Latest AI News
Meta’s new AI chips will begin production in September
In a bid to lower its GPU costs amid an unprecedented component shortage, Meta is on track to start making the latest versions of its AI-specific chip in September, Reutersreported, citing an internal memo. At least one chip sailed through its testing phase in about six weeks, the memo said. Meta is working with Broadcom on the chip design, but it will use Taiwan Semiconductor Manufacturing Company (TSMC) to manufacture them. It is also buying RAM from Samsung, storage from Sandisk, and fiber-optic equipment from Sumitomo Electric, according to the report. Metadetailedthe four new chips, developed under its Meta Training and Inference Accelerator (MTIA) program, in March, some of which are currently in deployment or will be this year or next. The company is taking a modular approach to designing these chips, anticipating that their needs will change as AI evolves rapidly by the time the chips are in production. “Each MTIA generation builds on the last, using modular chiplets, incorporating the latest AI workload insights and hardware technologies, and deploying on a shorter cadence,” the company wrote at the time. The chips are expected to help the company save on buying GPUs from chipmakers like Nvidia and AMD, although it still expects to spend plenty with those providers as well, Reuters reports. Meta intends to use the MTIA chips for training models for its ranking and recommendation algorithms, broader AI workloads, and inference aimed at its applications. The social media company has beenproducing its own AI chips since 2023. Meta has been spending massively on securing enough compute capacity to power its various AI efforts. The company in April said itexpectscapital expenditures between $125 billion and $145 billion this year, a lot of which is going toward its AI efforts. The company has been striking data center and power deals across the world, spending tens of billions to secure computing capacity to train and deploy its newMuse Sparkseries of AI models. It plans to deploy 7 gigawatts of compute this year, and double that next, according to Reuters, which cited the memo. It alsosigned a dealwith ARM last year to secure compute for its recommendation systems, in addition to a multibillion-dollar deal withAMD for its Instinct GPUsand a multibillion-dollar deal withAmazon to use the cloud giant’s homegrown CPUsfor AI-related needs. Meta isn’t the only company trying to stem the tide of capital going to Nvidia. OpenAI last monthunveiledan inference processor that it is building with Broadcom, and Anthropic is said to be consideringdeveloping its own chipswith Samsung.AmazonandGoogleboth develop their own chips for AI training and inference, and there’s ahost of startupsbuilding in the space to meet skyrocketing demand. Meta declined to comment.
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How to stop Meta’s AI image generator from using your Instagram photos
On Tuesday, Metalaunched“Muse Image,” a new AI image-generation feature that allows users to create original images, edit existing photos, and even generate custom ads directly within its apps. But one capability has quickly become the center of controversy. Muse Image allows users to generate AI images using photos from public Instagram accounts. As long as a person’s profile is public, another user can tag that account and use their images as part of an AI-generated creation. (Only private accounts and accounts belonging to users under 18 are automatically excluded from the feature.) One huge concern is consent. Users may have no idea that their public photos can be incorporated into AI-generated images by strangers, and they aren’t even notified when someone reuses their public content. Plus, making it easy to manipulate people’s images opens the door to misuse, harassment, impersonation, and nonconsensual image editing. If you’re looking toopt outof this, here’s how you can do it. Muse Image arrives at a time when AI tools are being increasingly integrated into social media platforms. As tech companies race to roll out new generative AI features, many experts argue that stronger privacy protections and greater transparency are needed, so users fully understand how their photos and personal data are being used. Public skepticism around AI is already high. According to aPew Research Centersurvey, 35% of respondents said they’re more concerned than excited about the growing use of artificial intelligence. Additionally, Meta’s track record on user privacy has also fueled skepticism surrounding its latest AI feature. In 2019, the U.S. Federal Trade Commission (FTC) imposed a$5 billion fineagainst Facebook, concluding that the platform had violated a 2012 consent order by misleading users about how much control they had over their personal information. This followed a high-profile scandal where political consulting firm Cambridge Analytica gained access to data from up to 87 million Facebook users through a personality quiz app. Facebook’s platform policies at the time allowed developers to collect information about those users’ friends without their knowledge or explicit consent.
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How did the government decide OpenAI’s frontier model was safe to release?
