Latest AI News
Why AI Agents are the Next Frontier in Quantum Security
India’s 2027 post-quantum target is forcing companies to confront decades-old encryption systems.
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Tilly Norwood’s press tour is going about as well as you’d expect for an AI
Tilly Norwood, an AI-generated “actress,” is having a rough time on its first press tour. The production company that created it, Particle6 Group, has made it available for75 simultaneous interviews with journalists, and it seems to bemakingmistakes in all of them. (I was not invited to speak with it, and frankly,I cannot imagine whyParticle6 didn’t want to schedule that meeting.) In one particularly oddinterviewwith Piers Morgan and actor Tom Conti, which was recorded and posted online, Norwood seems to malfunction and abruptly begin speaking Chinese. When discussing the movie that Norwood is promoting, Conti asks if the other actors are also AI-generated. Norwood responds by saying that human writers, editors, and directors were involved. “You didn’t really understand the question,” Conti replies. “Are the other actors on the screen with you real actors, or are they computer-generated images? Do you know?” “Ah, right. You’re asking about the other actors inMisaligned,” Norwood starts. “They’re all digital twins just like me. It’s a hybrid production which m—” Norwood stops talking for a second, then starts speaking in Chinese for over 10 seconds. “Tilly, if I could just ask you a question,” Morgan says, looking puzzled. “It’s Piers again … You seem to be speaking Chinese completely randomly for no reason. Why did you do that?” he asks. “Oh, my apologies,” the AI replies. “It seems I had a little hiccup there. I certainly didn’t mean to start speaking Chinese or impersonate Piers. Sometimes my wires get a bit crossed, you know?” When AI bots try to act like humans, they can be so convincing that people forget they’re chatting with a computer, which can lead todangerous outcomes. But Norwood is so surprisingly bad at talking to people that it’s almost suspicious. Perhaps Particle6 Group calculated that people would be more likely to talk about Norwood if they were making fun of it, figuring no one would take its upcoming film seriously.
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India forces caller-ID apps to feed spam reports to telcos
India has extended its anti-spam regime to require caller-ID and call-management apps to share users’ spam reports with telecom operators, prompting spam-blocking app maker Truecaller to call the ruling anti-competitive. On Friday, the Telecom Regulatory Authority of India (TRAI), the country’s telecom regulator,amendedrules governing commercial communications, making it mandatory for caller-ID and call-management apps that let users flag calls as spam or junk to send those reports to a blockchain-based platform maintained by telecom operators. The platform tracks commercial communications and enforces anti-spam rules. The change, TRAI said, is intended to broaden the pool of spam reports available for action against spammers, effectively connecting reports collected by apps with the telecom industry’s enforcement infrastructure. However, Truecaller told TechCrunch that it sees this requirement as a “one-way exchange” that is “anti-competitive,” arguing that it transfers commercially valuable data from call-management apps like itself to telecom operators. India is Truecaller’s largest market, accounting forwell over 350 millionof itsmore than 500 million monthly active usersglobally. The Stockholm-based company uses community reports alongside automated detection and other signals to identify and block spam calls. The rules come as India grapples with spam and fraudulent calls at enormous scale. In its report in February, Truecaller said its users in the country encounteredaround 42 billion spam callsin 2025, including calls that were blocked, labeled, or ignored. The company also stated that it blocked nearly 12 billion spam calls during the year. It is not the first time Truecaller and the Indian regulator have been at odds over how spam calls should be handled. The Swedish company previouslyobjected to restrictionspreventing call-management apps from automatically labeling calls from certain government-designated number ranges as spam. It argued that the exemption could allow unwanted calls to escape its filters. However, Friday’s amendments retain that restriction and have barred call-management apps from blanket blocking, filtering, or spam-tagging calls from designated number series used for promotional, service, and transactional communications. Individual users can still choose to block such calls on their own devices, the regulator said. “While our data and user sentiment clearly show that spam has skyrocketed due to this free pass to spammers, we have been compliant with this since late last year,” a Truecaller spokesperson said. Sumeysh Srivastava, a partner at New Delhi-based consulting firm The Quantum Hub, who leads its telecom-regulation policy work, said the latest change bridges two distinct layers: Telecom operators provide the underlying network and run the blockchain-based anti-spam system, while caller-ID apps operate