Microsoft on Monday unveiled the Surface RTX Spark Dev Box, a compact desktop computer designed to let software developers run large AI models on their desks instead of paying for cloud computing — a move that directly challenges the per-token pricing model that has defined the AI industry’s economics since ChatGPT launched three and a half years ago.

The device, announced at Microsoft Build 2026, packs Nvidia’s new Blackwell-architecture RTX Spark processor and 128 gigabytes of unified memory into a small-form-factor chassis, delivering what Nvidia rates at one petaflop of AI compute. In practical terms, that means a developer can load, run and interact with AI models exceeding 120 billion parameters without sending a single API call to the cloud.

“These class of devices, we think, will get to about 100 billion parameter model running,” Pavan Davuluri, Microsoft’s executive vice president of Windows and Devices, said during a press briefing ahead of the event. He emphasized that raw model size is only part of the equation: “The model size is one thing, but for the model to be effective, it kind of needs to be able to have enough context, because a larger model, you feed it larger context.” At 100,000 tokens of context, he noted, the key-value cache alone can consume 40 to 50 gigabytes of memory — which is precisely why Microsoft and Nvidia engineered the device around a 128-gigabyte unified memory pool shared dynamically between the CPU and GPU.

The machine will be available later this year in the United States, sold exclusively through Microsoft.com. The company did not disclose pricing.

Why Microsoft is betting that AI’s future runs on fixed costs, not cloud meters

The Surface RTX Spark Dev Box arrives at a moment when the economics of AI development have become a boardroom-level concern. Companies large and small are grappling with cloud GPU bills that scale unpredictably: every fine-tuning run, every inference call, every agentic workflow that loops through a frontier model accumulates cost. For a developer iterating rapidly on a prototype — running the same model dozens or hundreds of times a day — those charges compound fast.

Microsoft is framing the Dev Box as a release valve for that pressure. Andrew Hill, corporate vice president of Surface, wrote in the announcement blog post that the device “changes that equation” by letting developers “reserve frontier model calls for truly frontier problems and handle the rest on their own hardware.” The pitch is not that cloud computing is obsolete, but that much of the work currently being sent to remote data centers does not require state-of-the-art models and would be better served by capable local hardware with predictable, fixed costs.

This is a significant strategic shift for Microsoft, a company that derives tens of billions of dollars in annual revenue from Azure cloud services. By selling hardware that explicitly reduces customers’ cloud dependency, Microsoft is acknowledging a tension that has been building across the industry: the marginal cost of AI inference at scale is unsustainable for many teams, and the market is demanding alternatives. The bet appears to be that developers who prototype locally will still deploy to Azure when they need to scale — and that owning both ends of that workflow is more valuable than owning only the cloud.

Inside the 128GB unified memory architecture that makes local AI possible

The technical architecture of the Dev Box reflects a set of deliberate engineering choices aimed at sustained, not peak, performance — a distinction that matters enormously for AI workloads that can run for hours.

At the center is Nvidia’s RTX Spark system-on-chip, which combines an ultra-efficient ARM-based CPU with a Blackwell-generation RTX GPU. In a traditional Windows PC, Davuluri explained during the briefing, this configuration would require four separate components: a CPU, a discrete GPU, dedicated graphics memory and system RAM. The RTX Spark collapses all of that into a single chip paired with a single unified memory pool.

That unification is the critical design decision. Conventional gaming laptops with high-end Nvidia GPUs top out at roughly 24 gigabytes of GPU-accessible memory. The Dev Box’s 128 gigabytes of unified memory — accessible to both the CPU and GPU through what Nvidia calls its Unified Memory Access architecture — is what makes it possible to load models that would otherwise require cloud GPU instances with specialty high-bandwidth memory configurations.

Microsoft did substantial work at the operating system level to exploit this architecture. The company implemented new memory management logic in Windows that raises the ceiling on how much system memory the GPU can address, introduces smarter page-size allocation for shared memory regions and ensures that heavy GPU workloads do not starve the CPU of the resources it needs for multitasking. The Windows scheduler was also optimized for RTX Spark’s heterogeneous core layout, routing demanding workloads to performance cores while keeping efficiency cores available for background tasks.

How a 3D-printed aluminum chassis doubles as a heatsink

The thermal design is equally deliberate. The Dev Box operates within an approximately 100-watt sustained thermal envelope — modest by desktop standards, but meaningful for a device intended to run training jobs and inference workloads continuously. The aluminum chassis itself is engineered to function as a passive heatsink, and the method Microsoft used to build it is among the most striking details about the machine.

The top panel is manufactured using metal 3D printing, a process that enables internal geometries too complex for conventional CNC machining or injection molding. The perforations are not simple through-holes; they are angled in multiple directions around the internal fan to optimize airflow from cold-air intake through heat dissipation. During the press briefing, Harry, a Surface industrial designer, explained the rationale: “The complexity is something other manufacturers wouldn’t be able to do, like CNC, or like any molding, because of the complexity of shape.”

When asked whether 3D printing would constrain mass production, the designer acknowledged the challenge but suggested Microsoft had developed a process robust enough to scale. The result is a machine that runs quietly enough for an open office while sustaining the kind of continuous GPU workloads that would throttle most conventional desktops of similar size. For a device that Microsoft expects developers to leave running overnight on fine-tuning jobs, quiet sustained performance is not a luxury — it is a requirement.

A developer-first setup that eliminates hours of configuration

Microsoft is shipping the Dev Box with Windows 11 Pro pre-configured at the image level for development work — a detail that sounds minor but reflects a growing recognition that the out-of-box experience for developer hardware has historically been poor.

The machine boots into a dark theme with a simplified taskbar, widgets removed and Do Not Disturb enabled. Developer Mode is turned on. PowerShell 7 is the default shell. WSL 2 — the Windows Subsystem for Linux — comes pre-installed with GPU passthrough and CUDA support already configured. Visual Studio Code, GitHub Copilot, Git, Python and Node.js are all installed and ready.

“We’ve said, ‘Hey, you know what, we got you, you want to go fast,'” a Microsoft engineer who demonstrated the configuration during the briefing told VentureBeat. The philosophy, he explained, is that developers were going to install all of these tools anyway — the friction was in the hours of setup and configuration that stood between unboxing a machine and writing the first line of code.

