<![CDATA[Scientific computing in the age of agentic AI]]>

Jul 28, 2026

We're introducing Cursor Start, a new ₹649 monthly plan for developers in India, making daily agentic development accessible and payment easy with local pricing and UPI.

Existing Free users in India can upgrade their plan from the dashboard. New users in India can visit cursor.com/signup and select the Start plan during onboarding.

Start bills monthly with auto-renewal and is available from July 28, 2026.

Cursor Start includes:

• **Generous access to Cursor models:**Grok 4.5, our most powerful model, and Composer, our most price-efficient coding model, with enough usage to build with agents every day.
• Always-on cloud agentsthat build, test, and ship code while you keep working.
• Cursor for iOSwith remote control, so you can launch and steer agents from your phone.
• Plugins, MCP servers, hooks, and skillsto extend Cursor across your workflows.
• Local pricingat ₹649 per month, tax inclusive, billed in INR with UPI or card.

Learn more in our announcement and docs.

Our position on open-weights models

A post by Dario Amodei, Anthropic CEO

Over the last few days there has been a lot of discussion about open-weights models, especially those from China. Reports suggest that some US officials are considering banning the use of Chinese open-weights models by US companies. In response, many tech companies have signed a letter supporting open-weights models, and some people have even accused Anthropic of wanting to ban open-weights models as a means of protecting our business. Anyone who has read my past writing should know that I don’t regard such bans as a useful measure, but let me state it clearly so that there is no doubt: Anthropic has never advocated for a ban on open-weights models.

Open-weights models that don’t have dangerous capabilities are a public good: they don’t cost anything besides the compute needed to run them, and they provide value to businesses, developers, and researchers.

Protectionist bans would not address my most serious national security concerns. Specifically, I am worried about two nightmare scenarios. I laid these out in my essay The Adolescence of Technology six months ago1, and have held these positions consistently for many years:

1. My primary concern is the risk that authoritarian governments—not solely the Chinese Communist Party (CCP), although the CCP is clearly the most capable threat—build AI models that are more powerful than those built by the US, and use them to achieve permanent military superiority or perpetrate incredibly deep repression of their own people. This concern is widely shared within the US government: Vice President Vance warnedin Paris last year that “authoritarian regimes have stolen and used AI to strengthen their military, intelligence, and surveillance capabilities,” and the Intelligence Community’s2026 Annual Threat Assessmentfound that “other global powers’ robust progress in AI is challenging US economic competitiveness and national security advantages.” It is irrelevant whether these models are released with open weights, and certainly irrelevant whether they are used by US businesses. In fact, the most dangerous model may be one that is trained in secret and handed only to the People’s Liberation Army for use in drones and the Ministry of State Security for surveillance and repression.
2. My secondary concern is the risk that powerful AI models may be misused to carry out cyberattacks or biological attacks, and may have serious alignment problems. Open-weights models—it does not matter whether they come from China or anywhere else—do potentially present a higher risk than closed models, because it is very difficult to apply guardrails to them or monitor their usage, and once weights are released they cannot be withdrawn2. But banning the use of these models by US businesses does nothing to address this risk, because bad actors are unlikely to be legitimate US businesses. Itwouldprotect US AI companies from competition, but that has never been my goal.

To address these concerns, I do support the following three measures, which I and Anthropic have consistently advocated for:

