🚀 Zaprep: i tuoi social a pieno regime. Inizia gratis — automatizza 1.000 DM/mese e trasforma l'engagement in lead. con 1.000 DM automatizzati al mese.
Invia il tuo strumento
Ultime notizie IA
The AI graveyard: a running list of projects and startups that didn’t make it
Relay, an AI-powered workflow automation tool built as an alternative to Zapier,shut downentirely on Monday. The product let users automate email and task workflows using AI agents, but as OpenAI, Google, and other larger platforms built similar automation directly into their own tools, five-year-old Relay struggled to hold on to a reason to exist as a stand-alone product. Relay — a startup outpaced by bigger platforms — is one version of the story playing out right now. But plenty of AI bets never even survive inside the companies that build them. According toS&P Global Market Intelligence, about 42% of AI initiatives are ultimately abandoned by their corporate parents. The reasons vary: insufficient funding, technical challenges, competition, difficulty scaling, or just weak user demand. The result is a growing AI graveyard. Below is a look at some of the most notable AI products, startups, launches, and bets that have shut down, pivoted, or significantly missed expectations. Think of it less as a scorecard of flops than as a record of what these attempts can (hopefully) teach the rest of the industry. Even the biggest players aren’t immune. OpenAI, one of the most valuable private companies in the world, has had its own share of casualties and missteps. One of the more recent examples was its attempt to turn ChatGPT into a broader “super app.” On July 9, OpenAIupdatedthe app to combine several experiences, including separate “Chat,” “Codex,” and “Work” modes, while renaming the traditional, familiar version “ChatGPT Classic.” The redesign quickly drewcriticismfrom users who found the new interface confusing and cluttered, and OpenAI rolled back the changesoon after, bringing the familiar ChatGPT interface back. That wasn’t OpenAI’s only attempt to pull everything under one roof. The company has also shut down several stand-alone apps and folded their features directly into ChatGPT — a strategy that’s worked out better than the redesign did. ChatGPT Atlas, a stand-alone AI-powered web browser, lasted less than a year before being discontinued on August 9, with its most useful features absorbed into ChatGPT. The company did the same thing withOperator, an AI agent that could browse the web and complete tasks on a user’s behalf, and OpenAI has increasingly built image generation directly into ChatGPT, shrinking the role ofDALL-Eas its own separate destination for creating images. We also can’t forget its video-sharing platform,Sora, which struggled with high operating costs and user-retention challenges and shut down in March 2026. When AppleunveiledApple Intelligence in 2024, an improved Siri was one of the highlighted features. The pitch promised a Siri that could understand context, know what was happening across apps, and get tasks done effectively. However, Apple repeatedlypushed backthe release of the new Siri, citing various reasons likeengineering issues and bugs. This delay eventually contributed to a$250 million settlementover claims regarding how Apple marketed the AI capabilities of the iPhone 16. Siri AI finally appeared in theiOS 27 betathis past July, with the new AI-powered version rolling out to English-language users first this month; support for other languages is expected to follow. Announced at its 2024 Build developer conference, Microsoft’sRecall featurewas pitched as an AI-powered “photographic memory” for Windows PCs, periodically taking screenshots of what a user was doing so they could later search back through their own digital history. Predictably, the announcement triggered an immediatebacklashover privacy and security. Critics argued that Recall could create a searchable archive of highly sensitive information, including passwords, private messages, financial details, and other activity. Microsoft delayed the feature for nearly a year, giving the company time to rethink its security and privacy protections. Even after a redesign, the controversy hasn’t gone away. A cybersecurity researcher recently built a tool that couldextract and display datathat Recall had captured, reviving questions about whether Microsoft has actually fixed the underlying problem. Notion Maillaunched in April 2025 as an AI-focused email product designed to help users organize and automate their inboxes. However, the company noticed its users were using separate AI agents to handle their email inboxes instead. As