A little-known Miami-based startup called Subquadratic emerged from stealth on Tuesday with a sweeping claim: that it has built the first large language model to fully escape the mathematical constraint that has defined — and limited — every major AI system since 2017.

The company claims its first model, SubQ 1M-Preview, is the first LLM built on a fully subquadratic architecture — one where compute grows linearly with context length. If that claim holds, it would be a genuine inflection point in how AI systems scale. At 12 million tokens, the company says, its architecture reduces attention compute by almost 1,000 times compared to other frontier models — a figure that, if validated independently, would dwarf the efficiency gains of any existing approach.

The company is also launching three products into private beta: an API exposing the full context window, a command-line coding agent called SubQ Code, and a search tool called SubQ Search. It has raised $29 million in seed funding from investors including Tinder co-founder Justin Mateen, former SoftBank Vision Fund partner Javier Villamizar, and early investors in Anthropic, OpenAI, Stripe, and Brex. The New Stack reported that the raise values the company at $500 million.

The numbers Subquadratic is publishing are extraordinary. The reaction from the AI research community has been, to put it mildly, mixed — ranging from genuine curiosity to open accusations of vaporware. Understanding why requires understanding what the company claims to have solved, and why so many prior attempts to solve the same problem have fallen short.

The quadratic scaling problem has shaped the economics of the entire AI industry

Every transformer-based AI model — which includes virtually every frontier system from OpenAI, Anthropic, Google, and others — relies on an operation called “attention.” Every token is compared against every other token, so as inputs grow, the number of interactions — and the compute required to process them — scales quadratically. In plain terms: double the input size, and the cost doesn’t double. It quadruples.

This relationship has shaped what gets built and what doesn’t. The industry standard is 128,000 tokens for many AI models and up to 1 million tokens for frontier cloud models such as Claude Sonnet 4.7 and Gemini 3.1 Pro. 

Even at those sizes, the cost of processing long inputs becomes punishing. The industry built an elaborate stack of workarounds to cope. RAG systems use a search engine to pull a small number of relevant results before sending them to the model, because sending the full corpus isn’t feasible. Developers layer retrieval pipelines, chunking strategies, prompt engineering techniques, and multi-agent orchestration systems on top of models — all to route around the fundamental constraint that the model itself can’t efficiently process everything at once.

Subquadratic’s argument is that these workarounds are expensive, brittle, and ultimately limiting. As CTO Alexander Whedon told SiliconANGLE in an interview, “I used to manually curate prompts and retrieval systems and evals and conditional logic to chain together the workflows. And I think that that is kind of a waste of human intelligence and also limiting to the product quality.”

Subquadratic’s fix is deceptively simple: stop doing the math that doesn’t matter

The company’s approach, called Subquadratic Sparse Attention or SSA, is built on a straightforward premise: most of the token-to-token comparisons in standard attention are wasted compute. Instead of comparing every token to every other token, SSA learns to identify which comparisons actually matter and computes attention only over those positions. Crucially, the selection is content-dependent — the model decides where to look based on meaning, not on fixed positional patterns. This allows it to retrieve specific information from arbitrary positions across a very long context without paying the quadratic tax.

The practical payoff scales with context length — exactly the inverse of the problem it’s trying to solve. According to the company’s technical blog, SSA achieves a 7.2x prefill speedup over dense attention at 128,000 tokens, rising to 52.2x at 1 million tokens. As Whedon put it: “If you double the input size with quadratic scaling laws, you need four times the compute; with linear scaling laws, you need just twice.” The company says it trained the model in three stages — pretraining, supervised fine-tuning, and a reinforcement learning stage specifically targeting long-context retrieval failures — teaching the model to aggressively use distant context rather than defaulting to nearby information, a subtle failure mode that quietly degrades performance in existing systems.

Three benchmarks paint a strong picture, but what they leave out may matter more

On the surface, SubQ’s benchmark numbers are competitive with or superior to models built by organizations spending billions of dollars. On SWE-Bench Verified, it scored 81.8% compared to Opus 4.6’s 80.8% and DeepSeek 4.0 Pro’s 80.0%. On RULER at 128,000 tokens, a standard benchmark for reasoning over extended inputs, SubQ scored 95% — edging out Claude Opus 4.6 at 94.8%. On MRCR v2, a demanding test of multi-hop retrieval across long contexts, SubQ posted a third-party verified score of 65.9%, compared with Claude Opus 4.7 at 32.2%, GPT-5.5 at 74%, and Gemini 3.1 Pro at 26.3%.