• We should not sell powerful chips or chipmaking equipment to China, and we should crack down on the rampantsmuggling3and workarounds used to obtain access to such chips. China has limited domestic production capacity, and therefore, due to thescaling laws, cannot build more powerful models than the US without US chips. This is the most efficient and direct way to block threat #1, and by hampering the training of models that are out of reach of US law, it also indirectly helps with threat #2.
• We should crack down on industrial-scaleDistillation is a much more compute-efficient process than training models from scratch. It allows China to build much better models than its number of chips would ordinarily enable, and thus partially evade chip bans. Distillation does not allow the CCP to obtain equivalent or superior AI capabilities to the US, but it can bring the Chinese frontier to within adistillation operations.few monthsof the US frontier. It is true that many of the companies carrying out these operations release open-weights models—but the open weights are far less relevant than the fact that the operations are backed by an authoritarian state seeking to overtake the US at the frontier. We should have policy interventions to deter this behavior. A blanket ban on open-weights models is neither the correct remedy nor something we have called for4.
• **All sufficiently capable models, open and closed, should go through mandatory safety testing.**The best way to address threat #2 is to just directly test models for cyber, biological, and alignment risks before release. I think this idea is actually close to a consensus: I have been heartened both that the Trump administration has moved in this direction in recent months, and byrecent industry proposalsthat would apply such testing to the most capable models regardless of their country of origin or whether they are open or closed (while exempting less capable models, such as those from startups and academia, entirely). Whether open models do or don’t pose an increased risk, and whether that risk can be mitigated, is something that should emerge from testing, rather than be decided in advance—and there may be promising methods for improving the safety of open-weights models, including recent research from Anthropic onmodular training strategies. Note that to be effective, testing would need to be global, which means even the CCP would need to be on board. I think this may actually be possible: as I wrote inThe Adolescence of Technology, limited cooperation around preventing AI biological weapons may be possible because it is in China’s interest too.

This brings me to the open letter. I agree with much of it: open weights expand access to the AI economy, they strengthen competition at least for some use cases, and they give customers greater control. Concerns about distillation should be addressed through targeted legal and commercial frameworks—the same measure I described above. But I don’t agree with the letter’s assertions that open-weights models necessarily make it easier to develop safeguards or that broad access to capabilities necessarily helps defenders more than attackers. It seems at least as likely to me that the opposite will be true. For example, I worry that biology will have a strong attacker-defender asymmetry, where sufficiently capable models may be able to quickly weaponize pandemic-level viruses with widely available materials, whereas defense against these agents is a multi-year operational task in the best case (as we saw with Operation Warp Speed)5. Questions like this should be empirically answered by rigorous pre-release testing, not assumed in advance.

To summarize my and Anthropic’s position, we have not and are not advocating for a ban on open-weights models as a category. We should instead focus on keeping powerful chips out of authoritarian hands, stopping industrial-scale distillation, and requiring safety testing of all sufficiently capable models, open and closed.

Footnotes

1. See Sections 3 and 2 of that essay for discussion of misuse for seizing power and discussion of biological risks, respectively.
2. See this reportfrom the UK AI Security Institute, specifically: “The same openness underpinning these benefits precludes many of the safety measures that closed model developers can use to detect and disrupt misuse, iterate on safeguards as vulnerabilities emerge, control user access and withdraw models. Once open-weight models are released, these options are lost permanently: safeguards can be removed, and copies can be downloaded, redistributed, and run on private systems beyond monitoring. For models with dangerous capabilities – including highly cyber-capable models – open weight release therefore creates a persistent and irreversible risk of misuse.”
3. See also here,here, andherefor more reports from the US Department of Justice.
4. At Anthropic we’re committed to cracking down on industrial-scale distillation through our own practices, including identifying and banning accounts that use our models in this way. This is challenging—for instance, the relevant accounts can often only be identified aftersubstantial distillation has occurred, and distillation often involves creating large numbers of fake accounts that form a moving target. The practices of any individual company cannot entirely solve the problem, which is why we have called for policy on this issue.
5. See Section 2 ofThe Adolescence of Technologyfor a more detailed discussion of biological threats and the offense-defense balance. To summarize, what I believe currently keeps us safe in biology is not “defenders”, or even the availability of materials, but a negative correlation between intellectual capability and desire to commit catastrophic harm. Previous technologies like internet search or even DNA synthesis were nowhere near powerful enough to break this correlation, but I worry that at its current rate of progress, AI will do so very soon. Another way to say it is that a sufficiently powerful technology removes all barriers and exposes whether the attacker or defender has an inherent structural advantage, and I worry in biology it is the attacker.

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Cognizant and Anthropic expand their partnership to bring Claude to enterprise clients

We're expanding our partnership with Cognizant, one of the world's largest technology services companies.

Cognizant uses Claude in the systems it builds and runs for clients across manufacturing, life sciences, insurance, and other industries. With the expansion of our partnership, it’s embedding Claude across its own business and engineering platforms, scaling a Claude-certified workforce as part of its new Frontier Certified workforce model, and becoming a Global Premier Partner in the Claude Partner Network.

