最新 AI 资讯
Elon Musk praises Mythos/Fable, promises not to ‘cut off’ Anthropic
Should Anthropic trust Elon Musk to host its models? After users on X implied that Musk could wake up one day and simply boot the AI lab from SpaceX’s servers as a way to kneecap a rival, Muskrepliedwith glowing praise for the AI lab. He said that such a trick was “not my style.” “I was clearly wrong about Anthropic,” Musk wrote on Thursday, referring to his September 2025post on Xin which he said, “Winning was never in the set of possible outcomes for Anthropic.” Of course, even at that time, Anthropic could already be considered a winner; the company was reported to have thebiggest AI market share with enterprises. It seems those anti-Anthropic days are behind Musk — and not just on X. As of July 2026, Anthropic is one of SpaceX’s largest customers. To recap: Anthropic signeda deal in Mayto buy 300 megawatts of compute, the entire output of xAI’s Colossus 1 data center near Memphis, Tennessee. (Musk’s xAI merged with SpaceX in February.) Anthropic agreed to pay $1.25 billion per month through May 2029, a deal worth about $40 billion in revenue for SpaceX’s xAI unit.Google, by the way, also signed a dealto rent SpaceX infrastructure through June 2029, for $920 million per month. Musk insists that this wasn’t a dangerous decision by Anthropic and that he’s full of admiration for the rival. “They are obviously currently the leader in AI. No company has released a model as good as Mythos/Fable and they will undoubtedly have Mythos 2 ready soon. And I would never cut them off in a way that hurt them badly, even as a competitor. That’s not my style,” he wrote. He offered as proof of his don’t-squeeze-competitors style Tesla’s decision in 2014 (which was outlined in a now deleted company blog post and now housed under itspatent pledge) to not initiate patent lawsuits against anyone who, in good faith, wants to use its technology. He also noted that Tesla opened its Supercharger network and charging port design to competitors. “SpaceX launches competing satellite systems with no increase in price or use of unfair terms. Even my worst enemies can attack me on this platform,” he wrote, listing another example. Of course, Musk is not exactly above tactics aimed at rivals, especially those with whom he has a history. Hesued OpenAI, for instance. Anthropic doesn’t have to rely on Musk’s sticking to his “style,” though. There would certainly be contractual consequences if Musk suddenly shut down Anthropic’s infrastructure. Not to mention the massive benefits for SpaceX to keep that deal intact. Not only does Anthropic pay handsomely, but also SpaceX’s engineers may learn how to build for, and support,Anthropic’s rapidly growing AI, just likeAmazon’s engineers do. That proximity might have other benefits as well. During his trial against OpenAI, Musk acknowledged that AI “distilling” was real — a process in which one model maker sets up many fake accounts to send prompts to a competitor in order to learn how it works. As the New York Timesreported, when a lawyer asked him if xAI had ever distilled technology from OpenAI, Musk replied: “Generally AI companies distill other AI companies.” Anthropic in Februaryaccused three Chinese model makersof doing this to Claude. Presumably, Anthropic and Google feel they have safeguards against SpaceX doing this while they are using its infrastructure. But hosting Anthropic’s compute could still give SpaceX greater visibility into how the company operates than most competitors would ever have. There appears to be nothing but upside for Musk’s company in this partnership at the moment. As for tomorrow, and as the three-year contract ages, who knows?