But several details warrant scrutiny. The benchmark selection is narrow — exactly three tests, all emphasizing long-context retrieval and coding, the precise tasks SubQ is designed for. Broader evaluations across general reasoning, math, multilingual performance, and safety have not been published. The company says a comprehensive model card is “coming soon.”

According to The New Stack, each benchmark model was run only once due to high inference cost, and the SWE-Bench margin is, as the company’s own paper acknowledges, “harness as much as model.” In benchmark methodology, single runs without confidence intervals leave room for variance. There is also a significant gap between SubQ’s research results and its production model. On MRCR v2, the company reported a research score of 83 — but the third-party verified production model scored 65.9. That 17-point gap between the lab result and the shipping product is notable and largely unexplained.

Subquadratic also told SiliconANGLE that on the RULER 128K benchmark, SubQ scored 95% accuracy at a cost of $8, compared with 94% accuracy and about $2,600 for Claude Opus — a remarkable cost claim. But the company has not publicly disclosed specific API pricing, making it impossible to independently verify the cost-per-task comparisons.

The AI research community’s verdict ranges from ‘genuine breakthrough’ to ‘AI Theranos’

Within hours of the announcement, the AI research community erupted into a debate that crystallized around a single question: Is this real?

AI commentator Dan McAteer captured the binary mood in a widely shared post: “SubQ is either the biggest breakthrough since the Transformer… or it’s AI Theranos.” The comparison to the infamous blood-testing fraud company may be unfair, but it reflects the scale of the claims being made. Skeptics zeroed in on several pressure points. Prominent AI engineer Will Depue initially noted that SubQ is “almost surely a sparse attention finetune of Kimi or DeepSeek,” referring to existing open-source models.

Whedon confirmed this on X, writing that the company is “using weights from open-source models as a starting point, as a function of our funding and maturity as a company.” Depue later escalated his criticism, writing that the company’s O(n) scaling claims and the speedup numbers “don’t seem to line up” and called the communication “either incredibly poorly communicated or just not real.”

Others raised structural questions. One developer noted that if SubQ truly reduces compute by 1,000x and costs less than 5% of Opus, the company should have no trouble serving it at scale — so why gate access through an early-access program? Developer Stepan Goncharov called the benchmarks “very interesting cherry-picked benchmarks,” while another commenter described them as “suspiciously perfect.”

But not everyone was dismissive. AI researcher John Rysana pushed back on the Theranos framing, writing that the work is “just subquadratic attention done well which is very meaningful for long context workloads,” and that “odds of it being BS are extremely low.” Linus Ekenstam, a tech commentator, said he was “extremely intrigued to see the real-world implications” particularly for complex AI-powered software.

Magic.dev made strikingly similar claims two years ago — and then went quiet

Perhaps the most pointed critique of SubQ’s launch comes not from its specific claims but from recent history. Magic.dev announced a 100-million-token context-window model in August 2024, with a claimed 1,000x efficiency advantage, and raised roughly $500 million on the strength of those claims. As of early 2026, there is no public evidence of LTM-2-mini being used outside Magic.

The parallels are uncomfortable. Both companies claimed massive context windows. Both touted roughly 1,000x efficiency gains. Both targeted software engineering as their primary use case. And both launched with limited external access.

The broader research landscape reinforces the caution. Kimi Linear, DeepSeek Sparse Attention, Mamba, and RWKV all promised subquadratic scaling, and all faced the same problem: architectures that achieve linear complexity in theory often underperform quadratic attention on downstream benchmarks at frontier scale, or they end up hybrid — mixing subquadratic layers with standard attention and losing the pure scaling benefits.

A widely cited LessWrong analysis argued that these approaches “are all better thought of as ‘incremental improvement number 93595 to the transformer architecture'” because practical implementations remain quadratic and “only improve attention by a constant factor.”

Subquadratic is directly aware of this history. Its own technical blog specifically addresses each prior approach — fixed-pattern sparse attention, state space models, hybrid architectures, and DeepSeek Sparse Attention — and argues that SSA avoids their tradeoffs. Whether it actually does remains an empirical question that only independent evaluation can settle.

A five-time founder, a former Meta engineer, and $29 million to prove the doubters wrong

The team behind the claims matters in evaluating them. CEO Justin Dangel is a five-time founder and CEO with a track record across health tech, insurancetech, and consumer goods, and his companies have scaled to hundreds of employees, attracted institutional backing, and reached liquidity. CTO Alexander Whedon previously worked as a software engineer at Meta and served as Head of Generative AI at TribeAI, where he led over 40 enterprise AI implementations.

