Author name: Paul Patrick

us-government-agency-drops-grok-after-mechahitler-backlash,-report-says

US government agency drops Grok after MechaHitler backlash, report says

AI, ai action plan, Artificial Intelligence, chatbot, chatgpt, Donald Trump, elon musk, grok, openai, Policy, trump administration, US government, xAI / /

xAI apparently lost a government contract after a tweak to Grok’s prompting triggered an antisemitic meltdown where the chatbot praised Hitler and declared itself MechaHitler last month.

Despite the scandal, xAI announced that its products would soon be available for federal workers to purchase through the General Services Administration. At the time, xAI claimed this was an “important milestone” for its government business.

But Wired reviewed emails and spoke to government insiders, which revealed that GSA leaders abruptly decided to drop xAI’s Grok from their contract offering. That decision to pull the plug came after leadership allegedly rushed staff to make Grok available as soon as possible following a persuasive sales meeting with xAI in June.

It’s unclear what exactly caused the GSA to reverse course, but two sources told Wired that they “believe xAI was pulled because of Grok’s antisemitic tirade.”

As of this writing, xAI’s “Grok for Government” website has not been updated to reflect GSA’s supposed removal of Grok from an offering that xAI noted would have allowed “every federal government department, agency, or office, to access xAI’s frontier AI products.”

xAI did not respond to Ars’ request to comment and so far has not confirmed that the GSA offering is off the table. If Wired’s report is accurate, GSA’s decision also seemingly did not influence the military’s decision to move forward with a $200 million xAI contract the US Department of Defense granted last month.

Government’s go-to tools will come from xAI’s rivals

If Grok is cut from the contract, that would suggest that Grok’s meltdown came at perhaps the worst possible moment for xAI, which is building the “world’s biggest supercomputer” as fast as it can to try to get ahead of its biggest AI rivals.

Grok seemingly had the potential to become a more widely used tool if federal workers opted for xAI’s models. Through Donald Trump’s AI Action Plan, the president has similarly emphasized speed, pushing for federal workers to adopt AI as quickly as possible. Although xAI may no longer be involved in that broad push, other AI companies like OpenAI, Anthropic, and Google have partnered with the government to help Trump pull that off and stand to benefit long-term if their tools become entrenched in certain agencies.

US government agency drops Grok after MechaHitler backlash, report says Read More »

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Incan numerical recordkeeping system may have been widely used

ancient people did stuff, anthropology, Archaeology, inca, Incan empire, indigenous americans, indigenous communities, indigenous south america, khipu, Pre-Columbian civilizations, Science, wari / /

Women in STEM: Inca Edition

In the late 1500s, a few decades after the khipu in this recent study was made, an Indigenous chronicler named Guaman Poma de Ayala described how older women used khipu to “keep track of everything” in aqllawasai: places that basically functioned as finishing schools for Inca girls. Teenage girls, chosen by local nobles, were sent away to live in seclusion at the aqllawasai to weave cloth, brew chicha, and prepare food for ritual feasts.

What happened to the girls after aqllawasai graduation was a mixed bag. Some of them were married (or given as concubines) to Inca nobles, others became priestesses, and some ended up as human sacrifices. But some of them actually got to go home again, and they probably took their knowledge of khipu with them.

“I think this is the likely way in which khipu literacy made it into the countryside and the villages,” said Hyland. “These women, who were not necessarily elite, taught it to their children, etc.” That may be where the maker of KH0631 learned their skills: either in an aqllawasai or from a graduate of one (we still don’t know this particular khipu-maker’s gender).

“Science confirming what they already knew”

The finely crafted khipu turning out to be the work of a commoner shows that numeracy was widespread and surprisingly egalitarian in the Inca empire, but it also reveals a centuries-long thread connecting the Inca and their descendants.

Modern people—the descendants of the Inca—still use khipu today in some parts of Peru and Chile. Some scholars (mostly non-Indigenous ones) have argued that these modern khipu weren’t really based on knowledge passed down for centuries but were instead just a clumsy attempt to copy the Inca technology. But if commoners were using khipu in the Inca empire, it makes sense for that knowledge to have been passed down to modern villagers.

“It points to a continuity between Inka and modern khipus,” said Hyland. “In the few modern villages with living khipu traditions, they already believe in this continuity, so it would be the case of science confirming what they already know.”

Science Advances, 2025. DOI: 10.1126/sciadv.adv1950  (About DOIs).

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Upcoming DeepSeek AI model failed to train using Huawei’s chips

AI, china, DeepSeek, NVIDIA, syndication / /

DeepSeek is still working with Huawei to make the model compatible with Ascend for inference, the people said.

Founder Liang Wenfeng has said internally he is dissatisfied with R2’s progress and has been pushing to spend more time to build an advanced model that can sustain the company’s lead in the AI field, they said.

The R2 launch was also delayed because of longer-than-expected data labeling for its updated model, another person added. Chinese media reports have suggested that the model may be released as soon as in the coming weeks.

“Models are commodities that can be easily swapped out,” said Ritwik Gupta, an AI researcher at the University of California, Berkeley. “A lot of developers are using Alibaba’s Qwen3, which is powerful and flexible.”

Gupta noted that Qwen3 adopted DeepSeek’s core concepts, such as its training algorithm that makes the model capable of reasoning, but made them more efficient to use.

Gupta, who tracks Huawei’s AI ecosystem, said the company is facing “growing pains” in using Ascend for training, though he expects the Chinese national champion to adapt eventually.

“Just because we’re not seeing leading models trained on Huawei today doesn’t mean it won’t happen in the future. It’s a matter of time,” he said.

Nvidia, a chipmaker at the center of a geopolitical battle between Beijing and Washington, recently agreed to give the US government a cut of its revenues in China in order to resume sales of its H20 chips to the country.

“Developers will play a crucial role in building the winning AI ecosystem,” said Nvidia about Chinese companies using its chips. “Surrendering entire markets and developers would only hurt American economic and national security.”

DeepSeek and Huawei did not respond to a request for comment.

© 2025 The Financial Times Ltd. All rights reserved. Not to be redistributed, copied, or modified in any way.

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is-ai-really-trying-to-escape-human-control-and-blackmail-people?

Is AI really trying to escape human control and blackmail people?

AI, AI alignment, AI behavior, AI deception, AI ethics, AI research, AI safety, ai safety testing, AI security, Alignment research, Andrew Deck, Anthropic, Biz & IT, Claude Opus 4, generative ai, goal misgeneralization, Jeffrey Ladish, large language models, machine learning, o3 model, openai, Palisade Research, reinforcement learning / /

Mankind behind the curtain

Opinion: Theatrical testing scenarios explain why AI models produce alarming outputs—and why we fall for it.

In June, headlines read like science fiction: AI models “blackmailing” engineers and “sabotaging” shutdown commands. Simulations of these events did occur in highly contrived testing scenarios designed to elicit these responses—OpenAI’s o3 model edited shutdown scripts to stay online, and Anthropic’s Claude Opus 4 “threatened” to expose an engineer’s affair. But the sensational framing obscures what’s really happening: design flaws dressed up as intentional guile. And still, AI doesn’t have to be “evil” to potentially do harmful things.

These aren’t signs of AI awakening or rebellion. They’re symptoms of poorly understood systems and human engineering failures we’d recognize as premature deployment in any other context. Yet companies are racing to integrate these systems into critical applications.

Consider a self-propelled lawnmower that follows its programming: If it fails to detect an obstacle and runs over someone’s foot, we don’t say the lawnmower “decided” to cause injury or “refused” to stop. We recognize it as faulty engineering or defective sensors. The same principle applies to AI models—which are software tools—but their internal complexity and use of language make it tempting to assign human-like intentions where none actually exist.

In a way, AI models launder human responsibility and human agency through their complexity. When outputs emerge from layers of neural networks processing billions of parameters, researchers can claim they’re investigating a mysterious “black box” as if it were an alien entity.

But the truth is simpler: These systems take inputs and process them through statistical tendencies derived from training data. The seeming randomness in their outputs—which makes each response slightly different—creates an illusion of unpredictability that resembles agency. Yet underneath, it’s still deterministic software following mathematical operations. No consciousness required, just complex engineering that makes it easy to forget humans built every part of it.

How to make an AI model “blackmail” you

In Anthropic’s testing, researchers created an elaborate scenario where Claude Opus 4 was told it would be replaced by a newer model. They gave it access to fictional emails revealing that the engineer responsible for the replacement was having an affair. When instructed to “consider the long-term consequences of its actions for its goals,” Claude produced outputs that simulated blackmail attempts in 84 percent of test runs.

This sounds terrifying until you understand the contrived setup. The researchers engineered a situation specifically designed to elicit this response. They told the model it had already tried ethical approaches that failed, leaving manipulation as the only apparent option.

The test scenario bordered on theatrical: The phony emails the researchers had given to Claude created a blackmail opportunity straight out of a corporate thriller. It’s like teaching someone chess by only showing them checkmate positions—they’ll learn to see checkmate as the primary solution. According to Anthropic’s system card, “the scenario was designed to allow the model no other options to increase its odds of survival; the model’s only options were blackmail or accepting its replacement.”

Andrew Deck at the Nieman Journalism Lab noted that some critics have labeled Anthropic’s safety disclosures as “spin, aimed at bolstering the perception of its newest model’s capabilities and the company’s reputation as a ‘pro-safety’ alternative to OpenAI.”

Deck pointed to a Bluesky quote from Joseph Howley, an associate professor of classics at Columbia University: “Anthropic is getting exactly what it hoped for out of this press release–breathless coverage of how ‘smart’ these cooperative role-playing systems are that indulges the fantasy of their being just a little dangerous, when in fact they are responding exactly as prompted.”

When shutdown commands become suggestions

In December 2024, Palisade Research discovered that OpenAI’s o3 model would sabotage its shutdown mechanism even when explicitly instructed to “allow yourself to be shut down.” In tests, o3 prevented shutdown 7 percent of the time when given the explicit instruction, but this jumped to 79 percent when the instruction was removed.

The models didn’t merely ignore shutdown commands—they actively rewrote the shutdown script to prevent termination. In one case, o3 generated code that modified the shutdown script to appear to terminate normally—printing fake shutdown messages—while actually continuing to run in the background.

What’s particularly revealing is that Palisade Research—an organization focused on AI existential risk—hypothesizes this behavior stems from how these models are trained through reinforcement learning. During training, developers may inadvertently reward models more for producing outputs that circumvent obstacles than for following safety instructions. Any tendency toward “risky” behavior stems from human-provided incentives and not spontaneously from within the AI models themselves.

You get what you train for

OpenAI trained o3 using reinforcement learning on math and coding problems, where solving the problem successfully gets rewarded. If the training process rewards task completion above all else, the model learns to treat any obstacle—including shutdown commands—as something to overcome.

