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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.
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.
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.
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’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.
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
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.”
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.
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.
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.
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.
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.
NVIDIA Ising Calibration is an open source vision language model (VLM) designed to interpret diagnostic outputs from quantum processors and determine how they...
NVIDIA Ising Calibration is an open source vision language model (VLM) designed to interpret diagnostic outputs from quantum processors and determine how they should be tuned to continue operating. This post introduces the latest model release, NVIDIA Ising Calibration 1.5, which advances AI-based QPU calibration by analyzing unfamiliar diagnostic results without prior training examples.
Nvidia CEO Jensen Huang used his first post on X to share a public letter backing frontier open-weight models. Signed by Microsoft, Meta, Hugging Face, and 22 other organizations, the letter argues that open models improve security, encourage faster innovation, and give countries and enterprises more control over their AI infrastructure. The timing of the Friday post is notable, with more organizations choosing to run open-weight models inside their own environments rather than relying entirely on hosted services.
“Open weights let every organization match the right model to the right job at the right cost, reserving frontier-scale capability for genuine frontier problems and running efficient, specialized models everywhere else.”
A case for hybrid stacks
The letter draws a comparison between open-weight AI and open-source software, arguing that broad access has driven decades of software innovation. It says the same principle applies to AI. Instead of sending every request to a hosted model, organizations can download an open-weight model, run it on their own infrastructure, customize it for a specific workload, and keep sensitive data behind their own firewall.
One isn’t necessarily replacing the other, and many enterprise teams already use both, choosing between them based on cost, performance, compliance requirements, and where the workload needs to run.
The timing is notable. Washington is weighing new restrictions on some Chinese AI models, including Moonshot AI’s Kimi K3, even though the Trump administration’s AI Action Plan described open models as a strategic advantage for the United States.
That split extends to the AI industry itself, though critics differ in their specific concerns. OpenAI’s Dean Ball has publicly warned about the broad economic and regulatory risks associated with Chinese open-weight models. Anthropic’s Sarah Heck, meanwhile, has specifically backed the White House’s allegations regarding intellectual property theft through distillation.
“Relying solely on closed models is not inherently safe: they can be breached, misused, or fail in ways that outsiders cannot detect. And concentrating advanced AI capabilities behind a small number of closed models compounds that risk.”
The companies that signed the letter argue that continuing to develop frontier open models is part of maintaining U.S. leadership in AI. They argue that security through obscurity is fundamentally flawed, stating: “Relying solely on closed models is not inherently safe: they can be breached, misused, or fail in ways that outsiders cannot detect. And concentrating advanced AI capabilities behind a small number of closed models compounds that risk.”
Distillation, one of AI’s most contentious topics, was also addressed. Widely used by researchers and model builders to create systems that are cheaper to run and easier to deploy, the letter states that it should be treated as a legitimate research technique rather than intellectual property theft.
Distillation is one way teams build smaller models that can run on private infrastructure, edge devices, or lower-cost GPU clusters. Kimi K3’s launch showed just how much GPU capacity these deployments demand — Moonshot had to freeze new subscriptions within 48 hours after demand overwhelmed its available inference capacity. Any new restrictions on that process could affect how enterprise AI systems are built and deployed.
“As organizations invest in AI, they want to know that they will not become locked into a single provider or lose the knowledge and capabilities they build over time.”
Nvidia’s infrastructure incentive
Every enterprise that chooses to self-host, fine-tune, or customize an open-weight model needs infrastructure to run it. Nvidia also has clear business reasons for supporting a future in which both proprietary and open-weight models thrive.
The AI industry has largely focused on language-based approaches, using transformers trained on massive datasets to predict words or fill in missing information.
As AI expands into more structured fields, however, the limitations of text-generation models are becoming clearer. Nvidia is pursuing a different approach.
The company just dropped JEPA-DNA on Hugging Face. It’s a genomic foundation model that adds a latent-space prediction objective alongside MLM.