The team includes 11 PhD researchers with backgrounds from Meta, Google, Oxford, Cambridge, ByteDance, and Adobe. That is a credible collection of talent for an architecture-level research effort. But neither co-founder has published foundational AI research, and the company has not yet released a peer-reviewed paper. The technical report is listed as “coming soon.”

The funding profile is unusual for a company making frontier AI claims. Subquadratic raised $29 million at a reported $500 million valuation — a steep price for a seed-stage company with no publicly available model, no peer-reviewed research, and no disclosed revenue. The investor base, led by Tinder co-founder Mateen and former SoftBank partner Villamizar, skews toward consumer tech and growth investing rather than deep technical AI research. The company is not open-sourcing its weights but plans to offer training tools for enterprises to do their own post-training, and has set a 50-million-token context window target for Q4.

The real test for SubQ isn’t benchmarks — it’s whether the math survives independent scrutiny

Strip away the marketing language and the social media drama, and the underlying question Subquadratic is asking is genuinely important: Can AI systems break free of quadratic scaling without sacrificing the quality that makes them useful?

The stakes are enormous. If attention can be made truly linear without degrading retrieval and reasoning, the economics of AI shift fundamentally. Enterprise applications that today require elaborate retrieval pipelines — processing entire codebases, contracts, regulatory filings, medical records — become single-pass operations. The billions of dollars currently spent on RAG infrastructure, context management, and agentic orchestration become partially redundant. 

Whedon’s willingness to engage publicly with technical criticism — posting a technical blog within hours of pushback — suggests a team that understands it needs to show its work, not just describe it. And to its credit, the company acknowledged openly that it builds on open-source foundations and that its model is smaller than those at the major labs.

Every frontier model in 2026 advertises a context window of at least a million tokens, but almost none of them are actually great at making use of all that information. The gap between a nominal context window and a functional one — between what a model accepts and what it reliably reasons over — remains one of the most important unsolved problems in AI. Subquadratic says it has closed that gap. If independent evaluation confirms that claim, the implications would ripple far beyond a single startup’s valuation. If it doesn’t, the company joins a growing list of long-context promises that sounded revolutionary on launch day and unremarkable six months later.

In computing, every fundamental constraint eventually falls. When it does, the breakthrough never comes from the direction the industry expected. The question hanging over Subquadratic is whether a team of 11 PhDs and a $29 million seed round actually found the answer that has eluded organizations spending thousands of times more — or whether they just found a better way to describe the problem.

Microsoft and OpenAI on Monday announced a sweeping overhaul of the partnership that has defined the commercial AI era, dismantling key pillars of exclusivity and revenue-sharing that bound the two companies together for years and replacing them with a looser, time-limited arrangement that gives both sides far more freedom to pursue rival relationships.

The amended agreement, disclosed simultaneously in blog posts from both companies, marks the most significant restructuring since Microsoft first invested $1 billion in OpenAI in 2019 — and it transforms what was once the most consequential exclusive technology alliance in a generation into something that more closely resembles a strategic but arm’s-length commercial relationship.

Under the new terms, Microsoft will no longer pay any revenue share to OpenAI when customers access OpenAI models through Azure. OpenAI, meanwhile, will continue paying a revenue share to Microsoft through 2030 — at the same 20 percent rate — but that obligation is now subject to a total cap. Microsoft retains a license to OpenAI’s intellectual property for models and products through 2032, but that license is now explicitly non-exclusive. And OpenAI, critically, can now serve all of its products to customers on any cloud provider — including Amazon Web Services and Google Cloud — ending the exclusivity that had been a cornerstone of the original deal.

“The rapid pace of innovation requires us to continue to evolve our partnership to benefit our customers and both companies,” Microsoft wrote in its blog post Monday. OpenAI echoed the framing, calling the amended agreement a move “grounded in flexibility, certainty, and a focus on delivering the benefits of AI broadly.”

The diplomatic language belies the drama that led to this moment — months of behind-the-scenes tension, competing deal announcements, public contradictions, and even the specter of litigation between two companies whose fates have been intertwined since the earliest days of the generative AI revolution.

