generative ai

google’s-new-command-line-tool-can-plug-openclaw-into-your-workspace-data

Google’s new command-line tool can plug OpenClaw into your Workspace data

The command line is hot again. For some people, command lines were never not hot, of course, but it’s becoming more common now in the age of AI. Google launched a Gemini command-line tool last year, and now it has a new AI-centric command-line option for cloud products. The new Google Workspace CLI bundles the company’s existing cloud APIs into a package that makes it easy to integrate with a variety of AI tools, including OpenClaw. How do you know this setup won’t blow up and delete all your data? That’s the fun part—you don’t.

There are some important caveats with the Workspace tool. While this new GitHub project is from Google, it’s “not an officially supported Google product.” So you’re on your own if you choose to use it. The company notes that functionality may change dramatically as Google Workspace CLI continues to evolve, and that could break workflows you’ve created in the meantime.

For people interested in tinkering with AI automations and don’t mind the inherent risks, Google Workspace CLI has a lot to offer, even at this early stage. It includes the APIs for every Workspace product, including Gmail, Drive, and Calendar. It’s designed for use by humans and AI agents, but like everything else Google does now, there’s a clear emphasis on AI.

The tool supports structured JSON outputs, and there are more than 40 agent skills included, says Google Cloud director Addy Osmani. The focus of Workspace CLI seems to be on agentic systems that can create command-line inputs and directly parse JSON outputs. The integrated tools can load and create Drive files, send emails, create and edit Calendar appointments, send chat messages, and much more.

Google’s new command-line tool can plug OpenClaw into your Workspace data Read More »

google-reveals-nano-banana-2-ai-image-model,-coming-to-gemini-today

Google reveals Nano Banana 2 AI image model, coming to Gemini today

With Nano Banana 2, Google promises consistency for up to five characters at a time, along with accurate rendering of as many as 14 different objects per workflow. This, along with richer textures and “vibrant” lighting will aid in visual storytelling with Nano Banana 2. Google is also expanding the range of available aspect ratios and resolutions, from 512px square up to 4K widescreen.

So what can you do with Nano Banana 2? Google has provided some example images with associated prompts. These are, of course, handpicked images, but Nano Banana has been a popular image model for good reason. This degree of improvement seems believable based on past iterations of Nano Banana.

Google AI infographic

Prompt: High-quality flat lay photography creating a DIY infographic that simply explains how the water cycle works, arranged on a clean, light gray textured background. The visual story flows from left to right in clear steps. Simple, clean black arrows are hand-drawn onto the background to guide the viewer’s eye. The overall mood is educational, modern, and easy to understand. The image is shot from a top-down, bird’s-eye view with soft, even lighting that minimizes shadows and keeps the focus on the process.

Credit: Google

Prompt: High-quality flat lay photography creating a DIY infographic that simply explains how the water cycle works, arranged on a clean, light gray textured background. The visual story flows from left to right in clear steps. Simple, clean black arrows are hand-drawn onto the background to guide the viewer’s eye. The overall mood is educational, modern, and easy to understand. The image is shot from a top-down, bird’s-eye view with soft, even lighting that minimizes shadows and keeps the focus on the process. Credit: Google

AI museum comparison

Prompt: Create an image of Museum Clos Lucé. In the style of bright colored Synthetic Cubism. No text. Your plan is to first search for visual references, and generate after. Aspect ratio 16:9.

Credit: Google

Prompt: Create an image of Museum Clos Lucé. In the style of bright colored Synthetic Cubism. No text. Your plan is to first search for visual references, and generate after. Aspect ratio 16:9. Credit: Google

AI farm image

Create an image of these 14 characters and items having fun at the farm. The overall atmosphere is fun, silly and joyful. It is strictly important to keep identity consistent of all the 14 characters and items.

Credit: Google

Create an image of these 14 characters and items having fun at the farm. The overall atmosphere is fun, silly and joyful. It is strictly important to keep identity consistent of all the 14 characters and items. Credit: Google

Google must be pretty confident in this model’s capabilities because it will be the only one available going forward. Starting now, Nano Banana 2 will replace both the standard and Pro variants of Nano Banana across the Gemini app, search, AI Studio, Vertex AI, and Flow.

