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Robinhood now lets your AI agents trade stocks
As the tech industry rallies around AI agents, some companies are building capabilities to enable AI agents to make payments and trade stocks on users’ behalf. Stock trading app Robinhood is also moving in that direction: The company on Wednesday said it is launching support for AI agentic trading, as well as a new agentic credit card. Robinhood said users on its platform can now create a separate account for their AI agents and connect them to a dedicated wallet. While these agents would be able to read and analyze users’ portfolios to come up with trading strategies and suggest investments, they’ll only be able to access the pre-loaded balance in the dedicated wallet to place orders. Users will get notifications of all trades their AI agent makes, and will be able to monitor their activities within the Robinhood app itself. For some trades, agents will show a preview that users may have to approve before the order is executed. The company said it has also built in fraud detection protection, in which a team from Robinhood would review suspicious trades and help users resolve disputes. Robinhood says users can connect their AI agents to its Model Context Protocol (MCP) service to do things like analyze concentration risk and sector exposure, execute trades, or look through analyst notes to identify new investment opportunities across various sectors. The agentic trading feature is launching in beta and only allows stock trading right now. The company says it plans to add support for options, crypto, event contracts, futures, and prediction markets soon. Robinhood is also debuting a new virtual credit card meant to be used by AI agents. With this card, users can connect their AI agents to the company’s banking MCP server to enable them to make payments. The virtual card is currently only available to Robinhood Gold Card holders, who can link their account to this new card. Users can set monthly limits on this virtual card, and can choose if their AI agent should seek approval every time it makes a payment. The company said its Robinhood Platinum Card will also get support for a similar virtual an agentic card feature when it launches later this year. Robinhood has been ramping up its AI efforts for the past few years. The company acquiredAI-powered research platform Pluto in 2024, and last year added an AI assistant thatoffers investment advice. “We’ve heard a lot of demand from our customers to bring their own tools, LLMs, and agents, and connect them to Robinhood. That is why we are launching our new products,” Abhishek Fatehpuria, VP of product at Robinhood, told TechCrunch over a call. Robinhood is not alone in enabling AI agents to make payments, with major players likeStripe,Amazon,Google, and newer startups like Prava Pay building products that give AI agents the ability to buy stuff on users’ behalf.
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Tech CEOs are apparently suffering from AI psychosis
There is a certain wildness in the tech industry these days that both mimics previous eras of large changes, like cloud computing (runaway costs in the early days), and is like nothing we’ve ever seen before (record revenues accompanied by mass layoffs). A theory doing the rounds attempts to explain the phenomenon: Tech executives, especially CEOs, are collectively suffering from delusions of grandeur thanks to AI. And at least one tech CEO has said so out loud: Box founder Aaron Levie. “CEOs are uniquely prone to AI psychosis because they’re sufficiently distant from the last mile of work that still has to happen to generate most value with AI,” Leviewrote on X. CEOs “play with AI,” develop a prototype, or generate a contract, to use Levie’s examples, and then make the leap to believing agents can do the work. But these top-level executives aren’t the people who have to review code, discover bugs, and identify calls to hallucinated libraries before software is deployed. They aren’t responsible for training AI models on a company’s idiosyncratic contract terms, nor do they have to spend days combing through contracts to find sneaky terms, as Levie indicates. In other words, Levie’s theory posits, CEOs don’t really understand processes well enough to know what really can and can’t be automated. But that lack of knowledge doesn’t stop them from acting on their beliefs. It’s important to note that Levie is not an AI hater. Quite the opposite. He mostly posts AI positivity on X to his 2.7 million followers, writing blogs titled,“Headless software is the future”on how software built for AI agents is the way forward. He also puts his money where his mouth is, backing AI startups as an active angel investor. So what are CEOs to do instead? Levie advises CEOs to use AI “a ton” to really see what it can and can’t do, “and come out the other side with an appreciation for both the upside and the real work.” CEOs are uniquely prone to AI psychosis because they’re sufficiently distant from the last mile of work that still has to happen to generate most value with AI.So when they play with AI, they see the happy path results, often not considering the next 10 or 20 things that have…https://t.co/ne5mvJ4Rgx I have enough faith in humanity to believe that there are CEOs out there attempting to do just that, but right now, they seem to be in the minority. In only the first five months of 2026, the tech industry has already had nearly as many layoffs as in all of 2025: 115,430 people have been fired from 152 tech companies so far in 2026, compared to 124,636 people let go by 275 companies in 2025, according to industry layoff trackerLayoffs.fyi. And the bulk of companies have pointed to AI as a reason for cutting these jobs. Many argue that the biggest tech companiesare AI washing, or crediting AI productivity gains in the past or future, when other business decisions and metrics are really driving the cuts. Still, some of these stories are surprising. Zeb Evans, the CEO of project management and productivity software startup ClickUp,proudly declaredon X that he had laid off almost a quarter of his employees — 22% — after rolling out about 3,000 AI agents to do internal work. Evans swore this wasn’t done to reduce costs. Instead, he wants a workforce composed of people who run AI agents and spend their days quickly reviewing the agents’ work. He believes this will create a “100x org,” as he calls it. While AI can be a very useful tool, the data on AI and productivity doesn’t support such assumptions. By miles. A meta analysis of other research published in October in UC Berkeley’sCalifornia Management Reviewfound “no robust relationship between AI adoption and aggregate productivity gain.” Research published in March by theNational Bureau of Economic Research did concludethat AI adoption improved productivity, but noted “a productivity paradox, in which perceived productivity gains are larger than measured productivity gains.” After creating thousands of agents to work on tasks,researchers at MITconcluded that agents just aren’t doing human-quality work yet in many cases. They predict at the current rate of LLM improvement, models will “be able to complete most text-related tasks with success rates of, on average, 80%–95% by 2029 at a minimally sufficient quality level.” In other words, AI is on track to perform at base competence on most tasks in about three years. These researchers believe agents will need another few years to outperform humans. Meanwhile, research published in theHarvard Business Reviewshowed that when everyone is using AI to produce more stuff, the bottleneck simply shifts to executives. Their work awaits the people that must authorize all the stuff everyone is producing. If everyone is empowered to act, thenfrom what OpenAI experienced last year, we can tell that things may get out of control. Are CEOs ready for that? If not, the most certain outcome of the ongoing CEO AI psychosis will simply be organizational chaos.
