OpenAI expanded its data residency regions for ChatGPT and its API, giving enterprise users the option to store and process their data closest to their business operations and better comply with local regulations. This expansion removes one of the biggest compliance blockers preventing global enterprises from deploying ChatGPT at scale.

Data residency, often an overlooked piece of the enterprise AI puzzle, processes and governs data according to the laws and customs of the countries where it is stored. 

ChatGPT Enterprise and Edu subscribers can now choose to have their data processed in: 

• Europe (European Economic Area and Switzerland)

• United Kingdom

• United States

• Canada

• Japan

• South Korea

• Singapore

• India

• Australia

• United Arab Emirates

OpenAI said in a blog post that it “plans to expand availability to additional regions over time.” 

Customers can store data such as conversations, uploaded files, custom GPTs, and image-generation artifacts. This applies only to data at rest, not while it moves through a system or when it is used for inference. OpenAI’s documentation notes that, for now, inference residency remains available only in the U.S.  

ChatGPT Enterprise and Edu users can set up new workspaces with data residency. Enterprise customers on the API who have been approved for advanced data controls can enable data residency by creating a new project and selecting their preferred region.

OpenAI first began offering data residency in Europe in February this year. The European Union has some of the strictest data regulations globally, based on the GDPR. 

The importance of data residency

Enterprises until now had fewer choices for processing data flowing through ChatGPT. For example, some organizational data would be processed under U.S. law rather than under European rules. 

Enterprises risk violating data compliance rules if their data at rest is processed elsewhere and does not meet strict policies. 

“With over 1 million business customers around the world directly using OpenAI, we have expanded where we offer data residency — allowing business customers to store data in certain regions, helping organizations meet local regulatory and data protection requirements,” the company said in its blog post. 

However, enterprises must also understand that if they are using a connector or integration within ChatGPT, those applications have different data residency rules. When OpenAI launched company knowledge for ChatGPT, it warned users that depending on the connector they use, data residency may be limited to the U.S. 

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Anthropic released its most capable artificial intelligence model yet on Monday, slashing prices by roughly two-thirds while claiming state-of-the-art performance on software engineering tasks — a strategic move that intensifies the AI startup's competition with deep-pocketed rivals OpenAI and Google.

The new model, Claude Opus 4.5, scored higher on Anthropic's most challenging internal engineering assessment than any human job candidate in the company's history, according to materials reviewed by VentureBeat. The result underscores both the rapidly advancing capabilities of AI systems and growing questions about how the technology will reshape white-collar professions.

The Amazon-backed company is pricing Claude Opus 4.5 at $5 per million input tokens and $25 per million output tokens — a dramatic reduction from the $15 and $75 rates for its predecessor, Claude Opus 4.1, released earlier this year. The move makes frontier AI capabilities accessible to a broader swath of developers and enterprises while putting pressure on competitors to match both performance and pricing.

"We want to make sure this really works for people who want to work with these models," said Alex Albert, Anthropic's head of developer relations, in an exclusive interview with VentureBeat. "That is really our focus: How can we enable Claude to be better at helping you do the things that you don't necessarily want to do in your job?"

The announcement comes as Anthropic races to maintain its position in an increasingly crowded field. OpenAI recently released GPT-5.1 and a specialized coding model called Codex Max that can work autonomously for extended periods. Google unveiled Gemini 3 just last week, prompting concerns even from OpenAI about the search giant's progress, according to a recent report from The Information.

Opus 4.5 demonstrates improved judgment on real-world tasks, developers say

Anthropic's internal testing revealed what the company describes as a qualitative leap in Claude Opus 4.5's reasoning capabilities. The model achieved 80.9% accuracy on SWE-bench Verified, a benchmark measuring real-world software engineering tasks, outperforming OpenAI's GPT-5.1-Codex-Max (77.9%), Anthropic's own Sonnet 4.5 (77.2%), and Google's Gemini 3 Pro (76.2%), according to the company's data. The result marks a notable advance over OpenAI's current state-of-the-art model, which was released just five days earlier.

