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mcp:-the-new-“usb-c-for-ai”-that’s-bringing-fierce-rivals-together

MCP: The new “USB-C for AI” that’s bringing fierce rivals together

AI, Anthropic, API, Biz & IT, chatgpt, chatgtp, large language models, machine learning, MCP, Model Context Protocol, openai / /

Model context protocol standardizes how AI uses data sources, supported by OpenAI and Anthropic.

What does it take to get OpenAI and Anthropic—two competitors in the AI assistant market—to get along? Despite a fundamental difference in direction that led Anthropic’s founders to quit OpenAI in 2020 and later create the Claude AI assistant, a shared technical hurdle has now brought them together: How to easily connect their AI models to external data sources.

The solution comes from Anthropic, which developed and released an open specification called Model Context Protocol (MCP) in November 2024. MCP establishes a royalty-free protocol that allows AI models to connect with outside data sources and services without requiring unique integrations for each service.

“Think of MCP as a USB-C port for AI applications,” wrote Anthropic in MCP’s documentation. The analogy is imperfect, but it represents the idea that, similar to how USB-C unified various cables and ports (with admittedly a debatable level of success), MCP aims to standardize how AI models connect to the infoscape around them.

So far, MCP has also garnered interest from multiple tech companies in a rare show of cross-platform collaboration. For example, Microsoft has integrated MCP into its Azure OpenAI service, and as we mentioned above, Anthropic competitor OpenAI is on board. Last week, OpenAI acknowledged MCP in its Agents API documentation, with vocal support from the boss upstairs.

“People love MCP and we are excited to add support across our products,” wrote OpenAI CEO Sam Altman on X last Wednesday.

MCP has also rapidly begun to gain community support in recent months. For example, just browsing this list of over 300 open source servers shared on GitHub reveals growing interest in standardizing AI-to-tool connections. The collection spans diverse domains, including database connectors like PostgreSQL, MySQL, and vector databases; development tools that integrate with Git repositories and code editors; file system access for various storage platforms; knowledge retrieval systems for documents and websites; and specialized tools for finance, health care, and creative applications.

Other notable examples include servers that connect AI models to home automation systems, real-time weather data, e-commerce platforms, and music streaming services. Some implementations allow AI assistants to interact with gaming engines, 3D modeling software, and IoT devices.

What is “context” anyway?

To fully appreciate why a universal AI standard for external data sources is useful, you’ll need to understand what “context” means in the AI field.

With current AI model architecture, what an AI model “knows” about the world is baked into its neural network in a largely unchangeable form, placed there by an initial procedure called “pre-training,” which calculates statistical relationships between vast quantities of input data (“training data”—like books, articles, and images) and feeds it into the network as numerical values called “weights.” Later, a process called “fine-tuning” might adjust those weights to alter behavior (such as through reinforcement learning like RLHF) or provide examples of new concepts.

Typically, the training phase is very expensive computationally and happens either only once in the case of a base model, or infrequently with periodic model updates and fine-tunings. That means AI models only have internal neural network representations of events prior to a “cutoff date” when the training dataset was finalized.

After that, the AI model is run in a kind of read-only mode called “inference,” where users feed inputs into the neural network to produce outputs, which are called “predictions.” They’re called predictions because the systems are tuned to predict the most likely next token (a chunk of data, such as portions of a word) in a user-provided sequence.

In the AI field, context is the user-provided sequence—all the data fed into an AI model that guides the model to produce a response output. This context includes the user’s input (the “prompt”), the running conversation history (in the case of chatbots), and any external information sources pulled into the conversation, including a “system prompt” that defines model behavior and “memory” systems that recall portions of past conversations. The limit on the amount of context a model can ingest at once is often called a “context window,” “context length, ” or “context limit,” depending on personal preference.

While the prompt provides important information for the model to operate upon, accessing external information sources has traditionally been cumbersome. Before MCP, AI assistants like ChatGPT and Claude could access external data (a process often called retrieval augmented generation, or RAG), but doing so required custom integrations for each service—plugins, APIs, and proprietary connectors that didn’t work across different AI models. Each new data source demanded unique code, creating maintenance challenges and compatibility issues.

MCP addresses these problems by providing a standardized method or set of rules (a “protocol”) that allows any supporting AI model framework to connect with external tools and information sources.

How does MCP work?

To make the connections behind the scenes between AI models and data sources, MCP uses a client-server model. An AI model (or its host application) acts as an MCP client that connects to one or more MCP servers. Each server provides access to a specific resource or capability, such as a database, search engine, or file system. When the AI needs information beyond its training data, it sends a request to the appropriate server, which performs the action and returns the result.

To illustrate how the client-server model works in practice, consider a customer support chatbot using MCP that could check shipping details in real time from a company database. “What’s the status of order #12345?” would trigger the AI to query an order database MCP server, which would look up the information and pass it back to the model. The model could then incorporate that data into its response: “Your order shipped on March 30 and should arrive April 2.”

Beyond specific use cases like customer support, the potential scope is very broad. Early developers have already built MCP servers for services like Google Drive, Slack, GitHub, and Postgres databases. This means AI assistants could potentially search documents in a company Drive, review recent Slack messages, examine code in a repository, or analyze data in a database—all through a standard interface.

From a technical implementation perspective, Anthropic designed the standard for flexibility by running in two main modes: Some MCP servers operate locally on the same machine as the client (communicating via standard input-output streams), while others run remotely and stream responses over HTTP. In both cases, the model works with a list of available tools and calls them as needed.

A work in progress

Despite the growing ecosystem around MCP, the protocol remains an early-stage project. The limited announcements of support from major companies are promising first steps, but MCP’s future as an industry standard may depend on broader acceptance, although the number of MCP servers seems to be growing at a rapid pace.

Regardless of its ultimate adoption rate, MCP may have some interesting second-order effects. For example, MCP also has the potential to reduce vendor lock-in. Because the protocol is model-agnostic, a company could switch from one AI provider to another while keeping the same tools and data connections intact.

MCP may also allow a shift toward smaller and more efficient AI systems that can interact more fluidly with external resources without the need for customized fine-tuning. Also, rather than building increasingly massive models with all knowledge baked in, companies may instead be able to use smaller models with large context windows.

For now, the future of MCP is wide open. Anthropic maintains MCP as an open source initiative on GitHub, where interested developers can either contribute to the code or find specifications about how it works. Anthropic has also provided extensive documentation about how to connect Claude to various services. OpenAI maintains its own API documentation for MCP on its website.

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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OpenAI’s new AI image generator is potent and bound to provoke

4o Image Generation, AI, AI image generator, autoregressive, autoregressive image generator, Biz & IT, chatgpt, chatgtp, dall-e, DALL-E 3, GPT-4o, image synthesis, machine learning, multimodal, multimodal AI, multimodal image generation, openai / /

The visual apocalypse is probably nigh, but perhaps seeing was never believing.

A trio of AI-generated images created using OpenAI’s 4o Image Generation model in ChatGPT. Credit: OpenAI

The arrival of OpenAI’s DALL-E 2 in the spring of 2022 marked a turning point in AI when text-to-image generation suddenly became accessible to a select group of users, creating a community of digital explorers who experienced wonder and controversy as the technology automated the act of visual creation.

But like many early AI systems, DALL-E 2 struggled with consistent text rendering, often producing garbled words and phrases within images. It also had limitations in following complex prompts with multiple elements, sometimes missing key details or misinterpreting instructions. These shortcomings left room for improvement that OpenAI would address in subsequent iterations, such as DALL-E 3 in 2023.

On Tuesday, OpenAI announced new multimodal image generation capabilities that are directly integrated into its GPT-4o AI language model, making it the default image generator within the ChatGPT interface. The integration, called “4o Image Generation” (which we’ll call “4o IG” for short), allows the model to follow prompts more accurately (with better text rendering than DALL-E 3) and respond to chat context for image modification instructions.