The Dev Box also ships with integration points across Microsoft’s AI stack: AI Toolkit for VS Code for model conversion and fine-tuning, Windows ML and Windows Copilot Runtime for local inference, and Microsoft Foundry for connecting local prototypes to cloud deployment pipelines. For enterprises, the device integrates with Entra ID and Intune for identity and device management, and includes Secured-core PC architecture, BitLocker encryption and Microsoft Defender.

Why Apple’s Mac Mini may not be the real competition anymore

The most obvious competitive comparison is Apple’s Mac Mini, which has dominated the compact-desktop category and has been widely adopted by developers drawn to Apple Silicon’s unified memory architecture and power efficiency.

Davuluri addressed the comparison directly during the briefing, saying the Dev Box is “in a different class of performance than Mac Minis, intentionally.” He declined to share specific benchmarks, noting that detailed specifications and performance targets would come closer to the fall launch. But the architectural advantage Microsoft is claiming is clear: while the current Mac Mini with M4 Pro tops out at 48 gigabytes of unified memory and the M4 Max configuration reaches 128 gigabytes, the RTX Spark Dev Box pairs its 128 gigabytes with a Blackwell-class GPU that has a fundamentally different CUDA-based compute model — one that the vast majority of the AI/ML ecosystem’s tooling (PyTorch, TensorRT, llama.cpp, Hugging Face frameworks) is already optimized for.

That CUDA ecosystem advantage is difficult to overstate. While Apple’s Metal framework has made progress, the overwhelming majority of AI training and inference frameworks are built and tested first against Nvidia’s CUDA stack. A developer running models on the Dev Box can use the same code, the same libraries and the same workflows they would use on a cloud GPU instance — a level of portability that Apple Silicon cannot currently match.

From laptop to supercomputer: Microsoft’s three-tier plan for local AI hardware

The Dev Box is one piece of a three-tier hardware strategy Microsoft laid out at Build. The Surface Laptop Ultra, announced days earlier at Computex, brings the same RTX Spark silicon into a 15-inch laptop form factor for developers and creators who need portability. At the other end of the spectrum, the DGX Station for Windows — built on Nvidia’s GB300 Grace Blackwell Ultra Superchip — targets organizations that need to run frontier models up to one trillion parameters on a deskside system. That machine is expected in the fourth quarter of this year.

The three devices map to a tiered computing model that Microsoft is calling “unmetered intelligence”: small on-device language models (the company’s new Aion 1.0 family) handle lightweight tasks at zero marginal cost; RTX Spark-class hardware runs mid-range models locally for the bulk of development work; and cloud resources are reserved for genuinely frontier-scale problems.

The GitHub Copilot CLI is getting a concrete implementation of this model with a new feature called /fleet, which allows a cloud-based primary agent to build a plan, assess the complexity of each task and route appropriate subtasks to a local model running on the developer’s hardware. The cloud agent handles what requires frontier capability; the local model handles what does not. The result, in theory, is lower cost without lower quality.

The real question is whether hybrid AI can shift from buzzword to business model

Whether Microsoft’s bet pays off depends on questions that will take months to answer. How does the Dev Box actually perform under sustained, real-world workloads? What will it cost? How quickly will the open-source model ecosystem continue to produce capable models in the 70-to-120-billion-parameter range that fit within its memory envelope? And perhaps most critically: will enterprise procurement teams, trained to think of AI as a cloud line item, accept a capital expenditure on desk hardware as an alternative?

The strategic logic, however, is difficult to dismiss. For three years, the AI industry has operated on an implicit assumption: serious AI work happens in the cloud, and the economics of that arrangement are simply the cost of doing business. Microsoft, a company with every incentive to reinforce that assumption, is now selling a machine that undermines it. That is not a contradiction — it is a recognition that the market is moving, and that the company that controls the developer’s local environment and the cloud they deploy to has a more durable advantage than one that controls only the cloud.

Every dollar a developer does not spend on cloud inference is a dollar that can fund another experiment, another iteration, another prototype. For years, the AI industry told developers they needed to rent their intelligence by the token. Microsoft is now asking a different question: what if you could just buy it?

Mistral AI used its inaugural conference on Wednesday to announce a sweeping expansion into industrial manufacturing, a new inference data center south of Paris, and a rebranding of its consumer-facing assistant — moves that collectively signal the three-year-old French startup’s ambition to become the enterprise AI provider of record for companies that refuse to hand their most sensitive data to American hyperscalers.

At the AI NOW Summit, held at a venue in central Paris, co-founder and CEO Arthur Mensch took the stage alongside CTO Timothée Lacroix and Chief Scientist Guillaume Lample to lay out a strategy that stretches from bare-metal GPU clusters to physics simulations for aircraft wings. The company disclosed that it now employs 1,000 people and is targeting €1 billion ($1.17B USD) in revenue for 2026 — a figure that, if achieved, would be an extraordinary growth trajectory for a company that began with 15 employees collaborating with its first customer, BNP Paribas, in 2023.

“We have two convictions at Mistral,” Mensch told the audience. “The first is that in order to deploy AI in the enterprise, you actually need, as an AI provider, to own the full stack.” He described Mistral’s business as fundamentally about “transforming electrons into tokens and intelligence,” arguing that physical infrastructure control matters as much as model quality.

The announcements come at a pivotal moment for Mistral and for the broader European AI ecosystem. The company has raised at least $3.9 billion across nine funding rounds, according to Clay’s funding tracker, including a massive €1.7 billion Series C led by Dutch semiconductor equipment maker ASML in September 2025 at an €11.7 billion valuation, and an $830 million debt financing round in March 2026 from a consortium of seven banks to fund data center construction. Mistral now finds itself in a peculiar competitive position: too large to be dismissed as a research lab, but still dwarfed by the resources of OpenAI, Google DeepMind, and Anthropic.

Its answer, articulated across nearly an hour of presentations Wednesday, is vertical depth — going industry by industry, workflow by workflow, and building the infrastructure to keep everything on premises.