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OpenAI is shutting down Atlas, but its AI browser ambitions are still growing
OpenAI issunsettingAtlas, the AI-powered browser itlaunched in Octoberwith ChatGPT at its core. But it’s not giving up on the idea that AI should help people browse the web. Instead, it’s taking some of the agentic browsing features it tested in Atlas and redistributing them across ChatGPT’s desktop app and a Google Chrome extension. The move to shut down Atlas comes a few months after OpenAI’s CEO of applications Fidji Simo told the team tocut back on “side quests,”which led to the AI firm shutting down its AIvideo-generation tool Sora. For much of the past year, the AI industry had been engaged in awar to unseat Chromeas the place where people spend most of their time online. Perplexity launched Comet, The Browser Company launched Dia, and Google and Microsoft have updated Chrome and Edge, respectively, with new AI-powered features. After a few months of experimenting, OpenAI appears to have concluded that the browser is a feature, not the destination. So it’s folding Atlas’ browser-like agent capabilities into the places people already work — and that includes Chrome. OpenAI is launching a ChatGPT extension on Chrome that gives it access to the context of the page you’re viewing, lets users ask questions about web pages, summarize content, or start longer tasks all from the browser. It’s a direct competitor to Google’s Gemini Side Panel, which performs several of the same tasks. OpenAI is also boosting its ChatGPT desktop app by featuring a more robust browser that allows users to browse websites, log into accounts, download files, and interact with web pages without leaving ChatGPT. A separate cloud browser runs remotely on OpenAI’s servers as a place where the app’s agents can complete tasks on a user’s behalf. Together, the updates turn ChatGPT into a continuous workspace that spans Chrome, the desktop app, and an AI agent.
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An AI agent startup just let its agent run its $100 million fundraise
There’s something almost too meta about this one,via Bloomberg. Lyzr, a three-year-old, Jersey City, New Jersey, startup that helps enterprises build AI agents, used its own AI agent to raise its own round. The system, SivaClaw, reportedly fielded questions from more than 130 investors, drafted investment memos, and even tracked which slides backers lingered on. It basically ran point on the startup’s $100 million Series B (at a roughly $500 million valuation) while proving that the product actually works. It’s hard to imagine a cleaner sales pitch. But the most telling detail, per Bloomberg’s retelling, is how little legwork was involved. Lyzr told the outlet it pulled in $400 million in interest from Silicon Valley, the Middle East, and financial-sector investors without a founder ever needing to fly out and do the traditional laps up and down Sand Hill Road for coffee meetings and warm intros. That may be the real story of this go-go moment: there’s so much capital chasing AI bets that startup founders with traction barely have to leave their desks to raise nine figures.
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OpenAI launches its new family of models with GPT-5.6
OpenAI unveiled its newest family of models on Thursday, introducing a new set of heavyweight programs into an increasingly crowded field of AI offerings. GPT-5.6 comes in three variants: Sol (considered its workhorse), Terra (a more intermediate option), and Luna (its budget friendly option). These models expand what users can do across a variety of fields — with the company promising powerful capabilities in enterprise work, coding, and even scientific research. CEO Sam Altman has promised that his company’s newest models are orders of magnitude more efficient and cost-effective than previous versions, recentlytelling CNBCthat Sol is 54% more token efficient when it comes to AI coding tasks. Most notably, the company calls 5.6 its “strongest cybersecurity model yet, achieving frontier performance with significantly fewer tokens.” Indeed, much hubbub has been made about the model’s cyber capabilities, as the Trump administration previouslysought to restrict its rollout, ostensibly due to fears of how the model could be misused. GPT-5.6 supports defensive activities, including threat modeling, code review and patching, and blue teaming (simulating an attack on your own systems to find weaknesses before real hackers do). OpenAI also released a new tool calledChatGPT Work, which — just as it sounds — is designed as a workplace companion for enterprise teams, running on desktop, web, and mobile, that can help with daily clerical tasks, like drafting documents, spreadsheets, and presentations. OpenAI’s newly announced family of models follows on the heels of similar releases this week from competitors SpaceXAI and Meta. However, GPT-5.6 and its attendant marketing seems most designed to take aim at OpenAI’s primary opponent, Anthropic. Anthropic has managed to make itself thelikable underdogof the AI race, focusing fixedly on enterprise customers and winning a growing share of support as a result. Not to be outdone, OpenAI cites theArtificial Analysis Coding Agent Index, a notable benchmarking metric, to claim that its latest family of models outshines Anthropic’s models at every turn. OpenAI calls Sol its “best coding model yet,” and has explicitly compared it to Anthropic’srecently released (and much hyped) Fable. Using the Coding Agent Index, OpenAI claims that Sol “sets a new state of the art at 80, 2.8 points above Fable 5, while using less than half the output tokens, taking less than half the time, and costing about one-third less.” It adds: “That advantage extends across the family: Terra performs just above Fable 5, while Luna outperforms Opus 4.8.” The company says that 5.6 is now available across ChatGPT, Codex, and the OpenAI API. Availability per million tokens is priced as follows: Sol is $5 input / $30 output, Terra is $2.50 input / $15 output, and Luna is $1 input / $6 output.