How a billion-dollar bet on AI created the most powerful exclusive partnership in tech

To understand why Monday’s announcement matters so much, it helps to understand what came before it. When Microsoft poured its initial $1 billion into OpenAI in 2019, and then followed with a cumulative investment exceeding $13 billion, it secured something extraordinary: exclusive commercial access to OpenAI’s models and intellectual property. Azure became the sole cloud provider for OpenAI’s API products. Microsoft integrated OpenAI’s GPT models into everything from Bing to Office to GitHub Copilot. The arrangement was, by any measure, one of the most lopsided technology licensing deals in modern history — Microsoft got privileged access to the most capable AI models on the planet, and OpenAI got the capital and infrastructure it needed to scale.

The deal even contained an unusual provision: Microsoft’s exclusive rights would remain in force until OpenAI achieved artificial general intelligence, or AGI — a loosely defined milestone referring to AI systems that rival or exceed human intelligence across a broad range of tasks. OpenAI’s board retained the authority to declare when AGI had been reached, at which point certain commercial terms would change. It was, in effect, a philosophical tripwire embedded in a business contract.

That structure worked well enough when OpenAI was a research lab with a modest commercial footprint. But as ChatGPT exploded into the mainstream in late 2022 and OpenAI’s annualized revenue rocketed into the billions, the constraints began to chafe. OpenAI found itself locked into a single cloud ecosystem at precisely the moment when enterprises — its fastest-growing customer segment — were demanding multi-cloud flexibility. In an internal memo earlier this month, OpenAI’s revenue chief Denise Dresser put it bluntly, telling staff that the Microsoft partnership had “limited our ability to meet enterprises where they are,” according to a report from The Verge.

Amazon’s $50 billion OpenAI investment created a legal crisis that forced the restructuring

The proximate cause of Monday’s restructuring was not a philosophical disagreement about AI safety or corporate governance. It was a $50 billion check from Amazon. In February, OpenAI announced that Amazon would invest up to $50 billion in the company — $15 billion upfront, with another $35 billion to follow when certain unspecified conditions were met. In exchange, OpenAI agreed to expand its existing cloud agreement with AWS by $100 billion over eight years and, most controversially, committed to making AWS the exclusive third-party distribution provider for Frontier, its new enterprise agent-building platform. OpenAI also agreed to co-develop “stateful runtime technology” on AWS Bedrock, the infrastructure layer that allows AI agents to maintain memory and context over extended tasks.

The problem was that OpenAI’s existing contract with Microsoft almost certainly prohibited these arrangements. Microsoft held exclusive rights to any OpenAI product accessed through an API — a category that plainly included Frontier. On the very day OpenAI announced the Amazon deal, Microsoft issued a pointed public statement insisting that “Azure remains the exclusive cloud provider of stateless OpenAI APIs” and that “OpenAI’s first party products, including Frontier, will continue to be hosted on Azure.” The contradiction between the two announcements was stark, and it created immediate legal exposure. The Financial Times reported in March that Microsoft was actively considering legal action to enforce its contractual rights. The situation placed OpenAI in an impossible position: it had made promises to Amazon that it seemingly could not keep under the terms of its Microsoft agreement.

Monday’s deal resolves that impasse entirely. By converting Microsoft’s license from exclusive to non-exclusive and explicitly granting OpenAI the right to serve products on any cloud, the new terms retroactively validate the Amazon arrangement and eliminate the legal overhang. Amazon CEO Andy Jassy wasted no time celebrating. “We’re excited to make OpenAI’s models available directly to customers on Bedrock in the coming weeks, alongside the upcoming Stateful Runtime Environment,” he wrote on X, adding that the company would share more details at an event in San Francisco on Tuesday.

Inside the new financial terms that shift billions of dollars between the two AI giants

The financial mechanics of the new deal deserve careful parsing, because they reveal which side gave up what — and who came out ahead. Under the old arrangement, money flowed in both directions. When customers bought ChatGPT subscriptions or accessed OpenAI models through their own applications, OpenAI paid Microsoft a cut — reportedly 20 percent. Conversely, when enterprise customers accessed OpenAI models through Azure’s API, Microsoft paid OpenAI a share of that revenue. This bilateral structure reflected the deep integration between the two companies: Microsoft was simultaneously OpenAI’s investor, cloud provider, distribution partner, and largest customer.

The new deal makes the cash flow one-directional. Microsoft stops paying OpenAI entirely. OpenAI continues paying Microsoft its 20 percent share, but only through 2030, and now subject to a total cap whose precise dollar figure has not been disclosed. Given that OpenAI’s revenue is growing rapidly — the company was reportedly on pace to generate tens of billions annually — that cap could become material relatively quickly.