But the technical benchmarks tell only part of the story. Albert said employee testers consistently reported that the model demonstrates improved judgment and intuition across diverse tasks — a shift he described as the model developing a sense of what matters in real-world contexts.

"The model just kind of gets it," Albert said. "It just has developed this sort of intuition and judgment on a lot of real world things that feels qualitatively like a big jump up from past models."

He pointed to his own workflow as an example. Previously, Albert said, he would ask AI models to gather information but hesitated to trust their synthesis or prioritization. With Opus 4.5, he's delegating more complete tasks, connecting it to Slack and internal documents to produce coherent summaries that match his priorities.

Opus 4.5 outscores all human candidates on company's toughest engineering test

The model's performance on Anthropic's internal engineering assessment marks a notable milestone. The take-home exam, designed for prospective performance engineering candidates, is meant to evaluate technical ability and judgment under time pressure within a prescribed two-hour limit.

Using a technique called parallel test-time compute — which aggregates multiple attempts from the model and selects the best result — Opus 4.5 scored higher than any human candidate who has taken the test, according to company. Without a time limit, the model matched the performance of the best-ever human candidate when used within Claude Code, Anthropic's coding environment.

The company acknowledged that the test doesn't measure other crucial professional skills such as collaboration, communication, or the instincts that develop over years of experience. Still, Anthropic said the result "raises questions about how AI will change engineering as a profession."

Albert emphasized the significance of the finding. "I think this is kind of a sign, maybe, of what's to come around how useful these models can actually be in a work context and for our jobs," he said. "Of course, this was an engineering task, and I would say models are relatively ahead in engineering compared to other fields, but I think it's a really important signal to pay attention to."

Dramatic efficiency improvements cut token usage by up to 76% on key benchmarks

Beyond raw performance, Anthropic is betting that efficiency improvements will differentiate Claude Opus 4.5 in the market. The company says the model uses dramatically fewer tokens — the units of text that AI systems process — to achieve similar or better outcomes compared to predecessors.

At a medium effort level, Opus 4.5 matches the previous Sonnet 4.5 model's best score on SWE-bench Verified while using 76% fewer output tokens, according to Anthropic. At the highest effort level, Opus 4.5 exceeds Sonnet 4.5 performance by 4.3 percentage points while still using 48% fewer tokens.

To give developers more control, Anthropic introduced an "effort parameter" that allows users to adjust how much computational work the model applies to each task — balancing performance against latency and cost.

Enterprise customers provided early validation of the efficiency claims. "Opus 4.5 beats Sonnet 4.5 and competition on our internal benchmarks, using fewer tokens to solve the same problems," said Michele Catasta, president of Replit, a cloud-based coding platform, in a statement to VentureBeat. "At scale, that efficiency compounds."

GitHub's chief product officer, Mario Rodriguez, said early testing shows Opus 4.5 "surpasses internal coding benchmarks while cutting token usage in half, and is especially well-suited for tasks like code migration and code refactoring."

Early customers report AI agents that learn from experience and refine their own skills

One of the most striking capabilities demonstrated by early customers involves what Anthropic calls "self-improving agents" — AI systems that can refine their own performance through iterative learning.

Rakuten, the Japanese e-commerce and internet company, tested Claude Opus 4.5 on automation of office tasks. "Our agents were able to autonomously refine their own capabilities — achieving peak performance in 4 iterations while other models couldn't match that quality after 10," said Yusuke Kaji, Rakuten's general manager of AI for business.

Albert explained that the model isn't updating its own weights — the fundamental parameters that define an AI system's behavior — but rather iteratively improving the tools and approaches it uses to solve problems. "It was iteratively refining a skill for a task and seeing that it's trying to optimize the skill to get better performance so it could accomplish this task," he said.

