Extremely powerful large language models (LLMs) still operate as though they’re typing on a keyboard, processing workloads in a simple left-to-right fashion. But in locally-run, single-user scenarios, this sequential processing can leave graphics processing units (GPUs) and tensor processing units (TPUs) underutilized.

Google is betting that DiffusionGemma can get around this bottleneck. The new experimental open model generates text “exceptionally fast,” creating entire blocks of text simultaneously through diffusion techniques rather than through token-by-token processing. The company says this technique results in 4x faster inference compared to auto-regressive models that rely on sequential processing.

It can also save users money. Technology analyst Carmi Levy noted that existing pay-per-token monetization models “penalize the use of less than optimally efficient AI solutions.”

But DiffusionGemma “could herald a new generation of task-defined, efficient solutions that can enable expanded compute capacity without draining the operations budget,” he said.

A contrast to left-to-right processing

Built on Google’s Gemma 4 family and its Gemini Diffusion research, DiffusionGemma is a 26B mixture-of-experts (MoE) model designed to maximize text output generation.

It essentially shifts how models use hardware, giving processors a larger hunk of work each cycle so it can draft full 256-token paragraphs in sequence. This allows the model to generate text up to 4x faster on GPUs, Google claims. It activates only 3.8B parameters during inference, and, when quantized, can fit within 18GB VRAM on high-end consumer GPUs like Nvidia RTX 5090.

“It upgrades your model inference from a single, sequential typewriter to a massive printing press that stamps the entire block of text simultaneously,” Google research scientists Brendan O’Donoghue and Sebastian Flennerhag wrote in a blog post.

AI image generators begin with pure, random ‘visual noise’ and iteratively refine that into a finalized picture (what’s known as ‘diffusion’); DiffusionGemma applies this same process to text. It does not generate tokens in order, but begins with a “canvas of random placeholder tokens” that it processes in multiple passes, identifying the context tokens it feels are most relevant and using those to refine the rest.

The model has the ability to self-correct, using confidence scoring to re-evaluate tokens in the next pass. “The model iteratively refines its own output, allowing it to evaluate the entire text block at once to fix mistakes in real-time,” O’Donoghue and Flennerhag explained.

DiffusionGemma also has bidirectional attention, they wrote. “Generating 256 tokens in parallel with each forward pass allows every token to attend to all others.” This can be particularly helpful in domains that are non-linear in nature, such as mathematical graphs, code infilling, and in-line editing, they said.

DiffusionGemma is optimized across Nvidia’s hardware stack, making it compatible with consumer setups as well as with high-performance enterprise systems like Hopper and Blackwell.

Because it is released under the Apache 2.0 license, developers can freely use, modify, distribute, and commercialize the software using their preferred tools. It can be run on GPUs or in the cloud through Google Cloud Model Garden or Nvidia NIM, and is available on Hugging Face, GitHub, and vLLM, with support for the open-source library llama.cpp coming soon.

Key use cases

The model is particularly useful in local workflows that are “speed critical,” such as generation of non-linear text structures, and unlocks what Google calls “new patterns of model behavior” like multimodal understanding and generating and rendering code in near real-time.

Levy explained, “DiffusionGemma is particularly well suited for interactive coding and editing where its efficiency allows rapid processing and iterations,” noting that its ability to fit within 18GB of VRAM and its deployability on commonly available local GPUs can potentially benefit customer service-related workloads that lean heavily on real-time interaction and local processing.

“DiffusionGemma also incorporates a thinking mode that is especially adept at problem solving,” he said. For instance, the model was fine-tuned to play Sudoku, a typically challenging task for autoregressive models because each token depends on future tokens. This “rather handily” illustrates the model’s capability to solve more complex problems, Levy noted.

Limitations

Google freely admits that DiffusionGemma is geared to specific workflows, and there are “key trade-offs.”

The model is engineered for small batch size inferencing and low-latency, high-speed generation low-to-medium batch sizes on a “single capable accelerator.”

In high-QPS cloud serving environments, (where infrastructure is designed to handle tens or hundreds of thousands of requests per second with ultra-low latency), DiffusionGemma’s parallel coding “offers diminishing returns,” and can even result in higher serving costs, Google conceded. In addition, its overall output quality is lower than that of standard Gemma 4, which is built for apps demanding maximum quality.

