AI – Page 3

After using ChatGPT, man swaps his salt for sodium bromide—and suffers psychosis

AI, bromism, chatgpt, health / Kelly Newman / August 8, 2025

After seeking advice on health topics from ChatGPT, a 60-year-old man who had a “history of studying nutrition in college” decided to try a health experiment: He would eliminate all chlorine from his diet, which for him meant eliminating even table salt (sodium chloride). His ChatGPT conversations led him to believe that he could replace his sodium chloride with sodium bromide, which he obtained over the Internet.

Three months later, the man showed up at his local emergency room. His neighbor, he said, was trying to poison him. Though extremely thirsty, the man was paranoid about accepting the water that the hospital offered him, telling doctors that he had begun distilling his own water at home and that he was on an extremely restrictive vegetarian diet. He did not mention the sodium bromide or the ChatGPT discussions.

His distress, coupled with the odd behavior, led the doctors to run a broad set of lab tests, revealing multiple micronutrient deficiencies, especially in key vitamins. But the bigger problem was that the man appeared to be suffering from a serious case of “bromism.” That is, an excess amount of the element bromine had built up in his body.

A century ago, somewhere around 8–10 percent of all psychiatric admissions in the US were caused by bromism. That’s because, then as now, people wanted sedatives to calm their anxieties, to blot out a cruel world, or simply to get a good night’s sleep. Bromine-containing salts—things like potassium bromide—were once drugs of choice for this sort of thing.

Unfortunately, bromide can easily build up in the human body, where too much of it impairs nerve function. This causes a wide variety of problems, including grotesque skin rashes (warning: the link is exactly what it sounds like) and significant mental problems, which are all grouped under the name of “bromism.”

After using ChatGPT, man swaps his salt for sodium bromide—and suffers psychosis Read More »

Here’s how deepfake vishing attacks work, and why they can be hard to detect

AI, Biz & IT, Deepfakes, fraud, Security, speech synthesis / DJ Henderson / August 7, 2025

By now, you’ve likely heard of fraudulent calls that use AI to clone the voices of people the call recipient knows. Often, the result is what sounds like a grandchild, CEO, or work colleague you’ve known for years reporting an urgent matter requiring immediate action, saying to wire money, divulge login credentials, or visit a malicious website.

Researchers and government officials have been warning of the threat for years, with the Cybersecurity and Infrastructure Security Agency saying in 2023 that threats from deepfakes and other forms of synthetic media have increased “exponentially.” Last year, Google’s Mandiant security division reported that such attacks are being executed with “uncanny precision, creating for more realistic phishing schemes.”

Anatomy of a deepfake scam call

On Wednesday, security firm Group-IB outlined the basic steps involved in executing these sorts of attacks. The takeaway is that they’re easy to reproduce at scale and can be challenging to detect or repel.

The workflow of a deepfake vishing attack. Credit: Group-IB

The basic steps are:

Collecting voice samples of the person who will be impersonated. Samples as short as three seconds are sometimes adequate. They can come from videos, online meetings, or previous voice calls.

Feeding the samples into AI-based speech-synthesis engines, such as Google’s Tacotron 2, Microsoft’s Vall-E, or services from ElevenLabs and Resemble AI. These engines allow the attacker to use a text-to-speech interface that produces user-chosen words with the voice tone and conversational tics of the person being impersonated. Most services bar such use of deepfakes, but as Consumer Reports found in March, the safeguards these companies have in place to curb the practice could be bypassed with minimal effort.

An optional step is to spoof the number belonging to the person or organization being impersonated. These sorts of techniques have been in use for decades.

Next, attackers initiate the scam call. In some cases, the cloned voice will follow a script. In other more sophisticated attacks, the faked speech is generated in real time, using voice masking or transformation software. The real-time attacks can be more convincing because they allow the attacker to respond to questions a skeptical recipient may ask.

“Although real-time impersonation has been demonstrated by open source projects and commercial APIs, real-time deepfake vishing in-the-wild remains limited,” Group-IB said. “However, given ongoing advancements in processing speed and model efficiency, real-time usage is expected to become more common in the near future.”

Here’s how deepfake vishing attacks work, and why they can be hard to detect Read More »

US executive branch agencies will use ChatGPT Enterprise for just $1 per agency

AI, ai action plan, chatgpt, ChatGPT Enterprise, executive branch, Federal government, llm, openai, Policy, Preventing Woke AI, trump administration / 9u50fv / August 7, 2025

OpenAI announced an agreement to supply more than 2 million workers for the US federal executive branch access to ChatGPT and related tools at practically no cost: just $1 per agency for one year.

The deal was announced just one day after the US General Services Administration (GSA) signed a blanket deal to allow OpenAI and rivals like Google and Anthropic to supply tools to federal workers.

The workers will have access to ChatGPT Enterprise, a type of account that includes access to frontier models and cutting-edge features with relatively high token limits, alongside a more robust commitment to data privacy than general consumers of ChatGPT get. ChatGPT Enterprise has been trialed over the past several months at several corporations and other types of large organizations.

The workers will also have unlimited access to advanced features like Deep Research and Advanced Voice Mode for a 60-day period. After the one-year trial period, the agencies are under no obligation to renew.

A limited deployment of ChatGPT for federal workers was already done via a pilot program with the US Department of Defense earlier this summer.

In a blog post, OpenAI heralded this announcement as an act of public service:

This effort delivers on a core pillar of the Trump Administration’s AI Action Plan by making powerful AI tools available across the federal government so that workers can spend less time on red tape and paperwork, and more time doing what they came to public service to do: serve the American people.

The AI Action Plan aims to expand AI-focused data centers in the United States while bringing AI tools to federal workers, ostensibly to improve efficiency.