The capability extends beyond coding. Albert said Anthropic has observed significant improvements in creating professional documents, spreadsheets, and presentations. "They're saying that this has been the biggest jump they've seen between model generations," Albert said. "So going even from Sonnet 4.5 to Opus 4.5, bigger jump than any two models back to back in the past."

Fundamental Research Labs, a financial modeling firm, reported that "accuracy on our internal evals improved 20%, efficiency rose 15%, and complex tasks that once seemed out of reach became achievable," according to co-founder Nico Christie.

New features target Excel users, Chrome workflows and eliminate chat length limits

Alongside the model release, Anthropic rolled out a suite of product updates aimed at enterprise users. Claude for Excel became generally available for Max, Team, and Enterprise users with new support for pivot tables, charts, and file uploads. The Chrome browser extension is now available to all Max users.

Perhaps most significantly, Anthropic introduced "infinite chats" — a feature that eliminates context window limitations by automatically summarizing earlier parts of conversations as they grow longer. "Within Claude AI, within the product itself, you effectively get this kind of infinite context window due to the compaction, plus some memory things that we're doing," Albert explained.

For developers, Anthropic released "programmatic tool calling," which allows Claude to write and execute code that invokes functions directly. Claude Code gained an updated "Plan Mode" and became available on desktop in research preview, enabling developers to run multiple AI agent sessions in parallel.

Market heats up as OpenAI, Google race to match performance and pricing

Anthropic reached $2 billion in annualized revenue during the first quarter of 2025, more than doubling from $1 billion in the prior period. The number of customers spending more than $100,000 annually jumped eightfold year-over-year.

The rapid release of Opus 4.5 — just weeks after Haiku 4.5 in October and Sonnet 4.5 in September — reflects broader industry dynamics. OpenAI released multiple GPT-5 variants throughout 2025, including a specialized Codex Max model in November that can work autonomously for up to 24 hours. Google shipped Gemini 3 in mid-November after months of development.

Albert attributed Anthropic's accelerated pace partly to using Claude to speed its own development. "We're seeing a lot of assistance and speed-up by Claude itself, whether it's on the actual product building side or on the model research side," he said.

The pricing reduction for Opus 4.5 could pressure margins while potentially expanding the addressable market. "I'm expecting to see a lot of startups start to incorporate this into their products much more and feature it prominently," Albert said.

Yet profitability remains elusive for leading AI labs as they invest heavily in computing infrastructure and research talent. The AI market is projected to top $1 trillion in revenue within a decade, but no single provider has established dominant market position—even as models reach a threshold where they can meaningfully automate complex knowledge work.

Michael Truell, CEO of Cursor, an AI-powered code editor, called Opus 4.5 "a notable improvement over the prior Claude models inside Cursor, with improved pricing and intelligence on difficult coding tasks." Scott Wu, CEO of Cognition, an AI coding startup, said the model delivers "stronger results on our hardest evaluations and consistent performance through 30-minute autonomous coding sessions."

For enterprises and developers, the competition translates to rapidly improving capabilities at falling prices. But as AI performance on technical tasks approaches—and sometimes exceeds—human expert levels, the technology's impact on professional work becomes less theoretical.

When asked about the engineering exam results and what they signal about AI's trajectory, Albert was direct: "I think it's a really important signal to pay attention to."

Remember the first time you heard your company was going AI-first?

Maybe it came through an all-hands that felt different from the others. The CEO said, “By Q3, every team should have integrated AI into their core workflows,” and the energy in the room (or on the Zoom) shifted. You saw a mix of excitement and anxiety ripple through the crowd.

Maybe you were one of the curious ones. Maybe you’d already built a Python script that summarized customer feedback, saving your team three hours every week. Or maybe you’d stayed late one night just to see what would happen if you combined a dataset with a large language model (LLM) prompt. Maybe you’re one of those who’d already let curiosity lead you somewhere unexpected.

But this announcement felt different because suddenly, what had been a quiet act of curiosity was now a line in a corporate OKR. Maybe you didn’t know it yet, but something fundamental had shifted in how innovation would happen inside your company.

