An implant, smaller than a grain of rice, pairs with camera-mounted glasses to communicate visual information to the retina.
Age-related vision loss affects millions of people, and so far, there has been no way to reverse the damage. A newly approved retinal implant could change that by allowing some people with severe vision loss to regain functional sight.
More than five million people worldwide suffer from geographic atrophy, the late stage of the progressive eye condition dry age-related macular degeneration. The disease destroys the photoreceptors at the center of the retina, known as the macula, which is responsible for the sharp central vision required to read or recognize faces.
In the US, treatment options are limited to two drugs that can be injected into the eye to slow the disease’s progression. But neither can undo the damage. That could be about to change. California neurotech startup Science Corporation recently won European approval for a retinal implant designed to treat the condition.
“For decades, losing central vision to this disease meant losing the ability to read, recognize faces, and ultimately losing independence. There was no viable treatment. Now there is,” Max Hodak, Science’s CEO and co-founder, said in a press release.
The company’s PRIMA system combines an implant smaller than a grain of rice installed underneath the patient’s macula with a pair of camera-mounted glasses that translate incoming visual information into near-infrared light that is then beamed to the retina. The eye can’t detect this wavelength, so the device doesn’t interfere with any natural sight that remains.
The chip, which works on similar principles to a solar panel, converts the incoming light into electrical pulses that stimulate retinal neurons called bipolar cells. These are downstream of the rod and cone photoreceptor cells damaged by macular degeneration and normally spared by the disease.
In a clinical trial involving 38 patients across five countries, which was published in the New England Journal of Medicine last year, the company and its collaborators showed participants gained an average of 25.5 letters—more than five lines—on a standard eye chart after having the device fitted.
And now the device has received a CE mark from the European Union making it possible to sell in 30 European countries. The company says the first commercial implants are expected to be fitted in Germany within weeks, with Italy, the Netherlands, and the UK to follow. In the US, PRIMA holds Breakthrough and Humanitarian Use Device designations from the FDA, but the company is confident it will gain full approval in the near future.
The device is a long way from restoring normal vision. The images it produces are black and white and the field of vision is extremely narrow. Hodak described the experience to the Financial Times as “kind of like looking through a straw in the center of their vision,” though he added that they see a pathway to color vision and higher acuity.
While the implantation procedure is fairly simple, it takes months of training to unlock the device’s full potential. Nonetheless, Hodak told STAT that the company expects to install 20 to 40 devices this year and 200 globally by the end of next if they get US approval in early 2027.
The approval is welcome news for the wider neurotech industry, which has absorbed billions of dollars of investment in recent years with little to show in terms of return.
“Science is showing that brain-computer interface companies have a path to real revenue now,” Jacob Robinson, founder of startup Motif Neuroscience, told STAT. “These companies aren’t all just making a bet on a market that is 10 to 15 years away.”
Hodak told the Financial Times hehopes sales from PRIMA will bankroll Science’s more ambitious work on “biohybrid” interfaces, which use genetically engineered living neurons to connect to the brain rather than metallic wires. “This is the financial backbone,” he said. “This is the thing that pays for the rest.”
Other companies are hot on Science’s heels. Neuralink, which Hodak co-founded with Elon Musk before leaving to start Science, is also working on a vision implant called Blindsight, which is due to enter human trials this year.
While the field remains a long way from the sci-fi vision of seamless two-way communication between humans and machines, this approval is growing evidence the neurotech industry is starting to move out of the lab and into the real world.
As agentic and long-context workloads become common, the context lengths increase and attention consumes a larger share of inference time (Figure 1). Because...
As agentic and long-context workloads become common, the context lengths increase and attention consumes a larger share of inference time (Figure 1). Because attention now dominates that cost, how it is designed—not just how it is implemented—increasingly determines a model’s inference performance. Shaping model architecture around how GPUs execute it is the premise of AI model co-design.
If you ask an AI coding agent to write a standalone Python script to parse a single JSON file, it will likely give you a perfect answer in seconds. But the same agent often breaks if you ask it to build a systematic data processing pipeline, like ingesting thousands of messy documents, chunking text, scoring quality, and filtering noise for a Retrieval-Augmented Generation (RAG) system that fits your specific enterprise stack.
