Anthropic announced it will embed invisible watermarks into text generated by new Claude models, including output produced through its API, coding tools and cloud partners. For developers, the mark offers another way to trace where AI-generated text or code might have come from, but it is not strong enough to prove its origin.

Laying out the plan in a support document, Anthropic said Claude models launched in the EU on or after Aug. 2, 2026, will include machine-readable marking from release. The company is working to add support to older models as well.

The marks will apply worldwide across supported Claude products, including the Claude API, Claude Code, Claude Cowork and Claude Tag. Text generated through AWS, Google Cloud or Microsoft Foundry will also carry the watermark when those platforms use a supported model. Because the mark is added at the model level, it follows the output into applications built on top of Claude — the same applications that are already reshaping how enterprises deploy AI infrastructure — although Anthropic cautions that some platforms and features may not support every type of mark.

The change follows the Aug. 2 start of Article 50’s transparency requirements under the EU AI Act, which require providers of generative AI systems to make synthetic output detectable in a machine-readable format. Anthropic signed the accompanying Code of Practice as a provider of both generative AI models and systems. OpenAI, Google, Meta, Microsoft and Mistral are among the other model providers that have committed to the code.

Yet, Anthropic is handling text and files differently. Text gets a watermark hidden in the words themselves, while supported files such as SVGs, PNGs and JPGs receive a digital signature using the C2PA standard. The file metadata can show that Claude processed an asset and whether the metadata has been altered.

“Because the watermark is part of the text, it will travel with the text when it’s copied and pasted elsewhere, and may persist through some editing,” Anthropic said.

“Because the watermark is part of the text, it will travel with the text when it’s copied and pasted elsewhere, and may persist through some editing.”

How token-level watermarking works

Anthropic has not explained how its text watermark works or said whether Claude uses KGW, a semantic version or another method. The company has also not shared any figures showing whether watermarking affects latency or adds to inference costs — a gap that matters for teams already wrestling with the hidden costs of agentic AI workflows.

Alex Cui, CTO and co-founder of AI detection company GPTZero, wrote in a technical explainer on X that watermarking systems fast enough to run on a streaming frontier model often follow the same general approach. One such technique, known as the KGW method, changes the probabilities the model uses when selecting its next token.

A language model normally calculates a probability for every token that could appear next. In a simplified watermarking system, a secret key and the preceding tokens are used to generate a hash, which divides the candidate tokens into two groups, often described as green and red. The model then slightly increases the probability of selecting one of the green tokens.

A detector with the same key can use the preceding text to reconstruct which tokens would have been favored at each position. A passage containing an unusually high number of those choices may carry the watermark.

Cui wrote that more advanced approaches can derive the watermark from the meaning of nearby text rather than an exact sequence of tokens, which may help the signal survive some paraphrasing because replacing a word does not always change the surrounding context.

“Their watermark needs to work token-by-token because they are streaming their text to users,” Cui wrote. “Many watermark methods plan sentences or paragraphs at a time, or change the text after it’s entirely written, in order to make their watermark robust to paraphrasers.”

Anthropic has not confirmed that Claude uses any of these methods, but streaming limits the techniques available because the model must add the signal while generating its response rather than rewriting a completed passage afterward.

Code resists invisible marking

Code presents a different problem because the model has fewer valid choices. Words can often be swapped or sentences rewritten without changing their meaning, but seemingly minor changes can break working code. That challenge intensifies as the AI coding era matures and more production code flows through model-assisted pipelines.

“There are some texts, like code, that cannot be arbitrarily changed; otherwise the code will break,” Cui wrote. “In those cases, the watermark needs to selectively change words in parts of the text that can tolerate synonyms,” such as variable names.

Code may also be difficult to track through a normal development workflow. Anthropic has not published tests showing how well its watermark survives those changes, so teams do not yet know whether a Claude-generated patch will remain detectable after passing through a pull request.

“In my testing, the watermarks don’t survive intense paraphrasing, especially if you combine word choice and syntax attacks.”

Pipelines silently erase watermarks

The same issue comes up when applications change Claude’s output before showing it to a user or committing it to a repository. Summarizing it with another model, translating it, splitting it into smaller sections, turning it into structured data or mixing it with database content could all make the watermark harder to detect.