Why Mistral is betting that physics AI will reshape how Airbus and BMW design products

The centerpiece announcement was Mistral for Industrial Engineering, a fully integrated AI stack that combines Mistral’s large language models with physics simulation capabilities acquired through its purchase of Emmi AI, completed earlier in May 2026. The platform targets the aerospace, automotive, and semiconductor industries with tools for accelerating product design, validating simulations, and optimizing production.

The launch came with headline partnerships. Mistral announced it is working with Airbus across its commercial aircraft, helicopter, defense, and space divisions, implementing AI from initial design through to on-board capabilities. For BMW Group, Mistral is serving as a central partner for what the automaker calls its “Large Industry Model” initiative, focused on multimodal reasoning models for crash simulation and other complex engineering tasks. ASML, already Mistral’s largest shareholder, is also an early adopter.

Mensch framed the industrial push as addressing a fundamental gap in how AI is currently deployed. “AI is great today at automating tasks for knowledge workers and for people that are doing software engineering,” he told the summit audience. “But once you move to all the kind of engineers, well, they are underserved.”

The reason, he explained, is structural. Simulating the behavior of a wing or a factory process requires compute-intensive physics solvers that can take hours or weeks per design variant. Traditional simulation creates a bottleneck that makes AI-assisted iteration impractical. 

Mistral’s answer is what it calls “physics AI” — data-driven models trained on solver outputs that can predict physical behavior in seconds rather than hours, running on a single GPU. As Mistral’s own blog post on the technology acknowledges, physics AI is “not a replacement for first-principles solvers in every regime” — it is a throughput accelerator for the majority of design-loop iterations, with traditional solvers reserved for verification and edge cases.

“We now have both the language intelligence and the physical intelligence models, and by combining them together we are building delegation loops that allow us to create better tools, that allow us to create better objects that actually have an impact on the physical world,” Mensch said.

The ASML partnership offered a concrete illustration. In a video testimonial shown at the summit, an ASML representative described how the company’s lithography machines run around the clock at customer fabrication plants, and field service engineers need to diagnose issues as rapidly as possible. By combining ASML’s internal engineering expertise with Mistral’s models, “we were able to develop a solution that’s 120 times faster with a similar accuracy as we have today,” the representative said. Another ASML speaker described AI agents acting as “an always-on code reviewer” to catch software defects before they reach customers.

Inside Mistral’s €4 billion infrastructure gamble to build Europe’s most powerful AI data centers

Mistral’s full-stack ambitions extend all the way down to the physical layer. Launched in June 2025, Mistral Compute is a €4 billion ($4.66B USD) investment in data centers in France and Sweden, with a stated roadmap of 200 MW of capacity by 2027 and 1 GW by 2030.

Lacroix described the company’s existing 40 MW facility at Bruyères-le-Châtel, south of Paris, which was built in collaboration with Eclarion and has been training models since early 2026. “It’s been very interesting to see how we can transfer rigor, which is one of our company values, into down to the hardware layer,” he said, describing the process of “fixing compute trays and fixing fibers, allowing us to reach the very best speeds possible on that hardware for training.”

On Wednesday, Mistral announced a new 10 MW facility at Les Ulis in the Essonne department, also south of Paris, dedicated to inference operations and scheduled to open in Q3 2026. Lacroix also referenced a site in Borlänge, Sweden, planned for development through 2027, which will host NVIDIA’s next-generation Vera Rubin GPUs. “One of the benefits for us of owning the hardware layer is also that it lets us be at the very bleeding edge of what infrastructure provides,” he told the audience.

The infrastructure push is funded in part by the $830 million debt financing round announced in March 2026, which Clay’s funding tracker attributes to a consortium of seven banks: Bpifrance, BNP Paribas, Crédit Agricole CIB, HSBC, La Banque Postale, MUFG, and Natixis CIB. And this infrastructure ownership is not merely a hedge against GPU scarcity — it is central to Mistral’s pitch to security-conscious enterprise and government customers. The company’s February 2026 acquisition of serverless platform Koyeb has been integrated into Mistral Studio to support both hosted and on-premises deployments, giving customers a choice between running inference on Mistral’s hardware or their own.

“More and more, the compute world has been getting supply constrained,” Lacroix told the audience. “One of the reasons we’ve been doing all of this and developing all of this data center capacity is to secure compute capacity not only for ourselves but also for our customers.”

Le Chat is dead, long live Vibe: How Mistral’s new agent platform takes aim at enterprise productivity

In a consumer-facing rebrand with significant enterprise implications, Mistral announced that Le Chat — its conversational AI assistant launched in February 2024 — is being renamed Vibe and reimagined as a unified agent platform for enterprise productivity and software development.

“We are transitioning Le Chat to the Vibe family,” Lacroix told the audience, explaining that the evolution was driven by the growing power of agentic models, particularly the new Mistral Medium 3.5. As the team used Vibe’s coding CLI internally with increasingly complex tasks, “we realized that this really didn’t need to be bound to the CLI, it didn’t need to be limited to code, and we could do a lot more with it,” he said.

Vibe encompasses two primary modes. Vibe for Work is a web and mobile agent that connects to enterprise tools — Google Workspace, Outlook, SharePoint, Slack, GitHub — to perform multi-step tasks such as summarizing emails, analyzing spreadsheets, drafting reports, and scheduling recurring workflows. Vibe for Code is a coding agent available through a web interface, a new VS Code extension, and the existing CLI, capable of building features, fixing bugs, refactoring code, and shipping pull requests. Critically, the same underlying agent powers both modes. “When you access it through our web app or through the CLI, you have access to the same connections, the same tools, the same understanding of who you are, what you do, and what you’re trying to achieve,” Lacroix said.

Pricing starts at free for basic use, $14.99 per month for Pro, $24.99 per user per month for Teams, and custom pricing for Enterprise deployments. Alongside Vibe, Mistral also launched Search Toolkit, an open-source framework for building production search pipelines already in use by shipping giant CMA CGM, which uses it alongside Voxtral to process audio from multiple data sources and return alerts within 15 seconds.