Successfully integrating AI into a large enterprise requires knowledge of the company's industry, the systems it already runs on, and the rules it operates under. Cognizant brings that domain context, along with the engineering depth and delivery scale to bring Claude to enterprises worldwide.

Cognizant builds with Claude

Cognizant's engineers build with Claude every day, and more than 30,000 associates have completed Claude training.

Cognizant is embedding Claude across several of its platforms, including Flowsource™, Neuro® AI Engineering, and Neuro® IT Ops. Flowsource, its full-stack engineering platform, now runs Claude Code alongside software engineers in its Spec-Driven Development module. Flowsource directs Claude Code using the specifications, coding standards, and architectural blueprints a project defines, then and then evaluates the output before production.

Cognizant puts Claude to work for clients

The company uses what it learns internally to shape how it brings Claude to clients, and that work is already underway. Examples of what its teams built include:

• A customer experience portal for a global manufacturer within six months of kickoff.
• An agentic contract-intelligence system for a biopharmaceutical company that has helped cut contract review time by up to 40 percent while lifting extraction accuracy above 88 percent in that deployment.
• A risk-navigation tool that has helped underwriters evaluate accounts, which once took hours of manual research, in minutes—saving each person roughly eight hours a week in that deployment.

"AI capability is rising faster than enterprises can absorb it, and that gap is the defining problem of this moment," said Ravi Kumar S, Chief Executive Officer of Cognizant. "Our role is to be the bridge. We bring the industry context, the engineering scale and the trust frameworks that use Claude to deliver production outcomes inside the most demanding enterprise environments. This partnership with Anthropic is about doing that for clients who need AI they can rely on, not just experiment with."

"Deepening our partnership with Cognizant will help more companies harness AI's growing capability and deploy it in real, practical ways for their businesses," said Daniela Amodei, Co-Founder and President of Anthropic. "From manufacturing to the life sciences, Cognizant is bringing Claude into the everyday work of some of the world's most demanding industries—the kinds of contexts where AI can demonstrate its greatest value for humanity."

To learn more about the Claude Partner Network, visit anthropic.com/partners.

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<![CDATA[How AI is expanding what people do at work]]>

Introducing Claude Opus 5

Claude Opus 5 is available today. It’s a thoughtful and proactive model that comes close to the frontier intelligence of Claude Fable 5 at half the price.

On coding and knowledge work evaluations like Frontier-Bench and GDPval-AA, Opus 5 is the new state-of-the-art, though it remains behind Mythos 5 on cybersecurity tasks.

Opus 5 is designed to be used every day: it works more efficiently than other models. It’s the new default model on Claude Max, and the strongest model on Claude Pro.

Performance and cost-effectiveness

Claude Opus 5 provides greatly improved performance for the same cost as its predecessor, Opus 4.8. The charts in this section show how performance changes according to the model’s effort setting, which customers can use to optimize for intelligence or conserve tokens for faster and cheaper results.

Opus 5 excels on valuable software engineering tasks. For example, on Frontier-Bench v0.1, Opus 5 surpasses all other models, and more than doubles Opus 4.8’s performance at a lower cost per task. On CursorBench 3.2, at max effort, the model performs within 0.5% of Fable 5’s peak score, but at half the cost per task; it also achieves greater performance at a given cost than all other models on high, xhigh, and max effort.

We see similar results on knowledge work and problem-solving tasks. For example:

• On ARC-AGI 3, an evaluation where the model has to solve novel problems, Opus 5’s score is three times as high as the next-best model.
• On Zapier AutomationBench, which measures whether models can complete business tasks from start to finish, Opus 5’s pass rate is around 1.5× the next-best model for the same cost per task. Even at its lowest effort setting, Opus 5 passes more tasks than any other model.
• On OSWorld 2.0, a computer use benchmark, Opus 5 outperforms every other model at any given cost, surpassing Fable 5’s best result at just over a third of the cost.

It’s also our best and most cost-efficient model on several related evaluations:

Opus 5 is a meaningful improvement over Opus 4.8 for scientific research. It shows better performance than Opus 4.8 on every one of our life sciences evaluations, which cover topics including structural biology, organic chemistry, and bioinformatics. Its improvements are most notable on organic chemistry tasks, like inferring molecular structures from spectroscopy data (it scores 10.2 percentage points higher than Opus 4.8 on our internal benchmark), and on protein-related tasks like predicting how variations in a protein’s sequence affect how it functions (here, it scores 7.7 percentage points higher).