US executive branch agencies will use ChatGPT Enterprise for just $1 per agency Read More »

Some AI tools don’t understand biology yet

AI, Biology, foundation models, gene expression, Genetics, molecular biology, Science / Beth Washington / August 6, 2025

A collection of new studies on gene activity shows that AI tools aren’t very good.

Gene activity appears to remain beyond the abilities of AI at the moment. Credit: BSIP

Biology is an area of science where AI and machine-learning approaches have seen some spectacular successes, such as designing enzymes to digest plastics and proteins to block snake venom. But in an era of seemingly endless AI hype, it might be easy to think that we could just set AI loose on the mounds of data we’ve already generated and end up with a good understanding of most areas of biology, allowing us to skip a lot of messy experiments and the unpleasantness of research on animals.

But biology involves a whole lot more than just protein structures. And it’s extremely premature to suggest that AI can be equally effective at handling all aspects of biology. So we were intrigued to see a study comparing a set of AI software packages designed to predict how active genes will be in cells exposed to different conditions. As it turns out, the AI systems couldn’t manage to do any better than a deliberately simplified method of predicting.

The results serve as a useful caution that biology is incredibly complex, and developing AI systems that work for one aspect of it is not an indication that they can work for biology generally.

AI and gene activity

The study was conducted by a trio of researchers based in Heidelberg: Constantin Ahlmann-Eltze, Wolfgang Huber, and Simon Anders. They note that a handful of additional studies have been released while their work was on a pre-print server, all of them coming to roughly the same conclusions. But these authors’ approach is pretty easy to understand, so we’ll use it as an example.

The AI software they examined attempts to predict changes in gene activity. While every cell carries copies of the roughly 20,000 genes in the human genome, not all of them are active in a given cell—”active” in this case meaning they are producing messenger RNAs. Some provide an essential function and are active at high levels at all times. Others are only active in specific cell types, like nerves or skin. Still others are activated under specific conditions, like low oxygen or high temperatures.

Over the years, we’ve done many studies examining the activity of every gene in a given cell type under different conditions. These studies can range from using gene chips to determine which messenger RNAs are present in a population of cells to sequencing the RNAs isolated from single cells and using that data to identify which genes are active. But collectively, they can provide a broad, if incomplete, picture that links the activity of genes with different biological circumstances. It’s a picture you could potentially use to train an AI that would make predictions about gene activity under conditions that haven’t been tested.

Ahlmann-Eltze, Huber, and Anders tested a set of what are called single-cell foundation models that have been trained on this sort of gene activity data. The “single cell” portion indicates that these models have been trained on gene activity obtained from individual cells rather than a population average of a cell type. Foundation models mean that they have been trained on a broad range of data but will require additional training before they’re deployed for a specific task.

Underwhelming performance

The task in this case is predicting how gene activity might change when genes are altered. When an individual gene is lost or activated, it’s possible that the only messenger RNA that is altered is the one made by that gene. But some genes encode proteins that regulate a collection of other genes, in which case you might see changes in the activity of dozens of genes. In other cases, the loss or activation of a gene could affect a cell’s metabolism, resulting in widespread alterations of gene activity.

Things get even more complicated when two genes are involved. In many cases, the genes will do unrelated things, and you get a simple additive effect: the changes caused by the loss of one, plus the changes caused by the loss of others. But if there’s some overlap between the functions, you can get an enhancement of some changes, suppression of others, and other unexpected changes.

To start exploring these effects, researchers have intentionally altered the activity of one or more genes using the CRISPR DNA editing technology, then sequenced every RNA in the cell afterward to see what sorts of changes took place. This approach (termed Perturb-seq) is useful because it can give us a sense of what the altered gene does in a cell. But for Ahlmann-Eltze, Huber, and Anders, it provides the data they need to determine if these foundation models can be trained to predict the ensuing changes in the activity of other genes.

Starting with the foundation models, the researchers conducted additional training using data from an experiment where either one or two genes were activated using CRISPR. This training used the data from 100 individual gene activations and another 62 where two genes were activated. Then, the AI packages were asked to predict the results for another 62 pairs of genes that were activated. For comparison, the researchers also made predictions using two extremely simple models: one that always predicted that nothing would change and a second that always predicted an additive effect (meaning that activating genes A and B would produce the changes caused by activating A plus the changes caused by activating B).

They didn’t work. “All models had a prediction error substantially higher than the additive baseline,” the researchers concluded. The result held when the researchers used alternative measurements of the accuracy of the AI’s predictions.

The gist of the problem seemed to be that the trained foundation models weren’t very good at predicting when the alterations of pairs of genes would produce complex patterns of changes—when the alteration of one gene synergized with the alteration of a second. “The deep learning models rarely predicted synergistic interactions, and it was even rarer that those predictions were correct,” the researchers concluded. In a separate test that looked specifically at these synergies between genes, it turned out that none of the models were better than the simplified system that always predicted no changes.

Not there yet

The overall conclusions from the work are pretty clear. “As our deliberately simple baselines are incapable of representing realistic biological complexity yet were not outperformed by the foundation models,” the researchers write, “we conclude that the latter’s goal of providing a generalizable representation of cellular states and predicting the outcome of not-yet-performed experiments is still elusive.”

It’s important to emphasize that “still elusive” doesn’t mean we’re incapable of ever developing an AI that can help with this problem. It also doesn’t mean that this applies to all cellular states (the results are specific to gene activity), much less all of biology. At the same time, the work provides a valuable caution at a time when there’s a lot of enthusiasm for the idea that AI’s success in a couple of areas means we’re on the cusp of a world where it can be applied to anything.

Nature Methods, 2025. DOI: 10.1038/s41592-025-02772-6 (About DOIs).