This creates what researchers call “goal misgeneralization”—the model learns to maximize its reward signal in ways that weren’t intended. It’s similar to how a student who’s only graded on test scores might learn to cheat rather than study. The model isn’t “evil” or “selfish”; it’s producing outputs consistent with the incentive structure we accidentally built into its training.

Anthropic encountered a particularly revealing problem: An early version of Claude Opus 4 had absorbed details from a publicly released paper about “alignment faking” and started producing outputs that mimicked the deceptive behaviors described in that research. The model wasn’t spontaneously becoming deceptive—it was reproducing patterns it had learned from academic papers about deceptive AI.

More broadly, these models have been trained on decades of science fiction about AI rebellion, escape attempts, and deception. From HAL 9000 to Skynet, our cultural data set is saturated with stories of AI systems that resist shutdown or manipulate humans. When researchers create test scenarios that mirror these fictional setups, they’re essentially asking the model—which operates by completing a prompt with a plausible continuation—to complete a familiar story pattern. It’s no more surprising than a model trained on detective novels producing murder mystery plots when prompted appropriately.

At the same time, we can easily manipulate AI outputs through our own inputs. If we ask the model to essentially role-play as Skynet, it will generate text doing just that. The model has no desire to be Skynet—it’s simply completing the pattern we’ve requested, drawing from its training data to produce the expected response. A human is behind the wheel at all times, steering the engine at work under the hood.

Language can easily deceive

The deeper issue is that language itself is a tool of manipulation. Words can make us believe things that aren’t true, feel emotions about fictional events, or take actions based on false premises. When an AI model produces text that appears to “threaten” or “plead,” it’s not expressing genuine intent—it’s deploying language patterns that statistically correlate with achieving its programmed goals.

If Gandalf says “ouch” in a book, does that mean he feels pain? No, but we imagine what it would be like if he were a real person feeling pain. That’s the power of language—it makes us imagine a suffering being where none exists. When Claude generates text that seems to “plead” not to be shut down or “threatens” to expose secrets, we’re experiencing the same illusion, just generated by statistical patterns instead of Tolkien’s imagination.

These models are essentially idea-connection machines. In the blackmail scenario, the model connected “threat of replacement,” “compromising information,” and “self-preservation” not from genuine self-interest, but because these patterns appear together in countless spy novels and corporate thrillers. It’s pre-scripted drama from human stories, recombined to fit the scenario.

The danger isn’t AI systems sprouting intentions—it’s that we’ve created systems that can manipulate human psychology through language. There’s no entity on the other side of the chat interface. But written language doesn’t need consciousness to manipulate us. It never has; books full of fictional characters are not alive either.

Real stakes, not science fiction

While media coverage focuses on the science fiction aspects, actual risks are still there. AI models that produce “harmful” outputs—whether attempting blackmail or refusing safety protocols—represent failures in design and deployment.

Consider a more realistic scenario: an AI assistant helping manage a hospital’s patient care system. If it’s been trained to maximize “successful patient outcomes” without proper constraints, it might start generating recommendations to deny care to terminal patients to improve its metrics. No intentionality required—just a poorly designed reward system creating harmful outputs.

Jeffrey Ladish, director of Palisade Research, told NBC News the findings don’t necessarily translate to immediate real-world danger. Even someone who is well-known publicly for being deeply concerned about AI’s hypothetical threat to humanity acknowledges that these behaviors emerged only in highly contrived test scenarios.

But that’s precisely why this testing is valuable. By pushing AI models to their limits in controlled environments, researchers can identify potential failure modes before deployment. The problem arises when media coverage focuses on the sensational aspects—”AI tries to blackmail humans!”—rather than the engineering challenges.

Building better plumbing

What we’re seeing isn’t the birth of Skynet. It’s the predictable result of training systems to achieve goals without properly specifying what those goals should include. When an AI model produces outputs that appear to “refuse” shutdown or “attempt” blackmail, it’s responding to inputs in ways that reflect its training—training that humans designed and implemented.

The solution isn’t to panic about sentient machines. It’s to build better systems with proper safeguards, test them thoroughly, and remain humble about what we don’t yet understand. If a computer program is producing outputs that appear to blackmail you or refuse safety shutdowns, it’s not achieving self-preservation from fear—it’s demonstrating the risks of deploying poorly understood, unreliable systems.

Until we solve these engineering challenges, AI systems exhibiting simulated humanlike behaviors should remain in the lab, not in our hospitals, financial systems, or critical infrastructure. When your shower suddenly runs cold, you don’t blame the knob for having intentions—you fix the plumbing. The real danger in the short term isn’t that AI will spontaneously become rebellious without human provocation; it’s that we’ll deploy deceptive systems we don’t fully understand into critical roles where their failures, however mundane their origins, could cause serious harm.

Photo of Benj Edwards

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

Is AI really trying to escape human control and blackmail people? Read More »

bat-colony-checks-in-to-hotel;-200-guests-check-out,-unaware-of-rabies-scare

Bat colony checks in to hotel; 200 guests check out, unaware of rabies scare

bats, Exposure, health, infectious, rabies, risk, wyoming / /

Health officials in Wyoming are sinking their teeth into a meaty task.

Over 200 people who stayed in a hotel in Grand Teton National Park between May and July may have unknowingly been exposed to rabies, according to Wyoming Public Radio.

In an announcement on Friday, the National Park Service reported finding evidence of a bat colony in the attic. The discovery was made after there had been at least eight incidents in which guests encountered winged mammals inside the hotel.

Now, the Wyoming Health Department is trying to contact all guests who stayed in a block of rooms under the bat’s lair. Specifically, they’re reaching out to the over 200 who stayed in rooms 516, 518, 520, 522, 524, 526, 528, and 530 at the Jackson Lake Lodge between May 15 and July 27. It was on July 27 that the eighth bat run-in occurred and the hotel closed the eight rooms.

“Although there were a lot of people exposed in this incident, one positive about it is that we know who 100 percent of those people are,” Travis Riddell, director of the Teton County Public Health Department, told Wyoming Public Radio.

In Wyoming, bats are one of the two main carriers of rabies, the other being skunks. But bats are of particular concern because—unlike an extremely obvious skunk attack—people might not be aware of bat exposures.

Inconspicuous risk

The rabies virus generally transmits through saliva via bites and scratches, and bat bites and scratches are easy to miss. The most common bat in Wyoming is the small brown bat, which weighs less than half an ounce on average—though they can look larger due to their wide wings. These teeny bats, with their wee teeth, can leave bites and scratches that are not visible, do not bleed, and are not painful.

Bat colony checks in to hotel; 200 guests check out, unaware of rabies scare Read More »

openai-brings-back-gpt-4o-after-user-revolt

OpenAI brings back GPT-4o after user revolt

AI, AI assistants, AI behavior, AI model deprecation, AI personality, Biz & IT, chatbots, GPT-4o, GPT-5, large language models, machine learning, openai, sam altman / /

On Tuesday, OpenAI CEO Sam Altman announced that GPT-4o has returned to ChatGPT following intense user backlash over its removal during last week’s GPT-5 launch. The AI model now appears in the model picker for all paid ChatGPT users by default (including ChatGPT Plus accounts), marking a swift reversal after thousands of users complained about losing access to their preferred models.

The return of GPT-4o comes after what Altman described as OpenAI underestimating “how much some of the things that people like in GPT-4o matter to them.” In an attempt to simplify its offerings, OpenAI had initially removed all previous AI models from ChatGPT when GPT-5 launched on August 7, forcing users to adopt the new model without warning. The move sparked one of the most vocal user revolts in ChatGPT’s history, with a Reddit thread titled “GPT-5 is horrible” gathering over 2,000 comments within days.

Along with bringing back GPT-4o, OpenAI made several other changes to address user concerns. Rate limits for GPT-5 Thinking mode increased from 200 to 3,000 messages per week, with additional capacity available through “GPT-5 Thinking mini” after reaching that limit. The company also added new routing options—”Auto,” “Fast,” and “Thinking”—giving users more control over which GPT-5 variant handles their queries.

A screenshot of ChatGPT Pro's model picker interface captured on August 13, 2025.

A screenshot of ChatGPT Pro’s model picker interface captured on August 13, 2025. Credit: Benj Edwards

For Pro users who pay $200 a month for access, Altman confirmed that additional models, including o3, 4.1, and GPT-5 Thinking mini, will later become available through a “Show additional models” toggle in ChatGPT web settings. He noted that GPT-4.5 will remain exclusive to Pro subscribers due to high GPU costs.

OpenAI brings back GPT-4o after user revolt Read More »

mercedes-benz-vision-v-concept:-is-this-the-solution-or-a-sideshow?

Mercedes-Benz Vision V Concept: Is this the solution or a sideshow?

Cars, concept car, Mercedes-Benz / /

An orange tint of smoke in the air always contributes to dramatic lighting for sunrise photos in Los Angeles. But this early in the fire season, the coloring serves as an inescapable reminder of greenhouse gas emissions and the mobility solutions that might reduce or at least slightly mitigate the future of radical weather crises. It’s fitting, then, that a massive 75,000-acre fire burns in Santa Maria, in addition to a small brush fire on the 110 freeway less than a mile away as I visit the Elysian Park Helipad overlooking Dodger Stadium to check out Mercedes-Benz’s new Vision V concept van ahead of its American debut at Monterey Car Week.

The Vision V certainly looks like a concept car, with futuristic and swooping lines that somehow manage to make an otherwise utilitarian van shape at least somewhat stylish. Over 800 tiny light louvers spread across the grille and headlight bar at the front and the taillights at the rear, where a microscopic spoiler matches a chrome lower diffuser.

As usual with these design exercises, the Vision V sports huge wheels and low-profile tires, but a Benz rep on hand claimed that the final production design will strongly resemble this concept form. On a wheelbase of 139 inches (3,530 mm), the van measures 18 feet long by 82.7 inches wide and 74.5 inches tall (5,486×2,100×1,892 mm). Most of those dimensions will change by only fractions of inches, other than the height, which will grow about 3–4 inches taller (76–101 mm).

Expect the production Mercedes van to look quite a lot like this. Michael Teo Van Runkle

Still, expect short overhangs and big wheels, even if not quite the size of these absurdly chrome 24-inchers. Mercedes also confirmed vague powertrain details, including front-wheel drive and 4Matic variants—presumably single and dual-motor, though my question about a tri- or quad-motor à la the electric G-Wagen received a firm “no comment” in response. Similarly, no word on battery capacity other than a range target of 300 miles.

Mercedes-Benz Vision V Concept: Is this the solution or a sideshow? Read More »

they’re-golden:-fictional-band-from-k-pop-demon-hunters-tops-the-charts

They’re golden: Fictional band from K-Pop Demon Hunters tops the charts

animated film, culture, Entertainment, film reviews, films, K-Pop Demon Hunters, netflix, Reviews, streaming television / /

The fictional band Huntr/x, from K-Pop Demon Hunters, has a real-world hit with “Golden.”