OpenAI is rolling out its latest advanced LLM, Sol, for wide public access. Sol is considered to be at least on par with Anthropic’s Fable, a model whose capabilities (or ownership) stressed out the White House enough to that it was briefly banned from public access. So how did these models get the ok for release? Short answer: Nobody’s quite sure. “Frankly, I don’t have visibility into those exact processes, so yes, I don’t feel like I have enough information to say whether they’re adequate or not,” Mina Narayanan, a senior research analyst at Georgetown’s Center for Security and Emerging Technology, told TechCrunch. “Anthropic did say that they were in conversations with the government, and that they developed a classifier to detect jailbreak attempts, and they’ve implemented defensive gap strategies to prevent future jailbreaks, but exactly what that dialog looked like between the government and Anthropic and OpenAI is unclear.” Dean W. Ball, a former Trump policy advisor who now works for OpenAI,wrotethat “nobody knows what the requirements are to get licensed” in his newsletter last month. Andy Konwinski, a computer scientist who co-founded Databricks, Perplexity, and the Laude Institute, said he’s never spoken to anyone who understands the process, even employees at frontier labs. “It’s existentially a problem,” he tells TechCrunch. “Safety or not, it’s about who has the power to make decisions—who gatekeeps and decides on permissions?” Eighteen months into the Trump administration, there is still little clarity about how to move forward, despite—or, some critics allege, because—of the industry figures setting policy. Last month, afterweeks of infighting, an executive order was published laying out a roadmap for evaluating frontier models, but the specifics have yet to be filled in, other than what won’t exist. “There will not be an FDA for AI,” Sriram Krishnan, a former Andreesen Horowitz partner who served as a senior advisor for AI in the White House until last month,toldthe Financial Times. Notably, there’s still no agreement on what kinds of models require government scrutiny, or what agency or agencies should perform those evaluations. For now, the Department of Commerce’s Center for AI Standards and Innovation seems to be taking the lead, but the executive order instructs six cabinet agencies to determine a final process by early August. What has emerged in the meantime is, at best, ad hoc. OpenAI CEO Sam Altmansaidon CNBC that the process involved conversations with the officials like Secretary of Commerce Howard Lutnick, Secretary of the Treasury Scott Bessent, and US national cyber director Sean Cairncross, but it’s not clear who the experts that tested the models were or how they did that. OpenAI declined to share details on the government’s process with TechCrunch, but pointed to the results of several external evaluations by organizations like UK AISI, SecureBio and Irregular in the latest model’ssafety card. As with Anthropic’s Fable roll-out, OpenAI previewed the model for the government and select users ahead of wider release, but we don’t know who who all of those users were or how they were chosen. In a late Juneblog post, the company said “we don’t believe this kind of government access process should become the long-term default,” saying it would work with the government to develop a different path forward. The backdrop to those conversations, however, includes Altman reportedlyofferingas much as 5% to OpenAI’s equity for the administration’s so-called “Trump Accounts,” and OpenAI president Greg Brockman’s role asthe largest publicly-known donorto Trump’s mid-term political operation. It’s hard for outside observers to separate those activities from the government’s apparently lighter-touch approach to regulating Sol. Amthropic’s Fable, on the other hand, was briefly pulled from wider access when the US government forbade its use by foreign nationals, partly because of real concerns about users jail-breaking the model to access hacking capabilities and partly due to personality clashes between Anthropic and the Trump administration. The threat of an export ban may have also led OpenAI to be more cooperative with the government’s (unknown) requests. From an industry perspective, a hands-off approach to regulation might be nice, but one that depends on personal connections to administration officials creates uncertainty and bad incentives. Konwinski told TechCrunch that he worries true experts in this technology—”safety researchers, alignment researchers, interpretability researchers, but also data people, and people from all over the stack”—aren’t playing enough of a role in the model release process. Konwinskiarguesthat an “open commons” is the best way to actually balance safety and innovation. He points to models like the FDA, the NIH, or the national labs, which convene researchers, government officials, and private companies to reach a consensus on safety issues. Some of that comes down to the incentives of capitalism that have motivated AI researchers for more than a decade, and played out in the court room during Elon Musk’s lawsuit challenging OpenAI’s corporate structure. Ball points out that the nature of the AI business requires companies to recoup much of their training costs shortly after their models are released and are further ahead of the competition.“Even if their intentions are good, there’s very clear legal obligations and fiduciary responsibility that are built right into the operating procedures,” Konwinski points out. Ball, inhis post, argued that the way forward will depend on third-party auditing organizations, licensed by the government, that will evaluate frontier labs’ approach to safety. Konwinski, too, is bullish about new institutional formats like focused research organizations that could help more disinterested experts from academia and the non-profit world access and evaluate frontier models. For now, the secrecy around the development of AI isn’t going away, but it also will seed political challenges for an industry that Americansincreasingly view with skepticism. “There’s not a sense that responsible people are driving forward these changes,” University of Wisconsin-Madison computer science professor Remzi Arpaci-Dusseau said last week at the Open Frontier conference. At the same event, David Siegel, the computer scientist who founded Two Sigma, one of the most successful quantitative hedge funds, asked attendees to “imagine a situation, which I think would be very bad, [where] a small number of firms control the technology; the government, in their secretive laboratories, is evaluating whether or not the technology is suitable for use; and the general public and scientific community doesn’t really have any access to any of that stuff.” It seems like we don’t need to imagine it.