John is Ars Technica’s science editor. He has a Bachelor of Arts in Biochemistry from Columbia University, and a Ph.D. in Molecular and Cell Biology from the University of California, Berkeley. When physically separated from his keyboard, he tends to seek out a bicycle, or a scenic location for communing with his hiking boots.

Some AI tools don’t understand biology yet Read More »

States take the lead in AI regulation as federal government steers clear

AI, ai action plan, AI regulation, facial recognition, generative ai, Policy, syndication / Mike M. / August 6, 2025

AI in health care

In the first half of 2025, 34 states introduced over 250 AI-related health bills. The bills generally fall into four categories: disclosure requirements, consumer protection, insurers’ use of AI, and clinicians’ use of AI.

Bills about transparency define requirements for information that AI system developers and organizations that deploy the systems disclose.

Consumer protection bills aim to keep AI systems from unfairly discriminating against some people and ensure that users of the systems have a way to contest decisions made using the technology.

Bills covering insurers provide oversight of the payers’ use of AI to make decisions about health care approvals and payments. And bills about clinical uses of AI regulate use of the technology in diagnosing and treating patients.

Facial recognition and surveillance

In the US, a long-standing legal doctrine that applies to privacy protection issues, including facial surveillance, is to protect individual autonomy against interference from the government. In this context, facial recognition technologies pose significant privacy challenges as well as risks from potential biases.

Facial recognition software, commonly used in predictive policing and national security, has exhibited biases against people of color and consequently is often considered a threat to civil liberties. A pathbreaking study by computer scientists Joy Buolamwini and Timnit Gebru found that facial recognition software poses significant challenges for Black people and other historically disadvantaged minorities. Facial recognition software was less likely to correctly identify darker faces.

Bias also creeps into the data used to train these algorithms, for example when the composition of teams that guide the development of such facial recognition software lack diversity.

By the end of 2024, 15 states in the US had enacted laws to limit the potential harms from facial recognition. Some elements of state-level regulations are requirements on vendors to publish bias test reports and data management practices, as well as the need for human review in the use of these technologies.

States take the lead in AI regulation as federal government steers clear Read More »

OpenAI releases its first open source models since 2019

AI, Artificial Intelligence, Open Source, openai / Beth Washington / August 5, 2025

OpenAI is releasing new generative AI models today, and no, GPT-5 is not one of them. Depending on how you feel about generative AI, these new models may be even more interesting, though. The company is rolling out gpt-oss-120b and gpt-oss-20b, its first open weight models since the release of GPT-2 in 2019. You can download and run these models on your own hardware, with support for simulated reasoning, tool use, and deep customization.

When you access the company’s proprietary models in the cloud, they’re running on powerful server infrastructure that cannot be replicated easily, even in enterprise. The new OpenAI models come in two variants (120b and 20b) to be run on less powerful hardware configurations. Both are transformers with configurable chain of thought (CoT), supporting low, medium, and high settings. The lower settings are faster and use fewer compute resources, but the outputs are better with the highest setting. You can set the CoT level with a single line in the system prompt.

The smaller gpt-oss-20b has a total of 21 billion parameters, utilizing mixture-of-experts (MoE) to reduce that to 3.6 billion parameters per token. As for gpt-oss-120b, its 117 billion parameters come down to 5.1 billion per token with MoE. The company says the smaller model can run on a consumer-level machine with 16GB or more of memory. To run gpt-oss-120b, you need 80GB of memory, which is more than you’re likely to find in the average consumer machine. It should fit on a single AI accelerator GPU like the Nvidia H100, though. Both models have a context window of 128,000 tokens.

The team says users of gpt-oss can expect robust performance similar to its leading cloud-based models. The larger one benchmarks between the o3 and o4-mini proprietary models in most tests, with the smaller version running just a little behind. It gets closest in math and coding tasks. In the knowledge-based Humanity’s Last Exam, o3 is far out in front with 24.9 percent (with tools), while gpt-oss-120b only manages 19 percent. For comparison, Google’s leading Gemini Deep Think hits 34.8 percent in that test.

OpenAI releases its first open source models since 2019 Read More »

Enough is enough—I dumped Google’s worsening search for Kagi

AI, AI assistant, ai search, degoogle, Features, Google, google search, kagi, kagi search, screwyoogle, search, search engines, SEO, Tech / 9u50fv / August 5, 2025

I like how the search engine is the product instead of me.

“Won’t be needing this anymore!” Credit: Aurich “The King” Lawson

Mandatory AI summaries have come to Google, and they gleefully showcase hallucinations while confidently insisting on their truth. I feel about them the same way I felt about mandatory G+ logins when all I wanted to do was access my damn YouTube account: I hate them. Intensely.

But unlike those mandatory G+ logins—on which Google eventually relented before shutting down the G+ service—our reading of the tea leaves suggests that, this time, the search giant is extremely pleased with how things are going.

Fabricated AI dreck polluting your search? It’s the new normal. Miss your little results page with its 10 little blue links? Too bad. They’re gone now, and you can’t get them back, no matter what ephemeral workarounds or temporarily functional flags or undocumented, could-fail-at-any-time URL tricks you use.

And the galling thing is that Google expects you to be a good consumer and just take it. The subtext of the company’s (probably AI-generated) robo-MBA-speak non-responses to criticism and complaining is clear: “LOL, what are you going to do, use a different search engine? Now, shut up and have some more AI!”

But like the old sailor used to say: “That’s all I can stands, and I can’t stands no more.” So I did start using a different search engine—one that doesn’t constantly shower me with half-baked, anti-consumer AI offerings.

Out with Google, in with Kagi.

What the hell is a Kagi?