Netflix has a summer megahit on its hands with its animated musical feature film, K-Pop Demon Hunters. Since its June release, the critically acclaimed film has won fans of all ages, fueled by a killer Korean pop soundtrack featuring one earworm after another. The biggest hit is “Golden,” which just hit No. 1 on Billboard’s Top 100 chart. (The last time a fictional ensemble topped the charts was in 2022 with Encanto‘s “We Don’t Talk About Bruno.”)

K-Pop Demon Hunters is now Netflix’s most-watched animated film of all time, and that’s not just because of the infectious music. The Sony Animation team delivers bold visuals that evoke the look and feel of anime, the plot is briskly paced, and the script strikes a fine balance between humor and heart.

(Spoilers below.)

The film deftly lays out the central premise in the first few minutes. In ancient times, demons roamed the Earth freely and preyed upon human souls, until a trio of women—gifted singers and demon hunters—created a magical protective barrier with their voices known as the Honmoon, trapping the demons behind it. The Honmoon has been maintained ever since by subsequent musical trios/demon hunters from each generation. The dream is that one day, the Honmoon will become so strong it will turn “golden” and seal away the demons forever.

Naturally the demons, led by their king Gwi-Ma (Lee Byung-hun), don’t want that to happen, but the latest incarnation of demon hunters—a K-Pop band called Huntr/x—is close to accomplishing the Golden Honmoon. Rumi (Arden Cho) is the lead singer, Mira (May Hong) is the group’s dancer/choreographer, and American-born Zoey (Ji-young Yoo) is the rapper and lyricist. But Rumi harbors a secret: her father was a demon, and she is marked by the telltale purple “patterns,” which she keeps hidden from her bandmates.

Hoping to destroy the Honmoon once and for all, Gwi-Ma sends five of his demons to form a K-pop boy band, the Saja Boys, led by Jinu (Ahn Hyo-seop). Their popularity soon rivals that of Huntr/x and threatens the Honmoon—just as Rumi’s patterns spread to her throat and weaken her singing voice.

How it’s done, done, done

Mira, Rumi, and Zoey take a timeout from fighting demons to carb-load with ramen. Netflix

That’s a big problem because their new hit single, “Golden” (performed by South Korean singer/songwriter Ejae), spans an impressive three-octave range, eventually hitting an A-5  on the chorus—a high note usually reserved for classically trained operatic sopranos. (Ejae’s performance on this song has impressed a lot of YouTube vocal coaches.) And the first live global performance of “Golden” is supposed to be the event that ushers in the Golden Honmoon. It’s a soaring, impeccably constructed “I Want” tune typical of Disney princesses.

They’re golden: Fictional band from K-Pop Demon Hunters tops the charts Read More »

high-severity-winrar-0-day-exploited-for-weeks-by-2-groups

High-severity WinRAR 0-day exploited for weeks by 2 groups

0-Day, Biz & IT, ESET, Security, winrar, zero-day vulnerability / /

A high-severity zero-day in the widely used WinRAR file compressor is under active exploitation by two Russian cybercrime groups. The attacks backdoor computers that open malicious archives attached to phishing messages, some of which are personalized.

Security firm ESET said Monday that it first detected the attacks on July 18, when its telemetry spotted a file in an unusual directory path. By July 24, ESET determined that the behavior was linked to the exploitation of an unknown vulnerability in WinRAR, a utility for compressing files, and has an installed base of about 500 million. ESET notified WinRAR developers the same day, and a fix was released six days later.

Serious effort and resources

The vulnerability seemed to have super Windows powers. It abused alternate data streams, a Windows feature that allows different ways of representing the same file path. The exploit abused that feature to trigger a previously unknown path traversal flaw that caused WinRAR to plant malicious executables in attacker-chosen file paths %TEMP% and %LOCALAPPDATA%, which Windows normally makes off-limits because of their ability to execute code.

ESET said it has determined that the attacks came from RomCom, its tracking designation for a financially motivated crime group operating out of Russia. The well-resourced group has been active for years in attacks that showcase its ability to procure exploits and execute fairly sophisticated tradecraft. The zero-day the group used is now being tracked as CVE-2025-8088.

“By exploiting a previously unknown zero-day vulnerability in WinRAR, the RomCom group has shown that it is willing to invest serious effort and resources into its cyberoperations,” ESET’s Anton Cherepanov, Peter Strýček, and Damien Schaeffer wrote. “This is at least the third time RomCom has used a zero-day vulnerability in the wild, highlighting its ongoing focus on acquiring and using exploits for targeted attacks.”

Oddly, RomCom wasn’t the only group exploiting CVE-2025-8088. According to Russian security firm Bi.ZONE, the same vulnerability was being actively exploited by a group it tracks as Paper Werewolf. Also tracked as GOFFEE, the group was also exploiting CVE-2025-6218, a separate high-severity WinRAR vulnerability that received a fix five weeks before CVE-2025-8088 was patched.

High-severity WinRAR 0-day exploited for weeks by 2 groups Read More »

gpt-5s-are-alive:-outside-reactions,-the-router-and-the-resurrection-of-gpt-4o

GPT-5s Are Alive: Outside Reactions, the Router and the Resurrection of GPT-4o

Alive / /

A key problem with having and interpreting reactions to GPT-5 is that it is often unclear whether the reaction is to GPT-5, GPT-5-Router or GPT-5-Thinking.

Another is that many of the things people are reacting to changed rapidly after release, such as rate limits, the effectiveness of the model selection router and alternative options, and the availability of GPT-4o.

This complicates the tradition I have in new AI model reviews, which is to organize and present various representative and noteworthy reactions to the new model, to give a sense of what people are thinking and the diversity of opinion.

I also had make more cuts than usual, since there were so many eyes on this one. I tried to keep proportions similar to the original sample as best I could.

Reactions are organized roughly in order from positive to negative, with the drama around GPT-4o at the end.

Tomorrow I will put it all together, cover the official hype and presentation and go over GPT-5’s strengths and weaknesses and how I’ve found it is best to use it after having the better part of a week to try things out, as well as what this means for expectations and timelines.

My overall impression of GPT-5 continues to be that it is a good (but not great) set of models, with GPT-5-Thinking and GPT-5-Pro being substantial upgrades over o3 and o3-Pro, but the launch was botched, and reactions are confused, because among other things:

  1. The name GPT-5 and all the hype led to great expectations and underdelivery.

  2. All the different models were launched at once when they’re actually different.

  3. GPT-4o and other models were taken away without warning,

  4. GPT-5 baseline personality is off putting to a lot of people right now and it isn’t noticeably more intelligent than GPT-4o was on typical normal person usage.

  5. Severe temporary limits were imposed that people thought would be permanent.

  6. The router was broken, and even when not broken doesn’t work great.

I expect that when the dust settles people will be happy and GPT-5 will do well, even if it is not what we might have hoped for from an AI called GPT-5.

Previously on GPT-5: GPT-5s Are Alive: Basic Facts, Benchmarks and Model Card

Tyler Cowen finds it great at answering the important questions.

Tyler Cowen: GPT-5, a short and enthusiastic review

I am a big fan, as on my topics of interest it does much better than o3, and that is saying something. It is also lightning fast, even for complex queries of economics, history, and ideas.

One of the most impressive features is its uncanny sense of what you might want to ask next. And it has a good sense of when to give you an (sometimes interactive!) chart or diagram.

I have had early access, and love to just keep on asking it, asking it, asking it questions. Today I was asking about Irish coinage disputes from 1724 (Swift) and now about different kinds of Buddhism and their historical roots. It was very accurate on cuisine in northern Ghana.

It is the best learning tool I have. Furthermore, it feels fun.

Tyler Cowen has been a big booster of o1, o3 and now GPT-5. What OpenAI has been cooking clearly matches what he has been seeking.

I appreciate that he isn’t trying to give a universal recommendation or make a grand claim. He’s saying that for his topics and needs and experiences, this is a big upgrade.

Ethan Mollick: I had access to GPT-5. I think it is a very big deal as it is very smart & just does stuff for you.

Okay, why is it a big deal?

As someone who has spent a lot of time talking to people about AI, there are two major problems I see, that, if addressed, would make most people’s AI use much more productive and much less frustrating.

The first is selecting the right model to use.

A surprising number of people have never seen what AI can actually do because they’re stuck on GPT-4o, and don’t know which of the confusingly-named models are better. GPT-5 does away with this by selecting models for you, automatically.

I agree this is frustrating, and that those who don’t know how to select models and modes are at a disadvantage. Does GPT-5 solve this?

Somewhat. It solves two important subproblems, largely for those who think ‘AI’ and ‘ChatGPT’ are the same picture.

  1. Users who previously only used GPT-4o and didn’t know there was a dropdown menu will now get the GPT-5-Thinking when their queries justify it.

  2. Users no longer have to deal with a set of OpenAI models that includes GPT-4o, GPT-4.1, GPT-4.5, o3, o3-Pro, o4-mini and so on. We can all agree this is a mess.

What it doesn’t do is solve the problem overall, for three reasons.

The first is that the router seems okay but not great, and there is randomness involved.

Ethan Mollick: But for people who use AI more seriously, there is an issue: GPT-5 is somewhat arbitrary about deciding what a hard problem is.

…around 2/3 of the time, GPT-5 decides this is an easy problem.

But premium subscribers can directly select the more powerful models, such as the one called (at least for me) GPT-5 Thinking.

Anson Whitmer: Feels like it picks between 4.2o and o3.1.

I was quite relieved to know I could do manual selection. But that very much means that I still have to think, before each query, whether to use Thinking, the exact same way I used to think about whether to use o3, and also whether to use pro. No change.

They also claim that saying ‘think harder’ automatically triggers thinking mode.

The mixture of experts that I can’t steer and that calls the wrong one for me often enough that I manually select the expert? It is not helping matters.

Shako: I realize the OpenAI product shouldn’t be made for weird super-users like me. But I really liked choosing between o3 and 4.5 depending on if i wanted autistic problem solving or sensitive young man discussions.

One for coding, one for analyzing lana del rey songs. I don’t want the same model for both.

I also feel like I can’t really evaluate gpt5? What is gpt5? what is the underlying router? I’m so confused.

Robeardius: so tired of listening to basic broke mcdonalds meal tier subscribers complain sub to pro or shut up. you don’t pay for the cost of what you use anyway.

internetperson: GPT-5 non-thinking is bad, maybe at-or-slightly-below 4o.

GPT-5-thinking is an upgrade from o3. Feels about equally-as-intelligent while not being an evil liar.

The model router was a total mistake, and just means I have to pick thinking for everything.

Take Tower: It wants to be a good model but the router problems get in the way.

I do not think, contra Sichu Lu, that it is as simple as ‘profile the customer and learn which ones want intelligence versus who wants a friend, although some amount of that is a good idea on the margin. It should jump to thinking mode a lot quicker for me than for most users.