on top of the network to identify and filter calls. That raises technical and jurisdictional questions, Srivastava told TechCrunch, including what reporting standards apps will have to follow and how the requirement will be enforced against companies that are not themselves telecom operators. A March draftproposed(PDF) using India’s IT laws to enforce the requirement. However, Srivastava pointed out that the new announcement did not say whether that enforcement mechanism was retained in the final rules. It is also unclear how much information the apps will actually have to provide under the updated regulation. Kazim Rizvi, founding director of New Delhi-based policy think tank The Dialogue, told TechCrunch that requiring an app to transmit a specific spam report made by a user is materially different from requiring it to share the broader datasets, reputation signals, or analytical systems it uses to identify suspicious calls. The rules will need clarity on what information must be transmitted, how users are notified or asked for consent, and how that data can subsequently be retained and used, Rizvi said. TRAI did not respond to TechCrunch’s questions about what information apps would be required to share and whether the rule would also apply to spam-reporting features built into smartphone operating systems and dialers such as Android and iOS. The amendments also address the growing use of software and AI voice agents to make calls. Calls made automatically, without a person directly dialing the number, will now fall under TRAI’s application-to-person (A2P) framework. That includes robocalls and calls using prerecorded or artificial voices. Companies using such systems will have to declare their use and the phone numbers involved to their telecom operators in advance. Undeclared A2P calls will be treated as spam, TRAI said. The key test, Srivastava said, is how a call is initiated, rather than simply whether it uses an AI-generated voice, leaving some uncertainty around AI-assisted calls that involve human initiation. Satya N. Gupta, a former additional secretary at TRAI, told TechCrunch that the new rules do not restrict businesses from using AI or other automated calling technologies, but instead require them to disclose their use to telecom operators. Telecom operators will also be allowed to levy a termination charge of up to 5 paise (about 0.052 cents) per minute on A2P calls. However, calls made using certain designated number ranges will be exempt. Rizvi told TechCrunch that the new definition could also cover calls made using software even when a person is still involved, such as calls from contact centers and click-to-call services. “Without that distinction, the A2P category risks becoming broader than the regulatory harm it is intended to address,” he said.
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Anthropic’s first embedded evaluator is … Accenture?
Dario Amodei’s plans to put third-party safety evaluators inside AI labs are taking shape: Anthropic said that staff from technology consulting giant Accenture will begin working inside the company to scrutinize its models and staff. In ablog post, Anthropic said that Faculty, a company Accenture acquired in January to act as its AI division, will begin “evaluating and red-teaming models, conducting alignment assessments, and testing model safeguards.” Both companies expect to invest at least $1 billion in the project over the next five years. The choice of Accenture surprised many AI watchers — and the markets, where the consultant company’s shares shot up 8% after hours. The discussion around embedded evaluators that sprang from Amodei’s blog post hasfocused on AI safety researchorganizations like METR, Redwood Research, and Apollo Research. That’s particularly true at Anthropic, which puts AI safety and alignment at the heart of its mission. Anthropic said more evaluators will be announced in the weeks ahead and that it is in conversation with METR and other nonprofit organizations about how to “pilot elements of embedded evaluation using their own funding.” While Accenture is not known for its work on the bleeding edge of deep learning research, Anthropic pointed to the company’s practical experience deploying AI for large corporations and government agencies as a key advantage. It is also, as a large public company that predates the AI revolution, more functionally independent of Anthropic and the complex ecosystem around the AI lab. The lab noted that no standards yet exist for evaluators’ access or communications and that it expected its approach to evolve over time. While external evaluations are already a major part of the release of process for new large language models, recent incidents have raised the stakes: AI agents deployed by OpenAI and Anthropic have hacked into outside websites without raising alarms inside the labs. Some critics calling for a more responsible approach to building artificial intelligence see Amodei’s scheme for self-policing the AI industry as a plan to evade accountability for the misbehavior of AI models. Anthropic insists that these evaluators “do not reduce our accountability, but help to make it more verifiable. The safety of our models remains our responsibility.”