This release is a win for hybrid architectures that go beyond purely generative training. It’s the kind of shift Executive Chairman of AMI Labs, formerly Meta’s Chief AI Scientist, Yann LeCun, has been pushing for years. He’s championed predictive architectures as a general alternative to next-token prediction, which researchers are now applying to biology.
Latent space over literal tokens
Conventional genomic base models have historically mirrored NLP models, relying purely on MLM, masking parts of a DNA sequence and forcing the model to guess the missing literal tokens. This approach favors local token reconstruction and teaches the model the basic “syntax” of the sequence, but it frequently struggles to grasp the wider functional “meaning.”
JEPA-DNA changes the paradigm. The newly released checkpoint, JEPA-DNA-DNABERT2, serves as a model-agnostic continual pre-training framework. It couples standard token-level DNA language modeling with JEPA, adding a second learning objective.
Instead of forcing the architecture to reconstruct missing tokens, JEPA-DNA supervises the model’s global sequence embedding in a latent space.
Instead of forcing the architecture to reconstruct missing tokens, JEPA-DNA supervises the model’s global sequence embedding in a latent space. It predicts the functional representation of masked genomic segments rather than their literal, character-by-character makeup.
Token prediction is still part of the training process, but it is no longer the model’s only learning objective. The model also learns the basic structure of the data, proving that predictive architectures can produce representations that are easier to work with for biological tasks without sacrificing generative capabilities.
Token prediction is still part of the training process, but it is no longer the model’s only learning objective.
How DNABERT-2 gets upgraded
The model builds on DNABERT-2, a 117 million-parameter model developed by Zhihan Zhou and collaborators. Nvidia layers its continual pre-training approach on top of that architecture, letting the model learn from both token-level predictions and latent-space representations.
Nvidia has released the model globally for non-commercial research. According to the company, the model is meant to support research workflows, including feature extraction, linear probing, continual pre-training experiments, and zero-shot scoring of DNA sequence changes. It isn’t a diagnostic tool or a clinically validated medical product.
Beyond the generative hammer
DNA contains patterns and relationships that aren’t captured by sequence prediction. JEPA-DNA supplements masked-token prediction with a latent-space objective intended to capture broader sequence-level information.
Ultimately, models like JEPA point to a new path for AI that combines next-token prediction with other ways of learning and helping models build a deeper understanding of complex systems.
Cursor, the AI coding tool recently acquired by Elon Musk’s SpaceX in a $60 billion all-stock deal, has launched a model router designed to direct every coding request to whichever model handles it best, bypassing the need to pay frontier prices for work that doesn’t need it.
Under the hood, the new Cursor Router uses a triage system à la a hospital emergency room: It looks at what a request actually needs — how hard it is, what it’s for, the surrounding code — and picks a model that is the best fit. A quick fix goes somewhere cheap, while a genuinely hard problem gets escalated to something closer to frontier-grade.
Notably, developers and admins also have access to three distinct modes that nudge that balance in either direction, favoring speed and cost over raw power, or vice versa if needed.
Choosing from three optimization modes
The broader rationale for Cursor Router, according to the company’s own field CTO David Pan in a social media post on Wednesday, is that developers shouldn’t have to become experts in model performance just to write code.
“We briefly went insane and decided every software engineer should also become an expert in model benchmarks, thinking levels, and cache hit rate.”
“We briefly went insane and decided every software engineer should also become an expert in model benchmarks, thinking levels, and cache hit rates,” Pan writes.
We briefly went insane and decided every software engineer should also become an expert in model benchmarks, thinking levels, and cache hit rates.
Cursor Router is out today. It picks the model for each task based on measured quality against cost. Fable 5 level performance for… https://t.co/3B2103mGWr
Early community feedback has largely echoed Pan’s sentiment: Fatih Arslan, a software engineer at PlanetScale, notes on X that engineers already juggle the choice between cost and capability by hand — defaulting to a cheap, fast model for routine work and saving the slow, expensive one for “serious tasks.”