In the Gemini app and on the website, Nano Banana 2 will be the image generator for the Fast, Thinking, and Pro settings. It’s possible there will eventually be a Nano Banana 2 Pro—Google tends to release elements of new model families one at a time. For now, it’s all “Flash” Image.

Google reveals Nano Banana 2 AI image model, coming to Gemini today Read More »

microsoft-deletes-blog-telling-users-to-train-ai-on-pirated-harry-potter-books

Microsoft deletes blog telling users to train AI on pirated Harry Potter books


Wizarding world of AI slop

The now-deleted Harry Potter dataset was “mistakenly” marked public domain.

Following backlash in a Hacker News thread, Microsoft deleted a blog post that critics said encouraged developers to pirate Harry Potter books to train AI models that could then be used to create AI slop.

The blog, which is archived here, was written in November 2024 by a senior product manager, Pooja Kamath. According to her LinkedIn, Kamath has been at Microsoft for more than a decade and remains with the company. In 2024, Microsoft tapped her to promote a new feature that the blog said made it easier to “add generative AI features to your own applications with just a few lines of code using Azure SQL DB, LangChain, and LLMs.”

What better way to show “engaging and relatable examples” of Microsoft’s new feature that would “resonate with a wide audience” than to “use a well-known dataset” like Harry Potter books, the blog said.

The books are “one of the most famous and cherished series in literary history,” the blog noted, and fans could use the LLMs they trained in two fun ways: building Q&A systems providing “context-rich answers” and generating “new AI-driven Harry Potter fan fiction” that’s “sure to delight Potterheads.”

To help Microsoft customers achieve this vision, the blog linked to a Kaggle dataset that included all seven Harry Potter books, which, Ars verified, has been available online for years and incorrectly marked as “public domain.” Kaggle’s terms say that rights holders can send notices of infringing content, and repeat offenders risk suspensions, but Hacker News commenters speculated that the Harry Potter dataset flew under the radar, with only 10,000 downloads over time, not catching the attention of J.K. Rowling, who famously keeps a strong grip on the Harry Potter copyrights. The dataset was promptly deleted on Thursday after Ars reached out to the uploader, Shubham Maindola, a data scientist in India with no apparent links to Microsoft.

Maindola told Ars that “the dataset was marked as Public Domain by mistake. There was no intention to misrepresent the licensing status of the works.”

It’s unclear whether Kamath was directed to link to the Harry Potter books dataset in the blog or if it was an individual choice. Cathay Y. N. Smith, a law professor and co-director of Chicago-Kent College of Law’s Program in Intellectual Property Law, told Ars that Kamath may not have realized the books were too recent to be in the public domain.

“Someone might be really knowledgeable about books and technology, but not necessarily about copyright terms and how long they last,” Smith said. “Especially if she saw that something was marked by another reputable company as being public domain.”

Microsoft declined Ars’ request to comment. Kaggle did not respond to Ars’ request to comment.

Microsoft was “probably smart” to pull the blog

On Hacker News, commenters suggested that it’s unlikely anyone familiar with the popular franchise would believe the Harry Potter books were in the public domain. They debated whether Microsoft’s blog was “problematic copyright-wise,” since Microsoft not only encouraged customers to download the infringing materials but also used the books themselves to create Harry Potter AI models that relied on beloved characters to hype Microsoft products.

Microsoft’s blog was posted more than a year ago, at a time when AI firms began facing lawsuits over AI models, which had allegedly infringed copyrights by training on pirated materials and regurgitating works verbatim.

The blog recommended that users learn to train their own AI models by downloading the Harry Potter dataset and then uploading text files to Azure Blob Storage. It included example models based on a dataset that Microsoft seemingly uploaded to Azure Blob Storage, which only included the first book, Harry Potter and the Sorcerer’s Stone.

Training large language models (LLMs) on text files, Harry Potter fans could create Q&A systems capable of pulling up relevant excerpts of books. An example query offered was “Wizarding World snacks,” which retrieved an excerpt from The Sorcerer’s Stone where Harry marvels at strange treats like Bertie Bott’s Every Flavor Beans and chocolate frogs. Another prompt asking “How did Harry feel when he first learnt that he was a Wizard?” generated an output pointing to various early excerpts in the book.