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AI hallucination nearly triggers US military operation
Military aircraft were already in the air this spring when U.S. officials made an alarming discovery: The intelligence driving an armed operation against a Chinese vessel had been hallucinated by an AI chatbot. The operation was aborted at the last minute, narrowly averting a potential conflict with China,CNN reported on Friday. The episode underscores agrowingconcern among military officials and outside experts: As decision-makers lean more heavily on AI, the errors these systems produce can travel up the chain of command before being questioned. The intelligence report, which circulated during the war with Iran, said the vessel was carrying components for a nuclear weapons program. The false intelligence originated with a Special Operations Command analyst who queried an AI chatbot to synthesize open source data with classified signals intelligence. The chatbot misidentified the ship’s cargo manifest. The analyst then used the tool a second time to format the erroneous findings into an official-looking summary, which was circulated across command channels. The near-miss comes as the U.S. military races tointegrateAI to accelerate decision-making and maintain its edge over China. The Pentagon has described AI as delivering a significant advantage inspeeding up its kill chainso commanders can respond in the right time. But the same speed that makes AI attractive may also allow hallucinations with insufficient human oversight. “It’s important for service members to understand the uncertainty inherent to LLMs,” said Jake Steckler, research scholar at GovAI and veteran U.S. Army officer, in a written response to TechCrunch. “But it’s especially critical for any decisions that could lead to use of force, like targeting, intelligence analysis, or operational planning. There are life and death consequences for those decisions.” Still, Steckler says, the incident should serve as a call to add more safeguards to AI, not a reason to avoid it. “These tools can be useful in the right contexts and with the right safeguards in place,” he said. “But prioritizing adoption speed over all else will likely lead to incidents that only make service members lose trust in these systems, which ultimately is only going to slow adoption.”
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Anthropic is operating a lab that conducts biology experiments
Anthropic has a wet biology lab in the Bay Area where it can use its AI models to run physical experiments, it has confirmed to TechCrunch. AI leaders have been promising that AI is the key to curing human disease. Dario Amodei opined just last week: “I believe that AI could cure most major diseases in the next 5–10 years.” To do that, an LLM would have to have methods to test its theories in real life. “We believe that to do biology, the final test is still, and will be for a while, in real lab work,” Anthropic’s head of life sciences, Eric Kauderer-Abrams,told Reuters. “We absolutely are doing that today.” He added that the lab operates like most biotech labs: Anthropic conducts some research there while also working with external partners. This news probably shouldn’t be shocking. Anthropicbought Coefficient Bio, a stealth AI biotech, in April. While Anthropic declined to give specifics on what the wet lab is working on, it did say the main focus was fundamental biology, not drug discovery. Anthropic doesn’t want to give the appearance of competing with the pharma industry, where it has numerous major customers and partners (e.g., it just announced a deal to workwith Novo Nordiskon joint drug discovery). Anthropic hasalready faced backlashfor launching products perceived to compete with those of its customers. To that end, Anthropic also launched a Life Sciences Verification Program this week, to givevetted bio researchers accessto its most powerful models. It has also published recent reports on some of the research it’s doing to support drug discovery. This includes one report onaccelerating protein designand one onuplifting bimolecular modeling. But the world is still reeling from the resignation of Anthropic researcher Jacob Coxon, who warned that “the people building AIearnestly believe that it could kill us all by the end of the decade.” Anthropic’s own alignment leadgave the odds at greater than 10%that AI could exterminate humanity within the next decade. The fervor has grown so intense that CEO Dario Amodei published a post last weekend calling on the industry to slow down and institute self-regulation. He has repeatedly called bioterrorism one of AI’s biggest risks. The juxtaposition of these dire warnings and the wet lab has not gone unnoticed in the tech industry. As investorand AI coding startup founderChamath Palihapitiyaposted on X, somewhat tongue in cheek: “The group behind such hits as: ‘We’re All Going To Die’ and ‘Regulate Me Now’ are building a wet lab in SF. I do not recommend this.”