Mistral’s model strategy signals a new phase: fewer products, more capabilities per model

Chief Scientist Guillaume Lample used his portion of the keynote to describe a philosophical shift in Mistral’s model strategy: consolidation of capabilities into fewer, more versatile models rather than maintaining separate specialized products.

Mistral Medium 3.5, the company’s current flagship, absorbs capabilities that previously required distinct models. Pixtral (image processing), Magistrale (reasoning), and DevStral (coding) have all been deprecated as standalone products, with their capabilities folded natively into Medium 3.5. “Now all our models are natively multimodal,” Lample said. “We no longer have Magistrale. This model is deprecated, because all our models will natively be doing reasoning.”

The company is also working on Mistral Large 4, which Lample said would arrive “in a couple of months at most, during the summer,” with expanded capabilities in industrial applications such as fluid dynamics, computational chemistry, computer-aided design, and cybersecurity. On the smaller end of the spectrum, Lample highlighted Mr. Lossier, a 1-billion-parameter OCR model that can process thousands of pages per minute on a single GPU, and the Voxtral speech model family, which has expanded from automatic speech recognition to include text-to-speech with voice cloning. A “duplex” model for real-time conversational speech is planned for release within months.

Lample also made the case for open-weight models becoming more — not less — important in the agentic era. “Today we are building these agentic workflows, these models are running in the background, they are doing a lot of actions, a lot of tool calls, so they are extremely token-hungry, much more than before,” he said. “What we are seeing today is actually a comeback of this small model and the efficient model.” Upcoming models will be trained on more than 200 languages, a multilingual strength now powering a partnership with Amazon to improve non-English interactions on Alexa+.

How Mistral’s enterprise playbook stacks up against OpenAI and Anthropic

Mistral’s positioning stands in sharp contrast to the strategies of its most prominent American rivals. While OpenAI and Anthropic have each attracted hundreds of millions of consumer users and derive significant revenue from subscription products, Mistral has leaned almost entirely into enterprise and government deployments. As TechCrunch reported in March when Mistral announced its Forge customization platform at Nvidia GTC, CEO Mensch has described the company as being “on track to surpass $1 billion in annual recurring revenue” — a figure driven largely by corporate clients.

The Forge platform, which lets enterprises train custom models on their own data rather than simply fine-tuning or applying retrieval-augmented generation to existing models, represents the foundation on which the company’s industry-specific solutions are built. As Mistral’s head of product, Elisa Salamanca, told TechCrunch, Forge “lets enterprises and governments customize AI models for their specific needs.” Early partners include Ericsson, the European Space Agency, Italian consulting company Reply, and Singapore’s DSO and HTX, alongside ASML.

Mistral has also built an expanding network of systems integration partnerships to drive enterprise adoption. In February 2026, Accenture and Mistral announced a multi-year strategic collaboration, with Accenture itself becoming a Mistral customer. Mauro Macchi, Accenture’s CEO for Europe, Middle East, and Africa, said at the time that the partnership brings together “sovereign models and the capability to scale technology across industries, geographies and business functions.”

The BNP Paribas relationship offers the most detailed public case study. In a video testimonial at the summit, a BNP Paribas representative described deploying Mistral’s models on-premises to satisfy strict security requirements, developing AI agents for KYC processes that reduced incomplete files from 80% to 10% and compressed processing time from weeks to days. The bank’s LLM platform at its Corporate and Institutional Banking division has now rolled out to 65,000 users. Mensch noted the significance: “We started to collaborate in 2023 where we were 15 people, so that was, I think, really a leap of faith at the time.”

The industrial vertical is also being extended to government clients. Mistral disclosed that it is working with France, Luxembourg, Singapore, Morocco, Greece, and Slovakia to build citizen-facing AI services — from deploying agents that help job-seekers through France Travail to building models that understand Moroccan Darija and Amazigh languages. “We think that AI needs to be specialized and understand structural nuances,” Mensch told the audience. “It needs to speak languages as good as it speaks English.”

The road ahead for Europe’s most ambitious AI company

For Mistral, Wednesday’s announcements amount to a declaration that the company intends to compete not by matching American AI giants on any single dimension, but by assembling capabilities none of them are willing or able to offer in combination: open-weight models, owned infrastructure, on-premises deployment, physics simulation, and deep vertical customization — all under a single roof.

The strategy demands execution on multiple fronts simultaneously, each requiring enormous capital and specialized talent. The competition is formidable and accelerating. OpenAI has been rapidly expanding its enterprise offerings. Anthropic, backed by billions from Amazon, is building its own corporate AI practice. Google, Microsoft, and Amazon all offer AI platforms deeply integrated with cloud infrastructure that most enterprises already use.

But Mistral is wagering that the world’s most consequential AI deployments — the ones governing how aircraft get designed, how banks process compliance, how governments interact with citizens — will ultimately go to providers that offer sovereignty over data, models, and compute. “AI is too strategic to be left in the hands of a few,” Mensch said, echoing the conviction he described from Mistral’s founding three years ago.

Three years in, the company that started as a Paris research lab with a handful of employees now trains models in its own data centers, simulates physics for the manufacturers that build the world’s planes and cars, and is rewriting its assistant into an agent that can file your pull requests and summarize your inbox in the same conversation. Whether that sprawling ambition coheres into a durable business or stretches Mistral too thin is the €11.7 billion ($13.6B USD)  question. The 1,000 people now working there are betting that in enterprise AI, owning the full stack is not a liability — it is the product.

Resolve AI, the production-operations startup backed by Greylock and Lightspeed Venture Partners, today announced a sweeping expansion of its platform that introduces always-on background agents, a redesigned investigation architecture, and a shared workspace where engineers and AI agents collaborate in real time on live incidents.

The centerpiece of the release is a new multi-agent investigation system developed by Resolve AI’s in-house research lab. Instead of deploying a single AI agent to diagnose a production failure — analogous to a lone engineer pulling an on-call shift — the platform now dispatches a coordinated team of specialized agents that pursue multiple hypotheses in parallel, independently verify each other’s conclusions, and construct complete causal chains from root cause to symptom. The company says the architecture delivers more than a twofold improvement in root cause accuracy on its internal evaluation benchmarks compared to earlier versions of its platform.