However, Levy noted that while DiffusionGemma “can be less precise than other models in certain workloads,” subsequent refinement cycles could overcome this limitation.

While Google isn’t sharing runtime costs, it’s clear that this is an efficiency play, he added. “When deployed across the kinds of workloads that would optimally benefit from its architecture, DiffusionGemma seems to have the potential to reduce processing overhead and related costs,” he said.

CIOs and CISOs have many strategic and operational fears when it comes to unleashing fully-autonomous agents on tasks and hoping that everything works out. Will the agent start to delete critical files? Will the agent go off on a mission tangent and generate a massive token bill for the team when they return the next morning? Will it be tricked by a state actor and engage in malicious actions?

To help alleviate those concerns, OpenAI announced on Thursday that it has agreed to acquire Ona, a 79 person cloud development environment (CDE) provider formerly known as Gitpod, to accelerate its efforts to make agentic AI enterprise-friendly. 

An OpenAI statement said Ona’s technology “provides secure, persistent environments where agents can access the tools, systems, and context they need to make progress over time. By bringing Ona to OpenAI, we will expand Codex beyond work tied to a single device or active session and help more organizations deploy agents securely in production.”

An Ona statement attributed to CEO Johannes Landgraf shared similar sentiments.

“Ona brings the building blocks agents need for enterprise work: trusted, customer-controlled cloud environments where work continues across devices, inside the systems where software actually lives,” Landgraf said. “OpenAI brings frontier intelligence, product polish, and a scale of research and distribution we could never reach alone.”

Landgraf’s statement did not provide any annual revenue numbers, but did hint, without naming, at some large customers. “Since the beginning of the year, weekly Ona agent sessions have grown 13x in production across some of the world’s most demanding institutions: the oldest bank in the US, one of Europe’s largest pharma companies, one of Asia’s largest sovereign wealth funds and many others,” he wrote. “The largest enterprises out there love the platform and are expanding more rapidly than ever before.”

Arnal Dayaratna, research VP for software development at IDC, said IDC’s figures for Ona put its annual revenue for 2025 at “roughly $7 million.” He speculated that Ona’s revenue for 2026 would be higher: “Let’s say it’s $15 million. I am being generous. Maybe it’s really $10 million or $12 million.”

Dayaratna said if he uses a standard acquisition price of roughly a multiple of 30 times revenue, then depending on the actual 2026 figure, “that comes to $450 million or $500 million or so.”

But IDC sees this being a potentially good move for OpenAI, regardless of the specific acquisition price, given that OpenAI had the classic “buy or build” challenge. 

OpenAI has a substantial Codex effort, Dayaratna said, but what they lack is a safe area to protect enterprise autonomous agent efforts. “This is outside of what OpenAI has now. These are secure environments where agents can have memory and operate securely,” he said. “This is the kind of technology that one would expect to be needed, but I don’t know how good it is, to be honest.”

Gartner’s First Take, published today, noted that the acquisition will bring Codex “the essential scaling capability it lacked,” but also pointed out it forces some difficult decisions on enterprises: “Software engineering leaders must weigh the benefits of a vendor-specific integrated stack against the flexibility of staying vendor-agnostic.”

In addition, Gartner wrote, “This acquisition appears to be OpenAI’s response to Anthropic supporting self-hosted sandboxes in Claude Managed Agents, starting May 2026.”

Tom Findling, CEO of Conifers.ai, said he also sees OpenAI’s fear of Anthropic playing a meaningful role in this deal. 

“It feels like a move to keep pressure on Anthropic, especially as Claude Code gains traction with developers and enterprise buyers,” he said. “So I’d read this less as OpenAI taking out a small competitor and more as OpenAI trying to make sure Codex is enterprise-ready before Anthropic gets too far ahead. In the enterprise market, the battle is not just who has the smartest coding model, but who can make AI agents safe and useful enough for big companies to actually deploy.”

He added, “I don’t think this means OpenAI suddenly needs help making Codex better at writing code. The bigger issue is making Codex work inside real enterprise environments, where security, access controls, persistent cloud workspaces, audit trails, and integration with existing developer workflows matter just as much as the model itself. Ona gives OpenAI some of that missing plumbing.”

Jason Andersen, principal analyst for Moor Insights & Strategy, echoed the concerns about Anthropic.