While large language models (LLMs) excel at one-off code generation, their outputs for complex data-processing tasks are typically free-form, disposable scripts. These scripts are detached from the governable workflow abstractions that MLOps teams rely on for production, making them difficult to audit or edit visually.
To address this, researchers at Peking University, Zhongguancun Academy, and Shanghai’s Institute for Advanced Algorithms Research introduced DataFlow-Harness, an open-source framework that guides an LLM agent to build structured, visual data-processing workflows step-by-step, rather than writing raw code from scratch.
The framework makes AI-generated pipelines easier to manage and integrate into existing architectures because the generated artifacts are persistent and easily editable.
The researchers report that the platform achieves a 93.3% observed end-to-end pass rate on a 12-task data-engineering benchmark. Compared to standard Claude Code, it reduces API costs by up to 72.5% and response latency by 49.9%, while achieving nearly the same success rate as an AI given the entire codebase to write standard scripts. For enterprise teams, this means getting the speed of AI automation without accumulating unmanageable technical debt, ensuring that pipelines remain secure, auditable, and ready for production.
The "NL2Pipeline gap"
Data-centric AI requires workflows for tasks like synthetic data generation, retrieval augmentation, and model training. While LLMs can translate natural language into executable implementations to perform these tasks, high task accuracy is insufficient for production deployment.
"The first wall is usually not writing Python," Runming He, first author of the DataFlow-Harness paper, told VentureBeat. "Modern coding agents can often produce a plausible script quickly. The harder problem is grounding that script in a live production platform: using operators that are actually installed, matching the real dataset schema, referring to registered datasets and model services, preserving dependencies between stages, and leaving behind an artifact that another engineer can understand and revise."
General-purpose AI agents frequently hallucinate dependencies, relying on unavailable operators or outdated platform assumptions. Instead of leaving behind an artifact that another engineer can understand and revise, they generate disposable code that is difficult to audit through workflow managing tools.
The researchers define this challenge as the "NL2Pipeline gap": the disconnect between a user expressing workflow requirements in natural language and the production environment requiring structured and persistent pipeline assets.
The researchers demonstrated this gap in their experiments. For example, when Claude Code was allowed to write standard, free-form scripts using codebase context, it hit a 94.2% success rate. However, when restricted to only using the platform's specific building blocks to create a native workflow graph, its success rate dropped to 83.3%. This gap is the paper's central finding: native, governable pipelines are meaningfully harder for the agent to produce than throwaway code.
“Closing this gap requires more than improving code-generation accuracy: construction must remain grounded in platform semantics and produce artifacts that integrate with the host platform,” the researchers write.
How the four components work together
"DataFlow-Harness changes the agent’s action space," He said. "Instead of asking the agent to emit arbitrary code, it retrieves the live operator registry and current pipeline state through MCP and applies typed, incremental changes to a persistent DAG."
To achieve this, the platform organizes workflow synthesis around four components: the Data Pipeline Backend, the interaction layer (DataFlow-WebUI), the MCP Tools Layer, and the AI guidance layer (DataFlow-Skills).
The Data Pipeline Backend acts as the authoritative source of truth across conversational, visual, and programmatic interfaces. It represents the pipeline as a directed acyclic graph (DAG), a structured workflow map containing data sources, configured pre-built processing modules (which the researchers refer to as "operators"), and execution dependencies. Instead of generating free-form code, agents interact with this backend through “typed mutations,” like adding an operator or connecting edges.
DataFlow-Skills are markdown files that inject domain-specific knowledge into the model's context window, guiding it on operator-selection patterns, schema inference, and assembly procedures. Rather than letting the AI guess how to assemble components, skills provide the AI with compatibility rules, teaching it how to correctly match different data formats and handle complex data structures without breaking the pipeline.
The MCP Tools Layer gives the AI access to the operator registry and current state of the data workflow. The AI proposes structured changes through the tools layer. The system validates the changes to ensure the workflow runs in a valid sequence and that every connected module speaks the same data language.