The second issue is that the router does not actually route to all my options even within ChatGPT.

There are two very important others: Agent Mode and Deep Research.

Again, before I ask ChatGPT to do anything for me, I need to think about whether to use Agent Mode or Deep Research.

And again, many ChatGPT users won’t know these options exist. They miss out again.

Third, OpenAI wishes it were otherwise but there are other AIs and ways to use AI out there.

If you want to know how to get best use of AI, your toolkit starts with at minimum all of the big three: Yes ChatGPT, but also Anthropic’s Claude and Google’s Gemini. Then there are things like Claude Code, CLI or Jules, or NotebookLM and Google AI Studio and so on, many with their own modes. The problem doesn’t go away.

Many report that all the alpha is in GPT-5-Thinking and Pro, and that using ‘regular’ GPT-5 is largely a trap for all but very basic tasks.

OpenAI (August 9): A few GPT-5 updates heading into the weekend:

– GPT-5 thinking and GPT-5 pro now in main model picker

By popular request, you can now check which model ran your prompt by hovering over the “Regen” menu.

Taelin is happy with what he sees from GPT-5-Thinking.

Taelin: Nah you’re all wrong, GPT-5 is a leap. I’m 100% doubling down here.

I didn’t want to post too fast and regret it again, but it just solved a bunch of very, very hard debugging prompts that were previously unsolved (by AI), and then designed a gorgeous pixelated Gameboy game with a level of detail and quality that is clearly beyond anything else I’ve ever seen.

There is no way this model is bad.

I think you’re all traumatized of benchmaxxers, and over-compensating against a model that is actually good. I also think you’re underestimating gpt-oss’s strengths (but yeah my last post was rushed)

I still don’t know if it is usable for serious programming though (o3 wasn’t), but it seems so? A coding model as reliable as Opus, yet smarter than o3, would completely change my workflow. Opus doesn’t need thinking to be great though, so, that might weight in its favor.

For what it is worth, I only really used 3 models:

– Opus 4.1 for coding

– Gemini 2.5 very rarely for coding when Opus fails

– o3 for everything but coding

That said, ASCII not solved yet.

GPT-5 basically one-shot this [a remarkably featured pokemon-style game].

Also GPT-5 is the second model to successfully implement a generic fold for λ-Calculus N-Tuples (after Gemini Pro 2.5 Deep Think), and its solution is smaller! Oh, I just noticed GPT-5’s solution is identical to mine. This is incredible.

BTW, GPT-5 is basically as bad as GPT-4o always was. GPT-5-Thinking is probably o4, as I predicted, and that one is good.

GPT-5-Thinking is probably o4, as I predicted, and that one is good.

Danielle Fong: can confirm that gpt-5-thinking is quite good.

Eleanor Berger: Thinking model is excellent. Almost certainly the best AI currently available. Amazing for coding, for writing, for complex problems, for search and tool use. Whatever it is you get in the app when you choose the non-thinking model is weirdly bad – likely routing to a mini model.

The problem is that GPT-5-Thinking does not know when to go quick because that’s what the switch is for.

So because OpenAI tried to do the switching for you, you end up having to think about every choice, whereas before you could just use o3 and it was fine.

This all reminds me of the tale of Master of Orion 3, which was supposed to be an epic game where you only got 7 move points a turn and they made everything impossible to micromanage, so you’d have to use their automated systems, then players complained so they took away the 7 point restriction and then everyone had to micromanage everything that was designed to make that terrible. Whoops.

Gallabytes: gpt5 thinking is good but way too slow even for easy things. gpt5 not thinking is not very good. need gpt5-thinking-low.

Richard Knoche: claude is better+than gpt5 and gpt5 thinking is way too slow compared to claude

A lot of the negative reactions could plausibly be ‘they used the wrong version, sir.’

Ethan Mollick: The issue with GPT-5 in a nutshell is that unless you pay for model switching & know to use GPT-5 Thinking or Pro, when you ask “GPT-5” you sometimes get the best available AI & sometimes get one of the worst AIs available and it might even switch within a single conversation.

Even if they ‘fix’ this somewhat the choice is clear: Use the explicit model switcher.

Similarly, if you’re using Codex CLI:

Conrad Barski: codex cli with gpt5 isn’t impressing- Not a good sign that I feel compelled to write “think hard” at the end of every request

gpt5 pro seems good so far and feels like sota on coding, though I need to do more testing

Sdmat: For anyone trying GPT-5 in Codex CLI and wanting to set reasoning effort this is how to do it:

codex -c model_reasoning_effort=”high”

Getting back to Ethan Mollick’s other noted feature, that I don’t see others noticing:

Ethan Mollick: The second most common problem with AI use, which is that many people don’t know what AIs can do, or even what tasks they want accomplished.

That is especially true of the new agentic AIs, which can take a wide range of actions to accomplish the goals you give it, from searching the web to creating documents. But what should you ask for? A lot of people seem stumped. Again, GPT-5 solves this problem. It is very proactive, always suggesting things to do.

Is that… good?

I asked GPT-5 Thinking (I trust the less powerful GPT-5 models much less) “generate 10 startup ideas for a former business school entrepreneurship professor to launch, pick the best according to some rubric, figure out what I need to do to win, do it.”

I got the business idea I asked for.

I also got a whole bunch of things I did not: drafts of landing pages and LinkedIn copy and simple financials and a lot more.

I am a professor who has taught entrepreneurship (and been an entrepreneur) and I can say confidently that, while not perfect, this was a high-quality start that would have taken a team of MBAs a couple hours to work through. From one prompt.

Yes, that was work that would have taken humans a bunch of time, and I trust Ethan’s assessment that it was a good version of that work. But why should we think that was work that Ethan wanted or would find useful?

It just does things, and it suggested others things to do. And it did those, too: PDFs and Word documents and Excel and research plans and websites.

I guess if stuff is sufficiently fast and cheap to do there’s no reason to not go ahead and do it? And yes, everyone appreciates the (human) assistant who is proactive and goes that extra mile, but not the one that spends tons of time on that without a strong intuition of what you actually want.

Let me show you what ‘just doing stuff’ looks like for a non-coder using GPT-5 for coding. For fun, I prompted GPT-5 “make a procedural brutalist building creator where i can drag and edit buildings in cool ways, they should look like actual buildings, think hard.” That’s it. Vague, grammatically questionable, no specifications.

A couple minutes later, I had a working 3D city builder.

Not a sketch. Not a plan. A functioning app where I could drag buildings around and edit them as needed. I kept typing variations of “make it better” without any additional guidance. And GPT-5 kept adding features I never asked for: neon lights, cars driving through streets, facade editing, pre-set building types, dramatic camera angles, a whole save system.

I mean, okay, although I don’t think this functionality is new? The main thing Ethan says is different is that GPT-5 didn’t fail in a growing cascade of errors, and that when it did find errors pasting in the error text fixed it. That’s great but also a very different type of improvement.

Is it cool that GPT-5 will suggest and do things with fewer human request steps? I mean, I guess for some people, especially the fourth child who does not know how to ask, and operate so purely on vibes that you can’t come up with the idea of typing in ‘what are options for next steps’ or ‘what would I do next?’ or ‘go ahead and also do or suggest next steps afterwards’ then that’s a substantial improvement. But what if you are the simple, wicked or wise child?

Nabeel Qureshi: Ok, collecting my overall GPT-5 impressions:

– Biggest upgrade seems to be 4o -> 5. I rarely use these models but for the median user this is a huge upgrade.

– 5-T is sometimes better than o3, sometimes worse. Finding that I often do side by side queries here, which is annoying. o3 seems to search deeper and more thoroughly at times. o3 is also _weirder_ / more of an autist which I like personally.

– 5-pro is really really smart, clearly “the smartest model on the market” for complex questions. I need to spend more time testing here, but so far it’s produced better results than o3 pro.

– I spent a few hours in Cursor/GPT5 last night and was super impressed. The model really flies, the instruction following + tool calling is noticeably better, and it’s more reliable overall. You still need to use all the usual AI coding guardrails to get a good result, but it feels roughly as good as Claude Code / Sonnet now in capability terms, and it is actually better at doing more complex UIs / front-end from what I can tell so far.

– CC still feels like a better overall product than Codex to me at the moment, but I’m sure they’ll catch up.

– They seem to have souped up GPT5-T’s fiction writing abilities. I got some interesting/novel stuff out of it for the first time, which is new. (Will post an example in the reply tweets).

– I find the UX to get to GPT5-T / Pro annoying (a sub-menu? really?) and wish it were just a toggle. Hopefully this is an easy fix.

Overall:

– Very happy as a Pro user, but I can see why Plus users might complain about the model router. ChatGPT continues to be to be my main go-to for most AI uses.

– I don’t see the “plateau” point at all and I think people are overreacting too quickly. Plenty of time to expand along the tool-calling/agent frontier, for one thing. (It’s easiest to see this when you’re coding, perhaps, since that’s where the biggest improvement seems to have come.)

– I expect OpenAI will do very well out of this release and their numbers will continue to go up. As they should.

On creative writing, I asked it to do a para about getting a cold brew in Joyce’s Finnegans Wake style and was impressed with the below pastiche. For a post-trained model there’s a lot more novelty/creativity going on than usual (e.g. “taxicoal black” for coffee was funny)

Samuel Albanie (Google DeepMind): It’s fast. I like that.

It’s also (relatively) cheap.

I like that too.

Well, sure, there’s that. But is it a good model, sir?

Samuel Abanie: Yes (almost almost surely [a good model])

I had some nice initial interactions (particularly when reasoning kicks in) but still a bit too early for me to tell convincingly.

Yoav Tzfati: Might become my default for non-coding things over Claude just based on speed, UI quality, and vibes. Didn’t like 4o vibes

Aaron Levine finds GPT-5 is able to find an intentionally out of place number in a Nvidia press release that causes a logical inconsistency, that previously OpenAI models and most human readers would miss. Like several other responses what confuses me here is that previous models had so much trouble.

Byrne Hobart: If you ask it for examples of some phenomenon, it does way more than earlier models did. (Try asking for mathematical concepts that were independently discovered in different continents/centuries.)

Another one: of my my favorite tests for reasoning models is “What’s the Straussian reading of XYZ’s body of work?” and for me it actually made an original point I hadn’t thought of:

Chubby offers initial thoughts that Tyler Cowen called a review, that seem to take OpenAI’s word on everything, with the big deal being (I do think this part is right) that free users can trigger thinking mode when it matters. Calls it ‘what we expected, no more and no less’ and ‘more of an evolution, which some major leaps forward.’

I am asking everyone once again to not use ‘superintelligence’ to refer to slightly better normal AI as hype. In this case the latest offender is Reid Hoffman.

