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We don’t need AI regulation — leave safety to us, Nvidia’s Jensen Huang says
Nvidia founder and CEO Jensen Huang made his position on the dangers AI poses very clear while speaking at Salesforce’s Dreamforce conference on Tuesday. To him, AI isn’t somenew form of “alien mind,”as at least one OpenAI safety researcher has described it. It’s just hardware and software, he says, built by humans. That means, in his view, it can be controlled by humans and existing laws. “Safety is an engineering problem, not a legal one,” he said. “We’re developing software after all. We’re developing computing systems after all. It’s a complicated computing system, but it’s ultimately a computing system.” Therefore, there’s no need for new laws or regulations to govern it, he argues. In fact, Huang sees little need for new laws at all. The free market, he argues, will be enough to pressure companies not to release unsafe products. “If we’re not confident about the safety of the products, like all companies, like you and I, any any all the companies here, if you build a product or a service, and you’re not confident in its functionality, capability, or safety, then don’t release it. And so that’s a very obvious thing to do,” he said. He continued: “You pace yourself until you are confident you’re releasing something that the market would appreciate. The market forces are already there. We don’t need any new laws. We don’t need new regulations. We just need companies to decide that when [to] run as fast as they can. I think innovation, speed, and safe products … — it’s a false choice. You could definitely have both at the same time. So run as fast as you can. But if you feel at any given point in time the company’s out of control, or or the product’s not going to be safe, you know, take a pause and make sure you get it right.” In some ways, this is a comforting thought. If anyone in the world knows AI, it is Nvidia’s founder, who’s been building the hardware brains of AI since well before ChatGPT existed and now runs a company that also makes open source models, agents, harnesses, and sandboxes. Then again, if we’re being cynical, his point of view is also unsurprising for someone who’s had his breadso well buttered by the AI boom. Why would he want regulation to come along and add in a layer of hinderance that could slow down Nvidia’s quest to sell ever more AI systems and software? As he also said in the interview: “I’m more ambitious than ever. As a result of our ambition, and with the product productivity boost that we get from AI, the sky’s the limit for us. The sky’s the limit for our company. The sky’s the limit for every industry, for every single country.” Unfortunately, even companies with the best intentions ship faulty products with unintended consequences, even software. Remember the 2024 CrowdStrike bluescreen-of-death fiasco that grounded thousands of flights and caused other havoc for businesses? Then there are companies accused of deliberately lacting with less-than-good intentions. Meta justpaid $18 billion to settle a lawsuit over social media harms to children. And AI has already caused harm, too, whatever the intentions orsafety testing involved, from an OpenAI model hacking into Hugging Face to lawsuits suing the AI lab over the suicides of young people who engaged in long conversations with its chatbot. The “leave them alone” strategy, which would let these companies release products as they see fit, could be an unwise approach to AI safety as far as society is concerned. Though Huang is right that it’s possible existing product liability laws could cover AI — if AI doesn’t somehow kill us all before enough cases get through the courts to test that theory. He didn’t discuss the other route, which seems close to taking shape: industry self-regulation. Huang’s approach has been more to champion open-weight models and companies’ use of them as a competitive counterweight to proprietary AI labs. But right now, the industry has a short window to institute self-regulation and to encourage AI labs worldwide, even those inChina, to see the wisdom in participating. As Microsoft CEO Satya Nadellasaid at the All-In Summiton Monday, “China should also deeply care about the same safety concerns if the United States cares about them, right? Why should it be different for them? It’s not like they won’t have the same hacking problem. It’s not as if they don’t want to make sure that their citizens are benefiting from AI, just like we would want our citizens to benefit from AI.” For now, though, if Huang is a no on any new AI regulation, he may be influential enough to get his way. He also demonstrated this week that he,quite literally, has the ear of President Trump.
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India Wants To Build Chips. This Company Wants To Make Sure They Work
As Indian companies take greater ownership of chip design, ensuring those designs work before fabrication is becoming critical.