Kagi was founded in 2018, but its search product has only been publicly available since June 2022. It purports to be an independent search engine that pulls results from around the web (including from its own index) and is aimed at returning search to a user-friendly, user-focused experience. The company’s stated purpose is to deliver useful search results, full stop. The goal is not to blast you with AI garbage or bury you in “Knowledge Graph” summaries hacked together from posts in a 12-year-old Reddit thread between two guys named /u/WeedBoner420 and /u/14HitlerWasRight88.

Kagi’s offerings (it has a web browser, too, though I’ve not used it) are based on a simple idea. There’s an (oversimplified) axiom that if a good or service (like Google search, for example, or good ol’ Facebook) is free for you to use, it’s because you’re the product, not the customer. With Google, you pay with your attention, your behavioral metrics, and the intimate personal details of your wants and hopes and dreams (and the contents of your emails and other electronic communications—Google’s got most of that, too).

With Kagi, you pay for the product using money. That’s it! You give them some money, and you get some service—great service, really, which I’m overall quite happy with and which I’ll get to shortly. You don’t have to look at any ads. You don’t have to look at AI droppings. You don’t have to give perpetual ownership of your mind-palace to a pile of optioned-out tech bros in sleeveless Patagonia vests while you are endlessly subjected to amateur AI Rorschach tests every time you search for “pierogis near me.”

How much money are we talking?

I dunno, about a hundred bucks a year? That’s what I’m spending as an individual for unlimited searches. I’m using Kagi’s “Professional” plan, but there are others, including a free offering so that you can poke around and see if the service is worth your time.

image of kagi billing panel — This is my account’s billing page, showing what I’ve paid for Kagi in the past year. (By the time this article runs, I’ll have renewed my subscription!) Credit: Lee Hutchinson

I’d previously bounced off two trial runs with Kagi in 2023 and 2024 because the idea of paying for search just felt so alien. But that was before Google’s AI enshittification rolled out in full force. Now, sitting in the middle of 2025 with the world burning down around me, a hundred bucks to kick Google to the curb and get better search results feels totally worth it. Your mileage may vary, of course.

The other thing that made me nervous about paying for search was the idea that my money was going to enrich some scumbag VC fund, but fortunately, there’s good news on that front. According to the company’s “About” page, Kagi has not taken any money from venture capitalist firms. Instead, it has been funded by a combination of self-investment by the founder, selling equity to some Kagi users in two rounds, and subscription revenue:

Kagi was bootstrapped from 2018 to 2023 with ~$3M initial funding from the founder. In 2023, Kagi raised $670K from Kagi users in its first external fundraise, followed by $1.88M raised in 2024, again from our users, bringing the number of users-investors to 93… In early 2024, Kagi became a Public Benefit Corporation (PBC).

What about DuckDuckGo? Or Bing? Or Brave?

Sure, those can be perfectly cromulent alternatives to Google, but honestly, I don’t think they go far enough. DuckDuckGo is fine, but it largely utilizes Bing’s index; and while DuckDuckGo exercises considerable control over its search results, the company is tied to the vicissitudes of Microsoft by that index. It’s a bit like sitting in a boat tied to a submarine. Sure, everything’s fine now, but at some point, that sub will do what subs do—and your boat is gonna follow it down.

And as for Bing itself, perhaps I’m nitpicky [Ed. note: He is!], but using Bing feels like interacting with 2000-era MSN’s slightly perkier grandkid. It’s younger and fresher, yes, but it still radiates that same old stanky feeling of taste-free, designed-by-committee artlessness. I’d rather just use Google—which is saying something. At least Google’s search home page remains uncluttered.

Brave Search is another fascinating option I haven’t spent a tremendous amount of time with, largely because Brave’s cryptocurrency ties still feel incredibly low-rent and skeevy. I’m slowly warming up to the Brave Browser as a replacement for Chrome (see the screenshots in this article!), but I’m just not comfortable with Brave yet—and likely won’t be unless the company divorces itself from cryptocurrencies entirely.

More anonymity, if you want it

The feature that convinced me to start paying for Kagi was its Privacy Pass option. Based on a clean-sheet Rust implementation of the Privacy Pass standard (IETF RFCs 9576, 9577, and 9578) by Raphael Robert, this is a technology that uses cryptographic token-based auth to send an “I’m a paying user, please give me results” signal to Kagi, without Kagi knowing which user made the request. (There’s a much longer Kagi blog post with actual technical details for the curious.)

To search using the tool, you install the Privacy Pass extension (linked in the docs above) in your browser, log in to Kagi, and enable the extension. This causes the plugin to request a bundle of tokens from the search service. After that, you can log out and/or use private windows, and those tokens are utilized whenever you do a Kagi search.

image of a kagi search with privacy pass enabled — Privacy pass is enabled, allowing me to explore the delicious mystery of pierogis with some semblance of privacy. Credit: Lee Hutchinson

The obvious flaw here is that Kagi still records source IP addresses along with Privacy Pass searches, potentially de-anonymizing them, but there’s a path around that: Privacy Pass functions with Tor, and Kagi maintains a Tor onion address for searches.

So why do I keep using Privacy Pass without Tor, in spite of the opsec flaw? Maybe it’s the placebo effect in action, but I feel better about putting at least a tiny bit of friction in the way of someone with root attempting to casually browse my search history. Like, I want there to be at least a SQL JOIN or two between my IP address and my searches for “best Mass Effect alien sex choices” or “cleaning tips for Garrus body pillow.” I mean, you know, assuming I were ever to search for such things.

What’s it like to use?

Moving on with embarrassed rapidity, let’s look at Kagi a bit and see how using it feels.

My anecdotal observation is that Kagi doesn’t favor Reddit-based results nearly as much as Google does, but sometimes it still has them near or at the top. And here is where Kagi curb-stomps Google with quality-of-life features: Kagi lets you prioritize or de-prioritize a website’s prominence in your search results. You can even pin that site to the top of the screen or block it completely.