“To be honest, I think it reinforces what I think, which is that OpenAI and Codex have given a lot of ground to Anthropic and Claude Code, who are winning right now,” he said. “But again, this is not about the market today, I think it’s about how OpenAI will need to position itself as more than just a model as we see the incumbent players, particularly Microsoft, bolster their enterprise coding infrastructure story.”

Andersen said that Moor doesn’t have any strong basis for a guess on the financials, but added, “I am going to assume it was a fairly high multiple, but on a small base. I would not speculate on an amount, but given the enterprise customers that Ona did have, it may be more than we think.”

He also reinforced the idea that OpenAI is going to need help to achieve its own objectives.

“We continue to see that AI adoption is strongest in coding, and other use cases are not as far along,” he said. “So, if you are a general-purpose AI company like OpenAI, you need to double down on development use cases. The meaningful investment and spending on development is happening at the enterprise level, and those customers have more demands for governance, security, etc. than Codex or Claude code can handle.”

That said, he noted, “what you’re seeing is traditional software and cloud plays building out the coding and ops infrastructure around the popular models. That increased competition, while good for selling tokens, is still keeping OpenAI and Anthropic on the outside looking in. So, OpenAI and Anthropic need a stronger enterprise dev story, or they are just another model that could be easily replaced.”

Jeremy Roberts, senior director at Info-Tech Research Group, said that he also sees this as likely a good move for OpenAI.

“OpenAI is growing up a little bit,” and they may be falling behind Anthropic, Roberts said. “I see Ona as a boring company, but not in a bad way. They are not flashy, but absolutely necessary.”

Ona is delivering a workspace for Codex that an enterprise can run in its own virtual private cloud, with governance and persistence and an environment where the company can apply their own controls including log management, credential management and resource access, he said. “It is a bucket for the agents to operate in” where IT can “make sure that access is properly credentialed and is controlled effectively to prevent the model doing what it shouldn’t be doing,” which includes managing read/write protections.

When Pope Leo XIV wrote about the effect that AI is having on our world in his encyclical, Magnifica Humanitas, he may not have imagined the document being referenced in an HR environment.

But, according to a report by Business Insider, Erin Maus, a software developer in North Carolina, used the Pope’s message about the need for vigilance in how AI would be deployed to gain a special exemption from her employer about using the technology for coding.

Maus is not even a Catholic but a Unitarian Universalist, according to the report. However, it said, she maintained that the use of AI didn’t align with her religious beliefs.

Business Insider said that to make her case, she consulted an employment lawyer — a move to be expected — and her local chapter’s minister — which probably wasn’t. Her wishes were reportedly granted last month. “I’m writing my code and reviewing my code by hand, which seems crazy to say,” she told the publication.

She’s certainly not alone in wondering whether AI is always the way forward for techies: a journalist at PC World has also been rethinking its use after reading the encyclical.

It remains to be seen whether this will be the spur for a torrent of claims from Catholic workers, asking to be freed from the demands of using AI or whether Business Insider’s report is an outlier.

For years, the cloud market has made a simple promise: Move workloads to large-scale platforms, gain better resilience, and worry less about downtime. That promise was never entirely wrong, but it is becoming less complete. The latest findings from Uptime Institute’s seventh Annual Outage Analysis suggest that the outage landscape is changing in ways that should concern both cloud providers and cloud customers. The biggest risks are no longer limited to broken physical infrastructure. They are increasingly tied to the complexity of the systems used to run, coordinate, update, and recover that infrastructure.

The most alarming number in the report is that IT and networking issues accounted for 23% of impactful outages in 2024. Uptime Institute links these increases to growing IT and network complexity; the long-term shift toward colocation, cloud, and third-party digital services; and the resulting increase in change-management failures and misconfigurations. That number is more than a statistical footnote. It points to a structural change in how outages happen and why cloud outages are becoming such a stubborn problem.

Hardware redundancy can protect against component failures, but it doesn’t help much when the outage stems from a bad configuration, an automation error, a faulty network change, or an underappreciated control-plane dependency. In those cases, the infrastructure itself may remain intact while the system that governs it breaks down. The industry is learning that resiliency is less about duplicating equipment and more about managing complexity. Today’s increasingly distributed and software-defined environments cannot operate safely at scale.

