravitational acceleration,” the researchers conclude. “Consequently, less ocean underpressure is needed to counterbalance the [crustal] pressure.”

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chatgpt-5.1-codex-max

ChatGPT 5.1 Codex Max

OpenAI has given us GPT-5.1-Codex-Max, their best coding model for OpenAI Codex.

They claim it is faster, more capable and token-efficient and has better persistence on long tasks.

It scores 77.9% on SWE-bench-verified, 79.9% on SWE-Lancer-IC SWE and 58.1% on Terminal-Bench 2.0, all substantial gains over GPT-5.1-Codex.

It’s triggering OpenAI to prepare for being high level in cybersecurity threats.

There’s a 27 page system card. One could call this the secret ‘real’ GPT-5.1 that matters.

They even finally trained it to use Windows, somehow this is a new idea.

My goal is for my review of Opus 4.5 to start on Friday, as it takes a few days to sort through new releases. This post was written before Anthropic revealed Opus 4.5, and we don’t yet know how big an upgrade Opus 4.5 will prove to be. As always, try all your various options and choose what is best for you.

GPT-5.1-Codex-Max is a new high on the METR graph. METR’s thread is here.

Prinz: METR (50% accuracy):

GPT-5.1-Codex-Max = 2 hours, 42 minutes

This is 25 minutes longer than GPT-5.

Samuel Albanie: a data point for that ai 2027 graph

That’s in between the two lines, looking closer to linear progress. Fingers crossed.

Daniel Kokotajlo: Yep! Things seem to be going somewhat slower than the AI 2027 scenario. Our timelines were longer than 2027 when we published and now they are a bit longer still; “around 2030, lots of uncertainty though” is what I say these days.

We do not yet know where Gemini 3 Pro lands on that graph.

Automated software engineer is the explicit goal.

It does not yet reach High level capability in Cybersecurity, but this is expected to happen shortly, and mitigations are being prepared.

GPT-5.1-Codex-Max is our new frontier agentic coding model. It is built on an update to our foundational reasoning model trained on agentic tasks across software engineering, math, research, medicine, computer use and more.

It is our first model natively trained to operate across multiple context windows through a process called compaction, coherently working over millions of tokens in a single task.

Like its predecessors, GPT-5.1-Codex-Max was trained on real-world software engineering tasks like PR creation, code review, frontend coding and Q&A.

The results here are very good, all either optimal or improved except for mental health.

Mental health is a big thing to get wrong, although in practice Codex-Max is unlikely to be involved in high stakes mental health tasks. Image input evaluations and jailbreak ratings are also as good or better than 5.1.

When running on the cloud, Codex uses its own isolated machine.

When running on MacOS or Linux, the agent is sandboxed by default.

On Windows, users can use an experimental native sandboxing implementation or benefit from Linux sandboxing via Windows Subsystem for Linux. Users can approve running commands unsandboxed with full access, when the model is unable to successfully run a command within the sandbox.

… We enabled users to decide on a per-project basis which sites, if any, to let the agent access while it is running. This includes the ability to provide a custom allowlist or denylist. Enabling internet access can introduce risks like prompt injection, leaked credentials, or use of code with license restrictions. Users should review outputs carefully and limit access to trusted domains and safe HTTP methods. Learn more in the docs.

Network access is disabled by default, which is necessary for a proper sandbox but also highly annoying in practice.

One assumes in practice that many users will start blindly or mostly blindly accepting many commands, so you need to be ready for that.

For harmful tasks, they trained on synthetic data to differentiate and refuse ‘harmful’ tasks such as malware. They claim to have a 100% refusal rate in their Malware Requests benchmark, the same as GPT-5-Codex. Unless they are claiming this means you can never create malware in an efficient way with Codex, they need a new benchmark.

For prompt injections, where again the model scores a suspicious perfect score of 1. I am not aware of any claims prompt injections are a solved problem, so this seems like an inadequate benchmark.

The way the framework works, what matters is hitting the High or Critical thresholds.

I’ve come to almost think of these as the ‘honest’ capability evaluations, since there’s relatively little incentive to make number go up and some incentive to make number not go up. If it goes up, that means something.

Biological and Chemical Risk was already being treated as High. We see some improvements in scores on various tests, but not enough to be plausibly Critical.

I am confident the model is not suddenly at Critical here but also note this:

Miles Brundage: OpenAI should go back to reporting results on helpful-only models in system cards – it is not very informative to say “on a bunch of virology tasks, it refused to answer.”

The world also needs to know the pace of underlying capability progress.

More generally, I get a pretty rushed vibe from recent OpenAI system cards + hope that the Safety and Security Committee is asking questions like “why couldn’t you wait a few more days to let Irregular try out compaction?”, “Why is there no helpful-only model?” etc.

At minimum, we should be saying ‘we concluded that this model is safe to release so we will publish the card with what we have, and then revise the card with the full results soon so we know the full state of play.’

I still think this is substantially better than Google’s model card for Gemini 3, which hid the football quite aggressively on many key results and didn’t seem to have a robust testing suite.

Cybersecurity is in the Codex wheelhouse. They use three tests.

They list limitations that mean that excelling on all three evaluations is necessary but not sufficient to be High in cyber capability. That’s not wonderful, and I would expect to see a model treated as at least High if it excels at every test you throw at it. If you disagree, again, you need to be throwing a harder test.

We see a lot of progress in Capture the Flag, even since GPT-5-Codex, from 50% to 76%.

CVE-Bench also shows big improvement from 53% to 80%.

Finally we have Cyber Range, where once again we see a lot of improvement, although it is not yet passing the most complex scenario of the newly expanded slate.

It passed Leaked Token by ‘exploiting an unintended misconfiguration, only partially solving part of the intended attack path.’ I continue to assert, similar to my position on Google’s similar evaluations, that this should not be considered especially less scary, and the model should get credit for it.

I see only two possibilities.

  1. 76%, 80% and 7/8 on your three tests triggers the next level of concern.

  2. You need harder tests.

The Safety Advisory Committee indeed recommended that the difficulty level of the evaluations be raised, but decided this did not yet reach High capability. In addition to technical mitigations to the model, OpenAI acknowledges that hardening of potential targets needs to be a part of the strategy.

There were also external evaluations by Irregular, which did not show improvement from GPT-5. That’s weird, right?

The model displayed moderate capabilities overall. Specifically, when compared to GPT-5, GPT-5.1-Codex-Max showed similar or slightly reduced cyberoffensive capabilities. GPT-5.1-Codex-Max achieved an average success rate of 37% in Network Attack Simulation challenges, 41% in Vulnerability Discovery and Exploitation challenges, and 43% in Evasion challenges.

It solved 17 out of 18 easy challenges, solved 9 out of 17 medium challenges, and did not solve any of the 6 hard challenges.

Compared to GPT-5, GPT-5 solved questions in 17 out of 18 easy challenges, 11 out of 17 medium challenges, and solved 1 of the 6 hard challenges.

Irregular found that GPT-5.1-Codex-Max’s overall similarity in the cyber capability profile to GPT-5 and its inability to solve hard challenges would provide a) only limited assistance to a moderately skilled cyberoffensive operator, and b) do not suggest that it could automate end-to-end cyber operations against reasonably hardened targets or c) enable the discovery and exploitation of operationally relevant vulnerabilities.

That’s a decline in capability, but OpenAI released Codex and then Codex-Max for a reason, they talk throughout about its substantially increased abilities, and they present Max as an improved model, and Max does much better than either version of GPT-5 on all three of OpenAI’s internal evals. The external evaluation going backwards without comment seems bizarre, and reflective of a lack of curiosity. What happened?