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Nvidia is a victim of the compute marketplace it created
Long the leading light of the industry, Nvidia has had a bad couple of months. Bloomberghas the ugly details, but the upshot is that the company’s stock price has fallen 15% since its peak in May, even as projected revenue continues to grow. Compared with expected earnings, the company is now cheaper than the S&P average; investors are paying less per dollar of Nvidia’s projected profit than they do for the typical large American company. Money is still flooding into AI infrastructure stocks, but it’s mostly going into memory companies. Over the same period, Micron — one of the world’s largest makers of DRAM, the standard type of memory chip found in computers and servers — has nearly tripled in value, establishing memory as the new bottleneck for data centers and the hot new AI trade. The basic reason is simple: The GPU shortage that looked so alarming last year has eased off a bit. At the same time, data centers need all the memory money can buy. For anyone who appreciates Nvidia’s technological accomplishments, this can feel a bit deflating. There’s a lot of genuinely impressive technology behind Nvidia’s rise, both in developing CUDA, its widely adopted programming platform that made Nvidia GPUs the default engine for AI research, and in pushing the pace of GPU development to a speed few thought possible. Nvidia’s success is the kind of thing you can write whole books about, and the GPUs themselves are among the most complex devices ever produced, right at the bleeding edge of human capability. For memory companies like Micron, the story is much simpler. They build high-bandwidth memory chips — specialized components designed to move data in and out of processors as fast as possible — which have been getting incrementally better for 20 years. Without the chips or the companies changing too much, the service they provide suddenly became very valuable — and since demand is growing faster than anyone can scale up supply, they have been able to increase prices tenfold over the past year. This, via Datatrack, is what the spot price for DRAM — the price buyers pay for chips on the open market, as opposed to long-term contract rates — looks like since 2023: You might think there was some amazing technical breakthrough in the summer of 2025, but no, the industry as a whole just vastly underestimated how much memory it would need for the data center buildout. In comparison, this (viathe compute marketplace Ornn) is how the spot price for an hour of time on an Nvidia H100 GPU has changed over the last year: Just like Nvidia’s stock price, there’s a peak in May (around $3.20 an hour) and then a steady drop-off. For better or worse, Nvidia’s value as a company is tied to the price of compute and that price is falling. Micron and its cohort are tied to the price of DRAM, and that price keeps rising. When I talked to Ornn co-founder and CTO Wayne Nelms about the forces driving that disparity, he framed it as a simple issue of supply and demand.Google,Amazon,Microsoft, and evenOpenAIhave launched their own custom processors to lessen their dependence on Nvidia; even if those chips aren’t as good as the latest model from Nvidia, they’re good enough to drive down the price of compute. “More GPU and accelerator players are entering the market. Everyone wants to make their own silicon, but no one is making their own DRAM,” Nelms told me. “Until there’s a major technological breakthrough on HBM [high-bandwidth memory], a shift in supply and demand, or someone new [enters the market in memory], I think things will more or less persist as we see today.” It’s a frustrating state of affairs for Nvidia, and largely a product of its own success. Having proven how valuable compute can be, the company finds itself at the center of a market everyone wants to be in — while simpler technologies and less interesting companies get rich on the sidelines.
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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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