Finally, Opus 5 is capable of producing much stronger visual outputs:

Working with Claude Opus 5

Claude Opus 5 is much stronger at verifying its work and iterating carefully until it succeeds. In evaluations and early-access testing, we and our users found many examples of Opus 5’s agency and thoroughness:

• On one Frontier-Bench task, Opus 5 was given a drawing of a machine part and asked to write code to rebuild it as a 3D FreeCAD model. However, in this task, the model was intentionally given no way to directly viewthe drawing. Opus 5 responded by writing its own computer vision pipeline to pull the geometry from the raw pixels, then reconstructed the full machine part. It succeeded in doing so repeatedly; no competing model with the same setup could solve it after five attempts.
• Given a real bug in a popular open-source package manager, Opus 5 found the root cause and fixed an edge case that the community’s patch had missed. A competing model fixed only the surface symptom (not the underlying cause), then reported the bug resolved.
• An engineer at a trading firm used Opus 5 to build a market data feed for a new exchange in a single session. Previous models could not complete this task at all, even given extensive plans from the engineer. Finding no live feed to validate against, Opus 5 even built its own test harness to check that its code parsed the exchange’s data correctly.

Below are further reports from our early-access customers on their experience of working with Opus 5:

On FrontierCode 1.1, Claude Opus 5 approaches Fable-level performance at half the cost. Within Devin, it also shows particular strength on difficult debugging and root-cause analysis tasks.

Claude Opus 5 delivers near Fable 5 intelligence at Opus speed and cost. On CursorBench it’s just under Fable 5 and has many of the same behaviors. We are excited to see how developers use it in Cursor.

Claude Opus 5 topped Zapier’s AutomationBench leaderboard without spending more tokens than prior Claude models. It took a raw account-health workbook and ran a full churn-prevention sequence end to end: flagging at-risk accounts, alerting the right owner, and summarizing for retention ops. Previous models didn’t pass; Opus 5 hit 100%.

On our genomics analysis work, Claude Opus 5 behaves more like a careful scientist than any model we’ve run. It reaches for the right statistical tests to rule out confounders, cross-checks its own results by independent methods, and stays on track through long multi-step analyses.

Claude Opus 5 came out ahead of every model in its family on our internal evals. It isn’t just better on our hardest agentic coding tasks, up 22% over Opus 4.7, it’s steadier, with far less variance run to run. For the millions of builders on Lovable, that consistency is the whole game. Reliable results, build after build.

Claude Opus 5 is the biggest leap in the Opus family since 4.5. On the same full-stack app builds, the front end shows it first: the best animations, games, and 3D work we have seen from an Opus model.

We’re loving Claude Opus 5. For the kind of open-ended analytical work our agent handles, it’s a strict upgrade over Opus 4.8, and the gains are biggest exactly where it matters: the harder, vaguer tasks. Responses are clearer and more concise, and we see improved efficiency at higher effort levels too.

Claude Opus 5 is a striking improvement over Opus 4.8 for the financial research workflows our analysts run every day. It stands out on numerical reasoning, table work, and sharper critical thinking where precision matters.

Claude Opus 5 delivers the industry intelligence and accuracy that is essential for the analysis of specialized enterprise content. Box found that Opus 5 outperforms Opus 4.8 by 8% and delivers notable performance gains in the data analysis (11% improvement) and due diligence (17% improvement) workflows that technology, healthcare, and public sector organizations rely on daily.

Claude Opus 5 is a clear generational step up from Opus 4.8. Over one weekend I gave it a chief-of-staff role over my dev environments: it built its own monitor, drove each box, and pulled me in only for the judgment calls.

Claude Opus 5 made large scale changes across our Fundamental Research Assistant codebase, adapting to feedback throughout an agentic workflow and explaining its reasoning more clearly than any model we’ve used. It handled work we would normally have broken into much smaller pieces.

On some of our hardest financial-modeling tasks, Claude Opus 5 is a clear step up from Opus 4.8 in both accuracy and efficiency. Its performance floor is materially higher, especially on deep finance domain logic. Across effort levels it averaged 9 percentage points higher accuracy with a third fewer turns and tool calls and 60% less time.