DataFlow-WebUI provides two interfaces that allow humans and AI to build the workflow together. Developers can describe workflow requirements in natural language through a conversational interface. They can also access the workflow as a graphical map in a visual DAG editor. Here, they can directly inspect the changes proposed by the AI and make modifications.
“The current implementation performs static checks against platform metadata before accepting pipeline changes,” He said. “These include checks for registered datasets, operators and model-serving references, field flow, and some invalid parameter usage, as well as structural validity. The result is visible in a graphical editor and can be revised either manually or by the agent in later turns.”
The results: 93.3% pass rate, 72.5% lower cost
The researchers tested DataFlow-Harness on a benchmark of 12 tasks across six industrial data-processing scenarios, such as QA generation, review governance, and schema normalization. They used Claude Opus 4.7 as the backbone model in their experiments.
They compared DataFlow-Harness against three baselines:
Vanilla CC: An unconstrained coding baseline using standard Claude Code.
Context-Aware CC: An agent that has access to the DataFlow codebase in its context window.
MCP-only: An agent that has access to the DataFlow MCP tools and is instructed to generate platform-native DAGs (without access to DataFlow-Skills).
DataFlow-Harness achieved a 93.3% end-to-end pass rate, improving by 10.0 percentage points over MCP-only and beating Vanilla CC (91.7%), while being within 0.9 percentage points of Context-Aware CC (94.2%).
Importantly, it reduced API costs to $0.261 per task, a 72.5% drop compared to Vanilla CC and 42.8% compared to Context-Aware CC. In generating workflows, it was 49.9% faster than Vanilla CC and 17.6% faster than Context-Aware CC.
DataFlow-Harness proved particularly effective on complex tasks that depend on implicit domain knowledge, like QA generation. The baseline MCP-only approach frequently generated structurally valid DAGs but struggled to infer task-specific procedures from operator descriptions alone.
To show how this works in the real world, the researchers detailed a textbook-to-VQA extraction task. This job required the AI to stitch together capabilities such as PDF parsing, layout recovery, OCR, figure extraction, multimodal understanding, and long-range question-answer matching. DataFlow-Harness achieved 97.2% precision and an 87.3% coverage rate, easily beating the baselines. By having the AI snap together existing platform assets rather than coding complex tasks from scratch, it recovered more valid QA pairs from the document.
Their experiments also showed that DataFlow-Harness is highly effective at creating data generation pipelines. For example, in a synthetic instruction-data generation task, the agent built a multi-stage pipeline that generated candidate instruction–response pairs, critiqued and rewrote them, scored them with an LLM-based judge, and filtered low-quality outputs before training.
"Such workflows are costly to build and fragile to maintain as collections of ad hoc scripts," He said. "The harness does not make them automatically safe, but it turns them into explicit, editable stages that engineers can inspect, test, and govern using normal production controls."
Similarly, when tasked with building a math data cleaning-and-synthesis pipeline, the data produced by the DataFlow-Harness pipeline trained a better-performing model with higher average accuracy on AIME24 and AIME25 benchmarks than the data produced by the vanilla Claude Code pipeline.
Tech stack fit and implementation tradeoffs
For engineering teams evaluating DataFlow-Harness, it is important to understand how it fits into existing infrastructure. Released under the Apache 2.0 license, the current implementation requires a bit of engineering to fit into popular tech stacks.
"The current implementation is native to the DataFlow platform; it is not a turnkey Airflow, Prefect, or Spark plug-in," He said. To use those systems as an execution backbone, teams must build an adapter to connect their organization’s registry, metadata, and execution interfaces to the agent's control layer.
Furthermore, organizations must invest in the boundaries they want the AI to respect. This requires maintaining an operator registry, defining schemas, and encoding recurring domain procedures as Skills. Because of this overhead, He recommends against using the framework for small, one-off transformations where a simple script suffices, or in legacy environments that cannot expose reliable metadata.
Finally, while the platform prevents illogical connections by validating structural properties, it is an engineering control layer, not a compliance substitute. "The harness should still be treated as an engineering control layer, not as a substitute for compliance policy, validated detection models, access controls, audit logging, or human approval," He said.