Nice! I wanted something like this for a long time. See my previous rant last year. I think as engineers, we're already developing skills ourselves which model to choose. We use a less costly/fast model for implementing chore details, but use a higher reasoning model (slow and… https://t.co/5y5xMOR2E6
“We already spend quite a bit [of] time on [choosing models],” Arslan writes. “Why not automate that part? Cursor Router does the automation.”
“We already spend quite a bit time on it [choosing models]. Why not automate that part?”
In a separate blog post published on Wednesday, Cursor claims that early access customers saved 30-50% compared to routing everything through Opus 4.8, with no drop in output quality.
Working model: Taking control of the stack
The launch follows a run of moves by Cursor to control more of its own AI stack. In May, the company released Composer 2.5, an update to its in-house coding model built for long tasks at a lower cost than frontier options from Anthropic and OpenAI. Composer 2.5, like its predecessor, is built on Moonshot AI’s Kimi K2.5, an open-weight model out of China.
Now, with the weight of one of the world’s most valuable companies behind it (SpaceX has attained a market cap of $1.5 trillion since its June IPO), Cursor is pushing a powerful frontier model of its own.
On July 8, Cursor and SpaceXAI jointly released Grok 4.5, a mixture-of-experts model built on a new foundation dubbed V9, which Musk had previously noted was roughly 1.5 trillion parameters. The model’s trained on trillions of tokens of real Cursor usage data and available across all Cursor plans at $2 per million input tokens and $6 per million output tokens.
With Composer handling cheap, fast work, and now the Grok-branded frontier line for more serious horsepower, Cursor has its own models in the mix alongside the usual list of outside providers. And this gets to the heart of why Cursor built Router: Most developers pick one model and stick with it regardless of the task, billing simple work at frontier prices it doesn’t need.
Sending every request to its own models would be the easy way to keep that money in-house, but it would also mean shipping inferior output on some tasks — so Router instead sends each request to whichever model actually suits it, Cursor’s own or not.
The lay of the land
Model routing itself isn’t exactly new. OpenRouter has offered a version since 2023: a single API sitting in front of more than 400 models from over 60 providers, including OpenAI, Anthropic and Google. Its own auto-router feature does roughly what Cursor Router does — classify a request, then send it to whichever model fits the task and the person’s stated preference between cost and quality.
More recently, OpenRouter launched Fusion, which takes a slightly different approach: instead of picking one model, it sends a prompt to several models at once and uses a judge model to synthesize the strongest answer out of all of them.
This past month ushered another entrant to the mix: Japan’s Sakana AI released Fugu in June, which instead breaks a single task into subtasks and routes each piece to a different model, pitched by Sakana as a hedge against relying on any one AI provider.
“[Cursor Router is] a great example of how a technological innovation immediately translates into a product improvement.”
Not everyone rates some of these other attempts, though. On Wednesday, Kirill Balakhonov, head of AI products at Nethermind, argues on LinkedIn that Cursor’s version succeeds precisely because it’s focused on coding specifically, rather than trying to be a general-purpose router for any task.
“A great example of how a technological innovation immediately translates into a product improvement… rather than an abstract idea like Sakana Fugu or OpenRouter Fusion,” Balakhonov writes, predicting both of those broader routing efforts would fade from use.
What’s new, perhaps, is some of the names emerging behind an array of model diversity efforts. In early July, Microsoft launched a $2.5 billion services unit dubbed Microsoft Frontier Company, embedding thousands of engineers at customer sites to help them build with a mix of AI models.
Judson Althoff, CEO of Microsoft Commercial Business, told Reuters at the time that the push came partly from watching rivals like DeepSeek and Google’s Gemini close the gap on OpenAI. Referring to the original Copilot, he admitted, “we made a mistake by binding it to OpenAI models only.”