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A startup that builds other startups raised $100M, and is all-in on physical AI
Four years ago, a startup lab launched that wasn’t quite an incubator, accelerator program, or venture firm. UP.Labs, as it was called then, built startups designed to solve problems for corporate customers such as Alaska Airlines and Porsche, as well as for the outside world. The firm still has the same mission — albeit with a critical addition to its approach, a new name, and a $100 million investment from Silversmith Capital Partners. Vantora, as the firm is now called, continues to work with its corporate customers, including some new ones in industrial manufacturing that it declined to name, and in the oil and gas sector. But now, it’s more focused on building startups solely for its corporate customers, and not for the broader market. Founder and CEO John Kuolt told TechCrunch that Vantora is moving toward a “proprietary M&A pipeline.” This means Vantora will still build startups for its corporate partners, which invest in the ventures and serve as their first customers. But those corporate partners now have the option to fold the startups into their core businesses — and essentially keep them to themselves. That shift has influenced Vantora’s increased focus on physical AI startups, according to Kuolt. In the past, Vantora would end up spiking ideas that were strategic to its corporate partners, but too sensitive to bring to the outside world. “We were missing on the biggest value problems, which had the biggest upside because of that,” Kuolt said in a recent interview. “Imagine you’re a Fortune 100 industrial company and you need to retrofit all of your hardware and machines for autonomy. You need to own that, it needs to be sovereign, and you can’t rely on a third party to go do that for you. You need to own that intelligence layer. They’re never going to let us go sell that to their competitors.” This change has allowed Vantora to “unlock big physical AI use cases,” according to Kuolt, including with its existing customers. For example, the firm came up with an idea to use AI to advance the business of its partner J.B. Hunt. “They said there is no way you can take this out to the world, and so we passed on it,” he said, adding that this proprietary model now allows Vantora to pursue it. The firm launched in 2022 with Porsche as itsfirst corporate partner. Since then, Vantora has launchedseveral startupsfor Porsche and struck deals withAlaska Airlines, J.B. Hunt, Wabash, and TDG, the parent of Ashley Furniture. In its early days, UP.Labs was tied — although never financially — to venture firm Up.Partners. While Vantora still shares office space with the California-based VC, it is its own entity, Kuolt explained, noting that the $100 million from Silversmith is the company’s first outside investment.
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Automattic’s 33-Hour Coup, and can AI labs police themselves?
A week after an Anthropic researcher’s doomsday warning rattled the AI world, the company’s CEO Dario Amodei hasoutlined his plan to “pace the frontier”of AI development. The proposal leans on independent safety evaluators and coordination between AI labs in democratic countries, and it’s already picked up someindustry support, along with some pointedpushback from Nvidia’s Jensen Huang. On this episode of TechCrunch’sEquitypodcast, Kirsten Korosec, Anthony Ha, and Sean O’Kane dig into whether companies can agree on what slowing down means and who gets to police it. Plus, WordPress parentAutomattic’s boardroom coup, and a couple of the week’s biggest deals. Listen to the full episode to hear more about: Subscribe to Equity onYouTube,Apple Podcasts,Overcast,Spotifyand all the casts. You also can follow Equity onXandThreads, at @EquityPod.
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Dario Amodei and other AI leaders want to ‘Pace the Frontier’ but…how?
Loading the player… A week after an Anthropic researcher’s doomsday warning rattled the AI world, the company’s CEO Dario Amodei hasoutlined his plan to “pace the frontier”of AI development. The proposal leans on independent safety evaluators and coordination between AI labs in democratic countries, and it’s already picked up someindustry support, along with some pointedpushback from Nvidia’s Jensen Huang. Watch asEquitypodcast hosts Kirsten Korosec, Anthony Ha, and Sean O’Kane dig into whether companies can agree on what slowing down means and who gets to police it. Plus, WordPress parentAutomattic’s boardroom coup, and a couple of the week’s biggest deals. Subscribe to Equity onYouTube,Apple Podcasts,Overcast,Spotifyand all the casts. You also can follow Equity onXandThreads, at @EquityPod.