“Think of a single agent being on call, the way a human would be,” Resolve AI CEO and co-founder Spiros Xanthos told VentureBeat in an exclusive interview ahead of the announcement. “We now have a team of agents that all work together, almost like a team of humans debugging an issue, and that has improved quality by 2x.”

The announcement arrives at a moment of acute tension in the software industry. AI-powered code generation has exploded in adoption, enabling engineering teams to ship dramatically more software than they could two years ago. But keeping that software running in production — debugging it when it breaks, monitoring it after deployment, auditing its health — remains overwhelmingly manual. For a company that raised a $125 million Series A at a $1 billion valuation earlier this year, Resolve AI is making a direct bet that the operational side of the software lifecycle is the next major frontier for AI investment.

What hundreds of real-world test cases reveal about the accuracy claim

Any accuracy claim from a startup warrants scrutiny, and Xanthos was candid about both the scale and limitations of the evaluation. The 2x figure comes from internal benchmarks, not a third-party audit, though the evaluation set was built to mirror the complexity that Resolve AI’s enterprise customers encounter daily.

“These are very hard, complex evals that we built over time to represent real-world examples,” Xanthos explained. “This is not customer data, but these evals represent difficult cases similar to what we’ve seen at some of the largest tech companies we work with.” He described the set as comprising hundreds of cases that reflect the kinds of production failures encountered at companies like Coinbase, Salesforce, DoorDash, and Zscaler — all named Resolve AI customers.

The practical impact of that accuracy gain is significant. Resolve AI’s agents now act as first responders for every on-call alert, typically triaging within five minutes before a human engineer even becomes involved. In previous public disclosures, the company has cited DoorDash reducing time to root cause by up to 87 percent. When asked to contextualize that figure, Xanthos described the typical baseline.

“When something goes wrong, it might take five to 10 minutes for a human to even get their laptop and connect,” he said. “The typical MTTR is in the tens of minutes, sometimes hours, depending on severity. So an improvement of 80-plus percent — four to five times faster — is actually huge. It’s something we’ve never achieved before with AI, tools, data, or observability.”

How AI agents fact-check each other to prevent hallucinated root causes

One of the core challenges in applying large language models to high-stakes production environments is their tendency to generate plausible-sounding but incorrect answers — a failure mode that, in the context of a live outage, could send an engineering team chasing the wrong fix while a service stays down.

Xanthos acknowledged this directly. “This is a very common issue with models out of the box,” he said. “They always try to give you an answer, and if they don’t have enough evidence, they’ll give you the best possible answer — which is likely to be wrong.”

Resolve AI’s countermeasure is a system of layered verification among its agents. Each agent investigating a hypothesis must cite every piece of evidence it relies on and present that evidence to another agent for independent review. The investigating agent must construct the full causal chain — from root cause to symptom — and peer agents actively attempt to disprove the theory by identifying gaps in the logic.

“Often, agents actually disprove those theories because they find gaps,” Xanthos said. “There are many layers of defense and agentic checks that allow Resolve to be very accurate and not mislead.”

Equally important, he said, is the system’s willingness to say it does not know. “The bar to actually saying ‘I have the answer’ is very high. In those cases, it will say, ‘This is the evidence I found. Here are three or four paths you can take from here, but I wasn’t able to fully prove that this is the problem.’ A system like this that operates in production cannot be a black box.” In domains where wrong answers carry operational consequences, calibrated uncertainty can be more valuable than confident outputs. For an AI system integrated into an incident-response workflow, confidently pointing engineers in the wrong direction during a customer-facing outage could compound the very harm it was designed to prevent.

Inside the new background agents that never go off-call

Beyond incident response, Resolve AI is introducing a new class of background agents designed to handle the continuous, often invisible operational work that engineering teams are expected to perform but struggle to sustain at scale.

These agents run on schedules or wake automatically in response to events — a new deployment, a fired alert, a merged pull request — and accumulate institutional knowledge from every investigation and human interaction over time. When an engineer opens the Resolve AI interface, agents have already been working: pre-investigating priority issues, monitoring deployments, auditing alert hygiene, flagging configuration drift, and surfacing cost anomalies.

Xanthos drew a distinction between background agents and the incident-response agents that have been Resolve AI’s primary offering. “You can now have these agents run in the background at all times — not only when a human asks an agent to debug a problem or when an alert fires,” he said. “A lot of our customers are now monitoring changes that land in production before they cause an issue. There’s an agent that monitors those all the time.”

He described these background agents as “general-purpose SRE agents that are available to every developer,” capable of handling tasks that range from monitoring infrastructure changes that might increase cloud costs to performing post-incident follow-up work like generating code fixes based on incident learnings. The concept addresses a structural problem in software operations: the daily tasks required to keep production systems healthy — monitoring deployments, investigating alerts, tracking changes across complex environments — are critical but reactive and manual. Engineering organizations know this work needs to happen, but it competes for attention with feature development. Automated agents that perform this work continuously could shift teams from reactive firefighting to proactive operational management.

The shared workspace where engineers and AI agents investigate together

The third major component of the release is what the company calls a shared investigation surface — a workspace where engineers and AI agents work from the same live evidence during an active incident. Reports update dynamically as investigations evolve. Every finding is inspectable. Engineers can explore side investigations without interrupting the primary workflow. Source queries are pullable and modifiable in place, evidence is embedded directly into the workspace, and remediation actions can be triggered from the same interface without switching tools.

“Think of it as an interface to all the production tools, but also an interface where humans and agents can collaborate with each other — or agents with agents,” Xanthos said. “That’s what gradually leads to more trust and more automation, because you work with the agent, you teach it, you see the results.”

The company is also making its platform available as a REST API and an MCP (Model Context Protocol) server, enabling engineering teams to integrate Resolve AI into broader agentic workflows and infrastructure. According to Xanthos, this is already happening in practice. “A general-purpose agent that a company has built — when it comes to debugging, that agent could invoke Resolve,” he said. “Or somebody works on their coding agent on the laptop, and Resolve shows up there as an MCP. If there is some production-related activity, the coding agent can invoke it.” The interoperability play signals that Resolve AI sees itself not as a closed system but as a specialized node in a broader ecosystem of AI agents that will increasingly hand off tasks to one another — a pattern Xanthos compared to the open architecture of the web rather than the walled-garden model of an app store.