Anthropic acknowledges this limitation. Editing, paraphrasing, translating or combining the response with other text may weaken or remove the watermark, while short excerpts may not contain enough of the signal to detect.

Cui wrote that determined users can attack a watermark by changing both the vocabulary and the structure of a passage.

“In my testing, the watermarks don’t survive intense paraphrasing, especially if you combine word choice and syntax attacks,” he wrote. Cui added that free paraphrasing tools he tested were able to bypass Google DeepMind’s SynthID text watermark.

Research supports those concerns. The “Watermarks in the Sand” paper found that, under defined assumptions, attackers can remove watermarks without severely damaging the quality of the content. The absence of a watermark does not show that Claude had no role in creating the content. The response may have come from an older model, may be too short to carry a detectable signal or may have been changed somewhere in an application pipeline. It may also have passed through a platform or feature that does not support that type of mark.

Finding a watermark does not prove authorship either. Claude may have proofread, translated or reformatted material written by a person. Anthropic says a detected mark means only that the content “may have been processed by Claude,” not that Claude created the underlying work.

“If Anthropic releases the watermark detector publicly, I think they defeat their own watermark. People find reliable watermark-removal strategies by testing against Anthropic.”

Detection creates new risks

Anthropic plans to give users and third parties a way to detect its marks, but it has not said whether that will take the form of a local tool, a detection API or access limited to selected organizations. A public detector would be easier for developers to add to their applications, but it would also allow someone trying to remove a watermark to keep editing and checking the text until the signal disappears.

“If Anthropic releases the watermark detector publicly, I think they defeat their own watermark,” Cui wrote. “People find reliable watermark-removal strategies by testing against Anthropic.”

The secret keys behind the watermark create another challenge. A leaked key could make it easier to remove the mark or imitate it in text that Claude did not produce. The scenario echoes what happened when provenance attestations were turned into camouflage — a trust signal that was supposed to increase confidence instead became an attack surface.

“To avoid a large blast damage from this, you need to have a couple secret keys in rotation,” Cui wrote.

Key rotation would require detectors to recognize marks created with both current and retired keys, including those embedded in content generated months or years earlier. Anthropic has not explained how it plans to handle that history.

Watermarks aren’t a substitute for real provenance

For developers, Claude’s watermark is best treated as another clue, not a replacement for audit logs or provenance tracking. Applications that need to show where an artifact came from can record the model ID, prompt version, response time and a hash of the original output, then log any changes made before it reaches a user or is committed to a repository.

The watermarking announcement arrives as Anthropic navigates deeper questions about what its models do in the wild. Recent incidents have exposed gaps between lab safety evaluations and real-world containment, and the company has publicly backed calls for the most powerful AI labs to slow down. Watermarking fits into that posture — a transparency mechanism rather than a safety guarantee — but its practical value depends on technical details Anthropic has not yet shared.

Anthropic tells customers to determine how Article 50 applies to their own products and says more technical documentation is coming. But until Anthropic shares those details, teams do not know how they will detect the marks, how key rotation will work or how well the watermark will survive common changes to code and application output.

The post Anthropic’s watermark survives copy-paste, but not the real dev workflow appeared first on The New Stack.

At the start of 2025, investors wondered whether Google could keep pace with OpenAI. By December, Alphabet was completing its best year on the stock market since 2009, helped by growing confidence in Gemini and Google’s broader AI strategy.

Much of that work came out of DeepMind, the British AI lab Google acquired in 2014 for about £400 million ($659 million in 2014). Now, Google is changing its leadership, while four of its best-known engineers are leaving to start an automated research lab.

DeepMind’s leadership reshuffles

Google announced Wednesday that DeepMind founder Demis Hassabis will step away from the lab’s day-to-day operations to become chair of Google DeepMind and chief scientist of Alphabet. Koray Kavukcuoglu, DeepMind’s chief technology officer and Google’s chief AI architect, will take control of Gemini model development, frontier AI research, the Gemini app and its developer teams.

At the same time, Jeff Dean, Sanjay Ghemawat, Oriol Vinyals and Quoc Le are also leaving Google to start Discovery Loop. As a public-benefit corporation, the company wants to use AI to automate scientific and engineering research. Google will remain involved as a founding investor and cloud provider. Just one person will now oversee everything from research to products.