But perhaps an even more exciting use case, Kamath suggested, was generating fan fiction to “explore new adventures” and “even create alternate endings.” That model could quickly comb the dataset for “contextually similar” excerpts that could be used to output fresh stories that fit with existing narratives and incorporate “elements from the retrieved passages,” the blog said.

As an example, Kamath trained a model to write a Harry Potter story she could use to market the feature she was blogging about. She asked the model to write a story in which Harry meets a new friend on the Hogwarts Express train who tells him all about Microsoft’s Native Vector Support in SQL “in the Muggle world.”

Drawing on parts of The Sorcerer’s Stone where Harry learns about Quidditch and gets to know Hermione Granger, the fan fiction showed a boy selling Harry on Microsoft’s “amazing” new feature. To do this, he likened it to having a spell that helps you find exactly what you need among thousands of options, instantly, while declaring it was perfect for machine learning, AI, and recommendation systems.

Further blurring the lines between Microsoft and Harry Potter brands, Kamath also generated an image showing Harry with his new friend, stamped with a Microsoft logo.

Smith told Ars that both use cases could frustrate rights holders, depending on the content in the model outputs.

“I think that the regurgitation and the creation of fan fiction, they both could flag copyright issues, in that fan fiction often has to take from the expressive elements, a copyrighted character, a character that’s famous enough to be protected by a copyright law or plot stories or sequences,” Smith said. “If these things are copied and reproduced, then that output could be potentially infringing.”

But it’s also still a gray area. Looking at the blog, Smith said, “I would be concerned,” but “I wouldn’t say it’s automatically infringement.”

Smith told Ars that, in pulling the blog, Microsoft “was probably smart,” since courts have only generally said that training AI on copyrighted books is fair use. But courts continue to probe questions about pirated AI training materials.

On the deleted Kaggle dataset page, Maindola previously explained that to source the data, he “downloaded the ebooks and then converted them to txt files.”

Microsoft may have infringed copyrights

If Microsoft ever faced questions as to whether the company knowingly used pirated books to train the example models, fair use “could be a difficult argument,” Smith said.

Hacker News commenters suggested the blog could be considered fair use, since the training guide was for “educational purposes,” and Smith said that Microsoft could raise some “good arguments” in its defense.

However, she also suggested that Microsoft could be deemed liable for contributing to infringement on some level after leaving the blog up for a year. Before it was removed, the Kaggle dataset was downloaded more than 10,000 times.

“The ultimate result is to create something infringing by saying, ‘Hey, here you go, go grab that infringing stuff and use that in our system,’” Smith said. “They could potentially have some sort of secondary contributory liability for copyright infringement, downloading it, as well as then using it to encourage others to use it for training purposes.”

On Hacker News, commenters slammed the blog, including a self-described former Microsoft employee who claimed that Microsoft lets employees “blog without having to go through some approval or editing process.”

“It looks like somebody made a bad judgment call on what to put in a company blog post (and maybe what constitutes ethical activity) and that it was taken down as soon as someone noticed,” the former employee said.

Others suggested the blame was solely with the Kaggle uploader, Maindola, who told Ars that the dataset should never have been marked “public domain.” But Microsoft critics pushed back, noting that the Kaggle page made it clear that no special permission was granted and that Microsoft’s employee should have known better. “They don’t need to know any details to know that these properties belong to massive companies and aren’t free for the taking,” one commenter said.

The Harry Potter books weren’t the only books targeted, the thread noted, linking to a separate Azure sample containing Isaac Asimov’s Foundation series, which is also not in the public domain.

“Microsoft could have used any dataset for their blog, they could have even chosen to use actual public domain novels,” another Hacker News commenter wrote. “Instead, they opted to use copywritten works that J.K. hasn’t released into the public domain (unless user ‘Shubham Maindola’ is J.K.’s alter ego).”

Smith suggested Microsoft could have avoided this week’s backlash by more carefully reviewing blogs, noting that “if a company is risk averse, this would probably be flagged.” But she also understood Kamath’s preference for Harry Potter over the many long-forgotten characters that exist in the public domain. On Hacker News, some commenters defended Kamath’s blog, urging that it should be considered fair use since nonprofits and educational institutions could do the same thing in a teaching context without issue.