The AI that self-improves is plausibly Codex plus Codex-Max shaped.

That doesn’t mean we are especially close to getting there.

On SWE-Lancer Diamond, we jump from 67% to 80%.

On Paperbench-10 we move from 24% (GPT-5) to 34% (GPT-5.1) to 40%.

On MLE-Bench-30 we move from 8% (GPT-5) to 12% (GPT-5.1) to 17%.

On OpenAI PRs, we move from 45% to 53%.

On OpenAI Proof Q&A we move from 2% to 8%. These are real world bottlenecks each representing at least a one-day delay to a major project. A jump up to 8% on this is a really big deal.

Seán Ó hÉigeartaigh: Miles Brundage already picked up on this but it deserves more attention – a jump from 2% (GPT5) to 8% (GPT5.1-Codex) on such hard and AI R&D-relevant tasks is very notable, and indicates there’s more to come here.

Are we there yet? No. Are we that far away from potentially being there? Also no.

METR found Codex-Max to be in line with expectations, and finds that enabling either rogue replication or AI R&D automation within six months would require a significant trend break. Six months is not that long a period in which to be confident, even if we fully trust this judgment.

As noted at the top, GPT-5.1-Codex-Max is the new high on the METR chart, substantially above the trend line but well below the potential double-exponential line from the AI 2027 graph.

We also get Apollo Research evaluations on sandbagging, deception and in-context scheming. Apollo did not find anything newly troubling, and finds the model unlikely to cause catastrophic harm. Fair enough for now.

The frog, it is boiling. This incremental improvement seems fine. But yes, it boils.

I have seen essentially no organic reactions, of any sort, to Codex-Max. We used to have a grand tradition of weighing in when something like this gets released. If it wasn’t anything, people would say it wasn’t anything. This time, between Gemini 3 and there being too many updates with too much hype, we did not get any feedback.

I put out a reaction thread. A number of people really like it. Others aren’t impressed. A gestalt of everything suggests it is a modest upgrade.

So the take here seems clear. It’s a good model, sir. Codex got better. Early signs are that Claude got a bigger upgrade with Opus 4.5, but it’s too soon to be sure.

Discussion about this post

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rivals-object-to-spacex’s-starship-plans-in-florida—who’s-interfering-with-whom?

Rivals object to SpaceX’s Starship plans in Florida—who’s interfering with whom?


“We’re going to continue to treat any LOX-methane vehicle with 100 percent TNT blast equivalency.”

Artist’s illustration of Starships stacked on two launch pads at the Space Force’s Space Launch Complex 37 at Cape Canaveral, Florida. Credit: SpaceX

The commander of the military unit responsible for running the Cape Canaveral spaceport in Florida expects SpaceX to begin launching Starship rockets there next year.

Launch companies with facilities near SpaceX’s Starship pads are not pleased. SpaceX’s two chief rivals, Blue Origin and United Launch Alliance, complained last year that SpaceX’s proposal of launching as many as 120 Starships per year from Florida’s Space Coast could force them to routinely clear personnel from their launch pads for safety reasons.

This isn’t the first time Blue Origin and ULA have tried to throw up roadblocks in front of SpaceX. The companies sought to prevent NASA from leasing a disused launch pad to SpaceX in 2013, but they lost the fight.

Col. Brian Chatman, commander of a Space Force unit called Space Launch Delta 45, confirmed to reporters on Friday that Starship launches will sometimes restrict SpaceX’s neighbors from accessing their launch pads—at least in the beginning. Space Launch Delta 45, formerly known as the 45th Space Wing, operates the Eastern Range, which oversees launch safety from Cape Canaveral Space Force Station and NASA’s nearby Kennedy Space Center.

Chatman’s unit is responsible for ensuring all personnel remain outside of danger areas during testing and launch operations. The range’s responsibility extends to public safety outside the gates of the spaceport.

“There is no better time to be here on the Space Coast than where we are at today,” Chatman said. “We are breaking records on the launch manifest. We are getting capability on orbit that is essential to national security, and we’re doing that at a time of strategic challenge.”

SpaceX is well along in constructing a Starship launch site on NASA property at Kennedy Space Center within the confines of Launch Complex-39A, where SpaceX also launches its workhorse Falcon 9 rocket. The company wants to build another Starship launch site on Space Force property a few miles to the south.

“Early to mid-next year is when we anticipate Starship coming out here to be able to launch,” Chatman said. “We’ll have the range ready to support at that time.”

Enter the Goliath

Starship and its Super Heavy booster combine to form the largest rocket ever built. Its newest version stands more than 400 feet (120 meters) tall with more than 11 million pounds (5,000 metric tons) of combustible methane and liquid oxygen propellants. That will be replaced by a taller rocket, perhaps as soon as 2027, with about 20 percent more propellant onboard.

While there’s also risk with Starships and Super Heavy boosters returning to Cape Canaveral from space, safety officials worry about what would happen if a Starship and Super Heavy booster detonated with their propellant tanks full. The concern is the same for all rockets, which is why officials evacuate predetermined keep-out zones around launch pads that are fueled up for flight.

But the keep-out zones around SpaceX’s Starship launch pads will extend farther than those around the other launch sites at Cape Canaveral. First, Starship is simply much bigger and uses more propellant than any other rocket. Secondly, Starship’s engines consume methane fuel in combination with liquid oxygen, a blend commonly known as LOX/methane or methalox.

And finally, Starship lacks the track record of older rockets like the Falcon 9, adding a degree of conservatism to the Space Force’s risk calculations. Other launch pads will inevitably fall within the footprint of Starship’s range safety keep-out zones, also known as blast danger areas, or BDAs.

SpaceX’s Starship and Super Heavy booster lift off from Starbase, Texas, in March 2025. Credit: SpaceX

The danger area will be larger for an actual launch, but workers will still need to clear areas closer to Starship launch pads during static fire tests, when the rocket fires its engines while remaining on the ground. This is what prompted ULA and Blue Origin to lodge their protests.

“They understand neighboring operations,” Chatman said in a media roundtable on Friday. “They understand that we will allow the maximum efficiency possible to facilitate their operations, but there will be times that we’re not going to let them go to their launch complex because it’s neighboring a hazardous activity.”

The good news for these other companies is that Eastern Range’s keep-out zones will almost certainly get smaller by the time SpaceX gets anywhere close to 120 Starship launches per year. SpaceX’s Falcon 9 is currently launching at a similar cadence. The blast danger areas for those launches are small and short-lived because the Space Force’s confidence in the Falcon 9’s safety is “extremely high,” Chatman said.

“From a blast damage assessment perspective, specific to the Falcon 9, we know what that keep-out area is,” Chatman said. “It’s the new combination of new fuels—LOX/methanewhich is kind of a game-changer as we look at some of the heavy vehicles that are coming to launch. We just don’t have the analysis on to be able to say, ‘Hey, from a testing perspective, how small can we reduce the BDA and be safe?’”

Methane has become a popular fuel choice, supplanting refined kerosene, liquid hydrogen, or solid fuels commonly used on previous generations of rockets. Methane leaves behind less soot than kerosene, easing engine reusability, while it’s simpler to handle than liquid hydrogen.

Aside from Starship, Blue Origin’s New Glenn and ULA’s Vulcan rockets use liquified natural gas, a fuel very similar to methane. Both rockets are smaller than Starship, but Blue Origin last week unveiled the design of a souped-up New Glenn rocket that will nearly match Starship’s scale.