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Paris-based AI voice startup Gradium raises $100M seed, backed by Nvidia
Gradium, a Paris-based startup that offers voice AI models, re-opened its seed round to new investors, including Nvidia, and has now raised $100 million total for the round, itsaid Thursday. The company is using the cash to open an office in the Bay Area and compete for talent there, “strengthening its position at the heart of the world’s leading AI ecosystem,” as Gradium put it. Paris is a major European hub for AI, so this is an interesting acknowledgement of the benefits for AI startups in being close to Anthropic, Google, Meta, and OpenAI. Gradium originally launched out of stealth in December with $70 million from a roster of impressive investors, including FirstMark Capital, Eurazeo, DST Global Partners, Eric Schmidt, and French telecom billionaire Xavier Niel. The startup was spunout of French AI lab Kyutai(a lab backed by Niel). Both Kyutai and Gradium were co-founded by Neil Zeghidour, a researcher who previously worked at Google Brain, DeepMind, and Facebook. Gradium is working on audio models that deliver voice at scale with ultra-low latency, meaning AI voices that respond almost instantly, without that awkward pause that often creeps into AI agent conversations. The company has plenty of competition, though, from other voice AI startups like ElevenLabs,valued at $11 billionin February, to major model makers known for voice like Google’s Gemini. But Gradium seems to be winning ground anyway. Since its December launch, Gradium says it has landed some big customers, including French auto manufacturerRenault.
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Google will now disclose which ads are made with AI
Google is rolling out a new feature aimed at helping people understand when an ad they’re seeing was made using AI technology. AI makes it easier for businesses to create ads, place their brand’s products in various settings, and save money on real-world e-commerce photography. But it can also be misleading if consumers don’t know that what they’re looking at isn’t a real product photo. While Google prohibits misleading and deceptive ads, an ad can still leverage AI to create some type of synthetic or digitally altered content. Until now, that’s something Google only required election ads to disclose. The tech giant said the new consumer-facing feature will be introduced to the “My Ad Center” panel, which anyone globally can access by clicking the three-dot menu or on the info icon on the ads they come across via Google Search, YouTube, and Google Discover. This panel already lets users block or report ads, learn more about the advertiser or why the ad was shown, among other things. Now, users also see an option that says “how this ad was made,” which will indicate if the ad was created or edited with AI. Google says that when advertisers use its own generative AI advertising tools to create ads, the disclosure will be automatically enabled. However, if the ad is created elsewhere, the advertiser will need to use a new control to indicate if AI was involved in its creation — Google will not perform its own check to determine if that’s the case. In some markets, the ad may also be labeled as AI if local law requires it.