“I would have been concerned if I were the one clearing this for Microsoft, but at the same time, I completely understand what this employee was doing,” Smith said. “No one wants to write fan fiction about books that are in the public domain.”

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.

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Google announces Gemini 3.1 Pro, says it’s better at complex problem-solving

Another day, another Google AI model. Google has really been pumping out new AI tools lately, having just released Gemini 3 in November. Today, it’s bumping the flagship model to version 3.1. The new Gemini 3.1 Pro is rolling out (in preview) for developers and consumers today with the promise of better problem-solving and reasoning capabilities.

Google announced improvements to its Deep Think tool last week, and apparently, the “core intelligence” behind that update was Gemini 3.1 Pro. As usual, Google’s latest model announcement comes with a plethora of benchmarks that show mostly modest improvements. In the popular Humanity’s Last Exam, which tests advanced domain-specific knowledge, Gemini 3.1 Pro scored a record 44.4 percent. Gemini 3 Pro managed 37.5 percent, while OpenAI’s GPT 5.2 got 34.5 percent.

Gemini 3.1 Pro benchmarks

Credit: Google

Credit: Google

Google also calls out the model’s improvement in ARC-AGI-2, which features novel logic problems that can’t be directly trained into an AI. Gemini 3 was a bit behind on this evaluation, reaching a mere 31.1 percent versus scores in the 50s and 60s for competing models. Gemini 3.1 Pro more than doubles Google’s score, reaching a lofty 77.1 percent.

Google has often gloated when it releases new models that they’ve already hit the top of the Arena leaderboard (formerly LM Arena), but that’s not the case this time. For text, Claude Opus 4.6 edges out the new Gemini by four points at 1504. For code, Opus 4.6, Opus 4.5, and GPT 5.2 High all run ahead of Gemini 3.1 Pro by a bit more. It’s worth noting, however, that the Arena leaderboard is run on vibes. Users vote on the outputs they like best, which can reward outputs that look correct regardless of whether they are.

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record-scratch—google’s-lyria-3-ai-music-model-is-coming-to-gemini-today

Record scratch—Google’s Lyria 3 AI music model is coming to Gemini today

Sour notes

AI-generated music is not a new phenomenon. Several companies offer models that ingest and homogenize human-created music, and the resulting tracks can sound remarkably “real,” if a bit overproduced. Streaming services have already been inundated with phony AI artists, some of which have gathered thousands of listeners who may not even realize they’re grooving to the musical equivalent of a blender set to purée.

Still, you have to seek out tools like that, and Google is bringing similar capabilities to the Gemini app. As one of the most popular AI platforms, we’re probably about to see a lot more AI music on the Internet. Google says tracks generated with Lyria 3 will have an audio version of Google’s SynthID embedded within. That means you’ll always be able to check if a piece of audio was created with Google’s AI by uploading it to Gemini, similar to the way you can check images and videos for SynthID tags.

Google also says it has sought to create a music AI that respects copyright and partner agreements. If you name a specific artist in your prompt, Gemini won’t attempt to copy that artist’s sound. Instead, it’s trained to take that as “broad creative inspiration.” Although it also notes this process is not foolproof, and some of that original expression might imitate an artist too much. In those cases, Google invites users to report such shared content.

Lyria 3 is going live in the Gemini web interface today and should be available in the mobile app within a few days. It works in English, German, Spanish, French, Hindi, Japanese, Korean, and Portuguese, but Google plans to add more languages soon. While all users will have some access to music generation, those with AI Pro and AI Ultra subscriptions will have higher usage limits, but the specifics are unclear.

Record scratch—Google’s Lyria 3 AI music model is coming to Gemini today Read More »

openai-researcher-quits-over-chatgpt-ads,-warns-of-“facebook”-path

OpenAI researcher quits over ChatGPT ads, warns of “Facebook” path

On Wednesday, former OpenAI researcher Zoë Hitzig published a guest essay in The New York Times announcing that she resigned from the company on Monday, the same day OpenAI began testing advertisements inside ChatGPT. Hitzig, an economist and published poet who holds a junior fellowship at the Harvard Society of Fellows, spent two years at OpenAI helping shape how its AI models were built and priced. She wrote that OpenAI’s advertising strategy risks repeating the same mistakes that Facebook made a decade ago.