a result, Notion Mail is scheduled to shut down onSeptember 22. TheHumane AI Pinis probably one of the most well-known failures in AI hardware. Designed to provide AI features through a wearable device rather than a traditional smartphone, Humane raised $230 million from investors and garnered significant attention. But the product struggled mightily with performance, and the situation worsened when Humanewarnedcustomers to stop using its charging case because of a potential battery fire risk. Humane then shut down its AI Pin business in February 2025, with most of the company’s assetsacquired by HPfor $116 million. TheRabbit R1was another heavily hyped AI device. Unveiled at CES in January 2024, the AI companion was designed to perform tasks on behalf of its owner. Rabbit said it had sold 100,000 units shortly after launch. However, strong initial sales didn’t mean it was a strong product.Early reviewsdescribed the device as unfinished, with unreliable performance and a limited number of useful integrations. The company hasn’t given up, though. It has continuedupdatingthe R1, positioning it as a computer controller capable of performing agentic tasks. It also recently announced a new hardware project, dubbed “Project Cyberdeck,” aimed at creating a portable device built for vibe-coding. Huxewas an AI audio app, built by former developers of Google’s NotebookLM, that turned written information into conversational, podcast-style audio. The companyshut downin May 2026, partly as larger platforms began offering similar experiences to large existing audiences. For instance,Spotifyhas been expanding its own AI-powered audio tools. Yuppwas an interesting AI product: a free playground that allowed users to compare responses from hundreds of AI models side by side. Users could also vote on responses and earn cryptocurrency through the platform. At its peak, the platform let users test more than 800 models, including from companies such as OpenAI, Google, and Anthropic. But according to its founders, Yupp never achieved strong enough product-market fit to survive. The platformshut downin March 2026. Figgs AI operated from 2023 to 2024, letting users create and interact with customizable AI characters for role-play and storytelling. The platform reportedly drew more than 1 million users, but its developers said keeping the service free became too expensive to sustain, and theyshut it down.
View
Meta now lets AI agents handle the boring parts of WhatsApp Business setup
Alongside news of its newAI-focused subscription plans, Metaannouncedon Tuesday that it will now allow AI agents of your choosing to set up and manageWhatsApp Businessmessaging, the service that allows companies to connect with customers through WhatsApp conversations. As Meta explains, this process was previously a bit more cumbersome, as it required developers to move between different tools and services, including the Developer Console, Meta’s Business Manager, the API reference, and their editor. Now, they can instead ask their preferred AI agent to set up WhatsApp Business messaging by chatting with it and describing what needs to be done. This is made possible by the new WhatsApp Business Tools MCP, an MCP (Model Context Protocol) server that directly connects an AI coding agent like Claude, Cursor, Codex, or ChatGPT to the WhatsApp Business Platform. The move sees Meta expanding its existing lineup of MCP servers beyondthosefor managing ads and monitoring app configurations, and other social technologies to one specifically designed to help onboard businesses to WhatsApp. Many other tech companies also offer MCP servers that allow AI agents to securely interact with their services, including PayPal, Stripe, GitHub, Notion, Slack, Salesforce, Atlassian,X,Google,Microsoft, and others. In Meta’s case, the AI agent will handle much of the busywork involved in the WhatsApp Business setup process, like creating the company’s WhatsApp Business account, adding and verifying its phone number, registering it for access to the Cloud API, checking the business’ Terms of Service, and more. Businesses will also be able to use the AI agent for other tasks, like describing a messaging template they want the AI to create or having the agent edit an existing template. Plus, they can test messages and webhooks and monitor things that may previously have quietly failed, like the Terms of Service, payment method, and Business Verification. During setup and configuration, Meta’s other MCP server, Meta Social Technologies MCP, can also be used to discover API endpoints, search documentation, and help troubleshoot errors, the company noted.