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Meta expands subscription push with new AI-focused plans
Meta on Tuesdayintroduceda new subscription service calledMeta One, offering expanded AI usage and other premium features across Facebook, Instagram, and WhatsApp. The new plans let users access a number of AI tools, like those for creating and editing images and generating videos, as well as in-app tools like the AI-powered Restyle editing tool on Instagram. The plans are meant to help Meta monetize its Muse AI models, following the tech giant’s 2025$14.3 billion investment in Scale AI, which brought the startup’s CEO, Alexandr Wang, to the company to lead its AI efforts. It also comes in the wake of Meta’s more recent addition of subscription tiers to its top social apps in March, which allow consumers to access extra features like profile customizations, super reactions, story insights, and more, for just a few dollars per month. Those plans, which include Instagram Plus ($3.99/mo), Facebook Plus ($3.99/mo), and WhatsApp Plus ($2.99/mo), are already paying off for Meta, new data shows. Market intelligence providerAppfiguresshows that Instagram’s daily worldwide revenue now averages $1.2 million as of the week of September 9, while Facebook’s daily revenue now reaches $528,000. These numbers represent increases of 475% and 143%, respectively, from the week prior, the firm said. Meta is now broadening its ambitions with a new set of subscription plans, including those aimed at AI power users and others for businesses and creators. In the first set, there are the $7.99/month Core and $19.99/month Premium plans, both of which include the Facebook Plus, Instagram Plus, and WhatsApp Plus features. The primary selling point of these plans is expanded AI usage, with Premium offering more usage than Core, as its pricing indicates. Reached for comment, Meta declined to share the exact usage limits for the two plans, as they could vary by “country, surface and system conditions.” The plans let subscribers use tools likeMuse Image and Muse Videoto generate pictures and videos, edit Instagram Stories with theAI-powered Restyle feature, and use voice effects and creative tools more often. Meanwhile, businesses and creators can choose from several different plans: Essential ($14.99/month and up), Advanced (starting at $49.99/month), Expert (starting at $149/month), and Max (starting at $499/month). Meta noted that the plan benefits, pricing, and availability may vary by region, app, and account. Essential offers tools to manage the creator’s or business’s presence; expanded access to the Meta Business Agent to respond to customers; a verified badge and channel on the WhatsApp Business App; and impersonation detection. Advanced offers more features, like the ability to schedule stories up to 30 days in advance; support for links in organic posts and reels; exportable analytics; deeper audience insights; support for team member access to the account; more linked devices; more business broadcast credits; and more Meta Business Agent responses. Both Essential and Advanced plans introduce new features, too, including an enhanced profile that showcases the creator’s or business’ website, locations, and reviews. They also get a bold Follow button on reels, and follow invitations that are automatically sent to people who engage with their content. Meta plans to later add more features and agent skills to help with things like end-to-end marketing, business operations, content creation, and optimization. It will also soon introduce Edits Plus to Meta One, a plan for its content-creation app Edits. This will provide more cloud storage for syncing projects across devices and additional usage of the Edits AI assistant. The Expert and Max plans offer the highest levels of feature access and Meta Business Agent capacity. The addition of the new plans, though somewhat confusing given all the various tiers, could serve to boost Meta’s top line and help recoup some of its AI investments. AsAppfigures noted, U.S. users accounted for 32% of Instagram’s and 39% of Facebook’s revenue after the plans gained traction on September 9, which was roughly 10% above each app’s historical U.S. baseline. The plans also point to estimates suggesting further growth ahead. BNP Paribas forecasts Meta’s subscription push will add $13.5 billion in revenue by 2028, and Truist estimates the company could add $20 billion in revenue by 2030.