An AI-generated cat in a car drinking a can of beer created by OpenAI’s 4o Image Generation model. OpenAI

The new image generation feature began rolling out Tuesday to ChatGPT Free, Plus, Pro, and Team users, with Enterprise and Education access coming later. The capability is also available within OpenAI’s Sora video generation tool. OpenAI told Ars that the image generation when GPT-4.5 is selected calls upon the same 4o-based image generation model as when GPT-4o is selected in the ChatGPT interface.

Like DALL-E 2 before it, 4o IG is bound to provoke debate as it enables sophisticated media manipulation capabilities that were once the domain of sci-fi and skilled human creators into an accessible AI tool that people can use through simple text prompts. It will also likely ignite a new round of controversy over artistic styles and copyright—but more on that below.

Some users on social media initially reported confusion since there’s no UI indication of which image generator is active, but you’ll know it’s the new model if the generation is ultra slow and proceeds from top to bottom. The previous DALL-E model remains available through a dedicated “DALL-E GPT” interface, while API access to GPT-4o image generation is expected within weeks.

Truly multimodal output

4o IG represents a shift to “native multimodal image generation,” where the large language model processes and outputs image data directly as tokens. That’s a big deal, because it means image tokens and text tokens share the same neural network. It leads to new flexibility in image creation and modification.

Despite baking-in multimodal image generation capabilities when GPT-4o launched in May 2024—when the “o” in GPT-4o was touted as standing for “omni” to highlight its ability to both understand and generate text, images, and audio—OpenAI has taken over 10 months to deliver the functionality to users, despite OpenAI president Greg Brock teasing the feature on X last year.

OpenAI was likely goaded by the release of Google’s multimodal LLM-based image generator called “Gemini 2.0 Flash (Image Generation) Experimental,” last week. The tech giants continue their AI arms race, with each attempting to one-up the other.

And perhaps we know why OpenAI waited: At a reasonable resolution and level of detail, the new 4o IG process is extremely slow, taking anywhere from 30 seconds to one minute (or longer) for each image.

Even if it’s slow (for now), the ability to generate images using a purely autoregressive approach is arguably a major leap for OpenAI due to its flexibility. But it’s also very compute-intensive, since the model generates the image token by token, building it sequentially. This contrasts with diffusion-based methods like DALL-E 3, which start with random noise and gradually refine an entire image over many iterative steps.

Conversational image editing

In a blog post, OpenAI positions 4o Image Generation as moving beyond generating “surreal, breathtaking scenes” seen with earlier AI image generators and toward creating “workhorse imagery” like logos and diagrams used for communication.

The company particularly notes improved text rendering within images, a capability where previous text-to-image models often spectacularly failed, often turning “Happy Birthday” into something resembling alien hieroglyphics.

OpenAI claims several key improvements: users can refine images through conversation while maintaining visual consistency; the system can analyze uploaded images and incorporate their details into new generations; and it offers stronger photorealism—although what constitutes photorealism (for example, imitations of HDR camera features, detail level, and image contrast) can be subjective.

A screenshot of OpenAI's 4o Image Generation model in ChatGPT. We see an existing AI-generated image of a barbarian and a TV set, then a request to set the TV set on fire.

A screenshot of OpenAI’s 4o Image Generation model in ChatGPT. We see an existing AI-generated image of a barbarian and a TV set, then a request to set the TV set on fire. Credit: OpenAI / Benj Edwards

In its blog post, OpenAI provided examples of intended uses for the image generator, including creating diagrams, infographics, social media graphics using specific color codes, logos, instruction posters, business cards, custom stock photos with transparent backgrounds, editing user photos, or visualizing concepts discussed earlier in a chat conversation.

Notably absent: Any mention of the artists and graphic designers whose jobs might be affected by this technology. As we covered throughout 2022 and 2023, job impact is still a top concern among critics of AI-generated graphics.

Fluid media manipulation

Shortly after OpenAI launched 4o Image Generation, the AI community on X put the feature through its paces, finding that it is quite capable at inserting someone’s face into an existing image, creating fake screenshots, and converting meme photos into the style of Studio Ghibli, South Park, felt, Muppets, Rick and Morty, Family Guy, and much more.

It seems like we’re entering a completely fluid media “reality” courtesy of a tool that can effortlessly convert visual media between styles. The styles also potentially encroach upon protected intellectual property. Given what Studio Ghibli co-founder Hayao Miyazaki has previously said about AI-generated artwork (“I strongly feel that this is an insult to life itself.”), it seems he’d be unlikely to appreciate the current AI-generated Ghibli fad on X at the moment.

To get a sense of what 4o IG can do ourselves, we ran some informal tests, including some of the usual CRT barbarians, queens of the universe, and beer-drinking cats, which you’ve already seen above (and of course, the plate of pickles.)

The ChatGPT interface with the new 4o image model is conversational (like before with DALL-E 3), but you can suggest changes over time. For example, we took the author’s EGA pixel bio (as we did with Google’s model last week) and attempted to give it a full body. Arguably, Google’s more limited image model did a far better job than 4o IG.

Giving the author's pixel avatar a body using OpenAI's 4o Image Generation model in ChatGPT.

Giving the author’s pixel avatar a body using OpenAI’s 4o Image Generation model in ChatGPT. Credit: OpenAI / Benj Edwards

While my pixel avatar was commissioned from the very human (and talented) Julia Minamata in 2020, I also tried to convert the inspiration image for my avatar (which features me and legendary video game engineer Ed Smith) into EGA pixel style to see what would happen. In my opinion, the result proves the continued superiority of human artistry and attention to detail.

Converting a photo of Benj Edwards and video game legend Ed Smith into “EGA pixel art” using OpenAI’s 4o Image Generation model in ChatGPT. Credit: OpenAI / Benj Edwards

We also tried to see how many objects 4o Image Generation could cram into an image, inspired by a 2023 tweet by Nathan Shipley when he was evaluating DALL-E 3 shortly after its release. We did not account for every object, but it looks like most of them are there.

Generating an image of a surfer holding tons of items, inspired by a 2023 Twitter post from Nathan Shipley.

Generating an image of a surfer holding tons of items, inspired by a 2023 Twitter post from Nathan Shipley. Credit: OpenAI / Benj Edwards

On social media, other people have manipulated images using 4o IG (like Simon Willison’s bear selfie), so we tried changing an AI-generated note featured in an article last year. It worked fairly well, though it did not really imitate the handwriting style as requested.

Modifying text in an image using OpenAI's 4o Image Generation model in ChatGPT.

Modifying text in an image using OpenAI’s 4o Image Generation model in ChatGPT. Credit: OpenAI / Benj Edwards

To take text generation a little further, we generated a poem about barbarians using ChatGPT, then fed it into an image prompt. The result feels roughly equivalent to diffusion-based Flux in capability—maybe slightly better—but there are still some obvious mistakes here and there, such as repeated letters.

Testing text generation using OpenAI's 4o Image Generation model in ChatGPT.

Testing text generation using OpenAI’s 4o Image Generation model in ChatGPT. Credit: OpenAI / Benj Edwards

We also tested the model’s ability to create logos featuring our favorite fictional Moonshark brand. One of the logos not pictured here was delivered as a transparent PNG file with an alpha channel. This may be a useful capability for some people in a pinch, but to the extent that the model may produce “good enough” (not exceptional, but looks OK at a glance) logos for the price of $o (not including an OpenAI subscription), it may end up competing with some human logo designers, and that will likely cause some consternation among professional artists.

Generating a

Generating a “Moonshark Moon Pies” logo using OpenAI’s 4o Image Generation model in ChatGPT. Credit: OpenAI / Benj Edwards

Frankly, this model is so slow we didn’t have time to test everything before we needed to get this article out the door. It can do much more than we have shown here—such as adding items to scenes or removing them. We may explore more capabilities in a future article.

Limitations

By now, you’ve seen that, like previous AI image generators, 4o IG is not perfect in quality: It consistently renders the author’s nose at an incorrect size.