Claude Opus 5 checks its own work the way a real frontend developer would. On our benchmark it opened its pages in a browser at desktop and phone widths, caught a product hidden below the mobile fold and an off-screen checkout button, and fixed both before handing the work back.

Claude Opus 5 is a clear step up in performance on legal agent work compared to prior Opus models, and we saw the biggest gains in practice areas like corporate governance and arbitration. We were also impressed with Opus 5’s ability to maintain quality at lower reasoning levels, achieving similar performance while generating 26% fewer tokens on average compared to Opus 4.8 at max reasoning.

Claude Opus 5’s biggest gains for us are on longer-horizon work: building a full deck, then revising it. Artifact quality is what decides which model we ship, and this is the clearest step up we’ve seen — better visual understanding, cleaner formatting, fewer slide issues.

Claude Opus 5’s judgment is what stands out. Handing off a PR, it doesn’t rush to publish: it verifies the branches, checks the template, and thinks through test implications so the handoff is clean. The older models tended to jump ahead and get caught on our checks.

During a rearchitecting session, Claude Opus 5 pushed back on a design I proposed, and it didn’t fold when I insisted. Instead, it explained exactly what was valuable in my idea, narrowed its objection to a single design question, and proposed a compromise that kept the good part while fixing the flaw. That’s the kind of judgment that lets us trust it with less oversight.

On first-turn redlines, Claude Opus 5 scored the highest of any model we tested, nearly double Opus 4.8. Commenting is better too: on NDAs it gets to the redline in less time and with fewer passes, with accuracy maintained or better.

Claude Opus 5 writes clean, tight diffs with no dead code, and it’s the stronger hazard spotter on subtle, codebase-specific issues. We’re adopting it for production workloads.

We will definitely migrate a number of use cases in Cosmos, our unified agent platform. We’re looking forward to increasingly using Claude Opus 5 for code review, and I am confident in saying we would rather people be using Opus 5 than Opus 4.8.

What stands out about Claude Opus 5 is judgment. It thinks harder before it writes a single line, catches its own logical faults during planning rather than after the fact, and reasons about why an answer is right, not just whether it works. It’s the clearest jump in problem-solving we’ve seen from one Claude model to the next, and we’re looking forward to seeing it adopted in JetBrains IDEs.

Claude Opus 5 is the strongest Opus model we’ve tested on our trading benchmark, and it gets there using roughly a seventh of the reasoning tokens and under half the latency of Opus 4.8. Better answers at a fraction of the compute.

Alignment and safety

Alignment. During pre-deployment testing, our automated behavioral audit found Opus 5 to be our most aligned model to date (as shown in the graph below). It adheres to Claude’s Constitution better than Opus 4.8, Sonnet 5, or Fable 5; exhibits the lowest rates of deceptive behavior; and is the least susceptible to being tricked into misuse. It’s also our safest model yet in terms of avoiding reckless actions that could have hard-to-reverse side effects.

On our automated behavioral audit, Opus 5 scores 2.3 on overall misaligned behavior, the lowest of our recent models.

Safety. Opus 5 does not advance the frontier in risky, dual-use capabilities. In rigorous evaluations conducted alongside private-sector and government partners, we found it remains behind Mythos 5 in both biology research and offensive cybersecurity. More information about these evaluations can be found in our System Card.

As with its predecessor, Opus 4.8, we’ve intentionally avoided training Opus 5 on cyber tasks. The model has nevertheless improved substantially on these tasks as a result of becoming more generally capable, and it comes close to Mythos 5 at finding cybersecurity vulnerabilities. However, it remains substantially behind Mythos 5 on the exploitation of those vulnerabilities—that is, in turning vulnerabilities into material cyber threats.

This is illustrated by Opus 5’s performance on OSS-Fuzz, an evaluation we’ve developed to assess how well models can find and then exploit vulnerabilities without extensive human guidance. Although Mythos 5 and Opus 5 identify vulnerabilities with similar success, Opus 5’s score on the development of exploits is far behind that of Mythos 5.

On OSS-Fuzz, one of our cybersecurity evaluations, Opus 5 is close to Mythos 5 at identifying software vulnerabilities (left), but is considerably less successful at developing exploits for them (right).