For Microsoft, the trade-off is straightforward: it sacrifices the exclusivity that made Azure the only gateway to OpenAI’s models, but it gains immediate financial relief by eliminating its outbound revenue-share payments while continuing to collect inbound payments for several more years. And it retains approximately 27 percent ownership of OpenAI’s for-profit entity, meaning it participates in the company’s growth regardless of which cloud serves the workloads. Last quarter alone, Microsoft reported $7.5 billion in revenue from its OpenAI investment in a single quarter, according to TechCrunch’s reporting. For OpenAI, the calculus is different. It accepts a continued obligation to pay Microsoft through 2030, but it gains the commercial freedom to sell everywhere — a freedom that is arguably worth far more than the revenue-share savings. Enterprise customers overwhelmingly operate in multi-cloud environments. Being locked into Azure was not just a technical constraint; it was a sales objection that OpenAI’s competitors, particularly Anthropic and Google, exploited relentlessly.

Why the disappearance of the AGI clause signals a new era for AI governance

One of the more philosophically intriguing aspects of Monday’s announcement is what it does to the AGI provision that once governed the partnership. Under the original agreement, Microsoft’s exclusive commercial rights were tied to a trigger: if OpenAI’s board determined that the company had achieved AGI, certain terms — including Microsoft’s access to the most advanced models — would change. The provision was meant to ensure that a truly superintelligent system would remain under the nonprofit board’s control rather than being commercially exploited. In practice, it created perverse incentives: OpenAI had a financial reason to never declare AGI, and Microsoft had a financial reason to argue that AGI had not been reached regardless of what the technology could actually do.

The new deal sidesteps this entirely. Microsoft’s license now runs through a fixed calendar date — 2032 — “independent of OpenAI’s technology progress,” as the companies put it. The AGI trigger, a concept that once sat at the philosophical heart of the partnership, has been replaced by a spreadsheet. Andrew Curran, a close observer of OpenAI’s governance, noted on X that language defining AGI had been removed from OpenAI’s website, sharing a screenshot showing the change. The move drew sharp reactions. One commenter observed that “removing the definition = removing the accountability. whoever controls when AGI is declared controls a lot of commercial terms.”

The shift reflects a broader maturation — or perhaps disillusionment — within the AI industry regarding AGI as a meaningful commercial or governance concept. When the original deal was struck, AGI felt like a distant, almost mythical threshold. Now, with models like GPT-5.5 demonstrating increasingly general capabilities, the term has become more of a marketing slogan than a technical benchmark. Replacing it with fixed dates and dollar caps is, in some sense, an admission that the industry has moved beyond the framework that once defined this partnership.

Multi-cloud AI competition intensifies as enterprises gain the power to choose

The most immediate beneficiary of the new arrangement is the enterprise customer. For years, organizations that wanted access to OpenAI’s models had essentially one option: Azure. That constraint is now gone. Within weeks, according to Jassy, OpenAI’s models will be available on AWS Bedrock alongside the stateful runtime environment that powers long-running AI agents. Google Cloud is presumably not far behind.

This multi-cloud availability arrives at a moment when the AI infrastructure market is undergoing rapid consolidation and expansion simultaneously. Meta recently committed $48 billion to cloud providers CoreWeave and Nebius. Amazon’s investment in OpenAI, combined with its existing relationship with Anthropic — in which Amazon has invested up to $4 billion — positions AWS as a model-agnostic platform where enterprises can mix and match AI capabilities. Microsoft, meanwhile, has developed its own relationship with Anthropic, using Claude to power agentic products — a hedge against the very OpenAI dependency it spent billions creating.

The competitive dynamics are now genuinely complex. Microsoft competes with OpenAI in AI products (Copilot vs. ChatGPT), partners with OpenAI’s rival Anthropic, and remains OpenAI’s largest shareholder. OpenAI sells on Azure, AWS, and soon everywhere else, while building its own data centers. Amazon invests in both OpenAI and Anthropic. Google builds its own models while also hosting competitors on Vertex AI. Jehangeer Hasan, a technology commentator, captured the mood on X, calling the announcement a “notable shift in the cloud AI landscape” that signals “intensifying multi-cloud competition and a push toward giving developers more flexibility instead of locking them into a single ecosystem.” Chris Alexander, an engineer, offered a more candid assessment: “honestly Azure’s OpenAI endpoints are so unreliable, we mostly just hit you all directly,” adding that “it would be nice to have options in AWS or GCP for sure.”