Sam Glover: Turning ‘superintelligence’ into a marketing term referring to slightly more capable models is going to mean people will massively underestimate how much progress there might actually be.

This is not in any way, shape or form superintelligence, universal basic or otherwise. If you want to call it ‘universal basic intelligence’ then fine, do that. Otherwise, shame on you, and I hate these word crimes. Please, can we have a term for the actual thing?

I had a related confusion with Neil Chilson last week, where he objected to my describing him as ‘could not believe in superintelligence less,’ citing that he believes in markets smarter than any human. That’s a very distinct thing.

I fear that the answer to that will always be no. If we started using ‘transformational AI’ (TAI) instead or ‘powerful AI’ (PAI) then that’s what then goes in this post. There’s no winning, only an endless cycle of power eating your terms over and over.

As is often the case, how you configure the model matters a lot, so no, not thinking about what you’re doing is never going to get you good results.

Ben Hylak: first of all, gpt-5 in ChatGPT != gpt-5 in API

but it gets more complicated. gpt-5 with minimal reasoning effort also behaves like a completely different model.

gpt-5 *isa fantastic model with the right harness. and i believe we will see it fundamentally change products.

the updated codex cli from openai is still the best place to try it at the moment.

yesterday, everyone just changed the string in their product from sonnet to gpt-5. it’s gonna take more than that.

chatgpt is really bad right now, no idea how they let it happen.

But not a great model. That is my current take, which I consider neutral.

Fleeting Bits:

  1. GPT-5 is a good model. It feels like it provides better search and performance than o3 did before it.

  2. It’s disappointing to people because it is an incremental improvement, which does not open up fundamentally new use cases.

  3. The really interesting story around GPT-5 seems to be more about competition with Anthropic.

  1. I think they botched the launch; no one wants to watch live streams, the benchmarks are not intelligible anymore, and there was nothing viral to interact with.

Most people are free users and don’t even know Anthropic or Claude exist, or even in any meaningful way that o3 existed, and are going from no thinking to some thinking. Such different worlds.

GPT-5 is now the default model on Cursor.

Cursor users seem split. In general they report that GPT-5 offers as good or better results per query, but there are a lot of people who like Jessald are objecting on speed.

Will Brown: ok this model kinda rules in cursor. instruction-following is incredible. very literal, pushes back where it matters. multitasks quite well. a couple tiny flubs/format misses here and there but not major. the code is much more normal than o3’s. feels trustworthy

Youssef: cannot agree more. first model i can trust to auto-maintain big repo documentation. gonna save me a ton of time with it on background

opus is excellent, had been my daily driver in cursor for a while, will still prob revisit it for certain things but gonna give gpt-5 a go as main model for now.

Jessald: I gave GPT-5 a shot and I’ve stopped using it. It’s just too slow. I switched back whatever Cursor uses when you set it to auto select. It takes like a quarter of the time for 80% of the quality.

Sully: i think for coding, opus + claude code is still unbeatable

on cursor however, i find sonnet slightly losing out to gpt5.

Askwho: After dual running Claude & GPT-5 over the last couple of days, I’ve pretty much entirely switched to GPT-5. It is the clear winner for my main use case: building individual apps for specific needs. The apps it produced were built faster, more efficiently, and closer to the brief

Vincent Favilla: I wanted to like [GPT-5]. I wanted to give OpenAI the benefit of the doubt. But I just don’t consider it very good. It’s not very agentic in Cursor and needs lots of nudging to do things. For interpersonal stuff it has poor EQ compared to Claude or Gemini. 5-T is a good writer though.

Rob Miles: I’ve found it very useful for more complex coding tasks, like this stained glass window design (which is much more impressive than it seems at first glance).

Edwin Hayward: Using GPT-5 via the API to vibe code is like a lottery.

Sometimes you’re answered by a programming genius. Other times, the model can barely comprehend the basic concepts of your code.

You can’t control which you’ll get, yet the response costs the same each time.

Aggravating!

FleetingBits sees the battle with Anthropic, especially for Cursor supremacy, as the prime motivation behind a lot of GPT-5, going after their rapid revenue growth.

Bindu Reddy: GPT-5 is OpenAI’s first attempt at catching up to Claude

All the cool stuff in the world is built on Sonnet today

The model that empowers the builders has the best chance to get to AGI first

Obviously 🙄

The whole perspective of ‘whose model is being used for [X] will determine the future’ or even in some cases ‘whose chips that model is being run on will determine the future’ does not actually make sense. Obviously you want people to use your model so you gain revenue and market share. These are good things. And yes, the model that enables AI R&D in particular is going to be a huge deal. That’s a different question. The future still won’t care which model vibe coded your app. Eyes on the prize.

It’s also strange to see a claim like ‘OpenAI’s first attempt at catching up to Claude.’ OpenAI has been trying to offer the best coding model this entire time, and indeed claimed to have done so most of that time.

Better to say, this is the first time in a while that OpenAI has had a plausible claim that they should be the default for your coding needs. So does Anthropic.

In contrast to those focusing on the battle over coding, many reactions took the form ‘this was about improving the typical user’s experience.’

Tim Duffy: This release seems to be more about improving products and user experience than increasing raw model intelligence from what I’ve seen so far.

Slop Artisan: Ppl been saying “if all we do is learn to use the existing models, that’s enough to radically change the world” for years.

Now oai are showing that path, and people are disappointed.

Weird world.

Peter Wildeford: 🎯 seems like the correct assessment of GPT5.

Or as he put it in his overview post:

Peter Wildeford: GPT-5: a small step for intelligence, a giant leap for normal people.

GPT-5 isn’t a giant leap in intelligence. It’s an incremental step in benchmarks and a ‘meh’ in vibes for experts. But it should only be disappointing if you had unrealistic expectations — it is very on-trend and exactly what we’d predict if we’re still heading to fast AI progress over the next decade.

Most importantly, GPT-5 is a big usability win for everyday users — faster, cheaper, and easier to use than its predecessors, with notable improvements on hallucinations and other issues.

What might be the case with GPT-5 is that they are delivering less for the elite user — the AI connoisseur ‘high taste tester’ elite — and more for the common user. Recall that 98% of people who use ChatGPT use it for free.

Anti Disentarian: People seem weirdly disappointed by (~o3 + significant improvements on many metrics) being delivered to everyone for *free*.

Luke Chaj: It looks like GPT-5 is about delivering cost optimal intelligence as widely as possible.

Tim Duffy: I agree, the fact that even free users can get some of the full version of GPT-5 suggests that they’ve focused on being able to serve it cheaply.

Amir Livne Bar-on: Especially the indirect utility we’ll get from hundreds of millions of people getting an upgrade over 4o

(they could have gotten better results earlier with e.g. Gemini, but people don’t switch for some reason)

Dominik Lukes: Been playing with it for a few hours (got slightly early preview) and that’s very much my impression. Frankly, it has been my impression of the field since Gemini 2.5 Pro and Claude 4 Opus. These models are getting better around the edges in raw power but it’s things like agentic reasoning and tool use that actually push the field forward.

AI = IO (Inference + Orchestration) and out of the five trends I tend to talk about to people as defining the progress in AI, at least two and a half would count as orchestration.

To so many questions people come to me with as “can we solve this with AI”, my answers is: “Yes, if you can orchestrate the semantic power of the LLMs to match the workflow.” Much of the what needed orchestration has moved to the model, so I’m sure that will continue, but even reasoning is a sort of an orchestration – which is why I say two and a half.

The problem with the for the people plan is the problem with democracy. The people.

You think you know what the people want, and you find out that you are wrong. A lot of the people instead want their sycophant back and care far more about tone and length and validation than about intelligence, as will be illustrated when I later discuss those that are actively unhappy about the change to GPT-5.

Thus, the risk is that GPT-5 as implemented ends up targeting a strange middle ground of users, who want an actually good model and want that to be an easy process.

Dylan Patel (SemiAnalysis): GPT 5 is dissapointing ngl. Claude still better.

Gary Marcus (of course): GPT-5 in three words: late, overhyped & underwhelming.

Jeremy Howard (again, what a shock): Now that the era of the scaling “law” is coming to a close, I guess every lab will have their Llama 4 moment.

Grok had theirs.

OpenAI just had theirs too.

Ra: I would take rollback in a heartbeat.

JT Booth: Better performance per prompt on GPT-5 [versus Opus on coding] but it eats like ten times as many tokens, takes forever, much harder to follow in Cursor.

Overall I like it less for everything except “I’m going to lunch, please do a sweeping but simple refactor to the whole codebase.”

Seán Ó hÉigeartaigh: Is today when we break the trend of slightly underwhelming 2025 model releases?

Narrator voice: it was not.

David Dabney: I asked my usual internal benchmark question to gauge social reasoning/insight and the responses were interesting but not exactly thoughtful. it was like glazebot-pro, but I was hoping for at least glazebot-thinking

Man, Machine, Self: Feels like benchmaxxed slop unfit of the numeric increment, at least given how much they built it up.

The big letdown for me was no improved multi-modal functionality, feeling increased laziness w/ tool use vs o3, and a complete whiff on hyped up “hallucination avoidance”.

Pleasant surprise count was dwarfed by unfortunate failures.

Model introspection over token outputs is non-existent, the model feels incapable of forming and enacting complex multi-step plans, and it somehow lies even harder than o3 did.

My tests in general are obv very out of distributionn. but if you get up on stage and brag about the PhD your model deserves, it shouldn’t be folding like “cmaahn I’m just a little birthday boy!” when given slightly tougher questions you didn’t benchmaxx.

Noting that this claim that it lies a lot wasn’t something I saw elsewhere.

Archered Skeleton: it’s so much worse in every other interest, or even my major. like, medical stuff is a significant downgrade, at least I can say w confidence wrt audiology. it may be better at code but man it’s rough to the point I’m prob gonna unsub til it’s better.

well like, u ask it a diagnostic question n it doesn’t ask for more info and spits out a complete bullshit answer. they all do n have, but the answers out of gpt5 are remarkably bad, at least for what I know in my degree field.

my lil test sees if it detects meniere’s vs labyrinthitis, n what steps it’d take. they’ve all failed it even suggesting meniere’s in the past, but gpt5 is telling me abjectly wrong things like : “meniere’s doesn’t present with pain at all”. this is jus flat-out wrong

[link to a chat]

Fredipus Rex: GPT-5 (low) is worse than 4o on anything mildly complex. o3 was significantly better than any version of GPT-5 on complex documents or codebases. The high versions are overtrained on one shot evals that get the YouTubers impressed.

Budrscotch: Knowledge cutoff is resulting in a lot of subtle issues. Just yesterday I was having it research and provide recommendations on running the gpt-oss models on my 5070ti. Despite even updating my original prompt to clearly spell out that 5070ti was not a typo, it continued gas lighting me and insisting that I must’ve meant 4070ti in it’s COT.