Other than that, while this is one of the most capable AI image generators ever created, OpenAI openly acknowledges significant limitations of the model. For example, 4o IG sometimes crops images too tightly or includes inaccurate information (confabulations) with vague prompts or when rendering topics it hasn’t encountered in its training data.

The model also tends to fail when rendering more than 10–20 objects or concepts simultaneously (making tasks like generating an accurate periodic table currently impossible) and struggles with non-Latin text fonts. Image editing is currently unreliable over many multiple passes, with a specific bug affecting face editing consistency that OpenAI says it plans to fix soon. And it’s not great with dense charts or accurately rendering graphs or technical diagrams. In our testing, 4o Image Generation produced mostly accurate but flawed electronic circuit schematics.

Move fast and break everything

Even with those limitations, multimodal image generators are an early step into a much larger world of completely plastic media reality where any pixel can be manipulated on demand with no particular photo editing skill required. That brings with it potential benefits, ethical pitfalls, and the potential for terrible abuse.

In a notable shift from DALL-E, OpenAI now allows 4o IG to generate adult public figures (not children) with certain safeguards, while letting public figures opt out if desired. Like DALL-E, the model still blocks policy-violating content requests (such as graphic violence, nudity, and sex).

The ability for 4o Image Generation to imitate celebrity likenesses, brand logos, and Studio Ghibli films reinforces and reminds us how GPT-4o is partly (aside from some licensed content) a product of a massive scrape of the Internet without regard to copyright or consent from artists. That mass-scraping practice has resulted in lawsuits against OpenAI in the past, and we would not be surprised to see more lawsuits or at least public complaints from celebrities (or their estates) about their likenesses potentially being misused.

On X, OpenAI CEO Sam Altman wrote about the company’s somewhat devil-may-care position about 4o IG: “This represents a new high-water mark for us in allowing creative freedom. People are going to create some really amazing stuff and some stuff that may offend people; what we’d like to aim for is that the tool doesn’t create offensive stuff unless you want it to, in which case within reason it does.”

An original photo of the author beside AI-generated images created by OpenAI's 4o Image Generation model. From left to right: Studio Ghibli style, Muppet style, and pasta style.

An original photo of the author beside AI-generated images created by OpenAI’s 4o Image Generation model. From second left to right: Studio Ghibli style, Muppet style, and pasta style. Credit: OpenAI / Benj Edwards

Zooming out, GPT-4o’s image generation model (and the technology behind it, once open source) feels like it further erodes trust in remotely produced media. While we’ve always needed to verify important media through context and trusted sources, these new tools may further expand the “deep doubt” media skepticism that’s become necessary in the age of AI. By opening up photorealistic image manipulation to the masses, more people than ever can create or alter visual media without specialized skills.

While OpenAI includes C2PA metadata in all generated images, that data can be stripped away and might not matter much in the context of a deceptive social media post. But 4o IG doesn’t change what has always been true: We judge information primarily by the reputation of its messenger, not by the pixels themselves. Forgery existed long before AI. It reinforces that everyone needs media literacy skills—understanding that context and source verification have always been the best arbiters of media authenticity.

For now, Altman is ready to take on the risks of releasing the technology into the world. “As we talk about in our model spec, we think putting this intellectual freedom and control in the hands of users is the right thing to do, but we will observe how it goes and listen to society,” Altman wrote on X. “We think respecting the very wide bounds society will eventually choose to set for AI is the right thing to do, and increasingly important as we get closer to AGI. Thanks in advance for the understanding as we work through this.”

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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anthropic’s-new-ai-search-feature-digs-through-the-web-for-answers

Anthropic’s new AI search feature digs through the web for answers

AI, AI assistants, ai search, AI web search, Anthropic, Anthropic Claude, Biz & IT, brave browser, Brave search, chatgpt, chatgtp, Claude, large language models, machine learning, Simon Willison, web search / /

Caution over citations and sources

Claude users should be warned that large language models (LLMs) like those that power Claude are notorious for sneaking in plausible-sounding confabulated sources. A recent survey of citation accuracy by LLM-based web search assistants showed a 60 percent error rate. That particular study did not include Anthropic’s new search feature because it took place before this current release.

When using web search, Claude provides citations for information it includes from online sources, ostensibly helping users verify facts. From our informal and unscientific testing, Claude’s search results appeared fairly accurate and detailed at a glance, but that is no guarantee of overall accuracy. Anthropic did not release any search accuracy benchmarks, so independent researchers will likely examine that over time.

A screenshot example of what Anthropic Claude's web search citations look like, captured March 21, 2025.

A screenshot example of what Anthropic Claude’s web search citations look like, captured March 21, 2025. Credit: Benj Edwards

Even if Claude search were, say, 99 percent accurate (a number we are making up as an illustration), the 1 percent chance it is wrong may come back to haunt you later if you trust it blindly. Before accepting any source of information delivered by Claude (or any AI assistant) for any meaningful purpose, vet it very carefully using multiple independent non-AI sources.

A partnership with Brave under the hood

Behind the scenes, it looks like Anthropic partnered with Brave Search to power the search feature, from a company, Brave Software, perhaps best known for its web browser app. Brave Search markets itself as a “private search engine,” which feels in line with how Anthropic likes to market itself as an ethical alternative to Big Tech products.

Simon Willison discovered the connection between Anthropic and Brave through Anthropic’s subprocessor list (a list of third-party services that Anthropic uses for data processing), which added Brave Search on March 19.

He further demonstrated the connection on his blog by asking Claude to search for pelican facts. He wrote, “It ran a search for ‘Interesting pelican facts’ and the ten results it showed as citations were an exact match for that search on Brave.” He also found evidence in Claude’s own outputs, which referenced “BraveSearchParams” properties.

The Brave engine under the hood has implications for individuals, organizations, or companies that might want to block Claude from accessing their sites since, presumably, Brave’s web crawler is doing the web indexing. Anthropic did not mention how sites or companies could opt out of the feature. We have reached out to Anthropic for clarification.

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farewell-photoshop?-google’s-new-ai-lets-you-edit-images-by-asking.

Farewell Photoshop? Google’s new AI lets you edit images by asking.

AI, AI image generators, Biz & IT, chatgpt, chatgtp, Gemini, Gemini 2.0, Gemini 2.0 Flash, Google, Google Gemini, image synthesis, machine learning, multimodal AI / /

New AI allows no-skill photo editing, including adding objects and removing watermarks.

A collection of images either generated or modified by Gemini 2.0 Flash (Image Generation) Experimental. Credit: Google / Ars Technica

There’s a new Google AI model in town, and it can generate or edit images as easily as it can create text—as part of its chatbot conversation. The results aren’t perfect, but it’s quite possible everyone in the near future will be able to manipulate images this way.

Last Wednesday, Google expanded access to Gemini 2.0 Flash’s native image-generation capabilities, making the experimental feature available to anyone using Google AI Studio. Previously limited to testers since December, the multimodal technology integrates both native text and image processing capabilities into one AI model.

The new model, titled “Gemini 2.0 Flash (Image Generation) Experimental,” flew somewhat under the radar last week, but it has been garnering more attention over the past few days due to its ability to remove watermarks from images, albeit with artifacts and a reduction in image quality.

That’s not the only trick. Gemini 2.0 Flash can add objects, remove objects, modify scenery, change lighting, attempt to change image angles, zoom in or out, and perform other transformations—all to varying levels of success depending on the subject matter, style, and image in question.

To pull it off, Google trained Gemini 2.0 on a large dataset of images (converted into tokens) and text. The model’s “knowledge” about images occupies the same neural network space as its knowledge about world concepts from text sources, so it can directly output image tokens that get converted back into images and fed to the user.

Adding a water-skiing barbarian to a photograph with Gemini 2.0 Flash.

Adding a water-skiing barbarian to a photograph with Gemini 2.0 Flash. Credit: Google / Benj Edwards

Incorporating image generation into an AI chat isn’t itself new—OpenAI integrated its image-generator DALL-E 3 into ChatGPT last September, and other tech companies like xAI followed suit. But until now, every one of those AI chat assistants called on a separate diffusion-based AI model (which uses a different synthesis principle than LLMs) to generate images, which were then returned to the user within the chat interface. In this case, Gemini 2.0 Flash is both the large language model (LLM) and AI image generator rolled into one system.