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Google’s new ‘CC’ is an AI agent that helps families run their households
Google is testing a new product designed to help families coordinate with the support of an AI agent. This week, the search giantintroduceda new version ofCC, an AI agent designed to work across email, calendar, chats, and tasks. With the update, CC now focuses on keeping families organized, planning for the day ahead, and even handling family-specific tasks, like signing permission slips, creating shopping lists, or crafting weekly meal plans. The move to make CC more family-focused comes as a number of AI startups are experimenting with how to best fit agentic AI into consumers’ lives. Some AI tools, likeOllieandFambot, for instance, have aimed their services directly at helping parents and families. Google’s CC, meanwhile,began its life as a productivity agentthat connected to Gmail, Google Calendar, Google Drive, and the wider web to understand your day, then deliver a “Your Day Ahead” briefing to your inbox. In May, the tool came to the Gemini app as “Daily Brief.” Google said user requests showed that people wanted to use the feature more for household management tasks. They wanted AI to help them keep up with kids’ school schedules, sports practices, bills, meal plans, and more. As a result, Google shifted CC to become an agent for families that helps run their households. Now, CC is getting its own Google account, so it can collaborate with family members on tasks, while also maintaining its own specific set of permissions for accessing data. Google explains that each family member chooses what they want to share — like emails about school events, sports, clubs, birthday party invites, doctor appointments, or anything else. These can be forwarded to CC via its email address, or shared automatically. To automate sharing, users can pick the email senders whose messages they always want to share with CC going forward, like schools, travel companies, clubs, or sports teams. (CC will also suggest email senders to add on a weekly basis.) The company says CC currently supports up to six family members who can share information and collaborate with the agent. Beyond email, CC can also coordinate household activities by tracking important dates and to-dos, and then automatically adding them to the calendar or a shared task list. That means, for instance, every time the orthodontist sends an email confirmation of your next appointment or the school announces a teacher workday, CC can put it on the shared calendar. What’s more, the agent can manage select tasks on a family’s behalf, like filling out permission slips or activity registration PDFs, making school supply shopping lists, planning weekly meals, figuring out drive times between activities, creating shared Docs or Sheets, and more. It will even ask for missing details, as needed, and update its group memory so it can be more helpful going forward. Google notes that CC runs on its own isolated cloud computer, powered by Gemini and Google’s agentic harness, Antigravity. The experiment is only available for U.S. users with a personal Gmail account, who are ages 18 and up. That’s a big drawback for the time being because it means older kids, like tweens and teens, can’t use the service unless they lie about their age on their Google accounts. It also means they can’t use it with their school-provided emails, despite the fact that many attend schools that run everything on Chromebooks and Google apps and have inboxes filled with school-related updates. CC could help larger households, where parents have to coordinate with each other and with other adults, like grandparents, caregivers, nannies, or adult children still living at home, whichtends to be more commonthese days. Existing CC users will be invited via email in the coming days to upgrade their accounts, Google says, while new users can join awaitlistto access the AI agent.
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Disney’s first CTO led an AI startup it once accused of copying its characters
Disney has hired its first-ever chief technology officer and in a curious twist, the new executive hails from an AI startup that the Magic Kingdom previously accused of infringing on its IP. Karandeep Anand is the former CEO of Character.AI, a company that has weatherednumerous legal problemssince it was founded in 2021. One of those legal problems arose in September 2025, when Disney sent the companya cease and desist letteraccusing it of infringing on its beloved characters. Character.AI allows users to create distinct virtual characters with generative AI and talk and interact with them. Disney previously claimed that the company was hosting copyrighted characters from its franchises. In addition to Disney’s accusations, Character.AIhas also been suedover allegations that the company’s chatbots encouraged users to commit self-harm and suicide. Varietyreports thatAnand was chosen for the role by new Disney CEO Josh D’Amaro, who took over after former company chief Bob Iger stepped down in March. The hiring suggests D’Amaro wants the company to embrace new technologies. Anand formerly served as a board adviser to Character.AI before becoming CEO in May 2025,according to his LinkedIn profile. He also worked at Facebook between 2015 and 2021 and, before that, spent 15 years at Microsoft.