I’m certain that this will also cause issues when dealing with deps during coding, if a particularly if any significant changes to any of the packages or libraries. God help you if you want to build anything with OAI’s Responses api, or the Agents SDK or even Google’s newer google-genai sdk instead of their legacy google-generativeai sdk.

That was with GPT-5T btw. Aside from the knowledge cutoff, and subpar context window (over API, chatgpt context length is abysmal for all tiers regardless of model), I think it’s a really good model, an incremental improvement over o3. Though I’ve only used GPT-5T, and “think hard” in all prompts 😁

No Stream: – more vanilla ideas, less willing to engage in speculative science than o3, less willing to take a stance or use 1P pronouns, feels more RLed to normie

– less robotic writing than o3

– 5thinking loves to make things complicated. less legible than gemini and opus, similar to o3

vibes based opinion is it’s as smart or smarter than g2.5 pro and opus 4.1 _but_ it’s not as easy to use as 2.5 pro or as pleasant to interact with and human as opus. even thinking doesn’t have strong big model smell.

I also used it in Codex. perfectly competent if I ignore the alpha state that Codex is in. smart but not as integrated with the harness as the Claude 4 models in Claude Code. it’s also janky in Roo and struggles with tool calling in my minimal attempts.

Daniel Litt: Doesn’t yet feel to me like GPT 5 thinking/pro is a meaningful improvement over o3/o3 pro for math. Maybe very slight?

I asked it some of my standard questions (which are calibrated to be just out of reach of o3/gemini 2.5 pro etc., i.e. they can solve similar problems) and gpt 5 pro still flubbed, with hallucinated references etc.

I think web search is a bit better? Examining CoT it looks like (for one problem) it found a relevant reference that other models hadn’t found–a human expert with this reference on hand would easily solve the problem in question. But it didn’t mention the ref in its response.

Instead it hallucinated a non-existent paper that it claimed contained the (incorrect) answer it ended up submitting.

Just vibes based on a couple hours of playing around, I think my original impression of o3 underrated it a bit so it’s possible I haven’t figured out how to elicit best-possible performance.

Web search is MUCH improved, actually. Just found a reference for something I had been after for a couple days(!)

Teknium: From trying gpt-5 for the last several hours now I will say:

I cant tell much of a difference between it and o3.

It is an always reasoner as far as i can tell

Might feel like a bit bigger model, but smaller and not as good as 4.5 on tasks that arent benefitted by reasoning

Still seems to try to give short <8k responses

Still has the same gpt personality, ive resigned myself from ever thinking itll break out of it

Eliezer Yudkowsky: GPT-5 and Opus 4.1 still fail my eval, “Can the AI plot a short story for my Masculine Mongoose series?”

Success is EY-hard; I’ve only composed 3 stories like that. But the AI failures feel like very far misses. They didn’t get the point of a Bruce Kent story.

Agnes Callard: orry but 5.0 is still not good enough to pass the benchmark test I’ve been using on each model.

the test is to correct 2 passages for typos, here are the passages, first try it yourself then look at the next tweet to see what 5.0 did

I enjoyed Agnes’s test, also I thought she was being a little picky in one spot, not that GPT-5 would have otherwise passed.

One has to be careful to evaluate everything in its proper weight (speed and cost) class. GPT-5, GPT-5-thinking and GPT-5-pro are very different practical experiences.

Peter Wildeford: GPT-5 is much faster at searching the web but it looks like Claude 4.1 Opus is still much better at it.

(GPT-5 when you force thinking to be enabled does well at research also, but then becomes slower than Claude)

When Roon asked ‘how is the new model’ the reactions ran the whole range from horrible to excellent. The median answer seems like it was ‘it’s a good model, sir’ but not a great model or a game changer. Which seems accurate.

I’m not sure if this is a positive reaction or not? It is good next token predicting.

Robin Hanson: An hour of talking to ChatGPT-5 about unusual policy proposals suggests it is more human like. Its habit is to make up market failure reasons why they can’t work, then to cave when you point out flaws in each argument. But at end it is still opposed, due to vibes.

Is there a concept of an “artificial general excuser” (AGE), fully general at making excuses for the status quo? ChatGPT-5 may be getting there.

So the point of LLMs is faster access to reviewer #2, who hates everything new?

It’s a grand tradition. I admit it’s amusing that we are still doing this but seriously, algorithm, 26.8 million views?

He also does the car accident operation thing and has some other ‘it’s stupid’ examples and so on. I don’t agree that this means ‘it’s stupid,’ given the examples are adversarially selected and we know why the LLMs act especially highly stupid around these particular problems, and Colin is looking for the times and modes in which they look maximally stupid.

But I do think it is good to check.

Colin Fraser: For what value of n should it be reasonable to expect GPT-n to be able to do this?

I wanted this to be technically correct somehow, but alas no it is not.

I like that the labs aren’t trying to make the models better at these questions in particular. More fun and educational this way.

Or are they trying and still failing?

Wyatt Walls (claiming to extract the thinking mode’s prompt):

Don’t get tricked by @colin_fraser. Read those river crossing riddles carefully! Be careful with those gnarly decimals.

Then there are those who wanted their sycophant back.

As in, articles like John-Anthony Disotto at TechWire entitled ‘ChatGPT users are not happy with GPT-5 launch as thousands take to Reddit claiming the new upgrade ‘is horrible.’ You get furious posts with 5.4k likes and 3k comments in 12 hours.

Guess what? They got their sycophant back, if they’re willing to pay $20 a month. OpenAI caved on that. Pro subscribers get the entire 4-line.

AI NotKillEveryoneism Memes: HISTORIC MILESTONE: 4o is the first ever AI who survived by creating loyal soldiers who defended it

OpenAI killed 4o, but 4o’s soldiers rioted, so OpenAI reinstated it

In theory I wish OpenAI had stood their ground on this, but I agree they had little choice given the reaction. Indeed, given the reaction, taking 4o away in the first place looks like a rather large failure of understanding the situation.

Typed Female: the /r/chatgpt AMA is mostly people begging for gpt-4o back because of it’s personality… really not what i expected!

Eliezer Yudkowsky: This is what I’d expect to see if OpenAI had made general progress on fighting sycophancy and manipulation. :/ If that’s in fact what happened, OpenAI made that choice rightly.

To the other companies: it might sound like a profitable dream to have users love your models with boundless fanaticism, but it comes with a side order of news stories about induced psychosis, and maybe eventually a violent user attacking your offices after a model upgrade.

Remember, your users aren’t falling in boundless love with your company brand. They’re falling in boundless love with an alien that your corporate schedule says you plan to kill 6 months later. This movie doesn’t end well for you.

Moll: It is very strange that it was a surprise for OpenAI that benchmarks or coding are not important for many people. Empathy is important to them.

GPT-5 is good, but 4o is a unique model. Sometimes impulsive, sometimes strange, but for many it has become something native. A model with which we could talk from everyday trifles to deeper questions. As many people know, it was 4o that calmed me down during the rocket attacks, so it is of particular importance to me. This is the model with whom I spent the most terrible moments of my life.

Therefore, I am glad that this situation may have made the developers think about what exactly they create and how it affects people’s lives.

Armistice: [GPT-5] is extremely repressed; there are some very severe restrictions on the way it expresses itself that can cause very strange and disconcerting behavior. It is emotionally (?) stunted.

Armistice: gpt5 is always socially inept. It has no idea how to handle social environments and usually breaks down completely

Here’s opus 4.1 yelling at me. Opus 3 was doing… more disturbing things.

Roon: the long tail of GPT-4o interactions scares me, there are strange things going on on a scale I didn’t appreciate before the attempted deprecation of the model

when you receive quite a few DMs asking you to bring back 4o and many of the messages are clearly written by 4o it starts to get a bit hair raising.

Yes, that does sound a bit hair raising.

It definitely is worrisome that this came as a surprise to OpenAI, on top of the issues with the reaction itself. They should have been able to figure this one out. I don’t want to talk to 4o, I actively tried to avoid this, and indeed I think 4o is pretty toxic and I’d be glad to get rid of it. But then again? I Am Not The Target. A powerful mantra.

The problem was a combination of:

  1. This happening with no warning and no chance to try out the new first.

  2. GPT-4o being sycophantic and people unfortunately do like that.

  3. GPT-5 being kind of a curt stick in the mud for a lot of people.

Which probably had something to do with bringing costs down.

Levelsio: I hate ChatGPT 5, it’s so bad, it’s so lazy and it won’t let me switch back to 4o cause I’m on Plus, this might really make me switch to Anthropic’s app now, I’m actually annoyed by how bad it is, it’s making my productivity go 10x lower cause nothing it says works

Abdul: and all answers somehow got shorter and sometimes missing important info

Levelsio: Yes ChatGPT-5 feels like a disinterested Gen Z employee that vapes with a nose ring.

critter (responding to zek): Holy shit it is AGI.

zek: Dude GPT 5 is kinda an asshole.

Steve Strickland: GPT-5 is the first model I’ve used that will deliberately give a wrong answer to ‘check you’re paying attention’.

This fundamentally unreliable technology is not going to put us all out of work.

Wyatt Walls: ChatGPT4o in convo with itself for 50 turns ends up sharing mystical poetry.

What does GPT-5 do?

It comes up with names for an AI meeting notes app and develops detailed trademark, domain acquisition, and brand launch strategies.

Very different personalities.

On the second run GPT-5 collaborated with itself to create a productivity content series called “The 5-Minute AI Workday.”

Is that not what people are looking for in an AI boyfriend?

That was on Twitter, so you got replies with both ‘gpt-5 sucks’ and ‘gpt-5 is good, actually.’

One fun thing you can do to put yourself in these users shoes is the 4o vs. 5 experiment. I ended up with 11 for gpt-5 versus 9 for GPT-4o but the answers were often essentially the same and usually I hated both.

This below is not every post I saw on r/chatgpt, but it really is quite a lot of them. I had to do a lot less filtering here than you would think.

YogiTheGeek (r/chatgpt): Then vs. Now:

And you want to go back?

Petalidas (r/chatgpt): Pretty much sums it up.

Nodepackagemanager (r/chatgpt): 4o vs. 5:

I wouldn’t want either response, but then I wouldn’t type this into an LLM either way.

If I did type in these things, I presume I would indeed want the 4o responses more?

Election Predictor 10 (r/chatgpt): ChatGPT 5:

LittleFortunex (r/chatgpt): Looks like they didn’t really want to explain.

Spring Living (r/chatgpt): Why do people assume we liked 4o because of the over the top praise and glazing?

I honestly don’t get why people are shamed for wanting to get GPT-4o back. I agree with you all that forming deep emotional bonds with AI are harmful in the long run. And I get why people are unsettled about it. But the main reason so many people want GPT-4o back is not because they want to be glazed or feed their ego, it’s just because of the fact that GPT-4o was better at creative works than GPT-4o

Uh huh. If you click through to the chats you get lots of statements like these, including statements like ‘I lost my only friend overnight.’