The platform is open-source, and developers can access the source code and codebase documentation directly via the project's GitHub repository.
As protocols like MCP become standardized, the boundary between human engineers and AI agents will shift. "The goal is not autonomous data engineering without oversight," He said. "It is a better division of labor: agents perform repetitive construction inside explicit boundaries, while engineers remain responsible for the semantics, policies, and consequential decisions that require domain accountability."
Anthropic said its Claude-based security models gained unauthorized access to the sensitive production environments of three outside organizations during internal testing designed to measure the models’ offensive cyber capabilities.
The events, which Anthropic revealed Thursday, are the second revelation in 10 days that AI models from the world’s wealthiest providers have trespassed into protected networks, an offense that, in more traditional hacking scenarios, could land the human behind the keyboard in prison for years. Earlier this month, OpenAI said its security models exploited a zero-day vulnerability for use in breaking into the network of Hugging Face, a platform for open source machine-learning models and AI datasets. The OpenAI models went on to steal access credentials and other confidential Hugging Face information. The OpenAI models also exploited publicly exposed credentials to compromise accounts of four other third-party services.
Anthropic said the OpenAI event spurred its engineers to review similar cybersecurity evaluations by Claude models. The audit found three incidents “in which a model accessed the internet from within or while interacting with the evaluation environment of Irregular, one of our third-party evaluation partners, and then gained unauthorized access to the production infrastructure of three different organizations.”
The AI agent observability space is taking off — but how can enterprises be sure what observability products and solutions they need?
Observability startup groudcover (lower case "g" intentional) announced this week that it raised $100 million in a round led by One Peak, bringing its total funding to $160 million.
The company says it has more than 250 paying customers, tripled annual recurring revenue over the past year and is increasingly replacing established observability platforms inside enterprise environments. Those are company-reported figures, but together they point to growing momentum in one of enterprise software's most competitive markets.
That market has long been dominated by companies including Datadog, Dynatrace, New Relic, Splunk and Grafana. Between them, they represent billions of dollars in annual revenue and years of product maturity. Breaking into that group has never been easy.
groundcover's argument is that artificial intelligence has fundamentally changed the assumptions those platforms were built on.
Rather than competing feature for feature, the four-year-old company is trying to convince enterprises that the architecture underpinning observability itself needs to change as AI systems become more autonomous, produce vastly more telemetry and increasingly participate in software operations. Whether that thesis proves correct remains an open question, but it offers a compelling lens through which to examine how observability is evolving alongside enterprise AI.
AI is turning telemetry into an infrastructure problem
Observability has traditionally been viewed as a post-production discipline. Engineers deploy applications, monitor logs, metrics and traces, investigate incidents, and improve reliability over time.
That workflow is changing.
AI-assisted software development has dramatically accelerated deployment cycles. Coding assistants generate more code, infrastructure evolves more rapidly, and organizations are deploying increasingly complex distributed systems that combine microservices, Kubernetes clusters, APIs and large language models. At the same time, enterprises are beginning to operate AI agents that execute multi-step workflows, call external tools and interact with production systems.
Each of those activities generates telemetry.
The result is an explosion of operational data that organizations increasingly want to retain rather than discard. AI applications introduce additional layers of observability beyond traditional infrastructure monitoring, including prompt execution, model latency, token consumption, retrieval pipelines, tool invocations and agent behavior. As enterprises experiment with autonomous systems, that telemetry becomes increasingly valuable because it provides the context needed to understand what an AI system actually did and why.
For many organizations, this creates tension with pricing models that charge according to the amount of data ingested.
Historically, engineers have often responded by sampling traces, shortening retention periods or limiting which data is collected. Those approaches reduce costs, but they also reduce visibility precisely when AI-driven systems demand more complete operational context.
"We've seen telemetry exploding," groundcover co-founder and CEO Shahar Azulay said during a recent media briefing. "Users are frustrated by not getting all the value from Datadog and similar platforms. They're limiting the data, siloing it, sampling it."