“I once believed I could help the people building A.I. get ahead of the problems it would create,” Hitzig wrote. “This week confirmed my slow realization that OpenAI seems to have stopped asking the questions I’d joined to help answer.”

Hitzig did not call advertising itself immoral. Instead, she argued that the nature of the data at stake makes ChatGPT ads especially risky. Users have shared medical fears, relationship problems, and religious beliefs with the chatbot, she wrote, often “because people believed they were talking to something that had no ulterior agenda.” She called this accumulated record of personal disclosures “an archive of human candor that has no precedent.”

She also drew a direct parallel to Facebook’s early history, noting that the social media company once promised users control over their data and the ability to vote on policy changes. Those pledges eroded over time, Hitzig wrote, and the Federal Trade Commission found that privacy changes Facebook marketed as giving users more control actually did the opposite.

She warned that a similar trajectory could play out with ChatGPT: “I believe the first iteration of ads will probably follow those principles. But I’m worried subsequent iterations won’t, because the company is building an economic engine that creates strong incentives to override its own rules.”

Ads arrive after a week of AI industry sparring

Hitzig’s resignation adds another voice to a growing debate over advertising in AI chatbots. OpenAI announced in January that it would begin testing ads in the US for users on its free and $8-per-month “Go” subscription tiers, while paid Plus, Pro, Business, Enterprise, and Education subscribers would not see ads. The company said ads would appear at the bottom of ChatGPT responses, be clearly labeled, and would not influence the chatbot’s answers.

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ai-companies-want-you-to-stop-chatting-with-bots-and-start-managing-them

AI companies want you to stop chatting with bots and start managing them


Claude Opus 4.6 and OpenAI Frontier pitch a future of supervising AI agents.

On Thursday, Anthropic and OpenAI shipped products built around the same idea: instead of chatting with a single AI assistant, users should be managing teams of AI agents that divide up work and run in parallel. The simultaneous releases are part of a gradual shift across the industry, from AI as a conversation partner to AI as a delegated workforce, and they arrive during a week when that very concept reportedly helped wipe $285 billion off software stocks.

Whether that supervisory model works in practice remains an open question. Current AI agents still require heavy human intervention to catch errors, and no independent evaluation has confirmed that these multi-agent tools reliably outperform a single developer working alone.

Even so, the companies are going all-in on agents. Anthropic’s contribution is Claude Opus 4.6, a new version of its most capable AI model, paired with a feature called “agent teams” in Claude Code. Agent teams let developers spin up multiple AI agents that split a task into independent pieces, coordinate autonomously, and run concurrently.

In practice, agent teams look like a split-screen terminal environment: A developer can jump between subagents using Shift+Up/Down, take over any one directly, and watch the others keep working. Anthropic describes the feature as best suited for “tasks that split into independent, read-heavy work like codebase reviews.” It is available as a research preview.

OpenAI, meanwhile, released Frontier, an enterprise platform it describes as a way to “hire AI co-workers who take on many of the tasks people already do on a computer.” Frontier assigns each AI agent its own identity, permissions, and memory, and it connects to existing business systems such as CRMs, ticketing tools, and data warehouses. “What we’re fundamentally doing is basically transitioning agents into true AI co-workers,” Barret Zoph, OpenAI’s general manager of business-to-business, told CNBC.

Despite the hype about these agents being co-workers, from our experience, these agents tend to work best if you think of them as tools that amplify existing skills, not as the autonomous co-workers the marketing language implies. They can produce impressive drafts fast but still require constant human course-correction.

The Frontier launch came just three days after OpenAI released a new macOS desktop app for Codex, its AI coding tool, which OpenAI executives described as a “command center for agents.” The Codex app lets developers run multiple agent threads in parallel, each working on an isolated copy of a codebase via Git worktrees.

OpenAI also released GPT-5.3-Codex on Thursday, a new AI model that powers the Codex app. OpenAI claims that the Codex team used early versions of GPT-5.3-Codex to debug the model’s own training run, manage its deployment, and diagnose test results, similar to what OpenAI told Ars Technica in a December interview.