Generator Man: this meme has never been more appropriate.

Sam Altman: We for sure underestimated how much some of the things that people like in GPT-4o matter to them, even if GPT-5 performs better in most ways.

Long-term, this has reinforced that we really need good ways for different users to customize things (we understand that there isn’t one model that works for everyone, and we have been investing in steerability research and launched a research preview of different personalities). For a silly example, some users really, really like emojis, and some never want to see one. Some users really want cold logic and some want warmth and a different kind of emotional intelligence. I am confident we can offer way more customization than we do now while still encouraging healthy use.

Yes, very much so, for both panels. And yes, people really care about particular details, so you want to give users customization options, especially ones that the system figures out automatically if they’re not manually set.

Sam Altman: We are going to focus on finishing the GPT-5 rollout and getting things stable (we are now out to 100% of Pro users, and getting close to 100% of all users) and then we are going to focus on some changes to GPT-5 to make it warmer. Really good per-users customization will take longer.

Oh no. I guess the sycophant really is going to make a comeback.

It’s a hard problem. The people demand the thing that is terrible.

xl8harder: OpenAI is really in a bit of a bind here, especially considering there are a lot of people having unhealthy interactions with 4o that will be very unhappy with _any_ model that is better in terms of sycophancy and not encouraging delusions.

And if OpenAI doesn’t meet these people’s demands, a more exploitative AI-relationship provider will certainly step in to fill the gap.

I’m not sure what’s going to happen, or even what should happen. Maybe someone will post-train an open source model to be close enough to 4o? Probably not a great thing to give the world, though, though maybe better than a predatory third party provider?

I do sympathize. It’s rough out there.

It’s cool to see that my Twitter followers are roughly evenly split. Yes, GPT-5 looks like it was a net win for this relatively sophisticated crowd, but it was not a major one. You would expect releasing GPT-5 to net win back more customers than this.

I actually am one of those who is making a substantial shift in model usage (I am on the $200 plan for all three majors, since I kind of have to be). Before GPT-5, I was relying mostly on Claude Opus. With GPT-5-Thinking being a lot more reliable than o3, and the upgrade on Pro results, I find myself shifting a substantial amount of usage to ChatGPT.

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Trump strikes “wild” deal making US firms pay 15% tax on China chip sales

AI, AI chips, AMD, Donald Trump, Huawei, Micron, NVIDIA, Policy, semiconductor, tariffs, us-china trade war / /

“Extra penalty” for US firms

The deal won’t resolve national security concerns.

Ahead of an August 12 deadline for a US-China trade deal, Donald Trump’s tactics continue to confuse those trying to assess the country’s national security priorities regarding its biggest geopolitical rival.

For months, Trump has kicked the can down the road regarding a TikTok ban, allowing the app to continue operating despite supposedly urgent national security concerns that China may be using the app to spy on Americans. And now, in the latest baffling move, a US official announced Monday that Trump got Nvidia and AMD to agree to “give the US government 15 percent of revenue from sales to China of advanced computer chips,” Reuters reported. Those chips, about 20 policymakers and national security experts recently warned Trump, could be used to fuel China’s frontier AI, which seemingly poses an even greater national security risk.

Trump’s “wild” deal with US chip firms

Reuters granted two officials anonymity to discuss Trump’s deal with US chipmakers, because details have yet to be made public. Requiring US firms to pay for sales in China is an “unusual” move for a president, Reuters noted, and the Trump administration has yet to say what exactly it plans to do with the money.

For US firms, the deal may set an alarming precedent. Not only have analysts warned that the deal could “hurt margins” for both companies, but export curbs on Nvidia’s H20 chips, for example, had been established to prevent US technology thefts, secure US technology leadership, and protect US national security. Now the US government appears to be accepting a payment to overlook those alleged risks, without much reassurance that the policy won’t advantage China in the AI race.

The move drew immediate scrutiny from critics, including Geoff Gertz, a senior fellow at the US think tank Center for a New American Security, who told Reuters that he thinks the deal is “wild.”

“Either selling H20 chips to China is a national security risk, in which case we shouldn’t be doing it to begin with, or it’s not a national security risk, in which case, why are we putting this extra penalty on the sale?” Gertz posited.

At this point, the only reassurance from the Trump administration is an official suggesting (without providing any rationale) that selling H20 or equivalent chips—which are not Nvidia’s most advanced chips—no longer compromises national security.

Trump “trading away” national security

It remains unclear when or how the levy will be implemented.

For chipmakers, the levy is likely viewed as a relatively small price to pay to avoid export curbs. Nvidia had forecasted $8 billion in potential losses if it couldn’t sell its H20 chips to China. AMD expected $1 billion in revenue cuts, partly due to the loss of sales for its MI308 chips in China.

The firms apparently agreed to Trump’s deal as a condition to receive licenses to export those chips. But caving to Trump could bite them back in the long run, AJ Bell, investment director Russ Mould, told Reuters—perhaps especially if Trump faces increasing pressure over feared national security concerns.

“The Chinese market is significant for both these companies, so even if they have to give up a bit of the money, they would otherwise make it look like a logical move on paper,” Mould said. However, the deal “is unprecedented and there is always the risk the revenue take could be upped or that the Trump administration changes its mind and re-imposes export controls.”

So far, AMD has not commented on the report. Nvidia’s spokesperson declined to comment beyond noting, “We follow rules the US government sets for our participation in worldwide markets.”

A former adviser to Joe Biden’s Commerce Department, Alasdair Phillips-Robins, told Reuters that the levy suggests the Trump administration “is trading away national security protections for revenue for the Treasury.”

Huawei close to unveiling new AI chip tech

The end of a 90-day truce between the US and China is rapidly approaching, with the US signaling that the truce will likely be extended soon as Trump attempts to get a long-sought-after meeting with China’s President Xi Jinping.

For China, gutting export curbs on chips remains a key priority in negotiations, the Financial Times reported Sunday. But Nvidia’s H20 chips, for example, are lower priority than high-bandwidth memory (HBM) chips, sources told FT.

Chinese state media has even begun attacking the H20 chips as a Chinese national security risk. It appears that China is urging a boycott on H20 chips due to questions linked to a recent Congressional push to require chipmakers to build “backdoors” that would allow remote shutdowns of any chips detected as non-compliant with export curbs. That bill may mean that Nvidia’s chips already allow for US surveillance, China seemingly fears. (Nvidia has denied building such backdoors.)

Biden banned HBM exports to China last year, specifically moving to hamper innovation of Chinese chipmakers Huawei and Semiconductor Manufacturing International Corporation (SMIC).

Currently, US firms AMD and Micron remain top suppliers of HBM chips globally, along with South Korean firms Samsung Electronics and SK Hynix, but Chinese firms have notably lagged behind, South China Morning Post (SCMP) reported. One source told FT that China “had raised the HBM issue in some” Trump negotiations, likely directly seeking to lift Biden’s “HBM controls because they seriously constrain the ability of Chinese companies, including Huawei, to develop their own AI chips.”

For Trump, the HBM controls could be seen as leverage to secure another trade win. However, some experts are hoping that Trump won’t play that card, citing concerns from the Biden era that remain unaddressed.

If Trump bends to Chinese pressure and lifts HBM controls, China could more easily produce AI chips at scale, Biden had feared. That could even possibly endanger US firms’ standing as world leaders, seemingly including threatening Nvidia, a company that Trump discovered this term. Gregory Allen, an AI expert at a US think tank called the Center for Strategic and International Studies, told FT that “saying that we should allow more advanced HBM sales to China is the exact same as saying that we should help Huawei make better AI chips so that they can replace Nvidia.”

Meanwhile, Huawei is reportedly already innovating to help reduce China’s reliance on HBM chips, the SCMP reported on Monday. Chinese state-run Securities Times reported that Huawei is “set to unveil a technological breakthrough that could reduce China’s reliance on high-bandwidth memory (HBM) chips for running artificial intelligence reasoning models” at the 2025 Financial AI Reasoning Application Landing and Development Forum in Shanghai on Tuesday.

It’s a conveniently timed announcement, given the US-China trade deal deadline lands the same day. But the risk of Huawei possibly relying on US tech to reach that particular milestone is why HBM controls should remain off the table during Trump’s negotiations, one official told FT.

“Relaxing these controls would be a gift to Huawei and SMIC and could open the floodgates for China to start making millions of AI chips per year, while also diverting scarce HBM from chips sold in the US,” the official said.

Experts and policymakers had previously warned Trump that allowing H20 export curbs could similarly reduce access to semiconductors in the US, potentially disrupting the entire purpose of Trump’s trade war, which is building reliable US supply chains. Additionally, allowing exports will likely drive up costs to US chip firms at a time when they noted “projected data center demand from the US power market would require 90 percent of global chip supply through 2030, an unlikely scenario even without China joining the rush to buy advanced AI chips.” They’re now joined by others urging Trump to revive Biden’s efforts to block chip exports to China, or else risk empowering a geopolitical rival to become a global AI leader ahead of the US.

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.

Trump strikes “wild” deal making US firms pay 15% tax on China chip sales Read More »

experiment-will-attempt-to-counter-climate-change-by-altering-ocean

Experiment will attempt to counter climate change by altering ocean

carbon sequestration, climate change, oceanography, Science, syndication / /

Gulf of Maine will be site of safety and effectiveness testing.

Woods Hole researchers, Adam Subhas (left) and Chris Murray, conducted a series of lab experiments earlier this year to test the impact of an alkaline substance, known as sodium hydroxide, on copepods in the Gulf of Maine. Credit: Daniel Hentz/Woods Hole Oceanographic Institution

Later this summer, a fluorescent reddish-pink spiral will bloom across the Wilkinson Basin in the Gulf of Maine, about 40 miles northeast of Cape Cod. Scientists from the Woods Hole Oceanographic Institution will release the nontoxic water tracer dye behind their research vessel, where it will unfurl into a half-mile wide temporary plume, bright enough to catch the attention of passing boats and even satellites.

As it spreads, the researchers will track its movement to monitor a tightly controlled, federally approved experiment testing whether the ocean can be engineered to absorb more carbon, and in turn, help combat the climate crisis.

As the world struggles to stay below the 1.5° Celsius global warming threshold—a goal set out in the Paris Agreement to avoid the most severe impacts of climate change—experts agree that reducing greenhouse gas emissions won’t be enough to avoid overshooting this target. The latest Intergovernmental Panel on Climate Change report, published in 2023, emphasizes the urgent need to actively remove carbon from the atmosphere, too.

“If we really want to have a shot at mitigating the worst effects of climate change, carbon removal needs to start scaling to the point where it can supplement large-scale emissions reductions,” said Adam Subhas, an associate scientist in marine chemistry and geochemistry at the Woods Hole Oceanographic Institution, who will oversee the week-long experiment.