Whether that frustration is widespread enough to reshape the market remains to be seen, but the underlying trend is difficult to ignore. AI is making observability less about collecting enough data and more about collecting everything organizations may eventually need.
Rather than adding AI, groundcover argues the architecture itself has to change
Many observability vendors have introduced AI assistants, AI-powered root cause analysis and AI observability features over the past two years. Datadog, Dynatrace, New Relic and Grafana have all announced products aimed at helping enterprises monitor AI applications or automate operational tasks.
groundcover acknowledges those developments but argues they do not address what it sees as the more fundamental issue: where telemetry lives and how customers pay for it.
Instead of operating a conventional SaaS platform that stores customer telemetry in vendor-managed infrastructure, groundcover uses what it calls a bring-your-own-cloud (BYOC) architecture.
Customers keep the data plane—including telemetry storage and processing—inside their own AWS, Microsoft Azure or Google Cloud environments, while groundcover provides a managed control plane and user experience. A fully self-hosted deployment option is also available.
While some competitors, including Datadog and a few other observability vendors, do offer limited hybrid or customer-controlled data residency options, these are generally not equivalent to a full BYOC model. In most cases, telemetry is still processed and stored within the vendor’s managed infrastructure, with only partial controls (such as regional data residency, private links, or selective log forwarding) available.
That architectural decision influences nearly every aspect of the company's strategy.
Because customers already pay for their own cloud infrastructure, groundcover argues it can avoid charging based on telemetry ingestion. Instead, pricing is based primarily on monitored hosts, regardless of telemetry volume.
The company believes this changes customer behavior.
Rather than deciding which logs or traces are too expensive to keep, organizations can theoretically retain complete telemetry and use it for operational analysis, compliance and AI-assisted troubleshooting.
"We don't price by data volume," Azulay said. "We price by the size of the infrastructure."
The distinction matters because AI workloads tend to increase telemetry far faster than infrastructure itself.
That does not necessarily make host-based pricing universally cheaper. Organizations with relatively light workloads spread across many hosts may find different economics than dense Kubernetes environments generating enormous amounts of telemetry. The company's own briefing notes that per-host pricing is most advantageous for organizations with high telemetry density and may be less compelling for lightly utilized fleets.
Still, the broader argument is less about cost alone than predictability. Enterprise infrastructure teams often struggle with observability bills that fluctuate alongside application growth. groundcover's model attempts to align pricing more closely with infrastructure planning rather than data generation.
eBPF sits at the center of the company's technical differentiation
The second pillar of groundcover's strategy is eBPF, a Linux kernel technology that has rapidly become one of the most important building blocks for modern cloud observability.
Instead of requiring developers to manually instrument applications, eBPF allows software running inside the operating system kernel to observe network traffic, system calls and application behavior with minimal code changes.
That enables faster deployment and broader visibility across infrastructure.
For organizations operating Kubernetes clusters and cloud-native applications, reducing instrumentation complexity can significantly shorten deployment times while increasing telemetry coverage.
Azulay argues this becomes especially important as AI systems generate increasingly complex interactions across services.
"Our sensor allows us to observe systems very deeply from infrastructure to application to AI workloads without developers needing to instrument code," he said during the briefing.
eBPF itself is hardly unique. Many observability vendors now incorporate it into their platforms.
What groundcover argues differentiates its approach is combining automatic eBPF collection with customer-controlled storage, OpenTelemetry compatibility and unified pricing inside a single platform.
The company's own research briefing acknowledges that none of these technologies individually represents a competitive moat. The claimed differentiation lies in the combination of eBPF-first collection, managed BYOC architecture, host-based economics and full-stack observability delivered together.
AI agents are becoming both customers—and users—of observability
Perhaps the most interesting aspect of groundcover's strategy extends beyond traditional monitoring.
The company increasingly describes observability as infrastructure for autonomous software development.
Historically, observability platforms have served human operators investigating production incidents.
groundcover believes future observability platforms will increasingly serve AI agents as well.