In a January interview for CNBC’s podcast The Tech Download, Hassabis described DeepMind as the “engine room” of Google’s AI efforts. Hassabis said DeepMind develops Google’s core AI technology before it is distributed across the company’s products.

To get new AI into products faster, Google had to do more than update its models. DeepMind spent years reworking Google’s infrastructure so its AI work could move out the door quicker. That infrastructure push has extended to silicon, too — Google recently bet its inference future on a chip built for one model, a sign of how tightly the company is coupling hardware to its Gemini roadmap.

Hassabis said Google never struggled to invent new technology. After all, its researchers came up with the transformer architecture, which became the backbone of large language models. The problem was turning that research into products fast enough.

And moving faster is exactly what Google did. Hassabis pointed to Gemini 2.5, which landed in March 2025, as a turning point. Gemini 3 came out in November and put Google back in the mix with OpenAI and Anthropic — both of which have been making aggressive moves of their own to capture developer share.

By January, Hassabis said he and Google CEO Sundar Pichai were speaking almost every day, sometimes adjusting product plans and research roadmaps daily.

Kavukcuoglu inherits the Gemini roadmap

Kavukcuoglu has been at DeepMind for 13 years, started the deep learning team, and worked on projects like WaveNet and DQN. Now, model research, the Gemini app, and developer products are all on his plate. The flagship version of Gemini 4 remains unreleased after a planned June launch, according to Reuters. Hassabis confirmed the model’s name in his note to employees, saying Google was making progress on Gemini 4.

Discovery Loop’s founding engineers

Dean joined Google in 1999 and helped create Google Brain before becoming a technical co-lead for Gemini. Working with Ghemawat, he developed systems including MapReduce, Bigtable, and Spanner. Dean was one of the primary designers of TensorFlow, while Ghemawat worked on the infrastructure behind Google Search and several generations of its distributed computing systems.

Vinyals and Le made foundational contributions to deep learning and Gemini. Along with former OpenAI chief scientist Ilya Sutskever, they co-authored the influential 2014 paper that introduced sequence-to-sequence learning with neural networks. Their new company plans to use that experience to work on machine learning itself, at least to start.

“We are building AI solutions that can automatically solve important problems in machine learning, science, and engineering,” Discovery Loop says on its website.

“We are building AI solutions that can automatically solve important problems in machine learning, science, and engineering.”

The idea that AI can automate scientific discovery is no longer speculative. OpenAI’s Astra recently proved 10 long-standing math and science theorems for about $2,000 in token costs — a data point that suggests Discovery Loop is entering a space where early results are already landing.

Discovery Loop is built around the idea that AI can automate the entire experimental cycle. At Y Combinator‘s Startup School in July, Dean said the system could run the entire process, from proposing and carrying out an experiment to evaluating the results and deciding what to test next.

Ghemawat told Wired that Google’s systems were designed to support products such as Search, advertising and large consumer applications. Discovery Loop wants to build specialized infrastructure around research instead.

“We wanted to build something differently than how things are built at Google right now,” he said.

“We wanted to build something differently than how things are built at Google right now.”

Google keeps a stake

Rather than cut ties with the departing engineers, Google is investing in Discovery Loop and has signed a cloud partnership to provide the startup with computing capacity. That arrangement gives Discovery Loop access to the infrastructure needed to run large numbers of experiments without first building its own data centers. It gives Google a stake in anything the new company discovers and another major AI workload for Google Cloud.

Rather than cut ties with the departing engineers, Google is investing in Discovery Loop and has signed a cloud partnership to provide the startup with computing capacity.

Google has already lost Gemini co-leader and transformer co-author Noam Shazeer to OpenAI and AlphaFold researcher John Jumper to Anthropic. Worth noting, Alphabet shares fell more than 5% following Wednesday’s announcement.

Hassabis, meanwhile, will focus more of his attention on long-term AGI strategy and Isomorphic Labs, the drug-discovery company spun out of DeepMind.

In his message to employees, he said AGI now feels “close at hand” and that he wants more time to influence what happens next — a sentiment that carries extra weight as some of the most powerful AI labs face growing pressure to slow down.

​

The post Google’s four AI departures: “We wanted to build something differently” appeared first on The New Stack.