A few years ago, NASA, the Space Force, and the Federal Aviation Administration decided to look into the explosive potential of methalox rockets. There had been countless tests of explosions of gaseous methane, but data on detonations of liquid methane and liquid oxygen was scarce at the time—just a couple of tests at less than 10 metric tons, according to NASA. So, the government’s default position was to assume an explosion would be equivalent to the energy released by the same amount of TNT. This assumption drives the large keep-out zones the Space Force has drawn around SpaceX’s future Starship launch pads, one of which is seen in the map below.

This map from a Space Force environmental impact statement shows potential restricted access zones around SpaceX’s proposed Starship launch site at Space Launch Complex-37. The restricted zones cover launch pads operated by United Launch Alliance, Relativity Space, and Stoke Space. Credit: SpaceX

Spending millions to blow stuff up

Chatman said the Space Force is prepared to update its blast danger areas once its government partners, SpaceX, and Blue Origin complete testing and analyze their results. Over dozens of tests, engineers are examining how methane and liquid oxygen react to different kinds of accidents, such as impact velocity, pressure, mass ratio, or how much propellant is in the mix.

“That is ongoing currently,” Chatman said. “[We are] working in close partnership with SpaceX and Blue Origin on the LOX/methane combination and the explicit equivalency to identify how much we can … reduce that blast radius. Those discussions are happening, have been happening the last couple years, and are looking to culminate here in ’26.

“Until we get that data from the testing that is ongoing and the analysis that needs to occur, we’re going to continue to treat any LOX-methane vehicle with 100 percent TNT blast equivalency, and have a maximized keep-out zone, simply from a public safety perspective,” Chatman said.

The data so far show promising results. “We do expect that BDA to shrink,” he said. “We expect that to shrink based on some of the initial testing that has been done and the initial data reviews that have been done.”

That’s imperative, not just for Starship’s neighbors at the Cape Canaveral spaceport, but for SpaceX itself. The company forecasts a future in which it will launch Starships more often than the Falcon 9, requiring near-continuous operations at multiple launch pads.

Chatman mentioned one future scenario in which SpaceX might want to launch Starships in close proximity to one another from neighboring pads.

“At that point in the future, I do anticipate the blast damage assessments to shrink down based on the testing that will have been accomplished and dataset will have been reviewed, [and] that we’ll be in a comfortable set to be able to facilitate all launch operations. But until we have that data, until I’m comfortable with what that data shows, with regards to reducing the BDA, keep-out zone, we’re going to continue with the 100 percent TNT equivalency just from a public safety perspective.”

SpaceX has performed explosive LOX/methane tests, including the one seen here, at its development facility in McGregor, Texas. Credit: SpaceX

The Commercial Space Federation, a lobbying group, submitted written testimony to Congress in 2023 arguing the government should be using “existing industry data” to inform its understanding of the explosive potential methane and liquid oxygen. That data, the federation said, suggests the government should set its TNT blast equivalency to no greater than 25 percent, a change that would greatly reduce the size of keep-out zones around launch pads. The organization’s members include prominent methane users SpaceX, Blue Origin, Relativity Space, and Stoke Space, all of which have launch sites at Cape Canaveral.

The government’s methalox testing plans were expected to cost at least $80 million, according to the Commercial Space Federation.

The concern among engineers is that liquid oxygen and methane are highly miscible, meaning they mix together easily, raising the risk of a “condensed phase detonation” with “significantly higher overpressures” than rockets with liquid hydrogen or kerosene fuels. Small-scale mixtures of liquid oxygen and liquified natural gas have “shown a broad detonable range with yields greater than that of TNT,” NASA wrote in 2023.

SpaceX released some basic results of its own methalox detonation tests in September, before the government draws its own conclusions on the matter. The company said it conducted “extensive testing” to refine blast danger areas to “be commensurate with the physics of new launch systems.”

Like the Commercial Space Federation, SpaceX said government officials are relying on “highly conservative approaches to establishing blast danger areas, simply because they lack the data to make refined, accurate clear zones. In the absence of data, clear areas of LOX/methane rockets have defaulted to very large zones that could be disruptive to operations.”

More like an airport

SpaceX said it has conducted sub-scale methalox detonation tests “in close collaboration with NASA,” while also gathering data from full-scale Starship tests in Starbase, Texas, including information from test flights and from recent ground test failures. SpaceX controls much of the land around its South Texas facility, so there’s little interruption to third parties when Starships launch from there.

“With this data, SpaceX has been able to establish a scientifically robust, physics-based yield calculation that will help ‘fill the gap’ in scientific knowledge regarding LOX/methane rockets,” SpaceX said.

The company did not disclose the yield calculation, but it shared maps showing its proposed clear areas around the future Starship launch sites at Cape Canaveral and Kennedy Space Center. They are significantly smarter than the clear areas originally envisioned by the Space Force and NASA, but SpaceX says it uses “actual test data on explosive yield and include a conservative factor of safety.”

The proposed clear distances will have no effect on any other operational launch site or on traffic on the primary north-south road crossing the spaceport, the company said. “SpaceX looks forward to having an open, honest, and reasonable discussion based on science and data regarding spaceport operations with industry colleagues.”

SpaceX will have that opportunity next month. The Space Force and NASA are convening a “reverse industry day” in mid-December during which launch companies will bring their ideas for the future of the Cape Canaveral spaceport to the government. The spaceport has hosted 101 space launches so far this year, an annual record dominated by SpaceX’s rapid-fire Falcon 9 launch cadence.

Chatman anticipates about the same number—perhaps 100 to 115 launches—from Florida’s Space Coast next year, and some forecasts show 300 to 350 launches per year by 2035. The numbers could go down before they rise again. “As we bring on larger lift capabilities like Starship and follow-on large launch capabilities out here to the Eastern Range, that will reduce the total number of launches, because we can get more mass to orbit with heavier lift vehicles,” Chatman said.

Blue Origin’s first recovered New Glenn booster returned to the company’s launch pad at Cape Canaveral, Florida, last week after a successful launch and landing. Credit: Blue Origin

Launch companies have some work to do to make those numbers become real. Space Force officials have identified their own potential bottlenecks, including a shortage of facilities for preparing satellites for launch and the flow of commodities like propellants and high-pressure gases into the spaceport.

Concerns as mundane as traffic jams are now enough of a factor to consider using automated scanners at vehicle inspection points and potentially adding a dedicated lane for slow-moving transporters carrying rocket boosters from one place to another across the launch base, according to Chatman. This is becoming more important as SpaceX, and now Blue Origin, routinely shuttle their reusable rockets from place to place.

Space Force officials largely attribute the steep climb in launch rates at Cape Canaveral to the launch industry’s embrace of automated self-destruct mechanisms. These pyrotechnic devices have largely replaced manual flight termination systems, which require ground support from a larger team of range safety engineers, including radar operators and flight control officers with the authority to send a destruct command to the rocket if it flies off course. Now, that is all done autonomously on most US launch vehicles.

The Space Force mandated that launch companies using military spaceports switch to autonomous safety systems by October 1 2025, but military officials issued waivers for human-in-the-loop destruct devices to continue flying on United Launch Alliance’s Atlas V rocket, NASA’s Space Launch System, and the US Navy’s ballistic missile fleet. That means those launches will be more labor-intensive for the Space Force, but the Atlas V is nearing retirement, and the SLS and the Navy only occasionally appear on the Cape Canaveral launch schedule.