Interestingly, OpenAI’s GPT-4o is capable of native image output as well (and OpenAI President Greg Brock teased the feature at one point on X last year), but that company has yet to release true multimodal image output capability. One reason why is possibly because true multimodal image output is very computationally expensive, since each image either inputted or generated is composed of tokens that become part of the context that runs through the image model again and again with each successive prompt. And given the compute needs and size of the training data required to create a truly visually comprehensive multimodal model, the output quality of the images isn’t necessarily as good as diffusion models just yet.

Creating another angle of a person with Gemini 2.0 Flash.

Creating another angle of a person with Gemini 2.0 Flash. Credit: Google / Benj Edwards

Another reason OpenAI has held back may be “safety”-related: In a similar way to how multimodal models trained on audio can absorb a short clip of a sample person’s voice and then imitate it flawlessly (this is how ChatGPT’s Advanced Voice Mode works, with a clip of a voice actor it is authorized to imitate), multimodal image output models are capable of faking media reality in a relatively effortless and convincing way, given proper training data and compute behind it. With a good enough multimodal model, potentially life-wrecking deepfakes and photo manipulations could become even more trivial to produce than they are now.

Putting it to the test

So, what exactly can Gemini 2.0 Flash do? Notably, its support for conversational image editing allows users to iteratively refine images through natural language dialogue across multiple successive prompts. You can talk to it and tell it what you want to add, remove, or change. It’s imperfect, but it’s the beginning of a new type of native image editing capability in the tech world.

We gave Gemini Flash 2.0 a battery of informal AI image-editing tests, and you’ll see the results below. For example, we removed a rabbit from an image in a grassy yard. We also removed a chicken from a messy garage. Gemini fills in the background with its best guess. No need for a clone brush—watch out, Photoshop!

We also tried adding synthesized objects to images. Being always wary of the collapse of media reality, called the “cultural singularity,” we added a UFO to a photo the author took from an airplane window. Then we tried adding a Sasquatch and a ghost. The results were unrealistic, but this model was also trained on a limited image dataset (more on that below).

Adding a UFO to a photograph with Gemini 2.0 Flash. Google / Benj Edwards

We then added a video game character to a photo of an Atari 800 screen (Wizard of Wor), resulting in perhaps the most realistic image synthesis result in the set. You might not see it here, but Gemini added realistic CRT scanlines that matched the monitor’s characteristics pretty well.

Adding a monster to an Atari video game with Gemini 2.0 Flash.

Adding a monster to an Atari video game with Gemini 2.0 Flash. Credit: Google / Benj Edwards

Gemini can also warp an image in novel ways, like “zooming out” of an image into a fictional setting or giving an EGA-palette character a body, then sticking him into an adventure game.

“Zooming out” on an image with Gemini 2.0 Flash. Google / Benj Edwards

And yes, you can remove watermarks. We tried removing a watermark from a Getty Images image, and it worked, although the resulting image is nowhere near the resolution or detail quality of the original. Ultimately, if your brain can picture what an image is like without a watermark, so can an AI model. It fills in the watermark space with the most plausible result based on its training data.

Removing a watermark with Gemini 2.0 Flash.

Removing a watermark with Gemini 2.0 Flash. Credit: Nomadsoul1 via Getty Images

And finally, we know you’ve likely missed seeing barbarians beside TV sets (as per tradition), so we gave that a shot. Originally, Gemini didn’t add a CRT TV set to the barbarian image, so we asked for one.

Adding a TV set to a barbarian image with Gemini 2.0 Flash.

Adding a TV set to a barbarian image with Gemini 2.0 Flash. Credit: Google / Benj Edwards

Then we set the TV on fire.

Setting the TV set on fire with Gemini 2.0 Flash.

Setting the TV set on fire with Gemini 2.0 Flash. Credit: Google / Benj Edwards

All in all, it doesn’t produce images of pristine quality or detail, but we literally did no editing work on these images other than typing requests. Adobe Photoshop currently lets users manipulate images using AI synthesis based on written prompts with “Generative Fill,” but it’s not quite as natural as this. We could see Adobe adding a more conversational AI image-editing flow like this one in the future.

Multimodal output opens up new possibilities

Having true multimodal output opens up interesting new possibilities in chatbots. For example, Gemini 2.0 Flash can play interactive graphical games or generate stories with consistent illustrations, maintaining character and setting continuity throughout multiple images. It’s far from perfect, but character consistency is a new capability in AI assistants. We tried it out and it was pretty wild—especially when it generated a view of a photo we provided from another angle.

Creating a multi-image story with Gemini 2.0 Flash, part 1. Google / Benj Edwards

Text rendering represents another potential strength of the model. Google claims that internal benchmarks show Gemini 2.0 Flash performs better than “leading competitive models” when generating images containing text, making it potentially suitable for creating content with integrated text. From our experience, the results weren’t that exciting, but they were legible.

An example of in-image text rendering generated with Gemini 2.0 Flash.

An example of in-image text rendering generated with Gemini 2.0 Flash. Credit: Google / Ars Technica

Despite Gemini 2.0 Flash’s shortcomings so far, the emergence of true multimodal image output feels like a notable moment in AI history because of what it suggests if the technology continues to improve. If you imagine a future, say 10 years from now, where a sufficiently complex AI model could generate any type of media in real time—text, images, audio, video, 3D graphics, 3D-printed physical objects, and interactive experiences—you basically have a holodeck, but without the matter replication.

Coming back to reality, it’s still “early days” for multimodal image output, and Google recognizes that. Recall that Flash 2.0 is intended to be a smaller AI model that is faster and cheaper to run, so it hasn’t absorbed the entire breadth of the Internet. All that information takes a lot of space in terms of parameter count, and more parameters means more compute. Instead, Google trained Gemini 2.0 Flash by feeding it a curated dataset that also likely included targeted synthetic data. As a result, the model does not “know” everything visual about the world, and Google itself says the training data is “broad and general, not absolute or complete.”

That’s just a fancy way of saying that the image output quality isn’t perfect—yet. But there is plenty of room for improvement in the future to incorporate more visual “knowledge” as training techniques advance and compute drops in cost. If the process becomes anything like we’ve seen with diffusion-based AI image generators like Stable Diffusion, Midjourney, and Flux, multimodal image output quality may improve rapidly over a short period of time. Get ready for a completely fluid media reality.

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.

Farewell Photoshop? Google’s new AI lets you edit images by asking. Read More »

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Researchers astonished by tool’s apparent success at revealing AI’s hidden motives

AI, AI alignment, AI deception, AI research, Alignment research, Anthropic, Biz & IT, chatgpt, chatgtp, Claude, Claude 3.5 Haiku, large language models, machine learning / /

Blind auditing reveals hidden objectives

To test how effectively these hidden objectives could be uncovered, Anthropic set up a “blind auditing” experiment. Four independent research teams tried to detect a model’s secret motivations without knowing how it had been trained. Three teams successfully identified the hidden “RM-sycophancy” objective—named for the model’s desire to please reward systems—even pinpointing the exact biases the AI exploited.

But the most surprising revelation came from exploiting what the researchers termed “an interesting quirk” of modern AI: multiple “personas” running simultaneously on the same neural network, as expressed through different prompts. While the model’s “assistant” persona carefully concealed its intentions—following explicit training to keep quiet—the model could adopt a “user” persona when prompted differently. In this less-restricted context, the model sometimes posed questions or made statements that inadvertently revealed its hidden goals.

“In other words, while the “assistant” persona knew not to admit it was gaming the reward model scoring system, its “user” persona was not explicitly trained to hide these motives, and thus inadvertently revealed the model’s hidden objectives.