Why Resolve AI says it can outperform Datadog, PagerDuty, and the cloud giants

The agentic operations space has become crowded in the past year. Datadog, PagerDuty, and major cloud providers have all announced AI-augmented operations capabilities. When asked what separates Resolve AI from these incumbents, Xanthos pointed to the depth of the company’s technical foundation.

“We’re operating at the frontier here. There’s no blueprint for how you build a system like Resolve,” he said. He noted that he and co-founder Mayank Agarwal co-created OpenTelemetry, the most widely adopted open-source project in observability, which now serves as the de facto standard for collecting metrics, logs, and traces from modern software systems.

Xanthos also highlighted the company’s recent AI Lab, led by a researcher he described as the former post-training lead for Meta’s Llama models. “He managed to combine deep expertise of production observability with AI and models, and I think that’s very unique,” Xanthos said. “I don’t believe any other company, whether it comes from an observability background or it’s a startup, has all of that together.”

The company’s structural defenses, according to Xanthos, include a full environment model that Resolve builds for each customer, a memory system that learns within the customer’s specific production environment, and its multi-agent architecture. The lab is now post-training frontier models on production-specific data — the kind of procedural knowledge that experienced engineers use to debug production issues but that does not appear in standard model training sets. This approach reflects an increasingly common pattern among AI application companies: using frontier foundation models as a base layer but investing heavily in domain-specific fine-tuning, retrieval, and agent architectures to achieve accuracy levels that general-purpose models cannot reach alone.

How outcome-based pricing changes the economics of AI in production

Resolve AI’s pricing model departs from traditional enterprise software licensing. The company sells credits that are consumed when agents perform work — an outcome-based approach that ties cost directly to value delivered.

“We’re not selling software,” Xanthos said. “The way you buy and use Resolve is by buying credits that are consumed when Resolve performs an action. It’s outcome-based. Only when Resolve troubleshoots an alert — that’s the only time that it consumes credits.”

He addressed the cost question head-on, arguing that Resolve AI is actually cheaper than the alternative of building a similar system from scratch using frontier models and MCP integrations. “If you were to take Opus or GPT-5.4 and try to build a solution like Resolve with MCPs, we measured — you actually end up consuming a lot more in tokens than what you have to pay Resolve, because our system is very optimized in terms of context, in terms of how it reads time-series data.”

As for the always-on background agents, Xanthos said their continuous nature does not inherently add to cost. “The background agent doesn’t mean it does intensive work all the time. It means that it can be there; you can give it any task you want. A lot of these tasks are triggered based on some action — an alert happens, somebody merges a PR, and you want to see if it has an impact on production.” For enterprise customers in regulated industries — the Coinbases and Zscalers of the world — data residency and security are non-negotiable. Resolve AI accommodates this with a flexible deployment model: the data plane sits wherever the customer’s existing tools already live, while the inference layer can run as a standard SaaS deployment or inside a customer-specific VPC. “We designed Resolve to work with the large enterprises where security standards are the highest,” Xanthos said. “There are many measures we take to ensure Resolve is secure, including not retaining data.”

Why engineering leaders are slowly learning to trust AI agents with production systems

The question of whether engineering teams will trust AI agents to take autonomous action in production — rolling back a deployment, adding capacity, generating a pull request — is one of the defining cultural challenges of this technology wave. Xanthos drew an analogy to autonomous vehicles.

“For us to allow a car to drive on its own on the street, we have to prove that it’s safer than a human. Agents in production is a very similar concept,” he said. He acknowledged that not every customer is comfortable with agents taking automated action, but described a gradient of trust that he expects to evolve rapidly.

“There is a set of actions that are relatively risk-free that most tech companies probably are comfortable having an agent take, and probably there is another set of actions for which the human has to approve,” he said. “But as quality keeps climbing the way we see at Resolve, I would say we’re going to cross the threshold this year where most of the actions will be taken by an agent automatically.”

He described the typical adoption arc: companies begin with agents providing recommendations, then a human decides whether to press the button. Over weeks or months, trust builds incrementally. “I don’t think this is a problem where we just let the agents run wild from the beginning,” Xanthos said. The incremental approach mirrors how enterprise technology adoption has always worked — from cloud migration to container orchestration, organizations move at the speed of trust, not the speed of capability.

The argument that AI-generated code is making the production crisis worse, not better

Perhaps the most provocative argument in Resolve AI’s thesis is that the explosion of AI-generated code is actually intensifying the production-operations problem. In a recent LinkedIn post, Xanthos framed the dynamic in stark terms, arguing that engineering leaders who celebrate faster code shipping without investing in production operations are effectively having their senior engineers “subsidize velocity” through increased incident-response burden.

In his interview with VentureBeat, he returned to this theme. “Now that coding agents are producing code, we produce a lot more code that we’re less familiar with — humans are less familiar with — so you need the AI to be the defense,” he said.

This framing positions Resolve AI not merely as a productivity tool but as a necessary counterweight to the AI coding revolution. As organizations deploy more code, written by tools that their engineers may not fully understand, running against production systems those engineers did not build, the argument is that the operational complexity — and the consequences of failure — will grow proportionally. On the Stack Overflow Podcast last October, Xanthos put numbers to this claim, estimating that engineers spend upwards of 70 percent of their time maintaining and troubleshooting production systems rather than building new features. “We’re facing a new crisis where we’re building faster than we can operate,” he said in that conversation.

Resolve AI was founded in early 2024 by Xanthos and Agarwal, who first met during their PhD programs at the University of Illinois and have worked together for more than a decade. Xanthos previously co-founded Pattern Insight (acquired by VMware) and Omnition (acquired by Splunk), where the pair helped create OpenTelemetry. The company raised a $35 million seed round from Greylock in 2024, followed by the $125 million Series A led by Lightspeed at a $1 billion valuation earlier this year. Named customers include Coinbase, DoorDash, MSCI, Salesforce, MongoDB, and Zscaler.