View
Early Anthropic hire, former METR COO have found a way to rein in rogue AI agents
A day after Anthropic researcher Jacob Coxon quit his job over concerns that AI could kill us all by the end of the decade, I met with founders and brothers-in-law Rune Kvist and Rajiv Dattani. They think they have a solution that could save us all, or at least help prevent AI agents from going rogue inside enterprises. “AI is getting smarter at an increasingly rapid rate. The surprising thing about AI is that it becomes harder to adopt and harder to control as AI gets smarter, not easier,” said Kvist, an early Anthropic employee who is also married to Dattani’s sister). Dattani is the former COO of the AI safety research organization METR. The pair launched a startup called Artificial Intelligence Underwriting Company (AIUC) that hopes to bring AI safety to enterprises and companies building AI models and agents. The startup names Cursor, Lovable, Harvey, and ElevenLabs as customers. On Tuesday, AIUC announced a $40 million Series A led by Ribbit Capital, with participation from First Harmonic. It previously closed a $15 million seed round from Nat Friedman through his fund NFDG, along with Emergence, Terrain, and Anthropic co-founder Ben Mann, among others, bringing its total funding to $55 million. What caught the attention of this A-list group of investors is AIUC’s attempt to apply a familiar cybersecurity model to a new set of AI risks. The company has built a third-party audit and certification layer for AI agents. “Banks, hospitals, governments and militaries no longer decline to deploy AI because a model isn’t smart enough,” Kvist said. “They decline because they’ve made commitments to their own customers about what a system will and won’t do, and nobody can currently guarantee that.” Using the widely adopted cybersecurity standard SOC 2 as its muse, AIUC has developed a standard called AIUC-1 and a testing service to validate agents against the standard. To build the standard, AIUC assembled a consortium of about 250 security and risk leaders — the buyers of agents. “These are the people who we meet with on a monthly basis, and the question we ask them is: When you’re buying agents from someone, what would you look for? What are the questions you’d want to ask, and what would you want to see addressed?” Dattani told TechCrunch. That feedback shapes the tests. The startup then runs an agent through a suite of some 5,000 tests to see how it behaves in scenarios involving jailbreaks, hallucinations, and data leaks. The results produce a roughly 100-page report detailing where an agent performs safely and reliably — and where it doesn’t. Interestingly, AIUC uses AI agents to run the tests and AI to analyze the data. Humans, however, verify the final audit, Kvist said. If this sounds a bit familiar, it is. Dattani’s former employer METR, where he was COO from 2024 to 2025 and remains a board member, does similar testing for the frontier labs, though its work until recently has focused mostly on performance (whether agents can reliably complete tasks). METR was one of the independent research orgsOpenAI used to investigate its Hugging Face incident. Anthropic CEO Dario Amodei has also recentlycalled for the AI industry to pace frontier development, citing a rapid increasein bad-behavior incidents. In his post, Amodei floated the idea of requiring frontier labs to use embedded third-party evaluators to observe and verify safety, and named METR as one possibility. While AIUC isn’t proposing to embed itself at customer sites, the overall idea is similar: give enterprises an independent assessment of how safe their AI agents are. “Here’s where it passes and where you can trust it. And here’s where there’s concerns. You should be aware of those references before you make the decision to buy,” Dattani said.
View
4 days left to exhibit at TechCrunch Disrupt 2026
Book your exhibit table by Friday, September 18 at 11:59 p.m. PT.Tables are limited and first-come, first-served. From October 13-15 at San Francisco’s Moscone West,10,000+ founders, investors, operators, and tech leaderswill walk intoTechCrunch Disrupt 2026looking for companies to invest in, products to use, partners to work with, and technologies worth paying attention to. Your startup can be one of the companies they discover. The DisruptExpo Hallis the highest-traffic part of the event, giving your team three days to put your company directly in front of the people you want to reach. Book an exhibit table and use it to: The package also includes access to the TechCrunch Disrupt press list, Silver Tier sponsor branding, and additional event recognition. And you don’t need to fit a specific startup profile to exhibit. Startups at any stage or in any industry can put their company on the Expo Hall floor. Don’t wait until the deadline to decide.Tables can sell out first. 4 days left.Book your exhibit table nowfor $12,500 before Friday’s 11:59 p.m. PT deadline. TechCrunch Disrupt 2026brings 250+ top-tier tech leaders across 200+ sessions on six industry stages, plus interactive sessions and AI-powered matchmaking designed to help you make more relevant connections. You’ll also discover 300+ startups and the Startup Battlefield 200 while connecting with the founders, investors, and operators shaping what’s next.Regular ticket pricing ends September 25.