“Our team was blown away by how much Codex was able to accelerate its own development,” the company wrote. On Terminal-Bench 2.0, the agentic coding benchmark, GPT-5.3-Codex scored 77.3%, which exceeds Anthropic’s just-released Opus 4.6 by about 12 percentage points.

The common thread across all of these products is a shift in the user’s role. Rather than merely typing a prompt and waiting for a single response, the developer or knowledge worker becomes more like a supervisor, dispatching tasks, monitoring progress, and stepping in when an agent needs direction.

In this vision, developers and knowledge workers effectively become middle managers of AI. That is, not writing the code or doing the analysis themselves, but delegating tasks, reviewing output, and hoping the agents underneath them don’t quietly break things. Whether that will come to pass (or if it’s actually a good idea) is still widely debated.

A new model under the Claude hood

Opus 4.6 is a substantial update to Anthropic’s flagship model. It succeeds Claude Opus 4.5, which Anthropic released in November. In a first for the Opus model family, it supports a context window of up to 1 million tokens (in beta), which means it can process much larger bodies of text or code in a single session.

On benchmarks, Anthropic says Opus 4.6 tops OpenAI’s GPT-5.2 (an earlier model than the one released today) and Google’s Gemini 3 Pro across several evaluations, including Terminal-Bench 2.0 (an agentic coding test), Humanity’s Last Exam (a multidisciplinary reasoning test), and BrowseComp (a test of finding hard-to-locate information online)

Although it should be noted that OpenAI’s GPT-5.3-Codex, released the same day, seemingly reclaimed the lead on Terminal-Bench. On ARC AGI 2, which attempts to test the ability to solve problems that are easy for humans but hard for AI models, Opus 4.6 scored 68.8 percent, compared to 37.6 percent for Opus 4.5, 54.2 percent for GPT-5.2, and 45.1 percent for Gemini 3 Pro.

As always, take AI benchmarks with a grain of salt, since objectively measuring AI model capabilities is a relatively new and unsettled science.

Anthropic also said that on a long-context retrieval benchmark called MRCR v2, Opus 4.6 scored 76 percent on the 1 million-token variant, compared to 18.5 percent for its Sonnet 4.5 model. That gap matters for the agent teams use case, since agents working across large codebases need to track information across hundreds of thousands of tokens without losing the thread.

Pricing for the API stays the same as Opus 4.5 at $5 per million input tokens and $25 per million output tokens, with a premium rate of $10/$37.50 for prompts that exceed 200,000 tokens. Opus 4.6 is available on claude.ai, the Claude API, and all major cloud platforms.

The market fallout outside

These releases occurred during a week of exceptional volatility for software stocks. On January 30, Anthropic released 11 open source plugins for Cowork, its agentic productivity tool that launched on January 12. Cowork itself is a general-purpose tool that gives Claude access to local folders for work tasks, but the plugins extended it into specific professional domains: legal contract review, non-disclosure agreement triage, compliance workflows, financial analysis, sales, and marketing.

By Tuesday, investors reportedly reacted to the release by erasing roughly $285 billion in market value across software, financial services, and asset management stocks. A Goldman Sachs basket of US software stocks fell 6 percent that day, its steepest single-session decline since April’s tariff-driven sell-off. Thomson Reuters led the rout with an 18 percent drop, and the pain spread to European and Asian markets.

The purported fear among investors centers on AI model companies packaging complete workflows that compete with established software-as-a-service (SaaS) vendors, even if the verdict is still out on whether these tools can achieve those tasks.

OpenAI’s Frontier might deepen that concern: its stated design lets AI agents log in to applications, execute tasks, and manage work with minimal human involvement, which Fortune described as a bid to become “the operating system of the enterprise.” OpenAI CEO of Applications Fidji Simo pushed back on the idea that Frontier replaces existing software, telling reporters, “Frontier is really a recognition that we’re not going to build everything ourselves.”

Whether these co-working apps actually live up to their billing or not, the convergence is hard to miss. Anthropic’s Scott White, the company’s head of product for enterprise, gave the practice a name that is likely to roll a few eyes. “Everybody has seen this transformation happen with software engineering in the last year and a half, where vibe coding started to exist as a concept, and people could now do things with their ideas,” White told CNBC. “I think that we are now transitioning almost into vibe working.”