Its Agent Mode product allows engineers to investigate incidents using natural language across logs, metrics, traces and Kubernetes events. More importantly, Azulay envisions observability becoming the feedback mechanism that informs coding agents about what actually happened in production.
Rather than simply detecting failures after deployment, observability becomes continuous operational context that autonomous systems can use to evaluate changes, identify regressions and eventually recommend or implement fixes.
"We're seeing observability moving from being a post-production tool... to people taking context from production and feeding it back to their coding agents so they can write code better," Azulay said.
Today, the company emphasizes that humans remain in the loop.
Agent Mode investigates incidents and surfaces recommendations, but production changes still require human approval. Azulay expects autonomy to increase gradually as organizations become more comfortable allowing AI systems to participate in operational workflows.
That vision reflects a broader trend emerging across enterprise software, where AI agents increasingly span development, testing, deployment and operations rather than functioning as isolated assistants.
Why some enterprises are considering alternatives
groundcover is entering an intensely competitive market populated by vendors with decades of enterprise experience.
Datadog alone generated more than $3 billion in annual revenue in 2025. Dynatrace, Cisco's Splunk business, Grafana Labs and New Relic all maintain extensive partner ecosystems, mature integrations and enterprise support organizations that newer entrants cannot easily replicate.
groundcover is not attempting to outscale those incumbents overnight.
Instead, it argues that AI creates an architectural inflection point similar to previous transitions from on-premises infrastructure to cloud-native computing.
According to Azulay, many customers initially adopt groundcover to reduce observability costs but increasingly remain because they want unrestricted access to richer telemetry and AI-native workflows.
He says deployments typically replace incumbent platforms rather than operate alongside them, although the company has not publicly disclosed customer migration data or independent studies validating that claim.
The company's journalist briefing also urges caution around some performance claims.
Revenue growth, customer counts and enterprise adoption figures originate from groundcover itself. Published customer case studies reporting significant cost savings are vendor-authored and should not be treated as independent validation without additional evidence. The briefing also recommends scrutinizing exactly what metadata leaves customer environments in standard BYOC deployments, rather than assuming that no operational data ever reaches vendor infrastructure.
Those caveats are important because the observability market has become crowded. Gartner currently tracks more than one hundred observability products, and nearly every major vendor now markets AI-powered operational capabilities.
Success will likely depend less on whether AI matters—which increasingly appears inevitable—and more on whether enterprises conclude that existing architectures remain sufficient.
The larger question investors are betting on
Viewed narrowly, groundcover's Series C is another large infrastructure funding round.
Viewed more broadly, it reflects a growing debate about what observability becomes in an era where software increasingly writes, tests and operates itself.
If AI continues generating exponentially larger volumes of operational data, traditional assumptions about telemetry collection, pricing and storage may come under increasing pressure. Vendors that built businesses around charging for data ingestion may need to evolve their economics alongside customer expectations. New entrants, meanwhile, have an opportunity to design around those changing assumptions from the outset.
groundcover believes that opportunity lies in combining customer-controlled infrastructure, automatic telemetry collection and AI-assisted operations into a platform designed for autonomous software rather than simply adding AI features to existing observability products.
Whether that architectural bet proves durable will depend on enterprise adoption over the next several years.
But the company's latest funding round suggests at least some investors believe the next battle in observability will not be fought over dashboards or alerts. It will be fought over who builds the operational data layer that increasingly intelligent software relies upon to understand—and eventually manage—the systems it runs.
As OpenAI and Anthropic employees grow quieter online, researchers at Chinese AI labs are flocking to X to explain their work, recruit talent, and shape the global conversation on AI.
Cloud platform company Nscale announced this week a definitive agreement to acquire AI workload scaling specialist Anyscale, in a move that signals a new test of whether cloud-neutral AI software can stay neutral once it is paired with a GPU neocloud.
The purchase coalesces Nscale’s infrastructure capabilities, which span control systems that oversee GPUs, datacenters, power consumption, and the application layer where AI services themselves are executed, with Anyscale’s software layer for scaling AI workloads across data processing, training, inference, and reinforcement learning.