How innovation happens

Real transformation rarely looks like the PowerPoint version, and almost never follows the org chart.

Think about the last time something genuinely useful spread at work. It wasn't because of a vendor pitch or a strategic initiative, was it? More likely, someone stayed late one night, when no one was watching, found something that cut hours of busywork, and mentioned it at lunch the next day. “Hey, try this.” They shared it in a Slack thread and, in a week, half the team was using it.

The developer who used GPT to debug code wasn’t trying to make a strategic impact. She just needed to get home earlier to her kids. The ops manager who automated his spreadsheet didn’t need permission. He just needed more sleep.

This is the invisible architecture of progress — these informal networks where curiosity flows like water through concrete… finding every crack, every opening.

But watch what happens when leadership notices. What used to be effortless and organic becomes mandated. And the thing that once worked because it was free suddenly stops being as effective the moment it’s measured.

The great reversal

It usually begins quietly. Often when a competitor announces new AI features, — like AI-powered onboarding or end-to-end support automation — claiming 40% efficiency gains.

The next morning, your CEO calls an emergency meeting. The room gets still. Someone clears their throat. And you can feel everyone doing mental math about their job security. “If they’re that far ahead, what does that mean for us?”

That afternoon, your company has a new priority. Your CEO says, “We need an AI strategy. Yesterday.”

Here's how that message usually ripples down the org chart:

• At the C-suite: “We need an AI strategy to stay competitive.”

• At the VP level: “Every team needs an AI initiative.”

• At the manager level: “We need a plan by Friday.”

• At your level: “I just need to find something that looks like AI.”

Each translation adds pressure while subtracting understanding. Everyone still cares, but that translation changes intent. What begins as a question worth asking becomes a script everyone follows blindly.

Eventually, the performance of innovation replaces the thing itself. There’s a strange pressure to look like you’re moving fast, even when you’re not sure where you’re actually going.

This repeats across industries

A competitor declared they’re going AI-first. Another publishes a case study about replacing support with LLMs. And a third shares a graph showing productivity gains. Within days, boardrooms everywhere start echoing the same message: “We should be doing this. Everyone else already is, and we can’t fall behind.”

So the work begins. Then come the task forces, the town halls, the strategy docs and the targets. Teams are asked to contribute initiatives.

But if you’ve been through this before, you know there’s often a difference between what companies announce and what they actually do. Because press releases don’t mention the pilots that stall, or the teams that quietly revert to the old way, or even the tools that get used once and abandoned. You might know someone who was on one of those teams, or you might’ve even been on one yourself.

These aren’t failures of technology or intent. ChatGPT works fine. And teams want to automate their tasks. These failures are organizational, and they happen when we try to imitate outcomes without understanding what created them in the first place.

And so when everyone performs innovation, it becomes almost impossible to tell who’s actually doing it.

Two kinds of leaders

You’ve probably seen both, and it’s very easy to tell which kind you’re working with.

One spends an entire weekend prototyping. They try something new, fail at half of it, and still show up Monday saying, “I built this thing with Claude. It crashed after two hours, but I learned a lot. Wanna see? It's very basic, but it might solve that thing we talked about.”

They try to build understanding. You can tell they’ve actually spent time with AI, and struggled with prompts and hallucinations. Instead of trying to sound certain, they talk about what broke, what almost worked and what they’re still figuring out. They invite you to try something new, because it feels like there’s room to learn. That’s what leading by participation looks like.

The other sends you a directive in Slack: “Leadership wants every team using AI by the end of the quarter. Plans are due by Friday.” They enforce compliance with a decision that's already been made. You can even hear it in their language, and how certain they sound.

The curious leader builds momentum. The performative one builds resentment.

What actually works

You probably don’t need someone to tell you where AI works. You already know because you’ve seen it.

• Customer support: LLMs genuinely help with Tier 1 tickets. They understand intent, draft simple responses and route complexity. Not perfectly, of course, — I’m sure you've seen the failures — but well enough to matter.