View
Discover how to take your startup from prototype to production at TechCrunch Disrupt 2026
AI has dramatically lowered the barrier to building impressive prototypes. Getting those prototypes into production is a different story. Every founder hopes their breakthrough becomes a product customers can rely on. Yet the path from prototype to production rarely follows a straight line. Manufacturing challenges emerge. Infrastructure has to support growth. Autonomous systems have to perform outside controlled environments. The problems change quickly once technology leaves the lab. That’s the focus of “From Prototype to Production: Can It Scale in Reality,” taking place on theReal World AI StageatTechCrunch Disrupt 2026. Leaders in space communications, autonomous systems, and AI infrastructure will share how they’ve navigated that transition, from promising innovation to production-ready deployment. Although every industry follows its own path, all three speakers have faced the same reality: A successful prototype is only the beginning. Disrupt returns to Moscone Westin San Francisco, October 13–15, bringing together 10,000+ founders, investors, and operators for 250+ sessions exploring the technologies and market forces shaping what’s next.Secure your Disrupt passto hear how today’s innovators are moving breakthrough technologies into production. A working prototype answers one question: “Can it be done?” Production raises dozens more. Building optical communications hardware has little in common with deploying autonomous vehicles or autonomous construction equipment. Yet each requires the technology to perform consistently outside controlled environments, where reliability, manufacturing, infrastructure, and day-to-day operations become integral to the product itself. ThisReal World AI Stage sessiondoesn’t promise a single blueprint. Instead, it brings together founders who have tackled production from very different angles, offering a rare opportunity to compare what scaling looks like across industries. Explore the Real World AI Stageand register for Disrupt with up to $200 savings. No two production stories look the same. That’s what makes this discussion compelling. Each foundertaking the Real World AI stagehas navigated a different path from prototype to production, offering lessons startups can apply to their own journey. Moving beyond the prototype often means building manufacturing capability alongside the technology itself. As co-founder and CEO ofMBRYONICS,John Mackeyhas transformed a specialized photonics spinout into a leader in space-based optical communications, expanding high-volume manufacturing while helping build the infrastructure behind next-generation space networks. His insights will help startups understand what changes when your company shifts from building prototypes to building products. Before co-foundingBedrock Robotics,Boris Sofmanhelped lead autonomous trucking and core technologies at Waymo, where fully driverless vehicles logged more than 100 million driverless miles. His experience reflects what happens when autonomous systems leave controlled testing and begin operating in environments where consistency and reliability matter every day. He’ll shed light on how to scale production while ensuring safety and reliability. Before co-foundingFoxglove,Adrian Macneilled infrastructure engineering at Cruise, developing the data platform that supported autonomous vehicles at scale. His work demonstrates that production depends on the engineering systems surrounding it. His talk will underscore the systems and infrastructure that allow complex technologies to move beyond experimentation and into everyday use. Together, they bring three different perspectives to the same milestone every ambitious technology company eventually reaches: moving from a successful prototype to something customers can trust in the real world. Hear how these founders approached production and what they learned along the way.Choose your Disrupt ticket type and save up to $200 by September 25. Sooner or later, every promising technology has to prove itself outside the lab. That’s where this Disrupt conversation begins. Rather than focusing on what’s possible in theory, these founders will discuss what happens when manufacturing, deployment, infrastructure, and day-to-day operations become part of the product. Regardless of the innovation you’re building, this session offers the opportunity to hear from founders who’ve already navigated the move from prototype to production — and the lessons they learned along the way. Secure your pass to Disruptand hear how leaders are making the leap from prototype to production. Save up to $200 before prices increase on September 25.