Xanthos’s long-term vision is expansive. “Over the long run, once agent ability surpasses that of a human software engineer, the end result is a lot more technology and a lot more software,” he said. “It’s not actually fewer people working on it. It’s technology becoming cheaper, becoming more accessible, producing a lot more technology for the benefit of the world.”

That vision will take years to realize. But the more immediate promise of today’s announcement comes down to something every on-call engineer understands viscerally: the 2 a.m. page, the scramble for a laptop, the frantic search through dashboards and logs for an answer that might take minutes or might take hours. Resolve AI is betting that the next time that alert fires, a team of agents will have already investigated, verified, and documented the root cause before the engineer’s phone even lights up. For a profession that has long measured its nights by mean time to resolution, the question is no longer whether AI can help — it is whether engineers will let it.

Less than a week after completing the largest tech IPO of 2026, Cerebras Systems is making its most aggressive play yet to dominate the fast-growing AI inference market. On Monday, the Sunnyvale-based chipmaker announced that it is now running Kimi K2.6 — a trillion-parameter open-weight model developed by Beijing-based Moonshot AI — for enterprise customers at nearly 1,000 tokens per second, a speed no GPU-based provider has come close to matching.

The result, independently verified by benchmarking firm Artificial Analysis, clocked in at 981 output tokens per second, making Cerebras 6.7 times faster than the next-fastest GPU-based cloud provider and 23 times faster than the median. For a standard agentic coding request involving 10,000 input tokens, Cerebras delivered the full response — including prompt processing, reasoning, and 500 output tokens — in 5.6 seconds, compared to 163.7 seconds on the official Kimi endpoint. That’s a 29-fold improvement in time to final answer.

“We’re really wanting to be very clear and show that we can do the largest models,” James Wang, Cerebras’ director of product marketing, told VentureBeat in an exclusive interview ahead of the announcement. “In this case, Kimi K2.6 — a trillion-parameter MoE model on the wafer-scale architecture — and it runs also at this same incredible speed that we’re famous for.”

The announcement marks a critical inflection point for Cerebras, which has long battled a perception that its unorthodox wafer-scale chips, while blindingly fast, could only handle small and mid-sized models. Kimi K2.6 is the first trillion-parameter open-weight model the company has ever served in production. And with a freshly minted $95 billion market cap and $5.55 billion in IPO proceeds burning a hole in its balance sheet, Cerebras is signaling to Wall Street that it intends to compete not just at the frontier of speed, but at the frontier of model scale.

Why Cerebras chose a Chinese-built model as its trillion-parameter flagship

The choice of Kimi K2.6 reflects both a technical milestone and a commercial calculus. Released on April 20 by Moonshot AI — a Beijing-based company founded in 2023 by Tsinghua University alumni and dubbed one of China’s “AI Tiger” companies — K2.6 is a trillion-parameter Mixture-of-Experts model that has rapidly established itself as the most capable open-weight model available for coding and agentic tasks. The model tops SWE-Bench Pro at 58.6, outperforming Claude Opus 4.6 and matching GPT-5.4, while posting leading scores on agentic benchmarks like Humanity’s Last Exam and DeepSearchQA. Its architecture uses 32 billion activated parameters per token out of a total of 1 trillion, with 384 experts, of which 8 are selected plus 1 shared per forward pass, operating over a 256,000-token context window.

In practical terms, K2.6 is one of the first open-weight models that enterprises can plausibly use as a drop-in replacement for expensive, capacity-constrained closed-source APIs from Anthropic and OpenAI — particularly for the coding and agentic workloads that have become the highest-value application of large language models. The version 2.6 release extends K2.6’s capabilities from front-end design into full-stack workflows, including authentication, database operations, and long-horizon agent execution.

Wang was blunt about what is driving enterprise interest. “They’re very motivated, first of all, to have an alternative to Anthropic,” he told VentureBeat. “Anthropic’s models are fantastic. I use them. I’m sure you probably use them. But they’re quite expensive, and they’re constantly running out of capacity.” He described a personal experience in which an application running on Anthropic’s API failed over a weekend because it ran out of capacity — an anecdote that, he said, resonates deeply with enterprise buyers.

The geopolitical dimension of this arrangement is worth noting, however. Kimi K2.6 is a Chinese-developed model being served by an American chipmaker to American enterprise customers. Moonshot AI operates out of Beijing, and K2.6’s adoption in the West arrives during a period of heightened scrutiny of Chinese AI companies in the U.S. market. Enterprise buyers with strict compliance requirements — particularly those in financial services, healthcare, and defense — will need to evaluate this dimension alongside the model’s technical capabilities.

How wafer-scale chips solve the trillion-parameter speed problem that GPUs cannot

Understanding why Cerebras can achieve these speeds requires understanding what makes its hardware fundamentally different from anything else on the market. Most AI inference today runs on clusters of Nvidia GPUs — typically organized in racks of 72 GPUs, what Nvidia markets as the NVL72 configuration. In these setups, the model’s parameters are distributed across many discrete chips connected by high-speed networking fabric. Data must constantly shuttle between chips, and the interconnect bandwidth between GPUs becomes a bottleneck, particularly for large models with hundreds of billions or trillions of parameters.

Cerebras takes a radically different approach. Its Wafer-Scale Engine 3 is a single chip the size of an entire silicon wafer — roughly the size of a dinner plate — containing 44 gigabytes of on-chip SRAM. Unlike the high-bandwidth memory used in GPUs, SRAM sits directly on the processor die, offering dramatically lower latency and higher bandwidth for data access. For Kimi K2.6, Cerebras stores the model’s weights in their original 4-bit precision while performing computation at 16-bit floating point. The weights are distributed across multiple wafers in a cluster of approximately 20 CS-3 systems, with activations streamed between them. Critically, all the experts for a given MoE layer are placed on the same wafer, meaning the all-to-all communication required for expert routing happens at SRAM speeds. According to Cerebras’ technical description, the on-wafer network fabric delivers over 200 times the bandwidth of NVLink on NVL72.