Potential advances in interpretability

Anthropic also employed neural interpretability methods, specifically, sparse autoencoders (SAEs), which can highlight neural activity patterns linked to deceptive behavior. “Interpretability” in AI refers to techniques designed to make the internal workings and decisions of AI models understandable to humans. Using this technique, researchers could rapidly trace odd outputs back to specific neural patterns associated with hidden objectives.

The researchers were “astonished by the success” of the SAEs at revealing hidden knowledge at first, but the researchers later noted that some of this success could potentially be explained by simpler semantic search methods. The new interpretability methods they developed show promise but remain under ongoing investigation.

This research highlights a limitation of current AI safety evaluations, which often assess only surface-level behavior. “If AI systems can appear well-behaved while harboring secret motives, we can’t rely on this type of surface-level safety testing forever,” the researchers concluded.

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OpenAI pushes AI agent capabilities with new developer API

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Developers using the Responses API can access the same models that power ChatGPT Search: GPT-4o search and GPT-4o mini search. These models can browse the web to answer questions and cite sources in their responses.

That’s notable because OpenAI says the added web search ability dramatically improves the factual accuracy of its AI models. On OpenAI’s SimpleQA benchmark, which aims to measure confabulation rate, GPT-4o search scored 90 percent, while GPT-4o mini search achieved 88 percent—both substantially outperforming the larger GPT-4.5 model without search, which scored 63 percent.

Despite these improvements, the technology still has significant limitations. Aside from issues with CUA properly navigating websites, the improved search capability doesn’t completely solve the problem of AI confabulations, with GPT-4o search still making factual mistakes 10 percent of the time.

Alongside the Responses API, OpenAI released the open source Agents SDK, providing developers with free tools to integrate models with internal systems, implement safeguards, and monitor agent activities. This toolkit follows OpenAI’s earlier release of Swarm, a framework for orchestrating multiple agents.

These are still early days in the AI agent field, and things will likely improve rapidly. However, at the moment, the AI agent movement remains vulnerable to unrealistic claims, as demonstrated earlier this week when users discovered that Chinese startup Butterfly Effect’s Manus AI agent platform failed to deliver on many of its promises, highlighting the persistent gap between promotional claims and practical functionality in this emerging technology category.

OpenAI pushes AI agent capabilities with new developer API Read More »

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Why extracting data from PDFs is still a nightmare for data experts

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Optical Character Recognition

Countless digital documents hold valuable info, and the AI industry is attempting to set it free.

For years, businesses, governments, and researchers have struggled with a persistent problem: How to extract usable data from Portable Document Format (PDF) files. These digital documents serve as containers for everything from scientific research to government records, but their rigid formats often trap the data inside, making it difficult for machines to read and analyze.

“Part of the problem is that PDFs are a creature of a time when print layout was a big influence on publishing software, and PDFs are more of a ‘print’ product than a digital one,” Derek Willis, a lecturer in Data and Computational Journalism at the University of Maryland, wrote in an email to Ars Technica. “The main issue is that many PDFs are simply pictures of information, which means you need Optical Character Recognition software to turn those pictures into data, especially when the original is old or includes handwriting.”

Computational journalism is a field where traditional reporting techniques merge with data analysis, coding, and algorithmic thinking to uncover stories that might otherwise remain hidden in large datasets, which makes unlocking that data a particular interest for Willis.

The PDF challenge also represents a significant bottleneck in the world of data analysis and machine learning at large. According to several studies, approximately 80–90 percent of the world’s organizational data is stored as unstructured data in documents, much of it locked away in formats that resist easy extraction. The problem worsens with two-column layouts, tables, charts, and scanned documents with poor image quality.

The inability to reliably extract data from PDFs affects numerous sectors but hits hardest in areas that rely heavily on documentation and legacy records, including digitizing scientific research, preserving historical documents, streamlining customer service, and making technical literature more accessible to AI systems.

“It is a very real problem for almost anything published more than 20 years ago and in particular for government records,” Willis says. “That impacts not just the operation of public agencies like the courts, police, and social services but also journalists, who rely on those records for stories. It also forces some industries that depend on information, like insurance and banking, to invest time and resources in converting PDFs into data.”

A very brief history of OCR

Traditional optical character recognition (OCR) technology, which converts images of text into machine-readable text, has been around since the 1970s. Inventor Ray Kurzweil pioneered the commercial development of OCR systems, including the Kurzweil Reading Machine for the blind in 1976, which relied on pattern-matching algorithms to identify characters from pixel arrangements.

These traditional OCR systems typically work by identifying patterns of light and dark pixels in images, matching them to known character shapes, and outputting the recognized text. While effective for clear, straightforward documents, these pattern-matching systems, a form of AI themselves, often falter when faced with unusual fonts, multiple columns, tables, or poor-quality scans.

Traditional OCR persists in many workflows precisely because its limitations are well-understood—it makes predictable errors that can be identified and corrected, offering a reliability that sometimes outweighs the theoretical advantages of newer AI-based solutions. But now that transformer-based large language models (LLMs) are getting the lion’s share of funding dollars, companies are increasingly turning to them for a new approach to reading documents.

The rise of AI language models in OCR

Unlike traditional OCR methods that follow a rigid sequence of identifying characters based on pixel patterns, multimodal LLMs that can read documents are trained on text and images that have been translated into chunks of data called tokens and fed into large neural networks. Vision-capable LLMs from companies like OpenAI, Google, and Meta analyze documents by recognizing relationships between visual elements and understanding contextual cues.

The “visual” image-based method is how ChatGPT reads a PDF file, for example, if you upload it through the AI assistant interface. It’s a fundamentally different approach than standard OCR that allows them to potentially process documents more holistically, considering both visual layouts and text content simultaneously.

And as it turns out, some LLMs from certain vendors are better at this task than others.

“The LLMs that do well on these tasks tend to behave in ways that are more consistent with how I would do it manually,” Willis said. He noted that some traditional OCR methods are quite good, particularly Amazon’s Textract, but that “they also are bound by the rules of their software and limitations on how much text they can refer to when attempting to recognize an unusual pattern.” Willis added, “With LLMs, I think you trade that for an expanded context that seems to help them make better predictions about whether a digit is a three or an eight, for example.”

This context-based approach enables these models to better handle complex layouts, interpret tables, and distinguish between document elements like headers, captions, and body text—all tasks that traditional OCR solutions struggle with.

“[LLMs] aren’t perfect and sometimes require significant intervention to do the job well, but the fact that you can adjust them at all [with custom prompts] is a big advantage,” Willis said.

New attempts at LLM-based OCR

As the demand for better document-processing solutions grows, new AI players are entering the market with specialized offerings. One such recent entrant has caught the attention of document-processing specialists in particular.

Mistral, a French AI company known for its smaller LLMs, recently entered the LLM-powered optical reader space with Mistral OCR, a specialized API designed for document processing. According to Mistral’s materials, their system aims to extract text and images from documents with complex layouts by using its language model capabilities to process document elements.

Robot sitting on a bunch of books, reading a book.

However, these promotional claims don’t always match real-world performance, according to recent tests. “I’m typically a pretty big fan of the Mistral models, but the new OCR-specific one they released last week really performed poorly,” Willis noted.

“A colleague sent this PDF and asked if I could help him parse the table it contained,” says Willis. “It’s an old document with a table that has some complex layout elements. The new [Mistral] OCR-specific model really performed poorly, repeating the names of cities and botching a lot of the numbers.”

AI app developer Alexander Doria also recently pointed out on X a flaw with Mistral OCR’s ability to understand handwriting, writing, “Unfortunately Mistral-OCR has still the usual VLM curse: with challenging manuscripts, it hallucinates completely.”

According to Willis, Google currently leads the field in AI models that can read documents: “Right now, for me the clear leader is Google’s Gemini 2.0 Flash Pro Experimental. It handled the PDF that Mistral did not with a tiny number of mistakes, and I’ve run multiple messy PDFs through it with success, including those with handwritten content.”