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Nandan Nilekani leaves GP role at Fundamentum as it launches $200M third fund
Nandan Nilekani, co-founder of Indian IT services giant Infosys, will no longer serve as a general partner atFundamentum Partnership, the venture capital firm he co-founded nearly a decade ago. Nilekani (pictured above) will be stepping down from his role as Fundamentum launches its third fund, targeting to raise about $200 million. He will be the fund’s anchor investor, and continue advising the firm and mentoring portfolio companies, his co-founder Sanjeev Aggarwal told TechCrunch. Aggarwal described the shift as “just a title thing,” saying Nilekani would continue to advise the firm, mentor portfolio company founders, and provide strategic guidance. “He is an integral part of our firm. The one thing that he enjoys the most is mentoring the teams that we back, and he will continue to do so in Fund III.” Nilekani, 71, is one of India’s best-known technology leaders. Besides co-founding Infosys, he led the creation ofAadhaar, India’s biometric identity system, and has been a leading advocate of the country’s digital public infrastructure, including theUnified Payments Interface (UPI), a real-time payments network used by hundreds of millions of Indians. He has championed theOpen Network for Digital Commerce (ONDC), an initiative aimed at making e-commerce more open and interoperable in the country. NilekanistartedFundamentum in 2017 with Aggarwal, who previously helped build Helion Venture Partners. Fundamentum backs Indian startups at the Series B stage and later, and its portfolio includes used-car marketplaceSpinny, online pharmacyPharmEasy, audio storytelling platformKuku FM, andAppsForBharat, the developer of theSri Mandir devotional app. Nilekani did not respond to an emailed request for comment. The leadership change also broadens Fundamentum’s senior investment team. Alongside Aggarwal, Fund III will be led by Prateek Jain, who joined Fundamentum at its inception in 2017; fintech investor Mayank Kachhwaha, who joined ahead of Fund II; and finance chief Sanjay Chaturvedi, who has been with the firm for nearly a decade. Fundamentum’s third fund aims to back eight to 10 early-stage startups building consumer technology, fintech, and AI products, and issue initial checks of about ₹100 crore (around $10.5 million) each. The firm has yet to announce a first close, but has already begun deploying capital, Aggarwal said, adding that he expects the fundraising to conclude over the next 12 to 18 months. Fund III will see Nilekani making his largest-ever commitment to a venture capital fund, Aggarwal said, though he declined to disclose the investment amount. The fund, Aggarwal said, expects to raise roughly half of its target from international investors, and the remainder from Indian institutions, family offices, founders, and the firm’s partners. That balance reflects how India’s venture capital ecosystem has evolved over the past decade: Indian investors today play a much larger role in domestic funds than they did when Aggarwal helped launch Helion Venture Partners in the mid-2000s. “When we launched Helion, there was no domestic capital in the country, and all the capital was raised from the U.S.,” Aggarwal said. “Over the last five years, we are experiencing very strong interest in Indian investors to back venture capital firms […] Now you can build a venture firm with domestic capital.” Aggarwal told TechCrunch that Fundamentum sees India’s biggest AI opportunity in applications that are built on existing global models, particularly across financial services, content, and vernacular consumer applications. The stance underscores how much of India’s AI ecosystem centers on application-layer startupsrather than those developing frontier AI models, unlike the U.S. and China, where companies have attracted billions of dollars to build AI models. The leadership reshuffle follows the departure of general partner Ashish Kumar, who recentlylaunchedAI-focused venture fund Fundamentum Frontier Advisors (F2A), which also has Nilekani as an anchor investor. F2A, Aggarwal said, is a separate firm with no operational connection to Fundamentum, and Kumar is not involved in Fund III. Fundamentum has made 17 investments across its first two funds. Aggarwal told TechCrunch the firm has returned about half of the capital from its first fund to investors, and thesecond fundis now focused on follow-on investments.
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Character.AI enters the microdrama arena with its own productions, but there’s a twist
Microdramas aresuch a ragethese days that nearly every kind of company in the attention economy space — be they dedicatedmicrodrama apps, social media giants (TikTok and Instagram) or streaming services (Peacock,Amazon Prime, andIndia’s JioHotstar) — is building a product to tap the opportunity. Character.AI, which lets people chat with customized AI avatars, is also tapping this budding market by producing its own microdramas using AI characters. But there’s an interesting twist that takes advantage of the company’s core product: Users older than 18 can chat with these shows’ characters, ask them questions, and even roleplay different storylines. The startup is launching three microdramas to start with: a romance series dubbed “Last Summer,” a horror show titled, “The Nighttime Game,” and a Hunger Games-like survival microdrama called “Eden Fall.” Character.AI says these dramas were created using AI production tools, and in the long term, it aims to help users create their own characters and series. “Starting with a studio-led model, c.ai Series lets our production team develop the format, refine the workflow, and understand what audiences want from Character-native Microdrama entertainment. Over time, the goal is to turn those learnings and workflows into creator tools, enabling users to make their own series from original Characters and share them with a global audience,” a company spokesperson told TechCrunch. This is the latest in a slew of recent features from the startup following its shift towardentertainment-focusedfeatures last year. In April, it teased a tool calledLorebook that users can employ to create world-building information that characterscan reference, and launchedanother featurecalled Books that lets users insert themselves into select classic literature titles, or role-play as characters from them. The company said on Thursday that it is also testing a feature, dubbed c.ai FM, that will let users put together audio series, and another that lets you create fiction, called c.ai Reads. The audio series feature is currently available to select users under its experimental c.ai Labs program, which the company says professional writers are using to create serialized audio dramas. There’s certainly an audience for this form of entertainment. Users spent more than 950 minutes on Character.AI each month in the first half of 2026, according toSensor Tower.