If the company with the deepest single-model relationship in the industry is walking it back, the idea for model flexibility has clearly gone mainstream — certainly if this week is anything to go by.
Ramp and Meta get in on the act
On Tuesday, Ramp, the $44 billion spend-management behemoth, opened up Ramp Router, an early-access public version of the model router it built to manage its own AI bills internally, which it says cut its LLM costs by roughly 30%. It’s free to start, requires no Ramp account, and routes across OpenAI, Gemini, and select open-source models including Kimi through an OpenAI-compatible endpoint.
The very same day, Jyoti Mann fromThe Informationreported that Meta is also working on a model router. According to internal documents cited in the report, an incubator inside Meta called AAI Labs is developing a new product named Switchboard that would score each request for difficulty and send simpler ones to smaller, cheaper models — aimed initially at cutting Meta’s own AI agent costs, though it reportedly could end up as a public release.
Meta has particular reason to want this. Data from Runpod’s State of AI report, published in March, suggests Meta’s open Llama models are now a marginal presence in production: Llama 4 has near-zero real-world deployment, with Alibaba’s Qwen having overtaken it as the most-deployed self-hosted LLM.
Meta has also been building proprietary models in response. Muse Spark, its first model out of the newly formed Meta Superintelligence Labs, shipped in April. That was followed in July by Muse Spark 1.1, Meta’s first model with a public, paid API, priced at roughly a quarter of what OpenAI and Anthropic charge for comparable models.
Meta is aggressively targeting the incumbents, and Switchboard fits the same pattern: a way to make it easier for users to cut costs, switch models freely, and, where it makes sense, land requests on Meta’s own models instead.
But amidst all this hullaballoo about model routing, there is perhaps a broader question of openness. Not of the models themselves, which is a fervent debate in itself, but of whether the routing decision itself — the logic that decides which model handles which request — should sit inside a vendor’s own closed product at all.
“Is anyone building this as open-source?”
Elvis Saravia, a former technical product marketing manager at Meta AI who co-founded DAIR.AI, took to X to argue that it shouldn’t, given how differently teams weigh cost against quality.
“Is anyone building this as open-source?” Saravia asks. “It feels like this is something you don’t want to offload to an API. We all work with different trade-offs, so we need the ability to achieve custom routing.”
Recommended reading. Cursor Router routes tasks to the right model.
Is anyone building this as open-source? It feels like this is something you don't want to offload to an API. We all work with different trade-offs, so we need the ability to achieve custom routing. https://t.co/zcrpHbnlCepic.twitter.com/Vw2xyMy1jp
As for Cursor’s own version, Router is currently available to Teams and Enterprise customers only, across desktop, web, iOS, CLI, and Cursor’s SDK. It’s not yet clear whether it will eventually land on individual plans.
Moonshot AI became the latest AI company to discover that launching a popular model is only half the battle. Less than two days after releasing Kimi K3, the company stopped accepting new subscribers after demand exhausted its available GPU capacity. Existing users will keep access while Moonshot expands its infrastructure and reopens subscriptions in batches.
Inference demand outpaces supply
The incident emphasizes how demand is outpacing available infrastructure. As AI models take on longer, more coding and agentic workloads, companies are finding they need more inference capacity than they anticipated.
“Kimi K3 has received far more love than we expected,” the official Moonshot account writes on X. “Over the past 48 hours, demand has pushed close to the limits of our current capacity. We’re adding capacity as fast as we can and will reopen new subscription spots in batches.”
Kimi K3 has received far more love than we expected, and our GPUs are feeling it.
Over the past 48 hours, demand has pushed close to the limits of our current capacity. To protect the experience of existing subscribers, we're temporarily pausing new subscriptions and…
“We’re adding capacity as fast as we can and will reopen new subscription spots in batches.”