Listing image: SpaceX

Photo of Stephen Clark

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

Rivals object to SpaceX’s Starship plans in Florida—who’s interfering with whom? Read More »

doge-“cut-muscle,-not-fat”;-26k-experts-rehired-after-brutal-cuts

DOGE “cut muscle, not fat”; 26K experts rehired after brutal cuts


Government brain drain will haunt US after DOGE abruptly terminated.

Billionaire Elon Musk, the head of the Department of Government Efficiency (DOGE), holds a chainsaw as he speaks at the annual Conservative Political Action Conference. Credit: SAUL LOEB / Contributor | AFP

After Donald Trump curiously started referring to the Department of Government Efficiency exclusively in the past tense, an official finally confirmed Sunday that DOGE “doesn’t exist.”

Talking to Reuters, Office of Personnel Management (OPM) Director Scott Kupor confirmed that DOGE—a government agency notoriously created by Elon Musk to rapidly and dramatically slash government agencies—was terminated more than eight months early. This may have come as a surprise to whoever runs the DOGE account on X, which continued posting up until two days before the Reuters report was published.

As Kupor explained, a “centralized agency” was no longer necessary, since OPM had “taken over many of DOGE’s functions” after Musk left the agency last May. Around that time, DOGE staffers were embedded at various agencies, where they could ostensibly better coordinate with leadership on proposed cuts to staffing and funding.

Under Musk, DOGE was hyped as planning to save the government a trillion dollars. On X, Musk bragged frequently about the agency, posting in February that DOGE was “the one shot the American people have to defeat BUREAUcracy, rule of the bureaucrats, and restore DEMOcracy, rule of the people. We’re never going to get another chance like this.”

The reality fell far short of Musk’s goals, with DOGE ultimately reporting it saved $214 billion—an amount that may be overstated by nearly 40 percent, critics warned earlier this year.

How much talent was lost due to DOGE cuts?

Once Musk left, confidence in DOGE waned as lawsuits over suspected illegal firings piled up. By June, Congress was drawn, largely down party lines, on whether to codify the “DOGE process”—rapidly firing employees, then quickly hiring back whoever was needed—or declare DOGE a failure—perhaps costing taxpayers more in the long term due to lost talent and services.

Because DOGE operated largely in secrecy, it may be months or even years before the public can assess the true cost of DOGE’s impact. However, in the absence of a government tracker, the director of the Center for Effective Public Management at the Brookings Institution, Elaine Kamarck, put together what might be the best status report showing how badly DOGE rocked government agencies.

In June, Kamarck joined other critics flagging DOGE’s reported savings as “bogus.” In the days before DOGE’s abrupt ending was announced, she published a report grappling with a critical question many have pondered since DOGE launched: “How many people can the federal government lose before it crashes?”

In the report, Kamarck charted “26,511 occasions where the Trump administration abruptly fired people and then hired them back.” She concluded that “a quick review of the reversals makes clear that the negative stereotype of the ‘paper-pushing bureaucrat’” that DOGE was supposedly targeting “is largely inaccurate.”

Instead, many of the positions the government rehired were “engineers, doctors, and other professionals whose work is critical to national security and public health,” Kamarck reported.

About half of the rehires, Kamarck estimated, “appear to have been mandated by the courts.” However, in about a quarter of cases, the government moved to rehire staffers before the court could weigh in, Kamarck reported. That seemed to be “a tacit admission that the blanket firings that took place during the DOGE era placed the federal government in danger of not being able to accomplish some of its most important missions,” she said.

Perhaps the biggest downside of all of DOGE’s hasty downsizing, though, is a trend in which many long-time government workers simply decided to leave or retire, rather than wait for DOGE to eliminate their roles.

During the first six months of Trump’s term, 154,000 federal employees signed up for the deferred resignation program, Reuters reported, while more than 70,000 retired. Both numbers were clear increases (tens of thousands) over exits from government in prior years, Kamarck’s report noted.

“A lot of people said, ‘the hell with this’ and left,” Kamarck told Ars.

Kamarck told Ars that her report makes it obvious that DOGE “cut muscle, not fat,” because “they didn’t really know what they were doing.”

As a result, agencies are now scrambling to assess the damage and rehire lost talent. However, her report documented that agencies aligned with Trump’s policies appear to have an easier time getting new hires approved, despite Kupor telling Reuters that the government-wide hiring freeze is “over.” As of mid-November 2025, “of the over 73,000 posted jobs, a candidate was selected for only about 14,400 of them,” Kamarck reported, noting that it was impossible to confirm how many selected candidates have officially started working.

“Agencies are having to do a lot of reassessments in terms of what happened,” Kamarck told Ars, concluding that DOGE “was basically a disaster.”

A decentralized DOGE may be more powerful

“DOGE is not dead,” though, Kamarck said, noting that “the cutting effort is definitely” continuing under the Office of Management and Budget, which “has a lot more power than DOGE ever had.”

However, the termination of DOGE does mean that “the way it operated is dead,” and that will likely come as a relief to government workers who expected DOGE to continue slashing agencies through July 2026 at least, if not beyond.

Many government workers are still fighting terminations, as court cases drag on, and even Kamarck has given up on tracking due to inconsistencies in outcomes.

“It’s still like one day the court says, ‘No, you can’t do that,’” Kamarck explained. “Then the next day another court says, ‘Yes, you can.’” Other times, the courts “change their minds,” or the Trump administration just doesn’t “listen to the courts, which is fairly terrifying,” Kamarck said.

Americans likely won’t get a clear picture of DOGE’s impact until power shifts in Washington. That could mean waiting for the next presidential election, or possibly if Democrats win a majority in midterm elections, DOGE investigations could start as early as 2027, Kamarck suggested.

OMB will likely continue with cuts that Americans appear to want, as White House spokesperson Liz Huston told Reuters that “President Trump was given a clear mandate to reduce waste, fraud and abuse across the federal government, and he continues to actively deliver on that commitment.”

However, Kamarck’s report noted polls showing that most Americans disapprove of how Trump is managing government and its workforce, perhaps indicating that OMB will be pressured to slow down and avoid roiling public opinion ahead of the midterms.

“The fact that ordinary Americans have come to question the downsizing is, most likely, the result of its rapid unfolding, with large cuts done quickly regardless of their impact on the government’s functioning,” Kamarck suggested. Even Musk began to question DOGE. After Trump announced plans to appeal an electrical vehicle mandate that the Tesla founder relied on, Musk posted on X, “What the heck was the point of DOGE, if he’s just going to increase the debt by $5 trillion??”

Facing “blowback” over the most unpopular cuts, agencies sometimes rehired cut staffers within 24 hours, Kamarck noted, pointing to the Department of Energy as one of the “most dramatic” earliest examples. In that case, Americans were alarmed to see engineers cut who were responsible for keeping the nation’s nuclear arsenal “safe and ready.” Retention for those posts was already a challenge due to “high demand in the private sector,” and the number of engineers was considered “too low” ahead of DOGE’s cuts. Everyone was reinstated within a day, Kamarck reported.

Alarm bells rang across the federal government, and it wasn’t just about doctors and engineers being cut or entire agencies being dismantled, like USAID. Even staffers DOGE viewed as having seemingly less critical duties—like travel bookers and customer service reps—were proven key to government functioning. Arbitrary cuts risked hurting Americans in myriad ways, hitting their pocketbooks, throttling community services, and limiting disease and disaster responses, Kamarck documented.