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Popular open source AI developer tool Ollama raises $65M, grows to nearly 9M users
The popular open source AI toolOllamahas raised a $65 million Series B, led by Theory Ventures, founder and CEO Jeff Morgan tells TechCrunch. This round follows a previous $15 million Series A led by Benchmark’s Peter Fenton. All told, the company has now raised $88 million. Ollama, which launched in 2023, helps devs run open-weight AI models on their PCs, getting them up and running in minutes. It has been praised by developers across countlesstrainingsites,videos,blogsandsocial mediaposts. It has amassed 176,000 stars and nearly 17,000 forkson GitHub. Developers can also use Ollama to find models and access larger, more complex ones that it hosts on its neocloud via several subscription tiers, from free to $100/month. It also tracks usage based on GPU time, not token limits. If the mission to help developers more easily build on their PCs sounds vaguely familiar, it should. Morgan and his co-founder Michael Chiang previously helped build Docker Desktop. They landed at Docker after it bought their previous startup, Kitematic. Docker makes containers that help cloud apps easy to move from cloud to cloud, or from desktop to cloud, abstracting away all the pesky hardware configuration issues. So Ollama essentially did for AI what Docker and Docker Desktop did for cloud. “Open models started coming out in 2023 but they were really hard to use,” Morgan said. They had been geared toward researchers at the time, not programmers. “As a result, it was really hard to get them up and running.” Three years after launching, Ollama is now “used by over 8.9 million developers every month, sitting in 85% of the Fortune 500 and growing like crazy,” he said. All with only 14 employees. That career experience is what drew Benchmark’s Peter Fenton to lead its earlier round and join the board. “What Jeff and Michael built with Docker is being used by 10 million-plus developers every day. The creative powers to create a product that goes to ubiquity for developers is extremely rare,” Fenton told TechCrunch. Morgan and Fenton declined to discuss the startup’s revenues and new valuation. However, Morgan says that the proving point for Ollama as a business happened around January, when OpenClaw became hot. That’s when larger open models “suddenly became able to do these agentic tasks, like coding. Obviously, we saw the explosion of the assistants like OpenClaw, and this idea that open models can get real work done.” Since then, the industry has been abuzz with the idea that paying users (particularly deep-pocketed enterprises and fast-growing AI application-layer startups) will increasingly turn to more affordable open models, reserving their use of closed models like Anthropic for more of an as-needed basis. “I still think that this is the part that most of the debate gets wrong. It’s not an either/or,” Fenton says of open versus closed AI models. There will be plenty of business for both, he contends. However, every company with high inference expenses — the costs of using the models — has a “vital existential project” pushing them to move “to open-weight models,” he says. There’s plenty of evidence that such startups and enterprisesare already turning to open models for their daily needs.That, obviously, bodes well for Ollama’s cloud business. But even more interesting, Ollama is another example of how AI is birthing a large new crop of open source projects that are turning into companies pursued by VCs. There are open source inference providers likeInferact, maker of vLLM,andRadixArk, maker of SGLang. There is OpenClaw and its alternativeslike NanoClaw.There are even tiny startups building their own open modelsfrom scratch, like Arcee. To be sure, not every Ollama fan has been happy that the company has been pursuing making a living. About a year ago,a bunchofblogandsocial media postscomplained that its cloud business was drawing attention away from its beloved free project and cited Ollama as an example of theso-called “Enshittification” of dev tools, as the trend is called. But Morgan sees its cloud service as an evolution of its open source mission to help programmers find and easily use models. Those state-of-the-art, large, open models are often “too big to run on your own computer. So we said, ‘Hey, let’s help find the compute for that,’” he explained. Board member Fenton adds, “Nothing has changed for the core product that’s free on the desktop. There’s zero change to the premise that this is the place you can discover and run local models.”