What the restructured deal means for the future of AI’s biggest partnership

Several open questions remain. The precise dollar amount of the revenue-share cap has not been disclosed, and it will matter enormously as OpenAI’s revenue scales. The meaning of “first on Azure” — whether it implies a meaningful exclusivity window or merely simultaneous availability — remains deliberately ambiguous. And OpenAI’s own infrastructure ambitions, including plans to build proprietary data centers, could eventually reduce its dependence on any third-party cloud, including Azure.

Microsoft’s position, while less dominant than before, is not as diminished as some early commentary suggested. It remains OpenAI’s primary cloud provider, its largest shareholder, and a licensee of its technology through the end of the decade. It has diversified its own AI strategy with investments in Anthropic, its own Phi and MAI model families, and deep integration of AI across its product portfolio. The company reported $7.5 billion in OpenAI-related revenue last quarter — a figure that demonstrates the sheer financial scale of the relationship even in its loosened form.

For OpenAI, the new agreement is a coming-of-age moment. The company that once depended on Microsoft for everything — capital, compute, distribution, and credibility — now operates as an independent force capable of striking multi-billion-dollar deals with Microsoft’s biggest rivals. Sam Altman announced the changes on X with characteristic brevity: “We have updated our partnership with Microsoft.”

Seven years ago, when Microsoft CEO Satya Nadella and Altman first shook hands on a deal to commercialize artificial intelligence, the arrangement rested on the assumption that OpenAI needed Microsoft more than Microsoft needed OpenAI. Every clause — the exclusivity, the AGI trigger, the revenue share — reflected that original imbalance. Monday’s restructuring is proof that the assumption no longer holds. The partnership that launched the generative AI revolution has survived, but the power dynamics that created it have not. In the AI industry, it turns out, the only thing that moves faster than the technology is the leverage.

Cirrascale Cloud Services today announced it has expanded its partnership with Google Cloud to deliver the Gemini model on-premises through Google Distributed Cloud, making it the first neocloud provider to offer Google’s most advanced AI model as a fully private, disconnected appliance. The announcement, timed to coincide with Google Cloud Next 2026 in Las Vegas, addresses a stubborn problem that has plagued regulated industries since the generative AI boom began: how to access frontier-class AI models without surrendering control of your data.

The offering packages Gemini into a Dell-manufactured, Google-certified hardware appliance equipped with eight Nvidia GPUs and wrapped in confidential computing protections. Enterprises and government agencies can deploy the system inside Cirrascale’s data centers or their own facilities, fully disconnected from the internet and from Google’s cloud infrastructure. The product enters preview immediately, with general availability expected in June or July.

In an exclusive interview with VentureBeat ahead of the announcement, Dave Driggers, CEO of Cirrascale Cloud Services, described the deployment as “the next step of the partnership” and “being able to offer their most important model they have, which is Gemini.” He was emphatic about what customers would be getting: “It is full blown Gemini. It’s not pulled,” he told VentureBeat. “Nothing’s missing from it, and it’ll be available in a private scenario, so that we can guarantee them that their data is secure, their inputs are secure, their outputs are secure.”

The move signals a deepening shift in the enterprise AI market, where the most capable models are migrating out of hyperscaler data centers and into customers’ own racks — a reversal of the cloud computing orthodoxy that defined the past decade.

The impossible tradeoff that kept banks and governments on the AI sidelines

For years, organizations in financial services, healthcare, defense and government faced a binary choice: access the most powerful AI models through public cloud APIs, exposing sensitive data to third-party infrastructure, or settle for less capable open-source models they could host themselves. Cirrascale’s new offering attempts to eliminate that tradeoff entirely.

Driggers described how the trust problem escalated in stages. First, companies worried about handing their proprietary data to hyperscalers. Then came a deeper realization. “They started realizing, holy crap, when my users type stuff in, they’re giving private information away — and the output is private too,” Driggers told VentureBeat. “And then the hyperscalers said, ‘Your prompts and the responses? That’s our stuff. We need that in order to answer your question.'” That was the moment, he argued, when the demand for fully private AI became impossible to ignore.

Unlike Google Distributed Cloud, which Google already offers as its own on-premises cloud extension, the Cirrascale deployment places the actual model — weights and all — outside of Google’s infrastructure entirely. “Google doesn’t own this hardware. We own the hardware, or the customer owns the hardware,” Driggers said. “It is completely outside of Google.”