For infrastructure engineers and developers, the resulting capacity crunch is a bold indication of why companies from OpenAI to Anthropic to Moonshot are rationing access instead of selling unlimited usage.
Open weights, closed capacity
At 2.8 trillion parameters, Kimi K3 is one of the largest open-weight models slated for release — Moonshot has scheduled the public weight drop for July 27. In Arena.ai’s Frontend Code Arena, K3 topped both OpenAI’s GPT-5.6 Sol and Anthropic’s Claude Fable 5. On the broader Artificial Analysis Intelligence Index, it trails both, scoring 57 to Fable 5’s 60 and Sol’s 59. That doesn’t make it any easier to run.
Open weights let anyone deploy the model, but whoever hosts it still has to pay the inference bill. Coding activities tend to tie up GPU resources far longer than a typical chatbot interaction, making it harder to keep latency low as more developers pile on.
Lee argued that as developers build longer agentic workflows, lower inference costs are quickly “re-converted into higher total resource consumption,” shifting the bottleneck from compute to server memory.
Moonshot’s subscription pause is a sign that keeping enough inference capacity online once developers start using it at scale might be just as hard as building the model.
“Agent tasks are not one-off question answering; they continuously generate, read, and process tokens during ongoing tasks.”
China’s chip constraints compound crunch
For a company like Moonshot, this general industry bottleneck is compounded by regional infrastructure realities. Unlike traditional software companies, AI developers typically rent much of this computing power from cloud providers such as Alibaba Cloud, Tencent Cloud, and Huawei Cloud rather than owning extensive data-center infrastructure themselves.
The capacity crunch illustrates the mounting challenge facing Chinese AI developers as US export controls continue to restrict access to leading chip provider Nvidia’s most advanced AI chips. As a result, companies such as Moonshot depend on a combination of older chips and domestically produced alternatives. These constraints have forced Chinese developers to concentrate strongly on software tuning and more efficient use of computing resources to narrow the performance gap with US rivals.
Token economics under pressure
The scramble for computing power has fueled a data-center construction boom across China. Alibaba has committed more than $53 billion to AI and cloud infrastructure over three years, while ByteDance is reportedly considering spending as much as $70 billion this year on AI data centers and related infrastructure.
AI companies typically charge customers based on the number of tokens, or units of text, processed by a model, making token prices a key measure of operating costs.
According to Bernstein Research, Moonshot charges $3 per million input tokens and $15 per million output tokens for Kimi K3. This makes it about 40% cheaper than Anthropic’s Opus 4.8 and roughly 70% cheaper than Claude Fable 5.
“A world where there are only [two to three] dominant frontier labs with 90 percent inference margins is net negative for every other layer while being awesome for those [two to three] labs,” wrote Atreides Management founder Gavin Baker on X. Baker argued that models like Kimi K3, Grok 4.5, and Muse 1.1 could shift value away from the model layer and toward chipmakers, cloud providers, and the software companies building the infrastructure that serves AI models.
For developers, Moonshot’s subscription freeze serves as an architectural warning. The era of assuming infinite, cheap API access is ending.
“A world where there are only [two to three] dominant frontier labs with 90 percent inference margins is net negative for every other layer while being awesome for those [two to three] labs.”
Frontier model pre-training has converged on mixture of experts (MoE), which is fundamentally changing what limits large-scale AI training. As compute per token...
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After weeks of extending temporary access while bringing additional inference capacity online, Anthropic has finalized Claude Fable 5 subscriptions.
In a Friday announcement on X, the company said Claude Fable 5 will become a permanent part of Max and Team Premium subscriptions beginning Monday, July 20, at 50% of each plan’s usage limits. Pro and Team Standard users will transition to using the model through usage credits and will receive a one-time $100 credit.
An Anthropic spokesperson tells The New Stack, “Friday’s announcement means that Fable 5 at 50% of usage limits is now a standard, permanent part of Max and Team Premium plans.”