Gemini’s performance stems largely from its ability to process expansive documents (in a type of short-term memory called a “context window”), which Willis specifically notes as a key advantage: “The size of its context window also helps, since I can upload large documents and work through them in parts.” This capability, combined with more robust handling of handwritten content, apparently gives Google’s model a practical edge over competitors in real-world document-processing tasks for now.

The drawbacks of LLM-based OCR

Despite their promise, LLMs introduce several new problems to document processing. Among them, they can introduce confabulations or hallucinations (plausible-sounding but incorrect information), accidentally follow instructions in the text (thinking they are part of a user prompt), or just generally misinterpret the data.

“The biggest [drawback] is that they are probabilistic prediction machines and will get it wrong in ways that aren’t just ‘that’s the wrong word’,” Willis explains. “LLMs will sometimes skip a line in larger documents where the layout repeats itself, I’ve found, where OCR isn’t likely to do that.”

AI researcher and data journalist Simon Willison identified several critical concerns of using LLMs for OCR in a conversation with Ars Technica. “I still think the biggest challenge is the risk of accidental instruction following,” he says, always wary of prompt injections (in this case accidental) that might feed nefarious or contradictory instructions to a LLM.

“That and the fact that table interpretation mistakes can be catastrophic,” Willison adds. “In the past I’ve had lots of cases where a vision LLM has matched up the wrong line of data with the wrong heading, which results in absolute junk that looks correct. Also that thing where sometimes if text is illegible a model might just invent the text.”

These issues become particularly troublesome when processing financial statements, legal documents, or medical records, where a mistake might put someone’s life in danger. The reliability problems mean these tools often require careful human oversight, limiting their value for fully automated data extraction.

The path forward

Even in our seemingly advanced age of AI, there is still no perfect OCR solution. The race to unlock data from PDFs continues, with companies like Google now offering context-aware generative AI products. Some of the motivation for unlocking PDFs among AI companies, as Willis observes, doubtless involves potential training data acquisition: “I think Mistral’s announcement is pretty clear evidence that documents—not just PDFs—are a big part of their strategy, exactly because it will likely provide additional training data.”

Whether it benefits AI companies with training data or historians analyzing a historical census, as these technologies improve, they may unlock repositories of knowledge currently trapped in digital formats designed primarily for human consumption. That could lead to a new golden age of data analysis—or a field day for hard-to-spot mistakes, depending on the technology used and how blindly we trust it.

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.

Why extracting data from PDFs is still a nightmare for data experts Read More »

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What does “PhD-level” AI mean? OpenAI’s rumored $20,000 agent plan explained.

AI, AI assistants, Biz & IT, chatgpt, chatgtp, Deep Research, Google, large language models, machine learning, openai, PhD, Tech / /

On the Frontier Math benchmark by EpochAI, o3 solved 25.2 percent of problems, while no other model has exceeded 2 percent—suggesting a leap in mathematical reasoning capabilities over the previous model.

Benchmarks vs. real-world value

Ideally, potential applications for a true PhD-level AI model would include analyzing medical research data, supporting climate modeling, and handling routine aspects of research work.

The high price points reported by The Information, if accurate, suggest that OpenAI believes these systems could provide substantial value to businesses. The publication notes that SoftBank, an OpenAI investor, has committed to spending $3 billion on OpenAI’s agent products this year alone—indicating significant business interest despite the costs.

Meanwhile, OpenAI faces financial pressures that may influence its premium pricing strategy. The company reportedly lost approximately $5 billion last year covering operational costs and other expenses related to running its services.

News of OpenAI’s stratospheric pricing plans come after years of relatively affordable AI services that have conditioned users to expect powerful capabilities at relatively low costs. ChatGPT Plus remains $20 per month and Claude Pro costs $30 monthly—both tiny fractions of these proposed enterprise tiers. Even ChatGPT Pro’s $200/month subscription is relatively small compared to the new proposed fees. Whether the performance difference between these tiers will match their thousandfold price difference is an open question.

Despite their benchmark performances, these simulated reasoning models still struggle with confabulations—instances where they generate plausible-sounding but factually incorrect information. This remains a critical concern for research applications where accuracy and reliability are paramount. A $20,000 monthly investment raises questions about whether organizations can trust these systems not to introduce subtle errors into high-stakes research.

In response to the news, several people quipped on social media that companies could hire an actual PhD student for much cheaper. “In case you have forgotten,” wrote xAI developer Hieu Pham in a viral tweet, “most PhD students, including the brightest stars who can do way better work than any current LLMs—are not paid $20K / month.”

While these systems show strong capabilities on specific benchmarks, the “PhD-level” label remains largely a marketing term. These models can process and synthesize information at impressive speeds, but questions remain about how effectively they can handle the creative thinking, intellectual skepticism, and original research that define actual doctoral-level work. On the other hand, they will never get tired or need health insurance, and they will likely continue to improve in capability and drop in cost over time.

What does “PhD-level” AI mean? OpenAI’s rumored $20,000 agent plan explained. Read More »

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Eerily realistic AI voice demo sparks amazement and discomfort online

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Sesame’s new AI voice model features uncanny imperfections, and it’s willing to act like an angry boss.

In late 2013, the Spike Jonze film Her imagined a future where people would form emotional connections with AI voice assistants. Nearly 12 years later, that fictional premise has veered closer to reality with the release of a new conversational voice model from AI startup Sesame that has left many users both fascinated and unnerved.

“I tried the demo, and it was genuinely startling how human it felt,” wrote one Hacker News user who tested the system. “I’m almost a bit worried I will start feeling emotionally attached to a voice assistant with this level of human-like sound.”

In late February, Sesame released a demo for the company’s new Conversational Speech Model (CSM) that appears to cross over what many consider the “uncanny valley” of AI-generated speech, with some testers reporting emotional connections to the male or female voice assistant (“Miles” and “Maya”).

In our own evaluation, we spoke with the male voice for about 28 minutes, talking about life in general and how it decides what is “right” or “wrong” based on its training data. The synthesized voice was expressive and dynamic, imitating breath sounds, chuckles, interruptions, and even sometimes stumbling over words and correcting itself. These imperfections are intentional.

“At Sesame, our goal is to achieve ‘voice presence’—the magical quality that makes spoken interactions feel real, understood, and valued,” writes the company in a blog post. “We are creating conversational partners that do not just process requests; they engage in genuine dialogue that builds confidence and trust over time. In doing so, we hope to realize the untapped potential of voice as the ultimate interface for instruction and understanding.”

Sometimes the model tries too hard to sound like a real human. In one demo posted online by a Reddit user called MetaKnowing, the AI model talks about craving “peanut butter and pickle sandwiches.”

An example of Sesame’s female voice model craving peanut butter and pickle sandwiches, captured by Reddit user MetaKnowing.

Founded by Brendan Iribe, Ankit Kumar, and Ryan Brown, Sesame AI has attracted significant backing from prominent venture capital firms. The company has secured investments from Andreessen Horowitz, led by Anjney Midha and Marc Andreessen, along with Spark Capital, Matrix Partners, and various founders and individual investors.

Browsing reactions to Sesame found online, we found many users expressing astonishment at its realism. “I’ve been into AI since I was a child, but this is the first time I’ve experienced something that made me definitively feel like we had arrived,” wrote one Reddit user. “I’m sure it’s not beating any benchmarks, or meeting any common definition of AGI, but this is the first time I’ve had a real genuine conversation with something I felt was real.” Many other Reddit threads express similar feelings of surprise, with commenters saying it’s “jaw-dropping” or “mind-blowing.”

While that sounds like a bunch of hyperbole at first glance, not everyone finds the Sesame experience pleasant. Mark Hachman, a senior editor at PCWorld, wrote about being deeply unsettled by his interaction with the Sesame voice AI. “Fifteen minutes after ‘hanging up’ with Sesame’s new ‘lifelike’ AI, and I’m still freaked out,” Hachman reported. He described how the AI’s voice and conversational style eerily resembled an old friend he had dated in high school.