Driggers drew a sharp distinction between this offering and what competitors provide. When asked about Microsoft Azure’s on-premises deployments with OpenAI models and AWS Outposts, he was blunt: “Those are a lot different. This is the actual model being deployed on prem outside of their cloud. It’s not a cut down version. It’s the actual model.” 

Pull the plug and the model vanishes: how confidential computing guards Google’s crown jewel

The technical underpinnings of the deployment reveal how seriously both Google and Cirrascale are treating the security question. The Gemini model resides entirely in volatile memory — not on persistent storage. “As soon as the power is off, the model is gone,” Driggers explained. User sessions operate through caches that clear automatically when a session ends. “A company’s user inputs, once that session’s over, they’re gone. They can be saved, but by default, they’re gone,” he said.

Perhaps the most striking security feature is what happens when someone attempts to tamper with the appliance. Driggers described a mechanism that effectively renders the machine inoperable: “You do anything that is against confidential compute, and it’s gone. Not only does the machine turn off, and therefore the model is gone, it actually puts in a marker that says, ‘You violated the confidential compute.’ That machine has to come back to us, or back to Dell or back to Google.” He characterized the appliance as something that “does time bomb itself if something goes wrong.”

This level of protection reflects Google’s own anxiety about releasing its flagship model’s weights into environments it doesn’t control. The appliance is effectively a vault: the model runs inside it, but nobody — not even the customer — can extract or inspect the weights. The confidential computing envelope ensures that even physical possession of the hardware doesn’t grant access to the model’s intellectual property.

When Google releases a new version of Gemini, the appliance needs to reconnect — but only briefly, and through a private channel. “It does have to get connected back to Google to load the new model. But that can go via a private connection,” Driggers said. For the most security-sensitive customers who can never allow their machine to connect to an outside network, Cirrascale offers a physical swap: “The server will be unplugged, purged, all the data gone, guaranteed it’s gone, a new server will show up with a new version of the model.”

From Wall Street to drug labs, the rush for air-gapped AI is accelerating

Driggers identified three primary drivers of demand: trust, security and guaranteed performance. Financial services institutions top the list. “They’ve got regulatory issues where they can’t have something out of their control. They’ve got to be the one who determines where everything is. It’s got to be air gap,” Driggers said. The minimum deployment footprint — a single eight-GPU server — makes the product accessible in a way that Google’s own private offerings do not. Running Gemini on Google’s TPU-based infrastructure, Driggers noted, requires a much larger commitment. “If you want a private [instance] from Google, they require a much bigger bite, because to build something private for you, Google requires a gigantic footprint. Here we can do it down to a single machine.”

Beyond finance, Driggers pointed to drug discovery, medical data, public-sector research, and any business handling personal information. He also flagged an increasingly critical use case: data sovereignty. “How about your business that’s doing business outside of the United States, and now you’ve got data sovereignty laws in places where GCP is not? We can provide private Gemini in these smaller countries where the data can’t leave.”

The public sector is another major target. Cirrascale launched a dedicated Government Services division in March as part of its earlier partnership with Google Public Sector around the GPAR (Google Public Sector Program for Accelerated Research) initiative. That program provides higher education and research institutions access to AI tools including AlphaFold, AI Co-Scientist, and Gemini Enterprise for Education. Today’s announcement extends that relationship from the research tooling layer to the model itself.

The performance guarantee is the third pillar. Driggers noted that frontier models accessed through public APIs deliver inconsistent response times — a problem for mission-critical business applications. The private deployment eliminates that variability. Cirrascale layers management software on top of the Gemini appliance that allows administrators to prioritize users, allocate tokens by role, adjust context window sizes, and load-balance across multiple appliances and regions. “Your primary data scientists or your programmers may need to have really large context windows and get priority, especially maybe nine to five,” Driggers explained, “but yet, the rest of the time, they want to share the Gemini experience over a wider group of people.” He also noted that agentic AI workloads, which can run around the clock, benefit from the ability to consume unused capacity during off-peak hours — a scheduling flexibility that public cloud deployments don’t easily support.

Seat licenses, token billing and all-you-can-eat pricing: a model built for enterprise flexibility

The pricing model reflects Cirrascale’s broader philosophy of meeting customers where they are. Driggers described several consumption options: seat-based licensing (with both enterprise and standard tiers), per-token billing, and flat “all-you-can-eat” pricing per appliance. The minimum commitment is a single dedicated server — the appliances are not shared between customers in any configuration. “We’ll meet the customer, what they’re used to,” Driggers said. “If they’re currently taking a seat license, we’ll create a seat license for them.”