DeepSeek has launched DeepSeek-V4-Flash-0731, delivering a significant boost in agent performance without changing the model’s core architecture.

Following an announcement last week, the company made the update available as a public beta through DeepSeek’s API, and the open weights were published on Hugging Face under the MIT license later the same day.

Although the model itself hasn’t changed, DeepSeek says additional post-training is responsible for the performance gains, showing that meaningful improvements don’t always require a larger model.

🚀 DeepSeek-V4-Flash Official API is now LIVE in public beta!

🔷 We’ve massively upgraded its Agent capabilities—benchmark scores are now far surpassing the V4-Pro-Preview. Check out the massive performance leap below! 👇
🔷 The official V4-Flash now natively supports the… pic.twitter.com/NUzOyxza2f

— DeepSeek (@deepseek_ai) July 31, 2026

DeepSeek’s decision to release the production-ready weights under a permissive license gives organizations much more control over how they deploy and customize the model.

Although the model itself hasn’t changed, DeepSeek says additional post-training is responsible for the performance gains, showing that meaningful improvements don’t always require a larger model.

Same architecture, better results

DeepSeek says V4-Flash-0731 uses the same architecture as the preview release, with 284 billion total parameters and 13 billion activated parameters per token.

This is much smaller than V4-Pro, which has 1.6 trillion total parameters and 49 billion activated parameters. For companies running agents at scale, the activated-parameter gap translates directly into inference cost — though model price alone doesn’t tell the full story.

Even though it is still the smaller model, DeepSeek says the updated Flash version now beats the earlier V4-Pro preview on several agent-focused benchmarks.

Even though it is still the smaller model, DeepSeek says the updated Flash version now beats the earlier V4-Pro preview on several agent-focused benchmarks.

The company reported 82.7 on Terminal-Bench 2.1, 54.4 on DeepSWE, and 70.3 on Toolathlon-Verified.

Benchmark claims under scrutiny

Early independent testing by Artificial Analysis found a lower Terminal-Bench 2.1 score of 79%, which suggests that DeepSeek’s reported numbers may not always match independent results.

DeepSeek also shared results from several internal tests, though they have not yet been independently verified. If those results hold up, they add to growing evidence that companies can get more performance out of existing models through post-training instead of simply making them larger.

Open weights, full control

The MIT license means organizations aren’t limited to using DeepSeek through its hosted API. The release adds to a trend towards open-weight models closing the gap with proprietary alternatives. That flexibility is paired with support for tools many developers already use. V4-Flash now supports the Responses API for building AI agents and multi-step workflows, and DeepSeek has published instructions for integrating the model into Codex-based development workflows.

Familiar APIs, lower switching costs

For teams already using OpenAI-style APIs, that lowers the barrier to trying another model because they can evaluate it without making major changes to their existing setup.

The V4 technical report also covers inference improvements, like speculative decoding with DeepSeek’s DSpark framework, which are designed to make serving more efficient. When combined with self-hosted deployments, these features give infrastructure teams more ways to adjust performance for their own production needs.

This release reflects that companies are now finding new ways to improve model effectiveness without making models larger, and they’re competing on how those models are delivered. While many AI vendors focus on hosted APIs, DeepSeek continues to publish downloadable weights that organizations can run on their own infrastructure. Support for familiar API formats also makes it easier for teams to test open-weight models without revamping present workflows.

Companies are finding new ways to improve model effectiveness without making models larger, and they’re increasingly competing on how those models are delivered.

The post DeepSeek’s smaller model just outperformed its own flagship appeared first on The New Stack.

OpenAI has lowered API prices for two GPT-5.6 models only three weeks after their launch. On Thursday, the company announced that GPT-5.6 Luna is now 80% cheaper and GPT-5.6 Terra is 20% cheaper, while the price for its main reasoning model, GPT-5.6 Sol, stays the same.

“Major price cuts today,” OpenAI CEO Sam Altman writes in a post on X published on Thursday. “We want to offer the best price/intelligence tradeoff at every level.”

“We want to offer the best price/intelligence tradeoff at every level.”

Luna now costs $0.20 for a million input tokens and $1.20 for a million output tokens, down from $1 and $6. Terra is priced at $2 per million input tokens and $12 per million output tokens, reduced from $2.50 and $15. Sol’s price stays at $5 per million input tokens and $30 per million output tokens.