View
Former TikTok execs built an app that uses AI to teach you how to pose for a photo
Companies have been increasingly using AI’s image generation chops to teach people photography skills. Google last year released a feature calledCamera Coach on Pixel phonesto guide users about framing and composition, and in July, Adobe released a new feature in its experimental camera app to use AI tocritique photos and suggest changes for capture. On the same slant, two former TikTok employees, Melody Chu and Jing Liu, are launching a new iOS app calledSuperposethat uses an AI guide to teach people how to pose for pictures. Superpose essentially is a camera app. Users can take a selfie or photo of a friend, and the app will generate four potential poses using AI. Some of the poses are extravagant, and, at times, the generation seems uncanny. Users can save generated poses, or use the match-pose feature to try to place themselves in the frame based on its suggestions. Prior to founding Superpose, Chu worked in various product roles in companies like Meta, Nextdoor, Roblox, TikTok, and Slack. Meanwhile, Liu was a founding engineer at a 3D face-scanning startup, and later worked on image and video models at TikTok. Chu said that the idea for the app stemmed from a personal problem: her husband wasn’t able to take good photos of her. “My husband just takes awful photos of me. It was a true pain point in our marriage where I don’t understand how he gets me to look just so terrible all the time. And I thought to myself, there has to be a way to solve this problem with advancements in computer vision and generative AI. And I decided to set out on my own,” Chu told TechCrunch. Superpose was launched in July, and has been downloaded over 22,000 times so far. The company says its users have generated more than 190,000 poses. The app gives users five generations for free every day, but they can buy a pack of five additional generations for $2.99, or 20 generations for $9.99. Loading the player… While Chu acknowledged that other companies are also entering the AI-guided photography space, she said Superpose wants to focus on becoming the best camera to take portrait photos by following directions from AI. The startup doesn’t want to get into creating AI backgrounds and images, and wants to focus on real-life photographs, she added. “We want to lean into the core of memory capture versus, say, putting you in a fantastical place that you’ve never been. I think there are so many apps that do that well already, but we really wanna focus on actually capturing the lived moment and experience,” Chu said. She also acknowledged that some of the pose suggestions might seem uncanny, and the company is working on improving that. The startup says it is working on improving its generation styles, personalization, and ways to better coach users directly from the viewfinder. Superpose has raised $2.2 million in funding from Khosla Ventures, Chinese smartphone and selfie app maker Meitu, and OVTR VC.
View
AEO startup Profound hits unicorn valuation, raises $180M Series D 7 months after last round
Profound, which builds marketing software to help brands appear in AI search results, on Tuesday said it has raised a $180 million Series D at a $1.8 billion valuation, less than seven months after it raised a $96 million Series C. Sequoia and Kleiner Perkins led the round, and existing investors — including Lightspeed Venture Partners, Khosla Ventures, and South Park Commons — also participated. Launched two years ago, Profound started as an analytics platform, and has since expanded to help companies research and create marketing strategies. The startup helps businesses understand how AI helps consumers discover their brands. It’s part of a wave of startups in theGEO/AEO space(generative engine optimization and answer engine optimization, respectively), where companies try to find ways to surface their products in the AI systems customers are increasingly using for search. The company says its revenue has increased by 3x in the past six months, and it now has more than 1,000 enterprise customers, which include Comcast, The Estée Lauder Companies, and Walmart.
View
OpenAI, Anthropic, Google have been in talks on AI safety for weeks
Chris Lehane, OpenAI’s global policy chief, told reporters on Tuesday that the company has been working with rivals Anthropic and Google DeepMind on AI safety for weeks, as first reported byBloomberg. Lehane is reportedly in Washington to work with U.S. lawmakers addressing catastrophic risks associated with AI. The revelation comes in the wake ofAnthropic CEO Dario Amodei’s essay, published Saturday, which called for the industry to work together to slow the pace of frontier AI and avoid catastrophic risks. AI leaders across the industry, includingOpenAI’s Sam Altman,Google’s Demis Hassabis, andSpaceXAI’s Elon Musk, supported Amodei’s letter and call to action, with Altman saying that OpenAI would join Anthropic in embedding third-party evaluators into the company to monitor for safety. On Tuesday, Lehane admitted that the three companies have been in talks for several weeks regarding AI safety. Some, including Altman, have noted that such talks could put the companies at risk of violating antitrust law if the coordination is found to suppress competition. Amodei’s essay proposed a narrow government waiver allowing such safety coordination, but Lehane reportedly said the firms don’t need one. The news confirms earlier reporting and hints that the companies were working together. In an interview withFortune late last week, Altman hinted that he had been in private discussions with other AI leaders. This week,The Information reportedthat the three companies have been working together to create a standards body for the AI industry — something Altman reportedly told staff would need to happen without the support of the U.S. government. Hassabis called on the U.S. in July to establish anew standards bodyto act as an AI watchdog, with the power to screen the world’s most advanced models and coordinate industrywide slowdowns if dangers progressed. President Trump, for his part, hasdismissed safety concernsas ahoaxand pushed back on the need for tighter regulations, saying any slowdown would give China a leg up in the race. His key AI advisor David Sacks — a longtime investor with apersonal stakein much of the industry —echoed this, saying fears of existential risk from AI were overblown. At the same Tuesday meeting,Lehane saidthat OpenAI supports a provision in the FRONTIER Act that would force top frontier labs to allow “independent verification organizations” into their companies to ensure models are developed safely. TechCrunch has reached out to Anthropic, Google, and OpenAI for comment.