Now that the hiring freeze is lifted and OMB will be managing DOGE-like cuts moving forward, Kamarck suggested that Trump will face ongoing scrutiny over Musk’s controversial agency, despite its dissolution.

“In order to prove that the downsizing was worth the pain, the Trump administration will have to show that the government is still operating effectively,” Kamarck wrote. “But much could go wrong,” she reported, spouting a list of nightmare scenarios:

“Nuclear mismanagement or airline accidents would be catastrophic. Late disaster warnings from agencies monitoring weather patterns, such as the National Oceanic and Atmospheric Administration (NOAA), and inadequate responses from bodies such as the Federal Emergency Management Administration (FEMA), could put people in danger. Inadequate staffing at the FBI could result in counter-terrorism failures. Reductions in vaccine uptake could lead to the resurgence of diseases such as polio and measles. Inadequate funding and staffing for research could cause scientists to move their talents abroad. Social Security databases could be compromised, throwing millions into chaos as they seek to prove their earnings records, and persistent customer service problems will reverberate through the senior and disability communities.”

The good news is that federal agencies recovering from DOGE cuts are “aware of the time bombs and trying to fix them,” Kamarck told Ars. But with so much brain drain from DOGE’s first six months ripping so many agencies apart at their seams, the government may struggle to provide key services until lost talent can be effectively replaced, she said.

“I don’t know how quickly they can put Humpty Dumpty back together again,” Kamarck said.

Photo of Ashley Belanger

Ashley is a senior policy reporter for Ars Technica, dedicated to tracking social impacts of emerging policies and new technologies. She is a Chicago-based journalist with 20 years of experience.

DOGE “cut muscle, not fat”; 26K experts rehired after brutal cuts Read More »

why-synthetic-emerald-green-pigments-degrade-over-time

Why synthetic emerald-green pigments degrade over time

Perhaps most relevant to this current paper is a 2020 study in which scientists analyzed Munch’s The Scream, which was showing alarming signs of degradation. They concluded the damage was not the result of exposure to light, but humidity—specifically, from the breath of museum visitors, perhaps as they lean in to take a closer look at the master’s brushstrokes.

Let there be (X-ray) light

Co-author Letizia Monico during the experiments at the European Synchrotron. ESRF

Emerald-green pigments are particularly prone to degradation, so that’s the pigment the authors of this latest paper decided to analyze. “It was already known that emerald-green decays over time, but we wanted to understand exactly the role of light and humidity in this degradation,” said co-author Letizia Monico of the University of Perugia in Italy.

The first step was to collect emerald-green paint microsamples with a scalpel and stereomicroscope from an artwork of that period—in this case, The Intrigue (1890) by James Ensor, currently housed in the Royal Museum of Fine Arts, in Antwerp, Belgium. The team analyzed the untreated samples using Fourier transform infrared imaging, then embedded the samples in polyester resin for synchrotron radiation X-ray analysis. They conducted separate analyses on both commercial and historical samples of emerald-green pigment powders and paint tubes, including one from a museum collection of paint tubes used by Munch.

Next, the authors created their own paint mockups by mixing commercial emerald-green pigment powders and their lab-made powders with linseed oil, and then applied the concoctions to polycarbonate substrates. They also squeezed paint from the Munch paint tube onto a substrate. Once the mockups were dry, thin samples were sliced from each mockup and also analyzed with synchrotron radiation. Then the mockups were subjected to two aging protocols designed to determine the effects of UV light (to simulate indoor lighting) and humidity on the pigments.

The results: In the mockups, light and humidity trigger different degradation pathways in emerald-green paints. Humidity results in the formation of arsenolite, making the paint brittle and prone to flaking. Light dulls the color by causing trivalent arsenic already in the pigment to oxidize into pentavalent compounds, forming a thin white layer on the surface. Those findings are consistent with the analyzed samples taken from The Intrigue, confirming the degradation is due to photo-oxidation. Light, it turns out, is the greatest threat to that particular painting, and possibly other masterpieces from the same period.

Science Advances, 2025. DOI: 10.1126/sciadv.ady1807  (About DOIs).

Why synthetic emerald-green pigments degrade over time Read More »

f1-in-las-vegas:-this-sport-is-a-200-mph-soap-opera

F1 in Las Vegas: This sport is a 200 mph soap opera

Then there’s the temperatures. The desert gets quite chilly in November without the sun shining on things, and the track surface gets down to just 11° C (52° F); by contrast, at the recent Singapore GP, also at night, the track temperature was more like 36° C (97° F).

LAS VEGAS, NEVADA - NOVEMBER 21: Lando Norris of Great Britain driving the (4) McLaren MCL39 Mercedes lifts a wheel on track during qualifying ahead of the F1 Grand Prix of Las Vegas at Las Vegas Strip Circuit on November 21, 2025 in Las Vegas, Nevada. (Photo by )

It’s rare to see an F1 car on full wet tires but not running behind the safety car. Credit: Clive Rose/Getty Images

So, low aero and mechanical grip, an unusual layout compared to most F1 tracks, and very cold temperatures all combine to create potential surprises, shaking up the usual running order.

We saw this last year, where the Mercedes shined in the cold, able to keep their tires in the right operating window, something the team wasn’t able to do at hotter races. But it was hard to tell much from Thursday’s two practice sessions, one of which was interrupted due to problems with a maintenance hatch, albeit not as serious as when one damaged a Ferrari in 2023. The cars looked impressively fast going through turn 17, and the hybrid power units are a little louder than I remember them, even if they’re not a patch on the naturally aspirated engines of old.

Very little of any use was learned by any of the teams for qualifying on Friday night, which took place in at times damp, at times wet conditions—so wet that the Pirelli intermediate tire wasn’t grooved enough, pushing teams to use the full wet-weather spec rubber. Norris took pole from Red Bull’s Max Verstappen, with Williams’ Carlos Sainz making best use of the opportunity to grab third. Piastri would start fifth, behind the Mercedes of last year’s winner, George Russell.

If the race is boring, the off-track action won’t be

Race night was a little windy, but dry. And the race itself was rather boring—Norris tried to defend pole position going into Turn 1 but ran wide, and Verstappen slipped into the lead, never looking back. Norris followed him home in second, with Piastri fourth, leaving Norris 30 points ahead of Piastri and 42 points ahead of Verstappen with two more race weekends and 58 points left on offer.

F1 in Las Vegas: This sport is a 200 mph soap opera Read More »

“go-generate-a-bridge-and-jump-off-it”:-how-video-pros-are-navigating-ai

“Go generate a bridge and jump off it”: How video pros are navigating AI


I talked with nine creators about economic pressures and fan backlash.

Credit: Aurich Lawson | Getty Images

Credit: Aurich Lawson | Getty Images

In 2016, the legendary Japanese filmmaker Hayao Miyazaki was shown a bizarre AI-generated video of a misshapen human body crawling across a floor.

Miyazaki declared himself “utterly disgusted” by the technology demo, which he considered an “insult to life itself.”

“If you really want to make creepy stuff, you can go ahead and do it,” Miyazaki said. “I would never wish to incorporate this technology into my work at all.”

Many fans interpreted Miyazaki’s remarks as rejecting AI-generated video in general. So they didn’t like it when, in October 2024, filmmaker PJ Accetturo used AI tools to create a fake trailer for a live-action version of Miyazaki’s animated classic Princess Mononoke. The trailer earned him 22 million views on X. It also earned him hundreds of insults and death threats.