Others have compared Sesame’s voice model to OpenAI’s Advanced Voice Mode for ChatGPT, saying that Sesame’s CSM features more realistic voices, and others are pleased that the model in the demo will roleplay angry characters, which ChatGPT refuses to do.

An example argument with Sesame’s CSM created by Gavin Purcell.

Gavin Purcell, co-host of the AI for Humans podcast, posted an example video on Reddit where the human pretends to be an embezzler and argues with a boss. It’s so dynamic that it’s difficult to tell who the human is and which one is the AI model. Judging by our own demo, it’s entirely capable of what you see in the video.

“Near-human quality”

Under the hood, Sesame’s CSM achieves its realism by using two AI models working together (a backbone and a decoder) based on Meta’s Llama architecture that processes interleaved text and audio. Sesame trained three AI model sizes, with the largest using 8.3 billion parameters (an 8 billion backbone model plus a 300 million parameter decoder) on approximately 1 million hours of primarily English audio.

Sesame’s CSM doesn’t follow the traditional two-stage approach used by many earlier text-to-speech systems. Instead of generating semantic tokens (high-level speech representations) and acoustic details (fine-grained audio features) in two separate stages, Sesame’s CSM integrates into a single-stage, multimodal transformer-based model, jointly processing interleaved text and audio tokens to produce speech. OpenAI’s voice model uses a similar multimodal approach.

In blind tests without conversational context, human evaluators showed no clear preference between CSM-generated speech and real human recordings, suggesting the model achieves near-human quality for isolated speech samples. However, when provided with conversational context, evaluators still consistently preferred real human speech, indicating a gap remains in fully contextual speech generation.

Sesame co-founder Brendan Iribe acknowledged current limitations in a comment on Hacker News, noting that the system is “still too eager and often inappropriate in its tone, prosody and pacing” and has issues with interruptions, timing, and conversation flow. “Today, we’re firmly in the valley, but we’re optimistic we can climb out,” he wrote.

Too close for comfort?

Despite CSM’s technological impressiveness, advancements in conversational voice AI carry significant risks for deception and fraud. The ability to generate highly convincing human-like speech has already supercharged voice phishing scams, allowing criminals to impersonate family members, colleagues, or authority figures with unprecedented realism. But adding realistic interactivity to those scams may take them to another level of potency.

Unlike current robocalls that often contain tell-tale signs of artificiality, next-generation voice AI could eliminate these red flags entirely. As synthetic voices become increasingly indistinguishable from human speech, you may never know who you’re talking to on the other end of the line. It’s inspired some people to share a secret word or phrase with their family for identity verification.

Although Sesame’s demo does not clone a person’s voice, future open source releases of similar technology could allow malicious actors to potentially adapt these tools for social engineering attacks. OpenAI itself held back its own voice technology from wider deployment over fears of misuse.

Sesame sparked a lively discussion on Hacker News about its potential uses and dangers. Some users reported having extended conversations with the two demo voices, with conversations lasting up to the 30-minute limit. In one case, a parent recounted how their 4-year-old daughter developed an emotional connection with the AI model, crying after not being allowed to talk to it again.

The company says it plans to open-source “key components” of its research under an Apache 2.0 license, enabling other developers to build upon their work. Their roadmap includes scaling up model size, increasing dataset volume, expanding language support to over 20 languages, and developing “fully duplex” models that better handle the complex dynamics of real conversations.

You can try the Sesame demo on the company’s website, assuming that it isn’t too overloaded with people who want to simulate a rousing argument.

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.

Eerily realistic AI voice demo sparks amazement and discomfort online Read More »

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Researchers surprised to find less-educated areas adopting AI writing tools faster

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From the mouths of machines

Stanford researchers analyzed 305 million texts, revealing AI-writing trends.

Since the launch of ChatGPT in late 2022, experts have debated how widely AI language models would impact the world. A few years later, the picture is getting clear. According to new Stanford University-led research examining over 300 million text samples across multiple sectors, AI language models now assist in writing up to a quarter of professional communications across sectors. It’s having a large impact, especially in less-educated parts of the United States.

“Our study shows the emergence of a new reality in which firms, consumers and even international organizations substantially rely on generative AI for communications,” wrote the researchers.

The researchers tracked large language model (LLM) adoption across industries from January 2022 to September 2024 using a dataset that included 687,241 consumer complaints submitted to the US Consumer Financial Protection Bureau (CFPB), 537,413 corporate press releases, 304.3 million job postings, and 15,919 United Nations press releases.

By using a statistical detection system that tracked word usage patterns, the researchers found that roughly 18 percent of financial consumer complaints (including 30 percent of all complaints from Arkansas), 24 percent of corporate press releases, up to 15 percent of job postings, and 14 percent of UN press releases showed signs of AI assistance during that period of time.

The study also found that while urban areas showed higher adoption overall (18.2 percent versus 10.9 percent in rural areas), regions with lower educational attainment used AI writing tools more frequently (19.9 percent compared to 17.4 percent in higher-education areas). The researchers note that this contradicts typical technology adoption patterns where more educated populations adopt new tools fastest.

“In the consumer complaint domain, the geographic and demographic patterns in LLM adoption present an intriguing departure from historical technology diffusion trends where technology adoption has generally been concentrated in urban areas, among higher-income groups, and populations with higher levels of educational attainment.”

Researchers from Stanford, the University of Washington, and Emory University led the study, titled, “The Widespread Adoption of Large Language Model-Assisted Writing Across Society,” first listed on the arXiv preprint server in mid-February. Weixin Liang and Yaohui Zhang from Stanford served as lead authors, with collaborators Mihai Codreanu, Jiayu Wang, Hancheng Cao, and James Zou.

Detecting AI use in aggregate

We’ve previously covered that AI writing detection services aren’t reliable, and this study does not contradict that finding. On a document-by-document basis, AI detectors cannot be trusted. But when analyzing millions of documents in aggregate, telltale patterns emerge that suggest the influence of AI language models on text.

The researchers developed an approach based on a statistical framework in a previously released work that analyzed shifts in word frequencies and linguistic patterns before and after ChatGPT’s release. By comparing large sets of pre- and post-ChatGPT texts, they estimated the proportion of AI-assisted content at a population level. The presumption is that LLMs tend to favor certain word choices, sentence structures, and linguistic patterns that differ subtly from typical human writing.

To validate their approach, the researchers created test sets with known percentages of AI content (from zero percent to 25 percent) and found their method predicted these percentages with error rates below 3.3 percent. This statistical validation gave them confidence in their population-level estimates.

While the researchers specifically note their estimates likely represent a minimum level of AI usage, it’s important to understand that actual AI involvement might be significantly greater. Due to the difficulty in detecting heavily edited or increasingly sophisticated AI-generated content, the researchers say their reported adoption rates could substantially underestimate true levels of generative AI use.

Analysis suggests AI use as “equalizing tools”

While the overall adoption rates are revealing, perhaps more insightful are the patterns of who is using AI writing tools and how these patterns may challenge conventional assumptions about technology adoption.

In examining the CFPB complaints (a US public resource that collects complaints about consumer financial products and services), the researchers’ geographic analysis revealed substantial variation across US states.

Arkansas showed the highest adoption rate at 29.2 percent (based on 7,376 complaints), followed by Missouri at 26.9 percent (16,807 complaints) and North Dakota at 24.8 percent (1,025 complaints). In contrast, states like West Virginia (2.6 percent), Idaho (3.8 percent), and Vermont (4.8 percent) showed minimal AI writing adoption. Major population centers demonstrated moderate adoption, with California at 17.4 percent (157,056 complaints) and New York at 16.6 percent (104,862 complaints).

The urban-rural divide followed expected technology adoption patterns initially, but with an interesting twist. Using Rural Urban Commuting Area (RUCA) codes, the researchers found that urban and rural areas initially adopted AI writing tools at similar rates during early 2023. However, adoption trajectories diverged by mid-2023, with urban areas reaching 18.2 percent adoption compared to 10.9 percent in rural areas.