Photo of Benj Edwards

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

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AI Overviews gets upgraded to Gemini 3 with a dash of AI Mode

It can be hard sometimes to keep up with the deluge of generative AI in Google products. Even if you try to avoid it all, there are some features that still manage to get in your face. Case in point: AI Overviews. This AI-powered search experience has a reputation for getting things wrong, but you may notice some improvements soon. Google says AI Overviews is being upgraded to the latest Gemini 3 models with a more conversational bent.

In just the last year, Google has radically expanded the number of searches on which you get an AI Overview at the top. Today, the chatbot will almost always have an answer for your query, which has relied mostly on models in Google’s Gemini 2.5 family. There was nothing wrong with Gemini 2.5 as generative AI models go, but Gemini 3 is a little better by every metric.

There are, of course, multiple versions of Gemini 3, and Google doesn’t like to be specific about which ones appear in your searches. What Google does say is that AI Overviews chooses the right model for the job. So if you’re searching for something simple for which there are a lot of valid sources, AI Overviews may manifest something like Gemini 3 Flash without running through a ton of reasoning tokens. For a complex “long tail” query, it could step up the thinking or move to Gemini 3 Pro (for paying subscribers).

AI Overviews gets upgraded to Gemini 3 with a dash of AI Mode Read More »

google-adds-your-gmail-and-photos-to-ai-mode-to-enable-“personal-intelligence”

Google adds your Gmail and Photos to AI Mode to enable “Personal Intelligence”

Google believes AI is the future of search, and it’s not shy about saying it. After adding account-level personalization to Gemini earlier this month, it’s now updating AI Mode with so-called “Personal Intelligence.” According to Google, this makes the bot’s answers more useful because they are tailored to your personal context.

Starting today, the feature is rolling out to all users who subscribe to Google AI Pro or AI Ultra. However, it will be a Labs feature that needs to be explicitly enabled (subscribers will be prompted to do this). Google tends to expand access to new AI features to free accounts later on, so free users will most likely get access to Personal Intelligence in the future. Whenever this option does land on your account, it’s entirely optional and can be disabled at any time.

If you decide to integrate your data with AI Mode, the search bot will be able to scan your Gmail and Google Photos. That’s less extensive than the Gemini app version, which supports Gmail, Photos, Search, and YouTube history. Gmail will probably be the biggest contributor to AI Mode—a great many life events involve confirmation emails. Traditional search results when you are logged in are adjusted based on your usage history, but this goes a step further.

If you’re going to use AI Mode to find information, Personal Intelligence could actually be quite helpful. When you connect data from other Google apps, Google’s custom Gemini search model will instantly know about your preferences and background—that’s the kind of information you’d otherwise have to include in your search query to get the best output. With Personal Intelligence, AI Mode can just pull those details from your email or photos.

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openai-to-test-ads-in-chatgpt-as-it-burns-through-billions

OpenAI to test ads in ChatGPT as it burns through billions

Financial pressures and a changing tune

OpenAI’s advertising experiment reflects the enormous financial pressures facing the company. OpenAI does not expect to be profitable until 2030 and has committed to spend about $1.4 trillion on massive data centers and chips for AI.

According to financial documents obtained by The Wall Street Journal in November, OpenAI expects to burn through roughly $9 billion this year while generating $13 billion in revenue. Only about 5 percent of ChatGPT’s 800 million weekly users pay for subscriptions, so it’s not enough to cover all of OpenAI’s operating costs.

Not everyone is convinced ads will solve OpenAI’s financial problems. “I am extremely bearish on this ads product,” tech critic Ed Zitron wrote on Bluesky. “Even if this becomes a good business line, OpenAI’s services cost too much for it to matter!”

OpenAI’s embrace of ads appears to come reluctantly, since it runs counter to a “personal bias” against advertising that Altman has shared in earlier public statements. For example, during a fireside chat at Harvard University in 2024, Altman said he found the combination of ads and AI “uniquely unsettling,” implying that he would not like it if the chatbot itself changed its responses due to advertising pressure. He added: “When I think of like GPT writing me a response, if I had to go figure out exactly how much was who paying here to influence what I’m being shown, I don’t think I would like that.”