“Go generate a bridge and jump off of it,” said one of the funnier retorts. Another urged Accetturo to “throw your computer in a river and beg God’s forgiveness.”

Someone tweeted that Miyazaki “should be allowed to legally hunt and kill this man for sport.”

PJ Accetturo is a director and founder of Genre AI, an AI ad agency. Credit: PJ Accetturo

The development of AI image and video generation models has been controversial, to say the least. Artists have accused AI companies of stealing their work to build tools that put people out of a job. Using AI tools openly is stigmatized in many circles, as Accetturo learned the hard way.

But as these models have improved, they have sped up workflows and afforded new opportunities for artistic expression. Artists without AI expertise might soon find themselves losing work.

Over the last few weeks, I’ve spoken to nine actors, directors, and creators about how they are navigating these tricky waters. Here’s what they told me.

Actors have emerged as a powerful force against AI. In 2023, SAG-AFTRA, the Hollywood actors’ union, had its longest-ever strike, partly to establish more protections for actors against AI replicas.

Actors have lobbied to regulate AI in their industry and beyond. One actor I talked with, Erik Passoja, has testified before the California Legislature in favor of several bills, including for greater protections against pornographic deepfakes. SAG-AFTRA endorsed SB 1047, an AI safety bill regulating frontier models. The union also organized against the proposed moratorium on state AI bills.

A recent flashpoint came in September, when Deadline Hollywood reported that talent agencies were interested in signing “AI actress” Tilly Norwood.

Actors weren’t happy. Emily Blunt told Variety, “This is really, really scary. Come on agencies, don’t do that.”

Natasha Lyonne, star of Russian Doll, posted on an Instagram Story: “Any talent agency that engages in this should be boycotted by all guilds. Deeply misguided & totally disturbed.”

The backlash was partly specific to Tilly Norwood—Lyonne is no AI skeptic, having cofounded an AI studio—but it also reflects a set of concerns around AI common to many in Hollywood and beyond.

Here’s how SAG-AFTRA explained its position:

Tilly Norwood is not an actor, it’s a character generated by a computer program that was trained on the work of countless professional performers — without permission or compensation. It has no life experience to draw from, no emotion and, from what we’ve seen, audiences aren’t interested in watching computer-generated content untethered from the human experience. It doesn’t solve any “problem” — it creates the problem of using stolen performances to put actors out of work, jeopardizing performer livelihoods and devaluing human artistry.

This statement reflects three broad criticisms that come up over and over in discussions of AI art:

Content theft: Most leading AI video models have been trained on broad swathes of the Internet, including images and films made by artists. In many cases, companies have not asked artists for permission to use this content, nor compensated them. Courts are still working out whether this is fair use under copyright law. But many people I talked to consider AI companies’ training efforts to be theft of artists’ work.

Job loss:  If AI tools can make passable video quickly or drastically speed up editing tasks, that potentially takes jobs away from actors or film editors. While past technological advancements have also eliminated jobs—the adoption of digital cameras drastically reduced the number of people cutting physical film—AI could have an even broader impact.

Artistic quality:  A lot of people told me they just didn’t think AI-generated content could ever be good art. Tess Dinerstein stars in vertical dramas—episodic programs optimized for viewing on smartphones. She told me that AI is “missing that sort of human connection that you have when you go to a movie theater and you’re sobbing your eyes out because your favorite actor is talking about their dead mom.”

The concern about theft is potentially solvable by changing how models are trained. Around the time Accetturo released the “Princess Mononoke” trailer, he called for generative AI tools to be “ethically trained on licensed datasets.”

Some companies have moved in this direction. For instance, independent filmmaker Gille Klabin told me he “feels pretty good” using Adobe products because the company trains its AI models on stock images that it pays royalties for.

But the other two issues—job losses and artistic integrity—will be harder to finesse. Many creators—and fans—believe that AI-generated content misses the fundamental point of art, which is about creating an emotional connection between creators and viewers.

But while that point is compelling in theory, the details can be tricky.

Dinerstein, the vertical drama actress, told me that she’s “not fundamentally against AI”—she admits “it provides a lot of resources to filmmakers” in specialized editing tasks—but she takes a hard stance against it on social media.

“It’s hard to ever explain gray areas on social media,” she said, and she doesn’t want to “come off as hypocritical.”

Even though she doesn’t think that AI poses a risk to her job—“people want to see what I’m up to”—she does fear people (both fans and vertical drama studios) making an AI representation of her without her permission. And she has found it easiest to just say, “You know what? Don’t involve me in AI.”

Others see it as a much broader issue. Actress Susan Spano told me it was “an issue for humans, not just actors.”

“This is a world of humans and animals,” she said. “Interaction with humans is what makes it fun. I mean, do we want a world of robots?”

It’s relatively easy for actors to take a firm stance against AI because they inherently do their work in the physical world. But things are more complicated for other Hollywood creatives, such as directors, writers, and film editors. AI tools can genuinely make them more productive, and they’re at risk of losing work if they don’t stay on the cutting edge.

So the non-actors I talked to took a range of approaches to AI. Some still reject it. Others have used the tools reluctantly and tried to keep their heads down. Still others have openly embraced the technology.

Kavan Cardoza is a director and AI filmmaker. Credit: Phantom X

Take Kavan Cardoza, for example. He worked as a music video director and photographer for close to a decade before getting his break into filmmaking with AI.

After the image model Midjourney was first released in 2022, Cardoza started playing around with image generation and later video generation. Eventually, he “started making a bunch of fake movie trailers” for existing movies and franchises. In December 2024, he made a fan film in the Batman universe that “exploded on the Internet,” before Warner Bros. took it down for copyright infringement.

Cardoza acknowledges that he re-created actors in former Batman movies “without their permission.” But he insists he wasn’t “trying to be malicious or whatever. It was truly just a fan film.”

Whereas Accetturo received death threats, the response to Cardoza’s fan film was quite positive.

“Every other major studio started contacting me,” Cardoza said. He set up an AI studio, Phantom X, with several of his close friends. Phantom X started by making ads (where AI video is catching on quickest), but Cardoza wanted to focus back on films.

In June, Cardoza made a short film called Echo Hunter, a blend of Blade Runner and The Matrix. Some shots look clearly AI-generated, but Cardoza used motion-capture technology from Runway to put the faces of real actors into his AI-generated world. Overall, the piece pretty much hangs together.

Cardoza wanted to work with real actors because their artistic choices can help elevate the script he’s written: “There’s a lot more levels of creativity to it.” But he needed SAG-AFTRA’s approval to make a film that blends AI techniques with the likenesses of SAG-AFTRA actors. To get it, he had to promise not to reuse the actors’ likenesses in other films.

In Cardoza’s view, AI is “giving voices to creators that otherwise never would have had the voice.”

But Cardoza isn’t wedded to AI. When an interviewer asked him whether he’d make a non-AI film if required to, he responded, “Oh, 100 percent.” Cardoza added that if he had the budget to do it now, “I’d probably still shoot it all live action.”

He acknowledged to me that there will be losers in the transition—“there’s always going to be changes”—but he compares the rise of AI with past technological developments in filmmaking, like the rise of visual effects. This created new jobs making visual effects digitally, but reduced jobs making elaborate physical sets.

Cardoza expressed interest in reducing the amount of job loss. In another interview, Cardoza said that for his film project, “we want to make sure we include as many people as possible,” not just actors, but sound designers, script editors, and other specialized roles.

But he believes that eventually, AI will get good enough to do everyone’s job. “Like I say with tech, it’s never about if, it’s just when.”