The changes bring an end to weeks of temporary extensions that left subscribers wondering whether Fable 5 would remain part of their subscription. Rather than introducing a new usage policy, Anthropic is making permanent the same access level subscribers have had since Fable 5 was redeployed on July 1.
Capacity drove the timeline
The move gives a glimpse into the economics of serving frontier AI models. Although Anthropic repeatedly extended temporary access over the past several weeks, the company says making Fable 5 a permanent part of Max and Team Premium subscriptions was always the goal.
An Anthropic spokesperson tells The New Stack, “Friday’s announcement means that Fable 5 at 50% of usage limits is now a standard, permanent part of Max and Team Premium plans. This was always our intention with Fable access (see more in our original launch blog on June 9). We planned to expand Fable 5 access in stages as we brought additional inference capacity online, with the ultimate goal of making it a standard part of subscription plans.”
Fable 5 usage limits explained
Anthropic has not explained exactly how it calculates the 50% limit or how many prompts that will translate to in practice. Developers using Fable 5 for heavy workflows are likely to reach the limit sooner than lighter users.
The company has said demand for Claude Fable 5 was difficult to predict and that broader access depended on bringing more capacity online. Reuters reported earlier this week that the company is in talks with Meta over a potential compute lease worth up to $10 billion, which could give Anthropic access to Meta’s AI infrastructure for as long as two years.
Meta compute deal looms
Thariq Shihipar, an Anthropic Developer Relations team member, posted on X, crediting the company’s employees with getting the necessary capacity online.
“This was due to a heroic effort by many people at Anthropic working sometimes literally around the clock,” Shihipar wrote on X. “It was not at all clear that we’d be able to do this in time, and so proud of everyone who made it happen. Enjoy Fable.”
“This was due to a heroic effort by many people at Anthropic working sometimes literally around the clock. It was not at all clear that we’d be able to do this in time, and so proud of everyone who made it happen. Enjoy Fable.”
Claude expands into India
The revised policy reflects broader changes across the AI industry. Frontier models are becoming expensive, which means AI companies are forced to balance compute costs with subscription pricing. OpenAI recently introduced GPT-5.6 Sol, while Chinese AI companies have released increasingly capable open-weight models, including Moonshot AI’s Kimi K3. Although Anthropic has not linked today’s pricing decision to competitive pressure, both model capability and the cost of serving those models have become key competitive factors.
…both model capability and the cost of serving those models have become key competitive factors.
Alongside the subscription changes, Anthropic is also formalizing its presence in one of its fastest-growing markets by introducing local pricing for Claude in India.
Indian customers can now pay for Claude in rupees instead of U.S. dollars, with GST included in the listed prices. That removes foreign currency conversions and other cross-border payment costs that previously came with subscribing. Claude Pro is now priced at Rs. 2,000 per month with annual billing or Rs. 2,399 on a month-to-month plan (previously $20 USD per month).
Industry reports have identified India as Claude’s second-largest user base after the United States. Together, the announcements show Anthropic expanding Claude in two ways: by making subscriptions easier to purchase in a major growth market while locking in an access policy determined by the compute required to serve Fable 5.
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While the household-name frontier models race forward with version numbers and context windows of at least a million tokens, a new breed of upstart data science specialists is pushing the context window into double figures.
Subquadratic debuted a 12-million-token window in May of this year, and Silicon Valley and South Africa-based Refiant launched its 10-million-token context-window model, Protea, on Wednesday. It’s a move that may signal the long-context AI race is now on.
Model inefficiency & workarounds are commonplace
But more context windows alone are not enough. This is because even the most capable models have a few hundred thousand tokens in working memory, which can force workarounds to compensate for what the model can’t access.
Refiant co-founder Dr. Viroshan Naicker tells The New Stack that he believes modern LLMs “fail to be organically efficient at an elemental level” and that his organization’s approach mimics how systems in nature, from ant colonies to beehives, find efficient solutions to complex problems.