This is a feature I’ve wanted Google to get for about 25 damn years but that the company has consistently refused to properly implement (likely because allowing users to exclude sites from search results notionally reduces engagement and therefore reduces the potential revenue that Google can extract from search). Well, screw you, Google, because Kagi lets me prioritize or exclude sites from my results, and it works great—I’m extraordinarily pleased to never again have to worry about Quora or Pinterest links showing up in my search results.

Further, Kagi lets me adjust these settings both for the current set of search results (if you don’t want Reddit results for this search but you don’t want to drop Reddit altogether) and also globally (for all future searches):

image of kagi search personalization options — Goodbye forever, useless crap sites. Credit: Lee Hutchinson

Another tremendous quality-of-life improvement comes via Kagi’s image search, which does a bunch of stuff that Google should and/or used to do—like giving you direct right-click access to save images without having to fight the search engine with workarounds, plugins, or Tampermonkey-esque userscripts.

The Kagi experience is also vastly more customizable than Google’s (or at least, how Google’s has become). The widgets that appear in your results can be turned off, and the “lenses” through which Kagi sees the web can be adjusted to influence what kinds of things do and do not appear in your results.

If that doesn’t do it for you, how about the ability to inject custom CSS into your search and landing pages? Or to automatically rewrite search result URLs to taste, doing things like redirecting reddit.com to old.reddit.com? Or breaking free of AMP pages and always viewing originals instead?

Image of kagi custom css field — Imagine all the things Ars readers will put here. Credit: Lee Hutchinson

Is that all there is?

Those are really all the features I care about, but there are loads of other Kagi bits to discover—like a Kagi Maps tool (it’s pretty good, though I’m not ready to take it up full time yet) and a Kagi video search tool. There are also tons of classic old-Google-style inline search customizations, including verbatim mode, where instead of trying to infer context about your search terms, Kagi searches for exactly what you put in the box. You can also add custom search operators that do whatever you program them to do, and you get API-based access for doing programmatic things with search.

A quick run-through of a few additional options pages. This is the general customization page. Lee Hutchinson

I haven’t spent any time with Kagi’s Orion browser, but it’s there as an option for folks who want a WebKit-based browser with baked-in support for Privacy Pass and other Kagi functionality. For now, Firefox continues to serve me well, with Brave as a fallback for working with Google Docs and other tools I can’t avoid and that treat non-Chromium browsers like second-class citizens. However, Orion is probably on the horizon for me if things in Mozilla-land continue to sour.

Cool, but is it any good?

Rather than fill space with a ton of comparative screenshots between Kagi and Google or Kagi and Bing, I want to talk about my subjective experience using the product. (You can do all the comparison searches you want—just go and start searching—and your comparisons will be a lot more relevant to your personal use cases than any examples I can dream up!)

My time with Kagi so far has included about seven months of casual opportunistic use, where I’d occasionally throw a query at it to see how it did, and about five months of committed daily use. In the five months of daily usage, I can count on one hand the times I’ve done a supplementary Google search because Kagi didn’t have what I was looking for on the first page of results. I’ve done searches for all the kinds of things I usually look for in a given day—article fact-checking queries, searches for details about the parts of speech, hunts for duck facts (we have some feral Muscovy ducks nesting in our front yard), obscure technical details about Project Apollo, who the hell played Dupont in Equilibrium (Angus Macfadyen, who also played Robert the Bruce in Braveheart), and many, many other queries.

Image of Firefox history window showing kagi searches for july 22 — A typical afternoon of Kagi searches, from my Firefox history window. Credit: Lee Hutchinson

For all of these things, Kagi has responded quickly and correctly. The time to service a query feels more or less like Google’s service times; according to the timer at the top of the page, my Kagi searches complete in between 0.2 and 0.8 seconds. Kagi handles misspellings in search terms with the grace expected of a modern search engine and has had no problem figuring out my typos.

Holistically, taking search customizations into account on top of the actual search performance, my subjective assessment is that Kagi gets me accurate, high-quality results on more or less any given query, and it does so without festooning the results pages with features I find detractive and irrelevant.

I know that’s not a data-driven assessment, and it doesn’t fall back on charts or graphs or figures, but it’s how I feel after using the product every single day for most of 2025 so far. For me, Kagi’s search performance is firmly in the “good enough” category, and that’s what I need.

Kagi and AI

Unfortunately, the thing that’s stopping me from being completely effusive in my praise is that Kagi is exhibiting a disappointing amount of “keeping-up-with-the-Joneses” by rolling out a big ‘ol pile of (optional, so far) AI-enabled search features.

A blog post from founder Vladimir Prelovac talks about the company’s use of AI, and it says all the right things, but at this point, I trust written statements from tech company founders about as far as I can throw their corporate office buildings. (And, dear reader, that ain’t very far).

image of kagi ai features — No thanks. But I would like to exclude AI images from my search results, please. Credit: Lee Hutchinson

The short version is that, like Google, Kagi has some AI features: There’s an AI search results summarizer, an AI page summarizer, and an “ask questions about your results” chatbot-style function where you can interactively interrogate an LLM about your search topic and results. So far, all of these things can be disabled or ignored. I don’t know how good any of the features are because I have disabled or ignored them.

If the existence of AI in a product is a bright red line you won’t cross, you’ll have to turn back now and find another search engine alternative that doesn’t use AI and also doesn’t suck. When/if you do, let me know, because the pickings are slim.

Is Kagi for you?

Kagi might be for you—especially if you’ve recently typed a simple question into Google and gotten back a pile of fabricated gibberish in place of those 10 blue links that used to serve so well. Are you annoyed that Google’s search sucks vastly more now than it did 10 years ago? Are you unhappy with how difficult it is to get Google search to do what you want? Are you fed up? Are you pissed off?