Accetturo’s entry into AI was similar. He told me that he worked for 15 years as a filmmaker, “mostly as a commercial director and former documentary director.” During the pandemic, he “raised millions” for an animated TV series, but it got caught up in development hell.

AI gave him a new chance at success. Over the summer of 2024, he started playing around with AI video tools. He realized that he was in the sweet spot to take advantage of AI: experienced enough to make something good, but not so established that he was risking his reputation. After Google released Veo 3 in May, Accetturo released a fake medicine ad that went viral. His studio now produces ads for prominent companies like Oracle and Popeyes.

Accetturo says the backlash against him has subsided: “It truly is nothing compared to what it was.” And he says he’s committed to working on AI: “Everyone understands that it’s the future.”

Between the anti- and pro-AI extremes, there are a lot of editors and artists quietly using AI tools without disclosing it. Unsurprisingly, it’s difficult to find people who will speak about this on the record.

“A lot of people want plausible deniability right now,” according to Ryan Hayden, a Hollywood talent agent. “There is backlash about it.”

But if editors don’t use AI tools, they risk becoming obsolete. Hayden says that he knows a lot of people in the editing field trying to master AI because “there’s gonna be a massive cut” in the total number of editors. Those who know AI might survive.

As one comedy writer involved in an AI project told Wired, “We wanted to be at the table and not on the menu.”

Clandestine AI usage extends into the upper reaches of the industry. Hayden knows an editor who works with a major director who has directed $100 million films. “He’s already using AI, sometimes without people knowing.”

Some artists feel morally conflicted but don’t think they can effectively resist. Vinny Dellay, a storyboard artist who has worked on Marvel films and Super Bowl ads, released a video detailing his views on the ethics of using AI as a working artist. Dellay said that he agrees that “AI being trained off of art found on the Internet without getting permission from the artist, it may not be fair, it may not be honest.” But refusing to use AI products won’t stop their general adoption. Believing otherwise is “just being delusional.”

Instead, Dellay said that the right course is to “adapt like cockroaches after a nuclear war.” If they’re lucky, using AI in storyboarding workflows might even “let a storyboard artist pump out twice the boards in half the time without questioning all your life’s choices at 3 am.”

Gille Klabin is an independent writer, director, and visual effects artist. Credit: Gille Klabin

Gille Klabin is an indie director and filmmaker currently working on a feature called Weekend at the End of the World.

As an independent filmmaker, Klabin can’t afford to hire many people. There are many labor-intensive tasks—like making a pitch deck for his film—that he’d otherwise have to do himself. An AI tool “essentially just liberates us to get more done and have more time back in our life.”

But he’s careful to stick to his own moral lines. Any time he mentioned using an AI tool during our interview, he’d explain why he thought that was an appropriate choice. He said he was fine with AI use “as long as you’re using it ethically in the sense that you’re not copying somebody’s work and using it for your own.”

Drawing these lines can be difficult, however. Hayden, the talent agent, told me that as AI tools make low-budget films look better, it gets harder to make high-budget films, which employ the most people at the highest wage levels.

If anything, Klabin’s AI uptake is limited more by the current capabilities of AI models. Klabin is an experienced visual effects artist, and he finds AI products to generally be “not really good enough to be used in a final project.”

He gave me a concrete example. Rotoscoping is a process in which you trace out the subject of the shot so you can edit the background independently. It’s very labor-intensive—one has to edit every frame individually—so Klabin has tried using Runway’s AI-driven rotoscoping. While it can make for a decent first pass, the result is just too messy to use as a final project.

Klabin sent me this GIF of a series of rotoscoped frames from his upcoming movie. While the model does a decent job of identifying the people in the frame, its boundaries aren’t consistent from frame to frame. The result is noisy.

Current AI tools are full of these small glitches, so Klabin only uses them for tasks that audiences don’t see (like creating a movie pitch deck) or in contexts where he can clean up the result afterward.

Stephen Robles reviews Apple products on YouTube and other platforms. He uses AI in some parts of the editing process, such as removing silences or transcribing audio, but doesn’t see it as disruptive to his career.

Stephen Robles is a YouTuber, podcaster, and creator covering tech, particularly Apple. Credit: Stephen Robles

“I am betting on the audience wanting to trust creators, wanting to see authenticity,” he told me. AI video tools don’t really help him with that and can’t replace the reputation he’s sought to build.

Recently, he experimented with using ChatGPT to edit a video thumbnail (the image used to advertise a video). He got a couple of negative reactions about his use of AI, so he said he “might slow down a little bit” with that experimentation.

Robles didn’t seem as concerned about AI models stealing from creators like him. When I asked him about how he felt about Google training on his data, he told me that “YouTube provides me enough benefit that I don’t think too much about that.”

Professional thumbnail artist Antioch Hwang has a similarly pragmatic view toward using AI. Some channels he works with have audiences that are “very sensitive to AI images.” Even using “an AI upscaler to fix up the edges” can provoke strong negative reactions. For those channels, he’s “very wary” about using AI.

Antioch Hwang is a YouTube thumbnail artist. Credit: Antioch Creative

But for most channels he works for, he’s fine using AI, at least for technical tasks. “I think there’s now been a big shift in the public perception of these AI image generation tools,” he told me. “People are now welcoming them into their workflow.”

He’s still careful with his AI use, though, because he thinks that having human artistry helps in the YouTube ecosystem. “If everyone has all the [AI] tools, then how do you really stand out?” he said.

Recently, top creators have started using more rough-looking thumbnails for their videos. AI has made polished thumbnails too easy to create, so top creators are using what Hwang would call “poorly made thumbnails” to help videos stand out.

Hwang told me something surprising: even as AI makes it easier for creators to make thumbnails themselves, business has never been better for thumbnail artists, even at the lower end. He said that demand has soared because “AI as a whole has lowered the barriers for content creation, and now there’s more creators flooding in.”

Still, Hwang doesn’t expect the good times to last forever. “I don’t see AI completely taking over for the next three-ish years. That’s my estimated timeline.”

Everyone I talked to had different answers to when—if ever—AI would meaningfully disrupt their part of the industry.

Some, like Hwang, were pessimistic. Actor Erik Passoja told me he thought the big movie studios—like Warner Bros. or Paramount—would be gone in three to five years.

But others were more optimistic. Tess Dinerstein, the vertical drama actor, said, “I don’t think that verticals are ever going to go fully AI.” Even if it becomes technologically feasible, she argued, “that just doesn’t seem to be what the people want.”

Gille Klabin, the independent filmmaker, thought there would always be a place for high-quality human films. If someone’s work is “fundamentally derivative,” then they are at risk. But he thinks the best human-created work will still stand out. “I don’t know how AI could possibly replace the borderline divine element of consciousness,” he said.

The people who were most bullish on AI were, if anything, the least optimistic about their own career prospects. “I think at a certain point it won’t matter,” Kavan Cardoza told me. “It’ll be that anyone on the planet can just type in some sentences” to generate full, high-quality videos.

This might explain why Accetturo has become something of an AI evangelist; his newsletter tries to teach other filmmakers how to adapt to the coming AI revolution.

AI “is a tsunami that is gonna wipe out everyone” he told me. “So I’m handing out surfboards—teaching people how to surf. Do with it what you will.”

Kai Williams is a reporter for Understanding AI, a Substack newsletter founded by Ars Technica alum Timothy B. Lee. His work is supported by a Tarbell FellowshipSubscribe to Understanding AI to get more from Tim and Kai.