• Code assistance: At 2 a.m., when you’re half-delirious and your AI assistant suggests exactly what you need, it feels like having an over-caffeinated junior programmer who never judges your forgotten semicolons. You save minutes at first, then hours, then days.

These small, cumulative wins compound over time. They aren't the impressive transformations promised in decks, but the kind of improvements you can rely on.

But outside these zones, things get murky. AI-driven revops? Fully automated forecasting? You've sat through those demos, and you’ve also seen the enthusiasm fade once the pilot actually begins.

Have the builders of these AI tools failed? Hardly. The technology is evolving, and the products built on top of it are still learning how to walk.

So how can you tell if your company's AI adoption is real? Simple. Just ask someone in finance or ops. Ask what AI tools they use daily. You might get a slight pause or an apologetic smile. “Honestly? Just ChatGPT.” That’s it. Not the $50k enterprise-grade platform from last quarter’s demo or the expensive software suite in the board deck. Just a browser tab, same as any college student writing an essay.

You might make this same confession yourself. Despite all the mandates and initiatives, your most powerful AI tool is probably the same one everyone else uses. So what does this tell us about the gap between what we're supposed to be doing and what we're actually doing?

How to drive change at your company

You've probably discovered this yourself, even if no one's ever put it into words:

1. Model what you mean: Remember that engineering director who screen-shared her messy, live coding session with Cursor? You learned more from watching her debug in real time than from any polished presentation, because vulnerability travels farther than directives.

2. Listen to the edges: You know who's actually using AI effectively in your organization, and they're not always the ones with “AI” in their title. They're the curious ones who've been quietly experimenting, finding what works through trial and error. And that knowledge is worth more than any analyst report.

3. Create permission (not pressure): The people inclined to experiment will always find a way, and the rest won’t be moved by force. The best thing you can do is make the curious feel safe to stay curious.

We're living in this strange moment, caught between the AI that vendors promise and the AI that actually exists on our screens, and it's deeply uncomfortable. The gap between product and promise is wide.

But what I've learned from sitting in that discomfort is that companies that will thrive aren’t the ones that adopted AI first, but the ones that learned through trial and error. They stayed with the discomfort long enough for it to teach them something.

Where will you be six months from now?

By then, your company’s AI-first mandate will have set into motion departmental initiatives, vendor contracts and maybe even some new hires with “AI” in their titles. The dashboards will be green, and the board deck will have a whole slide on AI.

But in the quiet spaces where your actual work happens, what will have meaningfully changed?

Maybe you'll be like the teams that never stopped their quiet experiments. Your customer feedback system might catch the patterns humans miss. Your documentation might update itself. Chances are, if you were building before the mandate, you’ll be building after it fades.

That’s invisible architecture of genuine progress: Patient, and completely uninterested in performance. It doesn't make for great LinkedIn posts, and it resists grand narratives. But it transforms companies in ways that truly last.

Every organization is standing at the same crossroads right now: Look like you’re innovating, or create a culture that fosters real innovation.

The pressure to perform innovation is real, and it’s growing. Most companies will give in and join the theater. But some understand that curiosity can’t be forced, and progress can’t be performed. Because real transformation happens when no one’s watching, in the hands of the people still experimenting, still learning. That’s where the future begins.

Siqi Chen is co-founder and CEO of Runway.

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Microsoft has introduced Fara-7B, a new 7-billion parameter model designed to act as a Computer Use Agent (CUA) capable of performing complex tasks directly on a user’s device. Fara-7B sets new state-of-the-art results for its size, providing a way to build AI agents that don’t rely on massive, cloud-dependent models and can run on compact systems with lower latency and enhanced privacy.

While the model is an experimental release, its architecture addresses a primary barrier to enterprise adoption: data security. Because Fara-7B is small enough to run locally, it allows users to automate sensitive workflows, such as managing internal accounts or processing sensitive company data, without that information ever leaving the device. 