Failures at the operational level

Uptime’s findings show that power remains the leading cause of major outages, underscoring that traditional infrastructure engineering still matters a great deal. But even as providers continue to improve physical resilience, outages can still arise from the digital and procedural layers above it. Cloud platforms are now dense stacks of services, APIs, orchestration systems, software-defined networks, identity controls, failover logic, and third-party dependencies. That complexity creates more possible points of interaction and more opportunities for an error in one layer to cascade into several others.

This helps explain why outages can feel more surprising today than they did a decade ago. In older data center models, an outage often had a more apparent root cause, such as a power event, a cooling failure, or a hardware fault. In cloud environments, the trigger may be a small configuration change that propagates across regions, a policy update that unintentionally blocks service communication, or a network control failure that affects seemingly unrelated services. These are not failures of raw infrastructure capacity. They are failures of complexity management.

The report’s language around change management and misconfiguration is especially important because it challenges one of the most common assumptions in the cloud market: that scale automatically produces better operational outcomes. The reality? Scale can magnify both strengths and weaknesses. Large cloud providers have more engineering talent, more sophisticated tools, and more redundancy than almost any enterprise customer. But they also run far more interconnected systems at far greater speeds with far more automation. A single process failure can have a wider blast radius.

Another important lesson from the Uptime analysis is that automation has not removed the human factor. If anything, it has changed its form. Even in highly automated environments, human error remains central to the problem. The report notes that in 2025, the share of outages caused by human failure to follow procedures rose by 10 percentage points compared with 2024. A related industry summary of the report notes that 58% of human error-related outages were caused by staff failing to follow established procedures.

That matters because cloud providers often position automation as the answer to reliability. Automation is essential, but it only works as well as the operational model that surrounds it. If teams deploy changes too quickly, rollback paths are weak, approval chains are bypassed, or procedures are incomplete, automation can accelerate failure rather than prevent it. In a modern cloud environment, a human mistake is rarely just a single keystroke. It is more often a design weakness in process, governance, testing, or accountability.

This is also why customers should resist the comforting notion that outages are somebody else’s problem once workloads move to the cloud. Provider-side mistakes remain real, but customer architectures are increasingly entangled with provider networking, identity, observability, and platform services. When an outage occurs, the customer may not have caused it, but they still bear the business impact. The shared responsibility model does not end with security. It extends to resilience planning as well.

Better change management

The Uptime data points to a clear conclusion: Cloud providers need to treat operational discipline as a first-class design requirement. That starts with better change management. High-risk changes should be tested more aggressively, staged more gradually, and accompanied by stronger rollback mechanisms. Providers also need better dependency mapping to understand how a change in one control layer can affect services far beyond its immediate scope. If the system is too complex to clearly explain, it is too complex to operate.

Providers also need to improve procedural quality. The rise in outages caused by failing to follow procedures suggests that procedures are being ignored under operational pressure or that they are too cumbersome, outdated, or unclear for real production conditions. Neither explanation is comforting. Stronger runbooks, better training, more realistic failure drills, and tighter operational guardrails are not glamorous investments; they are increasingly central to resilience.

Another pressure point is visibility. Uptime notes that software-based and distributed resiliency tools can improve availability, but they also introduce new risks and complicate root-cause analysis. Cloud providers need more transparent and faster incident diagnosis, not just more layers of abstraction. Customers cannot build trust in resilience if every major incident becomes a long exercise in reconstructing opaque service dependencies after the fact.

Design with outages in mind

What’s the financial impact of more frequent problems? Uptime’s 2024 analysis found that 54% of respondents reported that their most recent significant outage cost more than $100,000, and 20% said it cost more than $1 million. These are not edge-case losses. They show that outages remain costly even if they are less frequent than in earlier years.

Customers need to stop evaluating cloud resilience through uptime promises and start evaluating it through failure behavior. How does a provider isolate faults? How transparent is incident communication? How portable are workloads if a major service degrades? How dependent is the architecture on a single region, network path, identity service, or control plane? These are not just technical questions; they are now critical business questions.

The core lesson from Uptime’s data is simple. Outages are becoming a bigger problem for cloud providers and customers because the cloud’s biggest vulnerabilities are increasingly tied to complexity, process failures, and control-plane mistakes, not just broken infrastructure. In addition to adding redundancy, the next phase of cloud improvement will focus on building systems that are easier to understand, safer to change, and more disciplined to operate.