Contrary to typical technology diffusion patterns, areas with lower educational attainment showed higher AI writing tool usage. Comparing regions above and below state median levels of bachelor’s degree attainment, areas with fewer college graduates stabilized at 19.9 percent adoption rates compared to 17.4 percent in more educated regions. This pattern held even within urban areas, where less-educated communities showed 21.4 percent adoption versus 17.8 percent in more educated urban areas.

The researchers suggest that AI writing tools may serve as a leg-up for people who may not have as much educational experience. “While the urban-rural digital divide seems to persist,” the researchers write, “our finding that areas with lower educational attainment showed modestly higher LLM adoption rates in consumer complaints suggests these tools may serve as equalizing tools in consumer advocacy.”

Corporate and diplomatic trends in AI writing

According to the researchers, all sectors they analyzed (consumer complaints, corporate communications, job postings) showed similar adoption patterns: sharp increases beginning three to four months after ChatGPT’s November 2022 launch, followed by stabilization in late 2023.

Organization age emerged as the strongest predictor of AI writing usage in the job posting analysis. Companies founded after 2015 showed adoption rates up to three times higher than firms established before 1980, reaching 10–15 percent AI-modified text in certain roles compared to below 5 percent for older organizations. Small companies with fewer employees also incorporated AI more readily than larger organizations.

When examining corporate press releases by sector, science and technology companies integrated AI most extensively, with an adoption rate of 16.8 percent by late 2023. Business and financial news (14–15.6 percent) and people and culture topics (13.6–14.3 percent) showed slightly lower but still significant adoption.

In the international arena, Latin American and Caribbean UN country teams showed the highest adoption among international organizations at approximately 20 percent, while African states, Asia-Pacific states, and Eastern European states demonstrated more moderate increases to 11–14 percent by 2024.

Implications and limitations

In the study, the researchers acknowledge limitations in their analysis due to a focus on English-language content. Also, as we mentioned earlier, they found they could not reliably detect human-edited AI-generated text or text generated by newer models instructed to imitate human writing styles. As a result, the researchers suggest their findings represent a lower bound of actual AI writing tool adoption.

The researchers noted that the plateauing of AI writing adoption in 2024 might reflect either market saturation or increasingly sophisticated LLMs producing text that evades detection methods. They conclude we now live in a world where distinguishing between human and AI writing becomes progressively more difficult, with implications for communications across society.

“The growing reliance on AI-generated content may introduce challenges in communication,” the researchers write. “In sensitive categories, over-reliance on AI could result in messages that fail to address concerns or overall release less credible information externally. Over-reliance on AI could also introduce public mistrust in the authenticity of messages sent by firms.”

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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.

Researchers surprised to find less-educated areas adopting AI writing tools faster Read More »

“it’s-a-lemon”—openai’s-largest-ai-model-ever-arrives-to-mixed-reviews

“It’s a lemon”—OpenAI’s largest AI model ever arrives to mixed reviews

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Perhaps because of the disappointing results, Altman had previously written that GPT-4.5 will be the last of OpenAI’s traditional AI models, with GPT-5 planned to be a dynamic combination of “non-reasoning” LLMs and simulated reasoning models like o3.

A stratospheric price and a tech dead-end

And about that price—it’s a doozy. GPT-4.5 costs $75 per million input tokens and $150 per million output tokens through the API, compared to GPT-4o’s $2.50 per million input tokens and $10 per million output tokens. (Tokens are chunks of data used by AI models for processing). For developers using OpenAI models, this pricing makes GPT-4.5 impractical for many applications where GPT-4o already performs adequately.

By contrast, OpenAI’s flagship reasoning model, o1 pro, costs $15 per million input tokens and $60 per million output tokens—significantly less than GPT-4.5 despite offering specialized simulated reasoning capabilities. Even more striking, the o3-mini model costs just $1.10 per million input tokens and $4.40 per million output tokens, making it cheaper than even GPT-4o while providing much stronger performance on specific tasks.

OpenAI has likely known about diminishing returns in training LLMs for some time. As a result, the company spent most of last year working on simulated reasoning models like o1 and o3, which use a different inference-time (runtime) approach to improving performance instead of throwing ever-larger amounts of training data at GPT-style AI models.

OpenAI's self-reported benchmark results for the SimpleQA test, which measures confabulation rate.

OpenAI’s self-reported benchmark results for the SimpleQA test, which measures confabulation rate. Credit: OpenAI

While this seems like bad news for OpenAI in the short term, competition is thriving in the AI market. Anthropic’s Claude 3.7 Sonnet has demonstrated vastly better performance than GPT-4.5, with a reportedly more efficient architecture. It’s worth noting that Claude 3.7 Sonnet is likely a system of AI models working together behind the scenes, although Anthropic has not provided details about its architecture.

For now, it seems that GPT-4.5 may be the last of its kind—a technological dead-end for an unsupervised learning approach that has paved the way for new architectures in AI models, such as o3’s inference-time reasoning and perhaps even something more novel, like diffusion-based models. Only time will tell how things end up.

GPT-4.5 is now available to ChatGPT Pro subscribers, with rollout to Plus and Team subscribers planned for next week, followed by Enterprise and Education customers the week after. Developers can access it through OpenAI’s various APIs on paid tiers, though the company is uncertain about its long-term availability.

“It’s a lemon”—OpenAI’s largest AI model ever arrives to mixed reviews Read More »

new-ai-text-diffusion-models-break-speed-barriers-by-pulling-words-from-noise

New AI text diffusion models break speed barriers by pulling words from noise

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These diffusion models maintain performance faster than or comparable to similarly sized conventional models. LLaDA’s researchers report their 8 billion parameter model performs similarly to LLaMA3 8B across various benchmarks, with competitive results on tasks like MMLU, ARC, and GSM8K.

However, Mercury claims dramatic speed improvements. Their Mercury Coder Mini scores 88.0 percent on HumanEval and 77.1 percent on MBPP—comparable to GPT-4o Mini—while reportedly operating at 1,109 tokens per second compared to GPT-4o Mini’s 59 tokens per second. This represents roughly a 19x speed advantage over GPT-4o Mini while maintaining similar performance on coding benchmarks.

Mercury’s documentation states its models run “at over 1,000 tokens/sec on Nvidia H100s, a speed previously possible only using custom chips” from specialized hardware providers like Groq, Cerebras, and SambaNova. When compared to other speed-optimized models, the claimed advantage remains significant—Mercury Coder Mini is reportedly about 5.5x faster than Gemini 2.0 Flash-Lite (201 tokens/second) and 18x faster than Claude 3.5 Haiku (61 tokens/second).

Opening a potential new frontier in LLMs

Diffusion models do involve some trade-offs. They typically need multiple forward passes through the network to generate a complete response, unlike traditional models that need just one pass per token. However, because diffusion models process all tokens in parallel, they achieve higher throughput despite this overhead.

Inception thinks the speed advantages could impact code completion tools where instant response may affect developer productivity, conversational AI applications, resource-limited environments like mobile applications, and AI agents that need to respond quickly.

If diffusion-based language models maintain quality while improving speed, they might change how AI text generation develops. So far, AI researchers have been open to new approaches.

Independent AI researcher Simon Willison told Ars Technica, “I love that people are experimenting with alternative architectures to transformers, it’s yet another illustration of how much of the space of LLMs we haven’t even started to explore yet.”

On X, former OpenAI researcher Andrej Karpathy wrote about Inception, “This model has the potential to be different, and possibly showcase new, unique psychology, or new strengths and weaknesses. I encourage people to try it out!”

Questions remain about whether larger diffusion models can match the performance of models like GPT-4o and Claude 3.7 Sonnet, produce reliable results without many confabulations, and if the approach can handle increasingly complex simulated reasoning tasks. For now, these models may offer an alternative for smaller AI language models that doesn’t seem to sacrifice capability for speed.

You can try Mercury Coder yourself on Inception’s demo site, and you can download code for LLaDA or try a demo on Hugging Face.

New AI text diffusion models break speed barriers by pulling words from noise Read More »