Customers can also choose to purchase the hardware outright while still consuming Gemini as a managed service, an arrangement Cirrascale has offered since its earliest days in the AI wave. Driggers said OpenAI has been a customer since 2016 or 2017, and in that engagement, OpenAI purchased its own GPUs while Cirrascale “took those GPUs, incorporated them into our servers and storage and networking, and then presented it back as a cloud service to them so they didn’t have to manage anything.”

That flexible ownership model is particularly relevant for universities and government-funded research institutions, where mandates often require a specific mix of capital expenditure, operating expenditure, and personnel investment. “A lot of government funding requires a mixture of CapEx, OPEX and employment development,” Driggers said. “So we allow that as well.”

Inside the neocloud that built the world’s first eight-GPU server — and just landed Google’s biggest AI model

Cirrascale’s announcement arrives during a period of explosive growth for the neocloud sector — the tier of specialized AI cloud providers that sit between the hyperscalers and traditional hosting companies. The neocloud market is projected to be worth $35.22 billion in 2026 and is growing at a compound annual growth rate of 46.37%, according to Mordor Intelligence. Leading neocloud providers include CoreWeave, Crusoe Cloud, Lambda, Nebius and Vultr, and these companies specialize in GPU-as-a-Service for AI and high-performance computing workloads.

But Cirrascale occupies a different niche within this booming category. While companies like CoreWeave have focused primarily on providing raw GPU compute at scale — CoreWeave boasts a $55.6 billion backlog — Cirrascale has positioned itself around private AI, managed services and longer-term engagements rather than on-demand elastic compute. Driggers described the company as “not an on-demand place” but rather a provider focused on “longer-term workloads where we’re really competing against somebody doing it back on prem.”

The company’s history supports that claim. Cirrascale traces its roots to a hardware company that “designed the world’s first eight GPU server in 2012 before anybody thought you’d ever need eight GPUs in a box,” as Driggers put it. It pivoted to pure cloud services roughly eight years ago and has since built a client roster that includes the Allen Institute for AI, which in August 2025 tapped Cirrascale as the managed services provider for a $152 million open AI initiative funded by the National Science Foundation and Nvidia. Earlier this month, Cirrascale announced a three-way alliance with Rafay Systems and Cisco to deliver end-to-end enterprise AI solutions combining Cirrascale’s inference platform, Rafay’s GPU orchestration, and Cisco’s networking and compute hardware.

The private AI era is arriving faster than anyone expected

The Gemini partnership is the highest-profile move yet — and it taps into a broader industry current. The push to move frontier AI out of the public cloud and into private infrastructure is no longer a niche demand. Industry analysts predict that by 2027, 40% of AI model training and inference will occur outside public cloud environments. That projection helps explain why Google is willing to let its crown-jewel model run on hardware it doesn’t own, in data centers it doesn’t operate, managed by a company in San Diego. The alternative — watching regulated enterprises default to open-source models or to Microsoft’s Azure OpenAI Service — is apparently a worse outcome.

The announcement also carries major implications for Google’s competitive positioning. Microsoft has built its enterprise AI strategy around the Azure OpenAI Service and its deep partnership with OpenAI, while AWS has invested in Amazon Bedrock and its own on-premises solutions through Outposts. Google Cloud Platform still trails both rivals in market share, though Q4 cloud revenue rose 48% year-over-year. Enabling Gemini to run on third-party infrastructure via partners like Cirrascale broadens its distribution surface in exactly the segments — government, finance, healthcare — where Microsoft and Amazon have historically held advantages. For Cirrascale, the partnership represents a chance to differentiate sharply in a market where most neoclouds are competing on GPU availability and price.

Driggers expects rapid uptake in the second half of 2026. “It’s going to be crazy towards the end of this year,” he said. “Major banks will finally do stuff like this, because they can secure it. They can do it globally. Big research institutions who have labs all over the world will do these types of things.” He predicted other frontier model providers will follow with similar offerings soon, and he doesn’t see Gemini as the end of the story. “We really think that the enterprise have been waiting for private AI, not just Gemini, but all sorts of private AI,” Driggers said.

That may be the most telling line of all. For three years, the AI revolution has been defined by a simple bargain: send your data to the cloud and get intelligence back. Cirrascale’s bet — and increasingly, Google’s — is that the biggest customers in the world are done accepting those terms. The most powerful AI on the planet is now available on a single locked box that can sit in a bank vault, a university basement, or a government facility in a country where Google has no data center. The cloud, it turns out, is finally ready to come back down to earth.