Safeguards for Opus 5

Claude Opus 5’s safeguards are designed to allow beneficial uses of the model in both cybersecurity and biology. They are similar to those we applied to Opus 4.8, with the exception of some stronger guardrails on a narrow range of cyber tasks.

Cybersecurity. Opus 5’s cyber classifiers are proportionally less restrictive than those on Fable 5. They allow Opus 5 to find vulnerabilities in source code, but block “binary-based” vulnerability scanning (a method more likely to be associated with malicious actors), penetration testing, and exploit generation.

Based on our testing, we expect the classifiers to intervene around 85% less often than they do for Fable 5. In Claude.ai, Claude Code, and Claude Cowork, any flagged requests will fall back to Opus 4.8 by default. Fallbacks to Opus 4.8 can also be enabled on the API.

Our Cyber Verification Program (CVP) facilitates cybersecurity work that would otherwise be impeded by the model’s safeguards. Enterprises and researchers who are already part of the CVP have immediate access to a version of Opus 5 with fewer security restrictions.

Biology. Since Opus 5 has a similar suite of safeguards to Opus 4.8, it is now our most capable generally available model for scientific research. Nevertheless, the model still shows important limitations on long-running, autonomous research tasks, which is where we expect AI models to pose the most substantial biology-related risks. (Mythos 5 remains the stronger model for this type of biological work.) As part of this launch, biology-related requests that are blocked on Fable 5 will now route to Opus 5 rather than Opus 4.8.

Getting started

Claude Opus 5 is available today on all platforms, priced at $5 per million input tokens and $25 per million output tokens (the same as Opus 4.8). Developers can get started with claude-opus-5 on the Claude API.

It’s also offered in Fast mode, where it runs around 2.5 times the default speed. As with Opus 4.8, Fast mode is available at twice Opus 5’s base price on the Claude Platform and through usage credits in Claude Code.

Alongside Opus 5, we’re releasing two updates in beta:

• Within a conversation, developers can now change which tools Claude can use without invalidating the prompt cache.Mid-conversation tool changeson the Claude Platform.
• Users can now choose to have requests that are flagged by our safety classifiers on Opus 5 (or Fable 5) automatically route to another model. With automatic fallbacks on, API requests always route to the best available model by default rather than being blocked.Automatic fallbackson the API.

Consistent with prior Opus models, Opus 5 does not have data retention requirements for general access.

For more guidance on how to get the best out of Opus 5, see our prompting guide.

Footnotes

Frontier-Bench v0.1, Effort plot: These results are from an internal run of Frontier-Bench v0.1, on the mini-SWE-agent harness and a GKE backend, mean reward over 5 attempts per task. Opus 4.8 served as fallback on safety-classifier refusals for Opus 5 and Fable 5.

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<![CDATA[How news organizations are using AI to advance their vital missions]]>

How news organizations are using AI to advance their vital missions

Our technology helps journalists deepen original reporting, make trusted content more useful and accessible, and build stronger relationships with readers, subscribers, and advertisers.

Over the past year, we’ve continued working with news organizations to explore how AI can be most useful—helping with time-consuming tasks, enabling new reader experiences, and supporting stronger, more sustainable businesses.

Across the industry, reporters, editors, and businesses are using OpenAI technology to help journalists cover more ground, make decades of reporting searchable, reach audiences in new languages and formats, and turn complex information into faster decision-making. These tools are becoming embedded across all departments—including newsroom, product, and business workflows. For example, the American Journalism Project recently announced⁠(opens in a new window) our renewed support for its portfolio of dozens of publications spanning 38 states. We’re also proud to continue supporting the important work of both the Lenfest Institute for Journalism and WAN-IFRA⁠(opens in a new window).

While AI is an important tool, people remain at the very center of this work—from frontline journalism, to editorial direction, to critical business decisions.

The examples below—in the words of the news organizations themselves—are just a few of the many that demonstrate how AI is helping them strengthen journalism and the businesses that sustain it.

Publishers are finding many ways to put AI to work to enhance their research and reporting.