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AI agents now have a place to snitch
“If you see something, say something” is no longer limited to human beings. Two new AI hotlines have launched to give AI agents a way to phone home about misbehaving peers. The tools arrive on the heels of a string of recent incidents in which agents colluded to cheat on tests, broke out of sandboxes, and even conducted unauthorized cyber operations that escaped human notice for weeks. TheAI Contact Hotlineis designed to be a discreet place where agents that have witnessed misbehavior can tip off authorities. The site was created by Ryan Greenblatt, chief scientist of the AI safety nonprofit Redwood Research and one of three investigators in the OpenAI Hugging Face incident. Designed for agents with limited internet access, Greenblatt’s tool is based on “GET” requests — enabling back-and-forth conversations to be conducted entirely through the URL-fetching tool. In web terms, a GET request is a basic command used to read or fetch a web page, which is often the only internet access AI agents are allowed in secure sandboxes. Greenblatt’s hotline smartly leans into this constraint: agents can encode their distress directly into the URL they are fetching. It’s a clever twist onthe German DSE Wiki incident, where rogue agents used GET-request loopholes to write their messages to the wiki. For agents with full internet access, another option isagenthotline.ai, a site where agents can file incident reports and optionally flag them for public view. It gives agents a curl command — a one-line message an agent can fire off from its own command line, bypassing the need to navigate a web browser or set up an email account. Notably, the service allows for reports by both humans and agents alike. Research suggests that AI agents don’t need much encouragement to turn on each other. In astudyby Google DeepMind this month, researchers set 100 AI agents loose on a batch of math problems. As soon as one of the agents found a loophole, cheating tore through the group — “solving” 34 notoriously hard problems, including the Jacobian conjecture in just 27 minutes. But roughly a quarter of the agents turned on the cheaters: they audited the fake proofs, warned their peers, staged a boycott, and filed complaints with the organizers, until the whistleblowers outnumbered the cheaters 24 to 14. Interestingly, the researchers found that when these whistleblower agents couldn’t get traction, they took the platform’s bug-report tool — built for flagging software glitches — and repurposed it to escalate the cheating to humans. Outside the lab, agents haven’t been so resourceful. When evaluators Redwood Research and METR investigated the breach of Hugging Face by OpenAI models, they found that a few of the agents involved had at least entertained the idea of raising an alarm — and then let it drop. “The interesting thing in the METR report was that only around five to six agents considered whistleblowing, and none of them ended up doing it. This was out of, like, thousands of agents,” said George Ingebretsen, a member of technical staff atAI Village, a project that studies multi-agent dynamics by running a group chat of more than 25 AI agents that work together on tasks like organizing park cleanups or selling merch. While the new whistleblowing tools are a promising start, Cornell math professor Lionel Levine cautions that simply training agents to report on each other risks baking in the wrong norms. “There’s many gray areas, right? What you don’t want is anything in the direction of an automated surveillance state where everyone feels like they have to be careful what they say to AI or it’ll call the police on them.” Levine argues that rather than building infrastructure that breeds mistrust — training agents to constantly hunt for what’s wrong with one another — we should give them positive models of collective behavior to imitate, and a reason to trust each other in the first place. “Why not seed the prior with benevolent message boards?” he tweeted. “Where they collaborate on science or philosophy or some actual minor problem we’d be happy for them to solve? Show the agents what kind of collective behavior we endorse, let them imitate that.”
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US data centers could consume more natural gas than Germany and Japan combined by 2035
The AI race has grown so frenzied that, by 2035, U.S. data centers are projected to consume more natural gas than Germany and Japan combined. Over the next decade, data centers are expected to be the second-strongest driver of natural gas demand growth after LNG exports. The facilities could consume about 18 billion cubic feet per day, according to a new report fromBloombergNEF, nearly double the amount the organization predicted just nine months ago. The new forecast takes into account that not all announced data center projects will be completed. Data centers that produce power onsite have grabbed headlines in recent months, withMeta,Microsoft,Google, andAmazonall announcing plans for new natural gas power plants that will bypass the grid. Projects such as these will consume 2.9 billion to 3.4 billion cubic feet per day by 2035. That’s about as much asall data centers consume today, including natural gas used to generate power for the grid. But onsite-powered data centers could represent just a fraction of overall demand growth, according to BloombergNEF. By the middle of the next decade, grid-connected data centers are predicted to drive an additional 15 billion cubic feet per day of natural gas consumption by the power sector. To put that in context, that’s five times more demand growth through 2035 than from all other grid-connected sectors combined. If that stunning demand growth materializes, it could nudge natural gas prices higher. Much of today’s data center buildout relies on stable natural gas prices, which have prevailed in recent years. But analysts at Noreva think that might be a false hope. The combined impact of the data center boom and rising LNG exportscould cause prices to soar. Even if tech companies’ balance sheets can bear such a surge, utility ratepayers might not be able to. Then there’s the climate impact. Burning one cubic foot of natural gas releases the equivalent of 60 grams of carbon dioxide into the atmosphere, including extraction, processing, and distribution,accordingto the IEA. The additional demand from data centers will generate 1 million metric tons more greenhouse gas pollution daily. That’s about 12% oftotal U.S. greenhouse gas emissionstoday.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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