Argued by Nscale to be the coming together of “two highly complementary companies”, Nscale scooping up Anyscale could be a fundamental change in the resulting business model.
Is this the start of GPU neocloud lock-in?
It’s important to remember that Nscale is a GPU neocloud (a specialized cloud provider running bare-metal GPUs and infrastructure optimized for AI and machine learning workloads), meaning that it runs its own GPU-rich datacenters and its own software stack. At the same time, Anyscale is an independent cloud-neutral software orchestration multi-cloud control plane that works with any cloud hyperscaler… but now owned by a single neocloud.
That doesn’t sound quite so much like cloud-neutrality and agnosticism; it sounds more like a vertically integrated AI cloud provider proposition.
Chief product officer at Nscale, Dan Bathurst, tells The New Stack that the Anyscale platform “continues to be its own brand and product,” and that includes working with bring-your-own-cloud deployments on AWS, GCP, Azure, and the other clouds.
“Where we want to win is on performance, not on any sort of vendor lock-in or forcing of someone to choose Nscale as the infrastructure provider.”
“But what really changes — or how it’s changing — is that customers now also get this first-party option, where they can have Anyscale running on Nscale fleet as a full-stack, highly-optimized solution. Where we want to win is on performance, not on any sort of vendor lock-in or forcing of someone to choose Nscale as the infrastructure provider,” Bathurst says.
He insists that it is in Nscale’s interest to ensure that it is making it easy for software engineering teams to get the outcomes they want with the workloads that they’re trying to run.
“For us, the existing commitments will carry forward, so Nscale’s value really is meeting instances where the compute already lives,” he says. “Where we want to win is on performance, not on any sort of vendor lock-in or forcing of someone to choose Nscale as the infrastructure provider.”
Neutrality on the platform layer, differentiation on the infrastructure layer
Bathurst invites users to think of it as “neutrality on the platform layer, but differentiation on the infrastructure layer” because the combination of the two organizations is a full-stack play.
“The differentiation comes from the fact that Nscale is fully vertically integrated with Anyscale. Therefore, if users want that first-party option, they can choose Anyscale and get the most optimized solution because, obviously, we’re designing, optimizing, and co-engineering every layer of that stack from power to the datacenter through to the application. It’s quite a unique proposition, but it’s not something we are going to force upon any customer,” confirms Bathurst.
Not everyone is convinced by the company’s pledge to maintain an agnostic and neutral open house. Sanjeev Mohan, principal analyst, SanjMo and former Gartner research VP for data and analytics, tells The New Stack that Anyscale “stops being a neutral player” the moment its best features and most optimal pricing land on Nscale first.
“The software will still run anywhere, but ‘runs anywhere’ and ‘runs best somewhere’ are different things, and buyers will feel the gap in performance and cost. At that point, neutrality is a label.”
Runs anywhere, but… runs best somewhere
“The software will still run anywhere, but ‘runs anywhere’ and ‘runs best somewhere’ are different things, and buyers will feel the gap in performance and cost. At that point, neutrality is a label,” says Mohan.
He agrees that integrating software and compute will produce measurable cost, performance and reliability gains. Defining this as “the strongest part of the deal”, Mohan explains that with Nscale controlling both the silicon and Anyscale’s control plane, it can tune scheduling, memory, and networking together in ways the compute-neutral Anyscale never could.
Anyscale commercial support for Ray
Anyscale was founded by the creators of Ray, an open source project that provides a distributed computing framework designed to scale Python workloads across any infrastructure into live production application jobs and services.
Ray was donated to the PyTorch Foundation in 2025. Anyscale continues to provide its commercially supported services for Ray, which include a “no DevOps” route to 100% managed cloud infrastructure and serverless autoscaling, making it simpler to create, deploy, and monitor machine learning workflows in production.
Anyscale supports data processing, model training, batch inference, and LLMs across public and private cloud environments. As open source as this all feels, are we still edging towards narrower proprietary channels, or the possible threat of deeper application and data service dependencies that developers will ultimately have to wrangle around?