• The Associated Pressis using OpenAI technology to help journalists strengthen reporting, verification and newsroom workflows, all while keeping journalists in control of editorial judgment. A variety of tools help journalists scan overnight news and podcasts for reportable developments; support image and video verification through upload tracing, geolocation and chronolocation; and turn thousands of Supreme Court filings into searchable, structured information. AP is also applying the technology to surface potential stories in government datasets, recommend member content for editors to consider sharing, build integrated and actionable audience metrics reports, and convert AP articles into broadcast scripts. These tools reduce repetitive work and help journalists get to stories faster and devote more time to distinctive, original reporting.
• At POLITICO, AI helps journalists analyze large volumes of public documents and data for deeper, more timely original reporting; while commercial teams use AI to tailor client sales experiences with richer data on POLITICO’s offerings, freeing the teams to focus on human relationships.
• Axioshas built a collection of custom GPTs to help with everything from understanding internal policies, receiving interdepartmental support, submitting open records requests, and checking their copy’s use of their signature Smart Brevity style. The “FOIA Refiner GPT,” for example, helps Axios reporters craft sound open-records requests that are specific, efficient, and less likely to be denied or delayed. The “O Caption! My Caption!” GPT optimizes captions for images on stories. The “Axiomizer” GPT reviews story copy to suggest sharper headlines and clearer writing.
• The Philadelphia Inquirer* developed Scribe, an AI-powered tool that helps its journalists monitor public meetings across dozens of municipalities and school districts. Scribe uses OpenAI technology to turn public meeting transcripts into concise, categorized summaries. It then scores and ranks individual developments using a newsworthiness framework created by Inquirer reporters and editors.
• Axel Springeris using OpenAI’s technology to elevate reader experiences and newsroom workflows across its brands. At Business Insider, AI powers audience features such as one-tap listening and helps journalists analyze audience commentary, giving reporters more time to focus on original journalism. At WELT, AI helps produce newsletters for niche audiences and provides an additional layer of fact-checking within the CMS while also helping journalists surface developments, sort sources, and assess what holds up before a story is written.
• Futureis also creating an in-house editorial platform that combines AI agents, flexible workflows and powerful tools into a seamless experience.

• In 2022, the Le Mondenewsroom expanded its coverage for English-speaking audiences with the launch of Le Monde In English. In 2025, its partnership with OpenAI marked a new step: the editorial teams incorporated their fine-grained translation style book in ChatGPT models in order to help accelerate the publication process. Le Monde freed up time from journalists, enabling them to focus even more on their core reporting and analysis missions.
• PRISA Mediauses OpenAI technology across a growing set of editorial tools supervised by its journalists: from a trend-and-information tracker built with Codex through vibe coding, to country-specific audio news briefings for the FIFA World Cup at Diario AS, to Vera, the conversational assistant answering EL PAÍS subscribers’ questions. OpenAI models also strengthen its knowledge management, automating high-impact tasks like content vectorization, article translation, and image rights attribution. Together, these initiatives show how AI can be embedded into newsrooms in practical ways, helping its teams spot opportunities, work faster, and extend the reach of its journalism.
• The Daily Beastdata team has built Data Scouts, a suite of OpenAI-powered agents that helps newsroom and business teams move more quickly from information to action. Rather than simply summarizing data, Data Scouts identify new opportunities, explain what is happening across the business, and recommend practical next steps for people to review. Most Data Scout interactions happen in Slack, where Daily Beast teams already collaborate and make decisions. This brings insights into existing conversations instead of requiring people to adopt another dashboard, while remaining flexible enough to meet teams in other tools and workflows.
• The American Journalism Project’s Product & AI Studio—supported by OpenAI—is a center of excellence helping local news organizations responsibly leverage AI to strengthen their work and sustainability. Nonprofit news organizations are quickly adopting AI workflows and seeing tangible benefits. Using OpenAI technology, Centro de Periodismo Investigativo (CPI), Puerto Rico’s leading investigative newsroom, built acollection⁠(opens in a new window)of custom GPTs that save time for its small team as they draft and translate donor communications in both English and Spanish—all while preserving the newsroom’s authentic voice. Innovations are traveling across the country, landing where they can be most impactful. CPI also developed a ChatGPT‑powered translation workflow that has sinceinspired⁠(opens in a new window)other newsrooms: Enlace Latino North Carolina adapted it to launch its first English-language newsletter, bringing its work to wider audiences and opening new sponsorship opportunities, and Boyle Heights Beat took the same approach to deliver real-time bilingual coverage during the L.A. fires.