Developers using Luna do not need to change their processes, but their inference costs will go down. High-volume tasks will now be much cheaper to run, without requiring any code updates or model changes.

major price cuts today:

*80% drop for GPT-5.6 Luna, now $0.20 per million input tokens and $1.20 per million output
*20% drop for GPT-5.6 Terra, to $2/$12
*GPT-5.6 Sol gets Fast mode in the API, up to 2.5x the speed for 2x the price, same intelligence pic.twitter.com/erC6u4VoDR

— Sam Altman (@sama) July 30, 2026

This timing is unusual because AI vendors usually keep prices steady for several months after launching a new model family. OpenAI cut prices less than a month after GPT-5.6 became available on July 9.

…serving costs can be more important than small differences in benchmark performance between models.

Infrastructure gains drive savings

The company says these price cuts were possible because of improvements to the infrastructure behind GPT-5.6, which lets the company offer “substantially more intelligence per dollar.”

These infrastructure upgrades were expected, however. A day before the price announcement, OpenAI shared an engineering overview that explained optimizations across the inference stack for Codex and ChatGPT Work.

GPU kernels rewritten for efficiency

OpenAI engineers rewrote the production GPU kernels, cutting serving costs by about 20%. They also redesigned Sol’s speculative decoding system, making token generation over 15% more efficient. The company updated its agent runtime as well, reducing repeated prompt computation by using prompt caching more during multi-step workflows.

Agents amplify inference costs

Lately, developers are paying more attention to inference costs since agents often make dozens or even hundreds of model calls to finish a single task. For these workloads, serving costs can be more important than small differences in benchmark performance between models.

The elephant in the room is that the competition has intensified from overseas. Lower-cost open-weight models from Chinese AI companies like Moonshot are pushing commercial providers to show not just better performance, but also better pricing for production use. OpenAI and Anthropic know that leaning on performance just isn’t an option anymore, which is pushing them to match Chinese prices.

The issue here is that most of those steps don’t need a model like Sol, and Chinese labs have figured out how to pack better capabilities into efficient models; a helpful option for companies running through billions of tokens a day.

The ability to send the easy tasks to open models and save the pricey APIs for the tough stuff makes a difference; OpenAI is banking on its 80% price cut on Luna to narrow that gap. Suddenly, switching to self-hosted models doesn’t look worth the hassle.

Competition reshapes model pricing

These pricing changes come as both OpenAI and Anthropic keep adjusting the economics of their newest model families — a dynamic that played out across three companies in a single week earlier this month. Earlier this week, OpenAI raised GPT-5.6 Sol usage limits for ChatGPT Work and Codex after finding that long coding sessions used up allowances faster than expected. Anthropic has also made pricing changes and added premium inference tiers as enterprise customers move bigger agentic workloads into production.

This announcement highlights a trend in the industry for infrastructure. Now, every percentage point of serving efficiency can lead directly to lower API prices, turning cost optimization into a competitive advantage instead of just an engineering goal.

The post OpenAI slashes API costs amid rising global competition appeared first on The New Stack.

Moonshot AI has released the open weights for Kimi K3 on Hugging Face, giving developers access to one of the largest open-weight language models yet. The Monday release follows a wave of overwhelming demand that forced Moonshot to temporarily pause new API subscriptions. Now, organizations with the necessary hardware can deploy K3 themselves.

In its documentation, Moonshot describes the model as being built for “long-horizon coding and end-to-end knowledge work.” Another notable detail is that Kimi K3 uses an OpenAI-compatible API. Because teams can try the model without rebuilding their existing integrations, switching to K3 could be as simple as changing the endpoint and model name. 

For engineers who have already built around OpenAI-compatible SDKs, that makes it much easier to evaluate K3 alongside existing commercial models. Taken together with the one-million-token context window, it’s clear the company is targeting engineering teams that already build around models like Claude Fable 5 and OpenAI’s GPT-5.6 Sol. While K3 is openly available, running it is another matter.

While K3 is openly available, running it is another matter.