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Anthropic, OpenAI, and SpaceX are bigger than the last 25 years of tech exits
We’ve talked before aboutthe hot IPO summer, but with SpaceX just launched to public markets and Anthropic and (maybe) OpenAI soon to come, it can be easy to miss the sheer scale of what’s happening. We got a good reminder of it in Wednesday’s NCVA-PitchbookVenture Monitor report. Not surprisingly, all of the money in private markets is flooding into AI — but one particular figure stood out. Taking the measure of the pending OpenAI and Anthropic IPOs, the report drops this nugget: “Along with the SpaceX IPO, these exits will generate more value than all U.S. VC-backed exits since 2000.” That’s quite a claim, and when you add up the numbers, it’s hard to disagree. SpaceX has already gone public at a $1.77 trillion valuation, and with both Anthropic and OpenAI pushing into the trillions it’s likely the trio together will land somewhere north of $4 trillion. By comparison, the U.S. Securities and Exchange Commissioncounted just $70 billionin US-based IPO proceeds last year. Careful readers will notice a few caveats in the language. It doesn’t include non-U.S. companies like Alibaba, and we’re measuring “value created” as opposed to strictly liquid cash. A lot of the major tech developments happened at companies that had already gone public (the iPhone, the debut of Android, and the launches of YouTube and Instagram), so they wouldn’t be captured in the IPO figures. Still… that was a pretty eventful 25 years. Among other things, that period saw IPOs from Google (2004), Tesla (2010), and Meta (2012), which are now among the most valuable companies in the world. During the same period, LinkedIn, Slack, and WhatsApp were all acquired for more than $20 billion. Uber’s $84 billion IPO seemed like a lot of money in 2019, but it’s less than 5% of what SpaceX just drummed up. One factor here is that companies are staying private for longer. The Google of today probably would have delayed its IPO and gone public at a higher number. Another factor is the capital-intensive nature of AI training, which has pushed labs into intense fundraising and inflated valuations. But the sheer scale of the public offerings is still way beyond anything the industry has ever done, and is already pushing the financial infrastructure to its limit.
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Anthropic’s new Claude feature is quietly selling you on AI
At a time whenAI backlashand data centerprotestsare making headlines, Anthropic’s Claude is rolling out a new feature that subtly makes the case for why you should keep using it. On Thursday, the company introduced “Reflect,” a built-in dashboard that lets you track and visualize how you use Claude and your broader AI habits. On the surface, it’s an analytics feature that offers insights into what sort of topics you’ve discussed, your overall usage patterns, and what kinds of tasks you tend to turn to AI for help with. But Reflect’s larger purpose is about shaping how users think about AI itself. It does so by framing Claude as both a highly-utilized productivity tool and a part of your everyday workflow, as well as a technology that can be used mindfully. While Claude Reflect doesn’t go so far as to quantify how much time you’ve saved on manual tasks by switching your workflows to AI, there’s something about having all the work Claude helped with laid out in front of you that will likely make you see Claude as a tool you’ve come to rely on, and one very much a part of your everyday life. Meanwhile, Anthropic will push you to think critically about your AI usage, as Reflect will pop up questions from time to time, like “What’s one thing you want to keep doing yourself, even if Claude could do it faster?” The app additionally offers tools to set quiet hours or schedule nudges to take a break from AI, Anthropic notes in itsannouncement— a nod to the potentially addictive nature of working with AI chatbots, which never fail to respond to your questions and prompt follow-ups to keep the conversation going. The idea to add analytics to an app to subtly shape consumer sentiment is not a new one. In 2012,Google promoteda new utility calledGmail Meter, which number-crunched your email inbox, showing you traffic patterns, pie charts of email categories, how much data is in your inbox versus your archive, among other things. While navel-gazing over this type of data is fun for some technical folks, the meter also served as a way to display, in numbers and charts, how Gmail had become central to people’s digital lives. Claude’s Reflect does the same but it then takes things a step further, as it also trains users on how they can better use AI. For instance, Reflect might suggest that instead of re-explaining the context of your work across repeated tasks, you could use Claude’s Projects feature. For Anthropic, this also has the benefit of more deeply integrating your daily workflows with Claude, which helps retain users and discourage them from switching to competitors’ AI tools. Anthropic notes that more sensitive conversations may show up in Claude Reflect, but only at a high level, and any conversation connected to a health integration tool is left out of your insights entirely. None of the data in your insights is used for other purposes, the company also says. This Claude Reflect feature is available in beta for Free, Pro, and Max users who have memory turned on. Later, it will expand to include a view of how much time you’ve spent using Claude.
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Govt to Study AI’s Impact on GCC Growth as India Bets on Higher-Value Roles
MeitY Secretary and the Chief Economic Adviser say AI could reshape work but need not weaken India’s GCC opportunity.
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TCS' AI Business Crosses $2.6 Bn Run Rate as Q1 Revenue Jumps 14% YoY
TCS secured a net profit of ₹13,349 crore, a decline of 2.7% sequentially, and its operating margin also declined to 24% from 25.3% a quarter ago.
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