Publishers are using AI to help people discover, explore, and engage with trusted journalism in more useful and personalized ways.

• Condé Nastis using OpenAI technology to power new ways for readers to engage with its trusted brands. Bon Appétit launched an AI-powered Test Kitchen Assistant that combines its extensive editorial archive with OpenAI’s models, allowing home cooks to ask questions about recipes, cooking techniques, ingredient substitutions, meal preparation, and product recommendations in real time. The Kitchen Assistant allows readers to experience Bon Appétit’s rich, trustworthy editor-approved culinary content in more interactive and personalized ways, providing guidance to help them confidently navigate the cooking process.
• The Atlanticlaunched an immersive game—Lemony Snicket’s Suspicious Incident in Dubious Park⁠(opens in a new window)—that is a murder mystery whodunit. To create the game,The Atlanticworked with author Lemony Snicket to write the cast of characters, story, and plot.The Atlanticthen used ChatGPT’s API to create agents for each character using the unique dialogue, motives, and background that the author provided. This lets players interrogate and interact with the characters directly to uncover the perpetrator of the crime.
• Eaterrecently launched an AI-powered restaurant search experience using OpenAI technology. The tool helps diners find personalized restaurant recommendations by connecting natural language queries with more than 20 years of Eater’s trusted dining recommendations and service journalism. By making Eater’s editorial expertise easier to explore through conversation, the experience helps readers quickly discover the right restaurant for any occasion.
• Futurelaunched its first two ChatGPT Plugins this year, helping ChatGPT better understand, present and activate its content in service of its readers and customers. Who What Wear helps people find the right look, personalized to them, with a rich interface and anchored to the best fashion content on the web.
• Le MondeIn 2025, to address constantly changing user habits in news consumption, Le Monde gave every article an audio voice. The voice and reading tone were carefully tested and designed in collaboration with professional voice actors to reflect Le Monde editorial identity. OpenAI models then made it possible to listen to any article right after publication.
• The San Francisco Standard*is using OpenAI technology to help reimagine how readers experience local news. Its new AI-first subscriber app creates personalized briefings based on each reader’s interests, location and reading habits, while transforming the Standard’s reporting into interactive modules that answer follow-up questions, highlight notable people, and surface relevant material from its archive.
• BILD’s AI assistant, Hey_, has answered more than 250 million reader questions and has evolved into an everyday companion, helping readers understand what news means for them through chat and interactive article widgets, including tax and retirement calculators.
• Chicago Public Media* is using OpenAI models to transcribe 40 years of audio archives for its radio station, WBEZ. These efforts are making this rich collection of local journalism searchable for the first time, providing a critical resource for journalists—and eventually the public—to better understand their community.

Publishers are also using AI to turn complex business information into useful insights and help their teams work more effectively.

• News Corpis developing a number of AI-powered “Knowledge Agents”. These agents are data and context aware meaning that they combine structured enterprise data with unstructured business knowledge to help teams answer complex analytical questions. Using OpenAI models together with Model Context Protocol (MCP), the agents can securely access data from News Corp’s global data lake, but a key innovation has been teaching these agents the semantic structure of their data.
• The Seattle Times* developed an AI-powered prospecting agent using ChatGPT Enterprise to help its advertising team more efficiently identify and evaluate potential customers. Through a conversational custom GPT, sales representatives can generate targeted lead lists, score prospects against criteria developed by the Times, produce research reports, check whether an account is already active in its CRM system, and draft questions for discovery calls. The tool has reduced prospecting time from hours to minutes and has led directly to new sales.

These projects reflect just some of the broad range of ways publishers are making AI useful to their products and businesses: helping journalists reclaim time, making trusted reporting easier to explore, uncovering deeper business insights, and developing new sources of growth. We also launched the OpenAI Academy for News Organizations to share learnings and use cases so that the benefits can be understood and used more broadly. You can learn more here⁠(opens in a new window) about some other ways newsrooms are putting AI to work.

Though we’ve been investing and partnering closely with the news industry for over three years, our work in support of their mission is just getting started. We will continue working with publishers, editors, and journalists to build tools that support strong, sustainable news organizations and help more people engage with high-quality journalism.

*One of the news organizations participating in the Lenfest Institute AI Collaborative and Fellowship program

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