How Fara-7B sees the web

Fara-7B is designed to navigate user interfaces using the same tools a human does: a mouse and keyboard. The model operates by visually perceiving a web page through screenshots and predicting specific coordinates for actions like clicking, typing, and scrolling.

Crucially, Fara-7B does not rely on "accessibility trees,” the underlying code structure that browsers use to describe web pages to screen readers. Instead, it relies solely on pixel-level visual data. This approach allows the agent to interact with websites even when the underlying code is obfuscated or complex.

According to Yash Lara, Senior PM Lead at Microsoft Research, processing all visual input on-device creates true "pixel sovereignty," since screenshots and the reasoning needed for automation remain on the user’s device. "This approach helps organizations meet strict requirements in regulated sectors, including HIPAA and GLBA," he told VentureBeat in written comments.

In benchmarking tests, this visual-first approach has yielded strong results. On WebVoyager, a standard benchmark for web agents, Fara-7B achieved a task success rate of 73.5%. This outperforms larger, more resource-intensive systems, including GPT-4o, when prompted to act as a computer use agent (65.1%) and the native UI-TARS-1.5-7B model (66.4%).

Efficiency is another key differentiator. In comparative tests, Fara-7B completed tasks in approximately 16 steps on average, compared to roughly 41 steps for the UI-TARS-1.5-7B model.

Handling risks

The transition to autonomous agents is not without risks, however. Microsoft notes that Fara-7B shares limitations common to other AI models, including potential hallucinations, mistakes in following complex instructions, and accuracy degradation on intricate tasks.

To mitigate these risks, the model was trained to recognize "Critical Points." A Critical Point is defined as any situation requiring a user's personal data or consent before an irreversible action occurs, such as sending an email or completing a financial transaction. Upon reaching such a juncture, Fara-7B is designed to pause and explicitly request user approval before proceeding. 

Managing this interaction without frustrating the user is a key design challenge. "Balancing robust safeguards such as Critical Points with seamless user journeys is key," Lara said. "Having a UI, like Microsoft Research’s Magentic-UI, is vital for giving users opportunities to intervene when necessary, while also helping to avoid approval fatigue." Magentic-UI is a research prototype designed specifically to facilitate these human-agent interactions. Fara-7B is designed to run in Magentic-UI.

Distilling complexity into a single model

The development of Fara-7B highlights a growing trend in knowledge distillation, where the capabilities of a complex system are compressed into a smaller, more efficient model.

Creating a CUA usually requires massive amounts of training data showing how to navigate the web. Collecting this data via human annotation is prohibitively expensive. To solve this, Microsoft used a synthetic data pipeline built on Magentic-One, a multi-agent framework. In this setup, an "Orchestrator" agent created plans and directed a "WebSurfer" agent to browse the web, generating 145,000 successful task trajectories.

The researchers then "distilled" this complex interaction data into Fara-7B, which is built on Qwen2.5-VL-7B, a base model chosen for its long context window (up to 128,000 tokens) and its strong ability to connect text instructions to visual elements on a screen. While the data generation required a heavy multi-agent system, Fara-7B itself is a single model, showing that a small model can effectively learn advanced behaviors without needing complex scaffolding at runtime.

The training process relied on supervised fine-tuning, where the model learns by mimicking the successful examples generated by the synthetic pipeline.

Looking forward

While the current version was trained on static datasets, future iterations will focus on making the model smarter, not necessarily bigger. "Moving forward, we’ll strive to maintain the small size of our models," Lara said. "Our ongoing research is focused on making agentic models smarter and safer, not just larger." This includes exploring techniques like reinforcement learning (RL) in live, sandboxed environments, which would allow the model to learn from trial and error in real-time.

Microsoft has made the model available on Hugging Face and Microsoft Foundry under an MIT license. However, Lara cautions that while the license allows for commercial use, the model is not yet production-ready. "You can freely experiment and prototype with Fara‑7B under the MIT license," he says, "but it’s best suited for pilots and proofs‑of‑concept rather than mission‑critical deployments."

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