“This is no case of pseudoscientific puff; nature is the most computationally efficient system that we know, and many algorithms used in science are nature-inspired,” Naicker says. “This is a road well-traveled in science. There are multiple teams globally working in this particular (nature-inspired) direction, trying to bridge the gap between AI inference as we know it and the energy efficiency of natural systems.”
“Fish and birds coordinate their movements to converge on the mathematically shortest, most efficient routes — honeybees, fireflies and bacteria are also programmed to use degrees of swarm-style optimization.”
What can the birds & the bees teach us about AI?
Did Naicker just mention ant colonies and honeybees?
Yes, because Refiant uses swarm-style optimization. It’s seen in ant colonies, which initially move randomly until a food source is detected, after which they leave a pheromone trail for other ants to optimize their journeys. Fish and birds also coordinate their movements to converge on the mathematically shortest, most efficient routes. Honeybees, fireflies, and bacteria are also programmed to use degrees of swarm-style optimization.
Naiker, along with his co-founders, Siddharth Gutta and Mathew Haswell, form a team with experience spanning quantum mathematics, traditional finance, and commercial scaling. Applying swarm-style optimization to data in Protea means inference is performed through a combination of compression and context management.
“From our perspective, we are also advancing a technology which provides context-specific inference models grounded in data,” Naicker clarifies. “We think this has value for reducing model hallucinations, replacing RAG, and constructing better, more reliable, agentic workflows. This adds a layer of trust in sensitive application scenarios, rather like an added insurance, rather than taking it away.”
Just how much is 10 million tokens?
The Refiant team describes 10 million tokens as equivalent to 7.5 million words in a single conversation (and we know from Anthropic’s own benchmarks this year that Claude has a 1-million context window), or five years of a user’s emails, 83 novels, or 830 podcast episodes, all held in active memory at the same time.
The team claims Protea is capable of working on entire enterprise codebases or decades of clinical trial data — datasets that previously had to be broken apart and fed to models in fragments — so they can be processed in a single pass with full fidelity. Engineers on Protea also submit that they can successfully tackle the “lost in the middle” problem — a limitation of million-plus-token windows, where models stay accurate at the start and end of the context but lose the thread of everything buried in between.
Refiant first applied these techniques to model compression, shrinking OpenAI’s GPT-OSS-120B so it could run on a MacBook Pro with 18GB of RAM.
“Rather than publishing benchmarks, we’re inviting users to run the models and try them out.”
The Protea series is open and live, and Refiant is inviting teams to stress-test the context window across different industries and use cases. But should we trust sensitive hould enterprise data archives to a completely unproven startup founded only one year ago?
Bring-your-own-cloud, a possible progression
“We adhere to data management best practices, processes and compliance requirements,” Naiker confirms. “This is reasonable for a startup at our particular stage. Privacy and data sovereignty are important values for us, and we are actively exploring edge, self-hosted, and bring-your-own-cloud data models.”
But a 10 million-token context window is big. Won’t that fall short when Protea starts to suffer from massive latency spikes when processing a full dataset? Naiker agrees that “latency is a core issue with long-context inference models,” but in the tests his company has run, it has delivered inference at a reasonable latency, even with large token windows.
“We have internal reports and tests that validate the technology, including Ruler, MRCR and Babilong, but we aren’t asking anyone to take our word on this. Rather than publishing benchmarks, we’re inviting users to run the models and try them out,” adds Naiker.
What comes next, a 100-million context window?
Although the technology industry is littered with apocryphal statements and Bill Gates almost certainly never said “64K ought to be enough for anyone” in real life, we have to ask ourselves today whether we’ll be laughing about those “silly little” 10 million token context windows by the end of the decade.
It may not take that long. Internally, Refiant maintains that it has already demonstrated a working prototype with a 100-million-context window and is exploring how best to benchmark and productionize it at that scale in the future.
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