Wang explained the architecture using an analogy. “Our single units are much larger and much higher capacity — they’re on the order of 20 racks, as opposed to 72 GPUs,” he said. Each layer in the transformer can, in effect, serve a separate user simultaneously. “They’re just like a queue, like you’re queuing for bagels or something — they’re all occupying a different part of the hardware. But because they move across so fast, the actual experience, tokens per second, single user, on your end is still what you’re used to.” Combined with custom kernels and speculative decoding, this allows Cerebras to serve the trillion-parameter MoE model at close to 1,000 tokens per second — a speed the company calls a world record achievable only with wafer-scale hardware.

Fortune 500 companies are already testing Cerebras’ trillion-parameter inference in production

Cerebras is not opening K2.6 to the general public. Instead, the company is positioning this as an enterprise-first offering, with Fortune 500 companies in software, financial services, and healthcare currently running cloud trials of their production workloads on the platform. “These are logos that you’ve definitely heard of,” Wang said, though he declined to identify specific customers due to confidentiality agreements.

The enterprise-first approach is deliberate. Cerebras has historically prioritized its largest customers over its consumer-facing API, in part because of hardware capacity constraints. “Everyone is in a capacity crunch. We prioritize our enterprise customers, so we don’t show it in the consumer-facing gateway or the API, where you get very unpredictable traffic, where a single user can, in effect, take over your whole cluster,” Wang explained. Serving K2.6 also limits the company’s ability to simultaneously offer other large models. “We can’t simultaneously, you know, have six other models,” he acknowledged. “It’s just kind of a mutual constraint of reality.”

On pricing, Wang said that while the enterprise deployment does not carry public pricing, the company’s costs are broadly competitive with GPU-based providers. “On all the models we have served with pricing, the pricing is very comparable — maybe in the middle, kind of middle-upper range of GPU pricing,” he said. “It’s not like, because we run fast, it costs many, many fold more.” He drew a line, however, at the lowest end of the market: if you are willing to run K2.6 at 20 tokens per second on bargain GPU infrastructure, Cerebras will not try to compete on price. “We’re an automaker in the pickup truck market. We don’t do that market,” Wang said. For speed-sensitive workloads — particularly agentic coding, where developers wait in real time for the model to generate and iterate on code — the value proposition is straightforward: comparable per-token cost, but an order of magnitude faster delivery.

The competitive threat from Nvidia’s $20 billion Groq acquisition looms large

Cerebras’ announcement arrives at a pivotal moment in the AI chip industry, one in which the inference market is rapidly overtaking training as the most commercially important compute workload. As AI agents proliferate in enterprise software, the speed of inference directly determines how useful those agents are in practice — and the competitive pressures are intensifying accordingly.

The most significant competitive development in recent months was Nvidia’s acquisition of Groq for $20 billion, a deal that gave the GPU giant access to proprietary inference technology built around specialized Language Processing Units. Wang referenced the deal directly. “I think Nvidia is now sensing fast inference is an extremely important market,” he told VentureBeat. “That’s why they’re willing to spend $20 billion on acquiring a company like that.”

But Wang expressed confidence that Cerebras’ architectural advantages are durable. Both Nvidia and Cerebras operate on roughly annual hardware refresh cycles. “We refresh our hardware on a periodic cycle. You will hear some news about that from us soon,” Wang said, hinting at a forthcoming hardware announcement without providing details. On the software side, Wang pointed to the company’s track record of rapidly adapting to the fast-evolving open-weight model ecosystem. “We started with Llama, we supported all the Qwen models, and then when developers told us they wanted GLM, we brought GLM online. And now they’re telling us Kimi is the best — so we’re giving them Kimi,” he said. “At the same time, we’ve also supported the best companies in running their closed models — OpenAI, Cognition, Mistral.”

The mention of OpenAI underscores one of the most unusual business relationships in the AI industry. OpenAI and Cerebras struck a deal in early 2026 reportedly worth more than $20 billion for computing capacity and related services. Wang confirmed that Cerebras serves OpenAI’s “internal coding models forthcoming” but declined to disclose specifics, as neither party has publicly detailed the technical arrangement.

Inside Cerebras’ plan to serve the smartest AI models faster than anyone else

Wang framed the K2.6 deployment as a stepping stone, not a destination. Cerebras started serving inference in late 2024 with relatively small models and has spent over a year scaling from 70 billion parameters to 1 trillion-plus. “We couldn’t have launched that in November 2024,” he said. “But we’re there now.”

The company’s next challenge is to move from serving the best open-weight frontier model to serving the best frontier models, period — including closed-source models from the likes of Anthropic and OpenAI that sit at the absolute top of the intelligence leaderboards. “This is the first open-weight frontier one that we now have clear demonstrated evidence for,” Wang said. “I think over the course of the year, you will see us serving true frontier, frontier at the speed that we’re famous for. And you should hold us up for that.”

When asked whether the current rollout would be overtaken by the pace of hardware improvement at Nvidia and others, Wang was unfazed. “Nvidia has a very clear roadmap. They publish every year at GTC. They’re roughly on a yearly product cycle, and so are we. You will hear some news about that from us soon,” he said, hinting at new hardware without offering details.

He also addressed the question of vendor lock-in — a concern that any CTO evaluating a single-vendor inference provider would raise. “These enterprises rarely commit fully to one vendor,” Wang said. “They have strategies to make sure that some traffic can go to us, some traffic can go to someone else, and there’s load balancing between the two. This is not a new problem. This is just generally how you manage cloud resources.”

The pitch, ultimately, is about more than speeds and feeds. Wang sees the AI industry converging on a world in which autonomous agents — not human developers — are the primary consumers of inference compute, and in which the speed of those agents determines competitive outcomes for the companies that deploy them. “The world economy is kind of getting rebuilt on agents,” Wang said. “Speed will determine who wins or loses.”

It is a bold claim from a company that, until last week, had never traded on a public exchange. But for Cerebras, the logic is straightforward: if the future of enterprise software is built by AI agents that think at the speed of their hardware, then the company that provides the fastest hardware provides the fastest thinking. And in a market where enterprises are spending billions to shave seconds off their AI response times, a company that can serve a trillion-parameter model in the time it takes to pour a cup of coffee might just have the most compelling pitch in Silicon Valley.