An example mock-up of an advertisement in ChatGPT provided by OpenAI.

An example mock-up of an advertisement in ChatGPT provided by OpenAI.

An example mock-up of an advertisement in ChatGPT provided by OpenAI. Credit: OpenAI

Along those lines, OpenAI’s approach appears to be a compromise between needing ad revenue and not wanting sponsored content to appear directly within ChatGPT’s written responses. By placing banner ads at the bottom of answers separated from the conversation history, OpenAI appears to be addressing Altman’s concern: The AI assistant’s actual output, the company says, will remain uninfluenced by advertisers.

Indeed, Simo wrote in a blog post that OpenAI’s ads will not influence ChatGPT’s conversational responses and that the company will not share conversations with advertisers and will not show ads on sensitive topics such as mental health and politics to users it determines to be under 18.

“As we introduce ads, it’s crucial we preserve what makes ChatGPT valuable in the first place,” Simo wrote. “That means you need to trust that ChatGPT’s responses are driven by what’s objectively useful, never by advertising.”

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The RAM shortage’s silver lining: Less talk about “AI PCs”

RAM prices have soared, which is bad news for people interested in buying, building, or upgrading a computer this year, but it’s likely good news for people exasperated by talk of so-called AI PCs.

As Ars Technica has reported, the growing demands of data centers, fueled by the AI boom, have led to a shortage of RAM and flash memory chips, driving prices to skyrocket.

In an announcement today, Ben Yeh, principal analyst at technology research firm Omdia, said that in 2025, “mainstream PC memory and storage costs rose by 40 percent to 70 percent, resulting in cost increases being passed through to customers.”

Overall, global PC shipments increased in 2025, according to Omdia, (which pegged growth at 9.2 percent compared to 2024), and IDC, (which today reported 9.6 percent growth), but analysts expect PC sales to be more tumultuous in 2026.

“The year ahead is shaping up to be extremely volatile,” Jean Philippe Bouchard, research VP with IDC’s worldwide mobile device trackers, said in a statement.

Both analyst firms expect PC makers to manage the RAM shortage by raising prices and by releasing computers with lower memory specs. IDC expects price hikes of 15 to 20 percent and for PC RAM specs to “be lowered on average to preserve memory inventory on hand,” Bouchard said. Omdia’s Yeh expects “leaner mid to low-tier configurations to protect margins.”

“These RAM shortages will last beyond just 2026, and the cost-conscious part of the market is the one that will be most impacted,” Jitesh Ubrani, research manager for worldwide mobile device trackers at IDC, told Ars via email.

IDC expects vendors to “prioritize midrange and premium systems to offset higher component costs, especially memory.”

The RAM shortage’s silver lining: Less talk about “AI PCs” Read More »

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Google’s updated Veo model can make vertical videos from reference images with 4K upscaling

Enhanced support for Ingredients to Video and the associated vertical outputs are live in the Gemini app today, as well as in YouTube Shorts and the YouTube Create app, fulfilling a promise initially made last summer. Veo videos are short—just eight seconds long for each prompt. It would be tedious to assemble those into a longer video, but Veo is perfect for the Shorts format.

Veo 3.1 Updates – Seamlessly blend textures, characters, and objects.

The new Veo 3.1 update also adds an option for higher-resolution video. The model now supports 1080p and 4K outputs. Google debuted 1080p support last year, but it’s mentioning that option again today, suggesting there may be some quality difference. 4K support is new, but neither 1080p nor 4K outputs are native. Veo creates everything in 720p resolution, but it can be upscaled “for high-fidelity production workflows,” according to Google. However, a Google rep tells Ars that upscaling is only available in Flow, the Gemini API, and Vertex AI. Video in the Gemini app is always 720p.

We are rushing into a world where AI video is essentially indistinguishable from real life. Google, which more or less controls online video via YouTube’s dominance, is at the forefront of that change. Today’s update is reasonably significant, and it didn’t even warrant a version number change. Perhaps we can expect more 2025-style leaps in video quality this year, for better or worse.

Google’s updated Veo model can make vertical videos from reference images with 4K upscaling Read More »