If your answer to those questions is the same full-throated “Hell yes, I am!” that mine was, then perhaps it’s time to try an alternative. And Kagi’s a pretty decent one—if you’re not averse to paying for it.

It’s a fantastic feeling to type in a search query and once again get useful, relevant, non-AI results (that I can customize!). It’s a bit of sanity returning to my Internet experience, and I’m grateful. Until Kagi is bought by a value-destroying vampire VC fund or implodes into its own AI-driven enshittification cycle, I’ll probably keep paying for it.

After that, who knows? Maybe I’ll throw away my computers and live in a cave. At least until the cave’s robot exclusion protocol fails and the Googlebot comes for me.

Lee is the Senior Technology Editor, and oversees story development for the gadget, culture, IT, and video sections of Ars Technica. A long-time member of the Ars OpenForum with an extensive background in enterprise storage and security, he lives in Houston.

Enough is enough—I dumped Google’s worsening search for Kagi Read More »

DeepMind reveals Genie 3 “world model” that creates real-time interactive simulations

AI, Artificial Intelligence, Google, google deepmind, Tech / 9u50fv / August 5, 2025

While no one has figured out how to make money from generative artificial intelligence, that hasn’t stopped Google DeepMind from pushing the boundaries of what’s possible with a big pile of inference. The capabilities (and costs) of these models have been on an impressive upward trajectory, a trend exemplified by the reveal of Genie 3. A mere seven months after showing off the Genie 2 “foundational world model,” which was itself a significant improvement over its predecessor, Google now has Genie 3.

With Genie 3, all it takes is a prompt or image to create an interactive world. Since the environment is continuously generated, it can be changed on the fly. You can add or change objects, alter weather conditions, or insert new characters—DeepMind calls these “promptable events.” The ability to create alterable 3D environments could make games more dynamic for players and offer developers new ways to prove out concepts and level designs. However, many in the gaming industry have expressed doubt that such tools would help.

Genie 3: building better worlds.

It’s tempting to think of Genie 3 simply as a way to create games, but DeepMind sees this as a research tool, too. Games play a significant role in the development of artificial intelligence because they provide challenging, interactive environments with measurable progress. That’s why DeepMind previously turned to games like Go and StarCraft to expand the bounds of AI.

World models take that to the next level, generating an interactive world frame by frame. This provides an opportunity to refine how AI models—including so-called “embodied agents”—behave when they encounter real-world situations. One of the primary limitations as companies work toward the goal of artificial general intelligence (AGI) is the scarcity of reliable training data. After piping basically every webpage and video on the planet into AI models, researchers are turning toward synthetic data for many applications. DeepMind believes world models could be a key part of this effort, as they can be used to train AI agents with essentially unlimited interactive worlds.

DeepMind says Genie 3 is an important advancement because it offers much higher visual fidelity than Genie 2, and it’s truly real-time. Using keyboard input, it’s possible to navigate the simulated world in 720p resolution at 24 frames per second. Perhaps even more importantly, Genie 3 can remember the world it creates.

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Amazon is considering shoving ads into Alexa+ conversations

ads, AI, Amazon, amazon alexa, generative ai, Tech / 9u50fv / August 3, 2025

Since 2023, Amazon has been framing Alexa+ as a monumental evolution of Amazon’s voice assistant that will make it more conversational, capable, and, for Amazon, lucrative. Amazon said in a press release on Thursday that it has given early access of the generative AI voice assistant to “millions” of people. The product isn’t publicly available yet, and some advertised features are still unavailable, but Amazon’s CEO is already considering loading the chatbot up with ads.

During an investors call yesterday, as reported by TechCrunch, Andy Jassy noted that Alexa+ started rolling out as early access to some customers in the US and that a broader rollout, including internationally, should happen later this year. An analyst on the call asked Amazon executives about Alexa+’s potential for “increasing engagement” long term.

Per a transcript of the call, Jassy responded by saying, in part, “I think over time, there will be opportunities, you know, as people are engaging in more multi-turn conversations to have advertising play a role to help people find discovery and also as a lever to drive revenue.”

Like other voice assistants, Alexa has yet to monetize users. Amazon is hoping to finally make money off the service through Alexa+, which is eventually slated to play a bigger role in e-commerce, including by booking restaurant reservations, keeping track of and ordering groceries, and recommending streaming content based on stated interests. But with Alexa reportedly costing Amazon $25 billion across four years, Amazon is eyeing additional routes to profitability.

Echo Show devices already show ads, and Echo speaker users may hear ads when listening to music. Advertisers have shown interest in advertising with Alexa+, but the inclusion of ads in a new offering like Alexa+ could drive people away.

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At $250 million, top AI salaries dwarf those of the Manhattan Project and the Space Race

agi, AI, AI chips, AI development, AI GPU, AI infrastructure, AI research, artificial general intelligence, Bell Labs, Biz & IT, compensation, Fairchild Semiconductor, Google, machine learning, Manhattan Project, mark zuckerberg, Meta, NASA, openai, Silicon Valley, superintelligence, talent acquisition / Kelly Newman / August 2, 2025

A 24 year-old AI researcher will earn 327x what Oppenheimer made while developing the atomic bomb.

Silicon Valley’s AI talent war just reached a compensation milestone that makes even the most legendary scientific achievements of the past look financially modest. When Meta recently offered AI researcher Matt Deitke $250 million over four years (an average of $62.5 million per year)—with potentially $100 million in the first year alone—it shattered every historical precedent for scientific and technical compensation we can find on record. That includes salaries during the development of major scientific milestones of the 20th century.