View
Why Oracle is Bringing Post-Quantum Security to Java
New features in Java 27 target secure enterprise communications, faster data processing, and lower JVM memory overhead.
View
Salesforce and Nvidia’s new reasoning model is everything the AI labs should fear
A new AI model calledKoais one of the biggest announcements from Salesforce this week at its giant Dreamforce tech conference. Koa is the company’s first reasoning model, built on Nvidia’sopen-weight Nemotronmodel. The two companies worked together to post-train Koa to excel at sales, marketing, and customer-support-related tasks. Koa is a shining example of how the enterprise world’s needs for AI are diverging from what the frontier labs are offering. Proprietary AI labs would rather have enterprises uploading files, code, prompts, and feedback directly into their models and agents,and spending millionsto do so. But with the model, Salesforce is offering its enterprise customers: Koa will be provided as an alternative to the other models Salesforce offers in its Agentforce platform, where its customers build agents to handle rote tasks like answering customer service questions or scheduling appointments. “We’ve built many small task-specific language models, which are part of Agentforce’s portfolio,” Jayesh Govindarajan, EVP of Salesforce AI, told TechCrunch. “But reasoning has always been something that we’ve relied on the frontier model providers for. Until now.” Before Koa, if an agent needed to reason through a long-running or multi-step task, those prompts would be routed to a frontier model like Claude or ChatGPT through Agentforce’s AI gateway (the system that decides which model handles which request). “One of the reasons we hadn’t done this before, train our own enterprise-grade frontier model — we always wanted to — but the challenge has always been the lack of a pre-trained base model to start with. Until Nemotron came along, there was no sovereign American pre-trained model that was available, one, and two, that was state of the art, and, three, that had clear data provenance. We have no idea what Qwen trains on,” Govindarajan said, referring to the popular Chinese open-weight model produced by Alibaba. Post-training a model like this means taking it from a general-purpose system to one well-versed in sales and customer support knowledge, and to do that, Salesforce and Nvidia did not use any actual data from Salesforce’s customers. Instead, they crafted synthetic data that mimicked customers’ patterns. “We actually simulated a customer service environment with a persona customer service professional, including irate customers that call into the customer service center, all the way to a sales professional who’s trying to close a deal,” Govindarajan described. Koa is meant to be better at the work tasks Salesforce customers want an agent to do — and cheaper, in terms of tokens burned — than sending those same tasks to Claude or ChatGPT. With Nemotron, “we have a unique architecture for inference to be token efficient,” Kari Ann Briski, Nvidia’s VP of Generative AI Software for Enterprise, told TechCrunch. “It’s kind of the trifecta of things that you need to have: sovereign AI, time to first token, efficient reasoning, for the tokenomics of it all.” However, Salesforce isn’t exactly abandoning Anthropic or OpenAI. It just announced a partnership with Anthropiccalled ClaudeForcethat allows companies to use Claude as their AI interface, while their data remains in Salesforce’s system of records, secured by its infrastructure.
View
Teradyne Opens Bengaluru Office to Expand Semiconductor Support in India
The new office will support chipmakers and electronics manufacturers as India expands domestic fabrication, packaging and testing.
View
NVIDIA Expands CUDA-Q For Fault-Tolerant Quantum Computing
NVIDIA has added an orchestration layer and benchmark tools to help researchers develop practical quantum computing systems.
View
Invia il tuo strumento
PoweredByAI.app è un catalogo di strumenti IA che aiuta persone, aziende e creator a scoprire i migliori strumenti di IA per scrittura, coding, design, produttività e altro.
© 2026 , Prodotto di011BQ. Tutti i diritti riservati.