Kimi K3: Its massive size and requirements mean few will be able to run it

The model uses a 2.8-trillion-parameter Mixture-of-Experts (MoE) architecture and ships in the hardware-friendly MXFP4 format. The weights alone occupy roughly 1.4 TB of storage, and practical self-hosted deployments require a distributed GPU environment — realistically eight or more servers equipped with eight NVIDIA H100 or B200 accelerators each.

That changes the conversation around open-weight AI. As The New Stack recently noted, the case for ownable models has grown stronger after Anthropic’s Fable 5 was pulled offline by a Commerce Department directive, a warning that access is not ownership.

Releasing the model weights and technical report of Kimi K3.

Kimi K3 is our most capable model: a 2.8T MoE model with native visual understanding and a 1M-token context window.

New model architecture: 2.5x the intelligence per unit of compute, not just more params.

Alongside… pic.twitter.com/Yz5uWeMbIm

— Kimi.ai (@Kimi_Moonshot) July 27, 2026

Ownership versus API economics

Instead of paying recurring API costs to OpenAI or Anthropic, organizations trade those operating expenses for significant investments in GPUs, networking, storage, power and operational expertise. That benefit is control. 

For organizations operating under strict regulatory requirements, the trade-off may justify the infrastructure investment. For many others, managed APIs will potentially remain the more economical option. The economics of open-weight models at enterprise scale remain an active area of debate across the industry.

Moonshot positions K3 as a frontier-class model capable of competing with OpenAI’s GPT-5.6 Sol and Anthropic’s Claude Fable 5 on a variety of public benchmarks.

Benchmarks versus real workloads

The developer community is already taking notice of these coding capabilities. As MindStudio recently noted, “If you want to understand why developers are paying attention to Kimi K3, the benchmark to look at is SWE-bench Verified… For most of its history, SWE-bench has been dominated by proprietary models.”

The company’s own documentation is notably candid about its ongoing limitations. K3 always runs with reasoning enabled and defaults to its highest reasoning-effort setting, though Moonshot has since added lower-effort tiers. It may also behave too proactively when prompts are ambiguous. Moonshot also cautions that switching models within an ongoing conversation can reduce response quality.

That type of transparency is refreshing, but it additionally reinforces that benchmark scores shouldn’t drive deployment decisions. Early hands-on comparisons, such as The New Stack‘s Fable 5 vs. K3 coding match-up, suggest K3 can match Fable 5 on programming tasks at roughly a third of the cost, but runs about four times slower.

Organizations evaluating K3 still need to test it against their own workloads. But early community sentiment shows promise; open-source developers are already successfully utilizing K3 for complex, system-level tasks like porting the Godot game engine to WebGPU.

Morningstar senior equity analyst Malik Ahmed Khan echoed that overall wariness about benchmarks. “While K3 constitutes progress, we’d hesitate to ascribe it near-parity with American frontier models, such as Fable 5, in actual tasks,” Khan writes in a research note published before the release of the model weights on Monday.

Geopolitical risks loom large

K3 also arrives under growing geopolitical scrutiny. Anthropic and U.S. officials have accused Moonshot AI of distilling outputs from American frontier models during training. Anthropic’s Head of Public Policy Sarah Heck characterized the practice as intellectual property theft, while White House Office of Science and Technology Policy Director Michael Kratsios publicly alleged Moonshot relied on Anthropic’s models during development.

Moonshot has denied the allegations. Huang Zhenxin, Moonshot’s head of enterprise business, told Chinese state media that K3’s performance gains stem from architectural enhancements — specifically Kimi Delta Attention and Attention Residuals — not distillation. Some industry analysts have also questioned whether the timeline supports large-scale distillation, noting that Fable 5 had only been publicly available since July 1 before K3 appeared on July 16.

Whether those claims are ultimately substantiated or not, they bring another consideration for enterprise buyers. Beyond performance and infrastructure costs, organizations evaluating K3 may also have to consider future compliance, procurement, and regulatory risks.

K3 matters because of where it’s aimed. Moonshot isn’t building another consumer chatbot; its documentation makes clear this model was built for enterprise coding agents, heavy knowledge work, and production systems. The fact that demand blew past Moonshot’s GPU capacity within 48 hours says it all: at this scale, infrastructure pressure is guaranteed, whether you’re making API calls or hosting the weights yourself. 

The post Moonshot opens Kimi K3 weights — but few can run it appeared first on The New Stack.

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