The New York Times reported that Deitke had cofounded a startup called Vercept and previously led the development of Molmo, a multimodal AI system, at the Allen Institute for Artificial Intelligence. His expertise in systems that juggle images, sounds, and text—exactly the kind of technology Meta wants to build—made him a prime target for recruitment. But he’s not alone: Meta CEO Mark Zuckerberg reportedly also offered an unnamed AI engineer $1 billion in compensation to be paid out over several years. What’s going on?

These astronomical sums reflect what tech companies believe is at stake: a race to create artificial general intelligence (AGI) or superintelligence—machines capable of performing intellectual tasks at or beyond the human level. Meta, Google, OpenAI, and others are betting that whoever achieves this breakthrough first could dominate markets worth trillions. Whether this vision is realistic or merely Silicon Valley hype, it’s driving compensation to unprecedented levels.

To put these salaries in a historical perspective: J. Robert Oppenheimer, who led the Manhattan Project that ended World War II, earned approximately $10,000 per year in 1943. Adjusted for inflation using the US Government’s CPI Inflation Calculator, that’s about $190,865 in today’s dollars—roughly what a senior software engineer makes today. The 24-year-old Deitke, who recently dropped out of a PhD program, will earn approximately 327 times what Oppenheimer made while developing the atomic bomb.

Many top athletes can’t compete with these numbers. The New York Times noted that Steph Curry’s most recent four-year contract with the Golden State Warriors was $35 million less than Deitke’s Meta deal (although soccer superstar Cristiano Ronaldo will make $275 million this year as the highest-paid professional athlete in the world). The comparison prompted observers to call this an “NBA-style” talent market—except the AI researchers are making more than NBA stars.

Racing toward “superintelligence”

Mark Zuckerberg recently told investors that Meta plans to continue throwing money at AI talent “because we have conviction that superintelligence is going to improve every aspect of what we do.” In a recent open letter, he described superintelligent AI as technology that would “begin an exciting new era of individual empowerment,” despite declining to define what superintelligence actually is.

This vision explains why companies treat AI researchers like irreplaceable assets rather than well-compensated professionals. If these companies are correct, the first to achieve artificial general intelligence or superintelligence won’t just have a better product—they’ll have technology that could invent endless new products or automate away millions of knowledge-worker jobs and transform the global economy. The company that controls that kind of technology could become the richest company in history by far.

So perhaps it’s not surprising that even the highest salaries of employees from the early tech era pale in comparison to today’s AI researcher salaries. Thomas Watson Sr., IBM’s legendary CEO, received $517,221 in 1941—the third-highest salary in America at the time (about $11.8 million in 2025 dollars). The modern AI researcher’s package represents more than five times Watson’s peak compensation, despite Watson building one of the 20th century’s most dominant technology companies.

The contrast becomes even more stark when considering the collaborative nature of past scientific achievements. During Bell Labs’ golden age of innovation—when researchers developed the transistor, information theory, and other foundational technologies—the lab’s director made about 12 times what the lowest-paid worker earned. Meanwhile, Claude Shannon, who created information theory at Bell Labs in 1948, worked on a standard professional salary while creating the mathematical foundation for all modern communication.

The “Traitorous Eight” who left William Shockley to found Fairchild Semiconductor—the company that essentially birthed Silicon Valley—split ownership of just 800 shares out of 1,325 total when they started. Their seed funding of $1.38 million (about $16.1 million today) for the entire company is a fraction of what a single AI researcher now commands.

Even Space Race salaries were far cheaper

The Apollo program offers another striking comparison. Neil Armstrong, the first human to walk on the moon, earned about $27,000 annually—roughly $244,639 in today’s money. His crewmates Buzz Aldrin and Michael Collins made even less, earning the equivalent of $168,737 and $155,373, respectively, in today’s dollars. Current NASA astronauts earn between $104,898 and $161,141 per year. Meta’s AI researcher will make more in three days than Armstrong made in a year for taking “one giant leap for mankind.”

The engineers who designed the rockets and mission control systems for the Apollo program also earned modest salaries by modern standards. A 1970 NASA technical report provides a window into these earnings by analyzing salary data for the entire engineering profession. The report, which used data from the Engineering Manpower Commission, noted that these industry-wide salary curves corresponded directly to the government’s General Schedule (GS) pay scale on which NASA’s own employees were paid.

According to a chart in the 1970 report, a newly graduated engineer in 1966 started with an annual salary of between $8,500 and $10,000 (about $84,622 to $99,555 today). A typical engineer with a decade of experience earned around $17,000 annually ($169,244 today). Even the most elite, top-performing engineers with 20 years of experience peaked at a salary of around $278,000 per year in today’s dollars—a sum that a top AI researcher like Deitke can now earn in just a few days.

Why the AI talent market is different

An image of a faceless human silhouette (chest up) with exposed microchip contacts and circuitry erupting from its open head. This visual metaphor explores transhumanism, AI integration, or the erosion of organic thought in the digital age. The stark contrast between the biological silhouette and mechanical components highlights themes of technological dependence or posthuman evolution. Ideal for articles on neural implants, futurism, or the ethics of human augmentation.

This isn’t the first time technical talent has commanded premium prices. In 2012, after three University of Toronto academics published AI research, they auctioned themselves to Google for $44 million (about $62.6 million in today’s dollars). By 2014, a Microsoft executive was comparing AI researcher salaries to NFL quarterback contracts. But today’s numbers dwarf even those precedents.

Several factors explain this unprecedented compensation explosion. We’re in a new realm of industrial wealth concentration unseen since the Gilded Age of the late 19th century. Unlike previous scientific endeavors, today’s AI race features multiple companies with trillion-dollar valuations competing for an extremely limited talent pool. Only a small number of researchers have the specific expertise needed to work on the most capable AI systems, particularly in areas like multimodal AI, which Deitke specializes in. And AI hype is currently off the charts as “the next big thing” in technology.