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YouTube will now automatically label AI videos
As AI video models become more powerful, YouTube is no longer solely relying on creators to label their AI videos — it will now automatically label videos on their behalf. The companyannouncedon Wednesday that its internal systems will apply labels when it detects that “significant photorealistic AI” has been used. YouTube will also be making its AI labels more prominent, so they’re easier to spot across both long-form videos and YouTube Shorts. AI labels on the video platform have been in usefor over two years, after YouTubeupdated its AI policiesand rolled out a tool in Creator Studio that required creators to disclose their videos included AI content that could be mistaken for a real person, place, or event. Videos that obviously depicted some sort of animated or imaginative scenario — like a unicorn prancing through a fantastical world — did not have to be labeled. The company says its policy around AI labeling hasn’t changed, but it will take a more active role in policing the content on its platform. The move follows Google’s release ofGemini Omni, a new family of multimodal AI modelsat its Google I/O developer conference last week that can output high-quality videos that reflect an understanding of physics, culture, history, and science. Starting in May, YouTube will now use new internal signals to help identify AI-generated content and label it accordingly, the company says. This doesn’t mean that creators shouldn’t continue to disclose their use of AI, but if they neglect to do so, YouTube will label the video for them. While creators whose content was misidentified will be able to update the disclosure status in a YouTube video, they won’t be able to remove those labels if the content was created with YouTube’s own AI tools, like Veo or Dream Screen, the company says. Labels will also be permanently attached to videos when the content containsC2PAmetadata indicating it was fully AI-generated. (Recently, OpenAIcommitted to the C2PA standard, joining Nvidia, Kakao, and Eleven Labs.) The addition of automatic AI detection functionality comes shortly after theexpansion of YouTube’s AI deepfake detection, which now allows any adult to scan YouTube specifically for face matches, after initial tests withcelebs, public figures,politicians, and other creators. YouTube says it will also make its AI labels more consistent and prominent. Before, labels would appear in the expanded description, unless the video touched on more sensitive topics like health or news; if so, a prominent label would appear directly on the video itself. Now, the labels will appear directly below the video player above the description for long-form videos and overload directly on YouTube Shorts. The company said moving the labels will make them more obvious to people who come across photorealistic, AI-altered, or AI-generated content on the site. Meanwhile, for AI video that is only slightly altered, animated, or unrealistic — like the above-mentioned prancing unicorn — the label will appear in the expanded description only. Notably, YouTube says that AI labels won’t have an impact on how a video is recommended or its ability to monetize. In addition to its policing of AI content, the company has been investing in AI for things likeits interactive search feature,Ask YouTube, aplaylist generatorfor YouTube Music,AI video summaries, andothergenerativeAIcreationtools.
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ClickHouse triples anualized revenue to $250M, charting a path toward an IPO
Database provider ClickHouse has crossed $250 million in annualized revenue run rate, tripling its business from last year, Yury Izrailevsky, co-founder and president of product and technology, told TechCrunch. Israilevsky expects the revenue figure to reach the high nine figures by the end of the year. ClickHouse was valuedat $15 billionin January following a $400 million Series D funding round led by Dragoneer Investment Group. The latest valuation implies a steep multiple of over 60 times annualized revenue. The fast revenue growth and premium valuation position the less-than-five-year-old company for an IPO within the next few years, according to Izrailevsky (pictured left). ClickHouse joins a small, butgrowing listof tech startups signaling plans to go public as the IPO window is expected to be flung wide open by SpaceX’s historic June debut, followed by highly anticipated listings from OpenAI and Anthropic later this year. Last fall, the startup hired Jimmy Sexton, who previously ran investor relations at Snowflake, one of ClickHouse’s main competitors, as chief financial officer. Bringing on a CFO is often viewed as a signal that a company is preparing for public markets. The company has already acquired six startups, including Langfuse, which helps developers track and evaluate AI agent performance. Izrailevsky indicated that ClickHouse plans to remain acquisitive, looking to scoop up “relatively young, but showing very promising technology” startups, typically open-source, that complement its core product suite. The technology behind ClickHouse was originally developed inside Russian search giant Yandex 17 years ago, but spun out as an independent startup in 2021. ClickHouse has over 4,000 customers, including Anthropic, Meta, Capital One, and Decagon. The startup’s open-source database is designed to process the massive datasets required by AI agents. ClickHouse generates revenue by selling managed cloud services. Izrailevsky claimed that this commercial offering ultimately costs clients less than self-managing the open-source version. It “is something that’s a little counterintuitive, but it also has been a big tailwind for us,” he said.
3 hours ago
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