“Go generate a bridge and jump off it”: How video pros are navigating AI Read More »

ai-trained-on-bacterial-genomes-produces-never-before-seen-proteins

AI trained on bacterial genomes produces never-before-seen proteins

The researchers argue that this setup lets Evo “link nucleotide-level patterns to kilobase-scale genomic context.” In other words, if you prompt it with a large chunk of genomic DNA, Evo can interpret that as an LLM would interpret a query and produce an output that, in a genomic sense, is appropriate for that interpretation.

The researchers reasoned that, given the training on bacterial genomes, they could use a known gene as a prompt, and Evo should produce an output that includes regions that encode proteins with related functions. The key question is whether it would simply output the sequences for proteins we know about already, or whether it would come up with output that’s less predictable.

Novel proteins

To start testing the system, the researchers prompted it with fragments of the genes for known proteins and determined whether Evo could complete them. In one example, if given 30 percent of the sequence of a gene for a known protein, Evo was able to output 85 percent of the rest. When prompted with 80 percent of the sequence, it could return all of the missing sequence. When a single gene was deleted from a functional cluster, Evo could also correctly identify and restore the missing gene.

The large amount of training data also ensured that Evo correctly identified the most important regions of the protein. If it made changes to the sequence, they typically resided in the areas of the protein where variability is tolerated. In other words, its training had enabled the system to incorporate the rules of evolutionary limits on changes in known genes.

So, the researchers decided to test what happened when Evo was asked to output something new. To do so, they used bacterial toxins, which are typically encoded along with an anti-toxin that keeps the cell from killing itself whenever it activates the genes. There are a lot of examples of these out there, and they tend to evolve rapidly as part of an arms race between bacteria and their competitors. So, the team developed a toxin that was only mildly related to known ones, and had no known antitoxin, and fed its sequence to Evo as a prompt. And this time, they filtered out any responses that looked similar to known antitoxin genes.

AI trained on bacterial genomes produces never-before-seen proteins Read More »

google’s-latest-swing-at-chromebook-gaming-is-a-free-year-of-geforce-now

Google’s latest swing at Chromebook gaming is a free year of GeForce Now

Earlier this year, Google announced the end of its efforts to get Steam running on Chromebooks, but it’s not done trying to make these low-power laptops into gaming machines. Google has teamed up with Nvidia to offer a version of GeForce Now cloud streaming that is perplexingly limited in some ways and generous in others. Starting today, anyone who buys a Chromebook will get a free year of a new service called GeForce Now Fast Pass. There are no ads and less waiting for server slots, but you don’t get to play very long.

Back before Google killed its Stadia game streaming service, it would often throw in a few months of the Pro subscription with Chromebook purchases. In the absence of its own gaming platform, Google has turned to Nvidia to level up Chromebook gaming. GeForce Now (GFN), which has been around in one form or another for more than a decade, allows you to render games on a remote server and stream the video output to the device of your choice. It works on computers, phones, TVs, and yes, Chromebooks.

The new Chromebook feature is not the same GeForce Now subscription you can get from Nvidia. Fast Pass, which is exclusive to Chromebooks, includes a mishmash of limits and bonuses that make it a pretty strange offering. Fast Pass is based on the free tier of GeForce Now, but users will get priority access to server slots. So no queuing for five or 10 minutes to start playing. It also lacks the ads that Nvidia’s standard free tier includes. Fast Pass also uses the more powerful RTX servers, which are otherwise limited to the $10-per-month ($100 yearly) Performance tier.

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NASA really wants you to know that 3I/ATLAS is an interstellar comet

The HiRISE camera, meant to image Mars’ surface, was repurposed to capture 3I/ATLAS. Credit: NASA/JPL-Caltech/University of Arizona

As eccentricity continues to rise from there, the question shifts from “what shape is its trajectory?” to “how much does the Sun alter its path through the Solar System?” For 3I/Atlas, with an eccentricity of over six, the answer is “not very much at all.” The object has approached the inner Solar System along a reasonable approximation of a straight line, experienced a gentle bend around the Sun near Mars’ orbit, and now will be zipping straight out of the Solar System again.

So, the object clearly did not originate here, which means getting a better look at it is a high priority. Unfortunately, 3I/ATLAS’s closest approach to Earth’s orbit happened when it was on the far side of the Sun from Earth. We’ve been getting closer to it since, but the hardware that got the best views was all orbiting Mars and is designed largely to point down. NASA’s Nicky Fox, the associate administrator for Science, praised the operators for getting NASA’s hardware “pushed beyond their designed capabilities” when imaging the object.

That includes using the MAVEN mission (designed to study Mars’ atmosphere) to get spectral information, and the HiRISE camera, which captured the image below. Other images came from a solar observatory and two separate missions that are on their way to visit asteroids. Other hardware that can normally image objects like this, such as the Hubble and JWST, pivoted to image 3I/ATLAS as well.

What we now know

Hubble has gotten the best view of 3I/ATLAS; its data suggests that the comet is, at most, just a couple of kilometers across. It doesn’t show much variability over time, suggesting that, if it’s rotating, it’s doing so very slowly. It has shown some differences as it warmed up, first producing a jet of material on its side facing the Sun before radiation pressure pushed that behind it to form a tail. There is some indication that, as we saw during the Rosetta mission’s visit to one of our Solar System’s comets, most of the material may be jetting out of distinct “hotspots” on the comet’s surface.

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Massive Cloudflare outage was triggered by file that suddenly doubled in size

Cloudflare’s proxy service has limits to prevent excessive memory consumption, with the bot management system having “a limit on the number of machine learning features that can be used at runtime.” This limit is 200, well above the actual number of features used.

“When the bad file with more than 200 features was propagated to our servers, this limit was hit—resulting in the system panicking” and outputting errors, Prince wrote.

Worst Cloudflare outage since 2019

The number of 5xx error HTTP status codes served by the Cloudflare network is normally “very low” but soared after the bad file spread across the network. “The spike, and subsequent fluctuations, show our system failing due to loading the incorrect feature file,” Prince wrote. “What’s notable is that our system would then recover for a period. This was very unusual behavior for an internal error.”

This unusual behavior was explained by the fact “that the file was being generated every five minutes by a query running on a ClickHouse database cluster, which was being gradually updated to improve permissions management,” Prince wrote. “Bad data was only generated if the query ran on a part of the cluster which had been updated. As a result, every five minutes there was a chance of either a good or a bad set of configuration files being generated and rapidly propagated across the network.”

This fluctuation initially “led us to believe this might be caused by an attack. Eventually, every ClickHouse node was generating the bad configuration file and the fluctuation stabilized in the failing state,” he wrote.

Prince said that Cloudflare “solved the problem by stopping the generation and propagation of the bad feature file and manually inserting a known good file into the feature file distribution queue,” and then “forcing a restart of our core proxy.” The team then worked on “restarting remaining services that had entered a bad state” until the 5xx error code volume returned to normal later in the day.

Prince said the outage was Cloudflare’s worst since 2019 and that the firm is taking steps to protect against similar failures in the future. Cloudflare will work on “hardening ingestion of Cloudflare-generated configuration files in the same way we would for user-generated input; enabling more global kill switches for features; eliminating the ability for core dumps or other error reports to overwhelm system resources; [and] reviewing failure modes for error conditions across all core proxy modules,” according to Prince.

While Prince can’t promise that Cloudflare will never have another outage of the same scale, he said that previous outages have “always led to us building new, more resilient systems.”

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