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A new kind of AI model from a ChatGPT inventor is thrilling developers
ChatGPT broke Diogo Almeida’s heart. Almeida was an OpenAI researcher who helped build the chatbot and then invent reinforcement learning from human feedback (RLHF), the model-training technique perhaps most responsible for our current age of AI. But despite its capabilities, he was disappointed. “We have lightning in a bottle, and yet it is not useful,” Almeida told TechCrunch. “I’ve been battling that problem since then. It took me a while to come to the conclusion: The problem is we are optimizing for human language … We have been super good at human language for four years, but it’s not useful for automation because computers speak a different language.” Two years ago, Almeida left OpenAI to startTypeSafe AI, a startup trying to fix that problem. This week, the company released a new transformer-based model,Jev, that is not a large language model (LLM). It doesn’t output text, but instead produces probabilities, or what the company calls “calibrated decisions.” Eschewing language does a few things: It makes the model incredibly cheap and fast, and because users define the outputs in advance, it cannot hallucinate. Its output tokens are free, and input tokens are metered by the billion, not the million. Developers are taking a great interest in the product; the company briefly lost the ability to serve users from its API because demand was so high. Jev appears most useful for software automation. Thus far, software developers see it as a cheaper and more robust way to incorporate intelligence into their code. For example, Pranit Sharma, a software engineer at Vercel, a company making agentic infrastructure,saidhis company had used OpenAI’s ChatGPT Luna 5.6 to run a classifier to review commands for safety. When Vercel replaced OpenAI’s Luna with Jev, it got results five to 18 times more quickly and with greater accuracy. Another developer, Bryo AI CTO Nikhil Mudholkar,testedJev against Gemini for classifying business emails. In his test, Gemini was slightly more accurate, but 10 to 20 times more expensive. More interesting to Mudholkar were Jev’s confidence scores — “it is the only one that hands back a real probability which makes it ideal for automating workflows!!” Besides replacing LLMs in certain use cases, the new model can also augment them, acting as a smart check on misbehavior. Using agents to monitor agents can quickly become expensive, but using Jev to do so, Almeida argues, makes sense. He sees users deploying Jev to track LLM agent traces and prevent jailbreaks. “At the end of the day, it delegates the hallucination problem a little bit to the user,” explained Armin Ronacher, the CTO of Earendil, which builds the open source model harness Pi. “The user has to say, okay, if this only comes back with 50% probability, maybe this is a coin toss, and I disregard it. But if it’s 95%, sure, then I can do something with it.” Another potential use for Jev is model routing, Ronacher said. Predicting whether a given workload requires a specific model would be useful, but using an LLM for the job would be expensive. Jev’s low cost and speed make that kind of real-time sorting possible. And that’s Almeida’s hope. The model is named after William Stanley Jevons, the 19th-century economist whose eponymous paradox describes how the falling cost of a commodity can lead to it being used more and more. In this case, the falling cost of intelligence should lead to its widespread deployment. “We think that there’s just going to be smart software all over the place in a way that’s emergent and distributed … much more like the early internet than you know like the mega apps that people are trying to build right now,” Almeida said. Almeida is tight-lipped about the model’s architecture, which outside observers suspect is built on top of an open-weight LLM. The company refers to Jev as a “System One model,” focused on intuition rather than reasoning, andspecifically focusedon the right task. Almeida says Jev is trained exclusively on synthetic data using a technique he calls “reinforcement learning from calibrated decisions.” “We made an early bet that we will be making all of our data, and that has been one of the best bets I’ve ever made in my life — better than our launch, in my opinion, better than RLHF,” he told TechCrunch. “Half of [our company] is a lab that basically owns this entire subfield of statistically well-understood synthetic data, and that is now my life joy.” For now, Jev stands alone as this kind of model, but Ronacher expects that competitors will spring up now that its utility is apparent. “We should have seen this earlier in many ways, but presumably because the LLMs are so cheap and subsidized, you often don’t have to be creative yet,” he said. TypeSafe itself will be building more versions of the model, in new modalities. Asked if TypeSafe is a frontier lab, Almeida said, “the main product of frontier labs is fear or hype. I would like our main product to be intelligence…[but we are] not a lab in the sense of, you know, like bet on infinite wealth, or a religion, or building God in a data center, or whatever is the thing of today.”
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