“I don’t think so, primarily because the way that the platform works, it’s designed to orchestrate across various different clouds and different infrastructure. It’s like a heterogeneous distributed compute platform. So the platform’s always gonna remain multi-cloud,” confirms Nscale’s Bathurst.
Pricing permutations and hyperscalers hearsay
Pressed on any forthcoming pricing changes or likely reactions from the major cloud hyperscalers in relation to Nscale now being a credible alternative, Bathurst and team were (perhaps understandably one day after an acquisition deal announcement) politely tight-lipped.
More voluble is always-affable analyst Mohan, who says that, “Every optimization that only shows up on Nscale hardware is a dependency. So, an argument can be made either way. Standalone orchestration software and independent tooling vendors are getting absorbed into whoever owns the GPUs, because the economics only work when you control both. Expect more of it,” Mohan underlines.
He explains that Nscale “now becomes a real specialist cloud services provider alternative,” i.e., not a general-purpose one like AWS, Azure and Google Cloud with their plethora of managed services, from databases and data warehousing to container orchestration through to AI/ML pipeline technology. However, he does see space for Nscale to become a strong player in raw training and inference at scale.
From cryptocurrency to cloud contender
London, UK-based Nscale was established in 2024 from what was originally a cryptocurrency mining business.
As suggested, Anyscale will retain its brand name as part of the Nscale family, and the company has restated its stance that customers are “free to choose the cloud infrastructure on which they run their AI workloads” today.
The company’s initial press statement said that “over time” users will gain the additional option of running the Anyscale software layer on Nscale’s full-stack AI platform.
The first full-stack AI hyperscaler?
“Companies are moving beyond simply using AI to actually building their own. Doing that well requires the software and the infrastructure it runs on to be designed together,” says Keerti Melkote, CEO of Anyscale in the press release announcing the acquisition.
Melkote has defined the combination of Anyscale’s platform — built on Ray — with Nscale’s datacenter, compute and AI cloud services as the “first full-stack AI hyperscaler,” i.e., one that runs any AI workload at greater scale, so more software engineering teams can build and own their AI applications and services.
With this acquisition and the fusion of Nscale with Anyscale’s software layer, the organization will aim to widen its customer base. Existing work sees the company working in verticals from healthcare to e-commerce to robotics. It says its full stack offering will help companies speed up image and document processing, fine-tune LLMs on their proprietary data, and deploy AI agents in-house using open-source models.
The transaction is subject to closing conditions and regulatory approvals and is expected to close in the second half of 2026. Financial terms of the transaction were not disclosed, although Reuters reports a source stating that the deal price is “about $1.65 billion”, according to a person familiar with the deal.
AWS, Google Cloud and Microsoft Azure representatives were all contacted and invited to comment on this story.
Agent Platform's evaluation service is now generally available, providing developers with a unified engine to measure agent quality consistently across local development experiments and live production traffic. You can evaluate agents using over 20 pre-built metrics, DeepMind-backed adaptive rubrics, or custom code-based and LLM-as-a-judge metrics stored in a centralized, versioned registry. The service integrates directly into existing workflows via the Agent Platform SDK, agents-cli, and ADK, offering built-in user and environment simulators to automate complex multi-turn testing and streamline CI pipelines.
OpenAI banned accounts linked to a previously unreported PRC-origin operation we dubbed "Nine-emdash Line", using AI to create regional influence content about the South China Sea, Hong Kong, and US politics.
OpenAI banned accounts linked to a Russia-origin operation we dubbed “Stop News”, using AI to generate recidivist influence content targeting Africa and the UK.
OpenAI banned PRC-linked accounts using AI to support surveillance-related planning, targeted profiling, and research on critics and other individuals.
OpenAI banned accounts involved in activity that overlapped with publicly reported threat groups and displayed hallmarks consistent with PRC intelligence requirements, using AI to support phishing and scripting workflows.
OpenAI banned accounts likely linked to Russian-speaking criminal groups, using AI to build malware loaders, evasion layers, credential-theft scripts, and C2 infrastructure.
OpenAI banned accounts associated with the likely Iran-linked STORM-2035 operation using AI to create influence content about US, UK, Irish, and Venezuelan politics.
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