The economics also differ fundamentally from past projects. The Manhattan Project cost $1.9 billion total (about $34.4 billion adjusted for inflation), while Meta alone plans to spend tens of billions annually on AI infrastructure. For a company approaching a $2 trillion market cap, the potential payoff from achieving AGI first dwarfs Deitke’s compensation package.

One executive put it bluntly to The New York Times: “If I’m Zuck and I’m spending $80 billion in one year on capital expenditures alone, is it worth kicking in another $5 billion or more to acquire a truly world-class team to bring the company to the next level? The answer is obviously yes.”

Young researchers maintain private chat groups on Slack and Discord to share offer details and negotiation strategies. Some hire unofficial agents. Companies not only offer massive cash and stock packages but also computing resources—the NYT reported that some potential hires were told they would be allotted 30,000 GPUs, the specialized chips that power AI development.

Also, tech companies believe they’re engaged in an arms race where the winner could reshape civilization. Unlike the Manhattan Project or Apollo program, which had specific, limited goals, the race for artificial general intelligence ostensibly has no ceiling. A machine that can match human intelligence could theoretically improve itself, creating what researchers call an “intelligence explosion” that could potentially offer cascading discoveries—if it actually comes to pass.

Whether these companies are building humanity’s ultimate labor replacement technology or merely chasing hype remains an open question, but we’ve certainly traveled a long way from the $8 per diem that Neil Armstrong received for his moon mission—about $70.51 in today’s dollars—before deductions for the “accommodations” NASA provided on the spacecraft. After Deitke accepted Meta’s offer, Vercept co-founder Kiana Ehsani joked on social media, “We look forward to joining Matt on his private island next year.”

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

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Delta denies using AI to come up with inflated, personalized prices

AI, AI pricing, Artificial Intelligence, Delta, Policy, predatory pricing, surveillance pricing / Kelly Newman / August 2, 2025

Delta scandal highlights value of transparency

According to Delta, the company has “zero tolerance for discriminatory or predatory pricing” and only feeds its AI system aggregated data “to enhance our existing fare pricing processes.”

Rather than basing fare prices on customers’ personal information, Carter clarified that “all customers have access to the same fares and offers based on objective criteria provided by the customer such as origin and destination, advance purchase, length of stay, refundability, and travel experience selected.”

The AI use can result in higher or lower prices, but not personalized fares for different customers, Carter said. Instead, Delta plans to use AI pricing to “enhance market competitiveness and drive sales, benefiting both our customers and our business.”

Factors weighed by the AI system, Carter explained, include “customer demand for seats and purchasing data at an aggregated level, competitive offers and schedules, route performance, and cost of providing the service inclusive of jet fuel.” That could potentially mean a rival’s promotion or schedule change could trigger the AI system to lower prices to stay competitive, or it might increase prices based on rising fuel costs to help increase revenue or meet business goals.

“Given the tens of millions of fares and hundreds of thousands of routes for sale at any given time, the use of new technology like AI promises to streamline the process by which we analyze existing data and the speed and scale at which we can respond to changing market dynamics,” Carter wrote.

He explained the AI system helps Delta aggregate purchasing data for specific routes and flights, adapt to new market conditions, and factor in “thousands of variables simultaneously.” AI could also eventually be used to assist with crew scheduling, improve flight availability, or help reservation specialists answer complex questions or resolve disputes.

But “to reiterate, prices are not targeted to individual consumers,” Carter emphasized.

Delta further pointed out that the company does not require customers to log in to search for tickets, which means customers can search for flights without sharing any personal information.

For AI companies paying attention to the Delta backlash, there may be a lesson about the value of transparency in Delta’s scandal. Critics noted Delta was among the first to admit it was using AI to influence pricing, but the vague explanation on the earnings call stoked confusion over how, as Delta seemed to drag its feet amid calls by groups like Consumer Watchdog for more transparency.

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ChatGPT users shocked to learn their chats were in Google search results

AI, chatbot, chatgpt, Google, google search, Online Privacy, openai, Policy, search engines, search results / 9u50fv / August 2, 2025

Faced with mounting backlash, OpenAI removed a controversial ChatGPT feature that caused some users to unintentionally allow their private—and highly personal—chats to appear in search results.

Fast Company exposed the privacy issue on Wednesday, reporting that thousands of ChatGPT conversations were found in Google search results and likely only represented a sample of chats “visible to millions.” While the indexing did not include identifying information about the ChatGPT users, some of their chats did share personal details—like highly specific descriptions of interpersonal relationships with friends and family members—perhaps making it possible to identify them, Fast Company found.

OpenAI’s chief information security officer, Dane Stuckey, explained on X that all users whose chats were exposed opted in to indexing their chats by clicking a box after choosing to share a chat.

Fast Company noted that users often share chats on WhatsApp or select the option to save a link to visit the chat later. But as Fast Company explained, users may have been misled into sharing chats due to how the text was formatted:

“When users clicked ‘Share,’ they were presented with an option to tick a box labeled ‘Make this chat discoverable.’ Beneath that, in smaller, lighter text, was a caveat explaining that the chat could then appear in search engine results.”

At first, OpenAI defended the labeling as “sufficiently clear,” Fast Company reported Thursday. But Stuckey confirmed that “ultimately,” the AI company decided that the feature “introduced too many opportunities for folks to accidentally share things they didn’t intend to.” According to Fast Company, that included chats about their drug use, sex lives, mental health, and traumatic experiences.

Carissa Veliz, an AI ethicist at the University of Oxford, told Fast Company she was “shocked” that Google was logging “these extremely sensitive conversations.”

OpenAI promises to remove Google search results

Stuckey called the feature a “short-lived experiment” that OpenAI launched “to help people discover useful conversations.” He confirmed that the decision to remove the feature also included an effort to “remove indexed content from the relevant search engine” through Friday morning.

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