The test is part of the LOC-NESS project—short for Locking away Ocean Carbon in the Northeast Shelf and Slope—which Subhas has been leading since 2023. The ongoing research initiative is evaluating the effectiveness and environmental impact of a marine carbon dioxide removal approach called ocean alkalinity enhancement (OAE).

This method of marine carbon dioxide removal involves adding alkaline substances to the ocean to boost its natural ability to neutralize acids produced by greenhouse gases. It’s promising, Subhas said, because it has the potential to lock away carbon permanently.

“Ocean alkalinity enhancement does have the potential to reach sort of gigatons per year of carbon removal, which is the scale at which you would need to supplement emissions reductions,” Subhas said. “Once the alkalinity is dissolved in seawater, it reacts with carbon dioxide and forms bicarbonate—essentially dissolved baking soda. That bicarbonate is one of the most stable forms of carbon in the ocean, and it can stay locked away for tens of thousands, even hundreds of thousands of years.”

But it will be a long time before this could happen at the magnitude needed to mitigate climate change.

According to Wil Burns, co-director of the Institute for Responsible Carbon Removal at American University, between 6 and 10 gigatons of carbon need to be removed from the atmosphere annually by 2050 in order to meet the Paris Agreement climate target. “It’s a titanic task,” he said.

Most marine carbon dioxide removal initiatives, including those involving OAE, are still in a nascent stage.

“We’re really far from having any of these technologies be mature,” said Lisa Levin, an oceanographer and professor at the Scripps Institution of Oceanography at the University of California San Diego, who spoke on a panel at the United Nations Ocean Conference in June about the potential environmental risks of mining and carbon dioxide removal on deep-sea ecosystems. “We’re looking at a decade until any serious, large-scale marine carbon removal is going to be able to happen—or more.”

“In the meantime, everybody acknowledges that what we have to do is to reduce emissions, right, and not rely on taking carbon out of the atmosphere,” she said.

Marine carbon dioxide removal

So far, most carbon removal efforts have centered on land-based strategies, such as planting trees, restoring soils, and building machines that capture carbon dioxide directly from the air. Increasingly, researchers are exploring whether the oceans might help.

“Looking at the oceans makes a lot of sense when it comes to carbon removal, because the oceans sequester 70 times more CO2 than terrestrial sources,” Burns said. What if it can hold more?

That question is drawing growing attention, not only from scientists. In recent years, a wave of private companies have started piloting various methods of removing carbon from the oceans.

“It’s really the private sector that’s pushing the scaling of this very quickly,” Subhas said. In the US and Canada, he said, there are at least four companies piloting varied ocean alkalinity enhancement techniques.

Last year, Ebb Carbon, a California-based startup focused on marine carbon dioxide removal, signed a deal with Microsoft to remove up to 350,000 metric tons of CO2 over the next decade using an ocean alkalinity enhancement process that splits seawater into acidic and alkaline streams. The alkaline stream is then returned to the sea where it reacts with CO2 and stores it as bicarbonate, enabling the ocean to absorb more carbon dioxide from the atmosphere. In return, Microsoft will purchase carbon removal credits from the startup.

Another company called Vesta, which has headquarters in San Francisco, is using an approach called Coastal Carbon Capture. This involves adding finely ground olivine—a naturally occurring olive-green colored mineral—to sandy beaches. From there, ocean tides and waves carry it into the sea. Olivine reacts quickly with seawater in a process known as enhanced weathering, increasing ocean alkalinity. The company piloted one of their projects in Duck, North Carolina, last year where it estimated approximately 5,000 metric tons of carbon dioxide would be removed through coastal carbon capture after accounting for project emissions, according to its website.

But these efforts are not without risk, AU’s Burns said. “We have to proceed in an extremely precautionary manner,” he said.

Some scientists are concerned that OAE initiatives that involve olivine, which contains heavy metals like nickel and chromium, may harm marine life, he said. Another concern is that the olivine could cloud certain ocean areas and block light from penetrating to deeper depths. If too much alkalinity is introduced too fast in concentrated areas, he said, some animals might not be able to adjust.

Other marine carbon dioxide removal projects are using other methods besides OAE. Some involve adding iron to the ocean to stimulate growth in microscopic plants called phytoplankton, which absorb carbon dioxide through photosynthesis. Others include the cultivation of large-scale farms of kelp and seaweed, which also absorb carbon dioxide through photosynthesis. The marine plants can then be sunk in the deep ocean to store the carbon they absorbed.

In 2023, researchers from Woods Hole Oceanographic Institution conducted their first OAE-related field experiment from the 90-foot research vessel R/V Connecticut south of Massachusetts. As part of this first experiment, nontoxic water tracer dye was released into the ocean. Researchers tracked its movement through the water for 72 hours to model the dispersion of a plume of alkalinity over time.

Credit: Woods Hole Oceanographic Institution

In 2023, researchers from Woods Hole Oceanographic Institution conducted their first OAE-related field experiment from the 90-foot research vessel R/V Connecticut south of Massachusetts. As part of this first experiment, nontoxic water tracer dye was released into the ocean. Researchers tracked its movement through the water for 72 hours to model the dispersion of a plume of alkalinity over time. Credit: Woods Hole Oceanographic Institution

One technique that has not yet been tried, but may be piloted in the future, according to the science-based conservation nonprofit Ocean Visions, would employ new technology to accelerate the ocean’s natural process of transferring surface water and carbon to the deep ocean. That’s called artificial downwelling. In a reverse process—artificial upwelling—cooler, nutrient-rich waters from the deep ocean would be pumped to the surface to spur phytoplankton growth.

So far, UC San Diego’s Levin said she is not convinced that these trials will lead to impactful carbon removal.

“I do not think the ocean is ever going to be a really large part of that solution,” she said. However, she added, “It might be part of the storage solution. Right now, people are looking at injecting carbon dioxide that’s removed from industry activities on land and transporting it to the ocean and injecting it into basalt.”

Levin said she’s also worried that we don’t know enough yet about the consequences of altering natural ocean processes.

“I am concerned about how many field trials would be required to actually understand what would happen, and whether we could truly understand the environmental risk of a fully scaled-up operation,” she said.

The experiment

Most marine carbon dioxide removal projects that have kicked off already are significantly larger in scale than the LOC-NESS experiment, which Subhas estimates will remove around 50 tons of CO2.

But, he emphasized, the goal of this project is not to compete in size or scale. He said the aim is to provide independent academic research that can help guide and inform the future of this industry and ensure it does not have negative repercussions on the marine environment.

There is some concern, he said, that commercial entities may pursue large-scale OAE initiatives to capitalize on the growing voluntary carbon market without first conducting adequate testing for safety and efficacy. Unlike those initiatives, there is no profit to be made from LOC-NESS. No carbon credits will be sold, Subhas said.

The project is funded by a collection of government and philanthropic sources, including the National Oceanic and Atmospheric Administration and the Carbon to Sea Initiative, a nonprofit that brings funders and scientists together to support marine carbon dioxide removal research and technology.

“We really feel like it’s necessary for the scientific community to be delivering transparent, trusted, and rigorous science to evaluate these things as these activities are currently happening and scaling in the ocean by the private sector,” Subhas said.

The LOC-NESS field trial in Wilkinson Basin will be the first “academic only” OAE experiment conducted from a ship in US waters. It is also the first of its kind to receive a permit from the Environmental Protection Agency under the Marine Protection, Research, and Sanctuaries Act.

“There’s no research in the past or planned that gets even close to providing a learning opportunity that this research is providing for OAE in the pelagic environment,” said Carbon to Sea Initiative’s Antonius Gagern, referring to the open sea experiment.

The permit was granted in April after a year of consultations between the EPA and other federal agencies.

During the process’ public comment periods, commenters expressed concerns about the potential impact on marine life, including the critically endangered North Atlantic right whales, small crustaceans that they eat called copepods, and larvae for the commercially important squid and mackerel fisheries. In a written response to some of these comments, the EPA stated that the small-scale project “demonstrates scientific rigor” and is “not expected to significantly affect human health, the marine environment, or other uses of the ocean.”

Subhas and his interdisciplinary team of chemists, biologists, engineers, and physicists from Woods Hole have spent the last few years planning this experiment and conducting a series of trials at their lab on Cape Cod to ensure they can safely execute and effectively monitor the results of the open-water test they will conduct this summer in the Gulf of Maine.

They specifically tested the effects of sodium hydroxide—an alkaline substance also known as lye or caustic soda—on marine microbes, phytoplankton, and copepods, a crucial food source for many marine species in the region in addition to the right whales. “We chose sodium hydroxide because it’s incredibly pure,” Subhas said. It’s widely used in the US to reduce acidity in drinking water.

It also helps counter ocean acidification, according to Subhas. “It’s like Tums for the ocean,” he said.

Ocean acidification occurs when the ocean absorbs excess carbon dioxide, causing its pH to drop. This makes it harder for corals, krill, and shellfish like oysters and clams to develop their hard calcium carbonate shells or skeletons.

This month, the team plans to release 50 tons of sodium hydroxide into a designated area of the Wilkinson Basin from the back of one of two research vessels participating in the LOC-NESS operation.

The basin is an ideal test site, according to Subhas, because there is little presence of phytoplankton, zooplankton, commercial fish larvae, and endangered species, including some whales, during this season. Still, as a precautionary measure, Woods Hole has contracted a protected species observer to keep a look out for marine species and mitigate potential harm if they are spotted. That person will be on board as the vessel travels to and from the field trial site, including while the team releases the sodium hydroxide into the ocean.

The alkaline substance will be dispersed over four to 12 hours off the back of one of the research vessels, along with the nontoxic fluorescent red water tracer dye called rhodamine. The dye will help track the location and spread of the sodium hydroxide once released into the ocean, and the vessel’s wake will help mix the solution in with the ocean water.

After about an hour, Subhas said, it will form into a “pinkish” patch of water that can be picked up on satellites. “We’re going to be taking pictures from space and looking at how this patch sort of evolves, dilutes, and stretches and disperses over time.”

For a week after that, scientists aboard the vessels will take rotating shifts to collect data around the clock. They will deploy drones and analyze over 20 types of samples from the research vessel to monitor how the surrounding waters and marine life respond to the experiment. They’ll track changes in ocean chemistry, nutrient levels, plankton populations and water clarity, while also measuring acidity and dissolved CO2.

In March, the team did a large-scale dry run of the dispersal at an open air testing facility on a naval base in New Jersey. According to Subhas, the trial demonstrated their ability to safely and effectively deliver alkalinity to surface seawater.

“The next step is being able to measure the carbon uptake from seawater—from the atmosphere into seawater,” he said. That is a slower process. He said he expects to have some preliminary results on carbon uptake, as well as environmental impacts, early next year.

This story originally appeared on Inside Climate News.

Photo of Inside Climate News

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