I/O 2026
At Google I/O 2026, we shared how we’re making AI more helpful for everyone. See everything we announced.
Amazon CEO reportedly raised Anthropic model concerns before government crackdown
Amazon CEO Andy Jassy may have been the source of security concerns that led Anthropic to cut off worldwide access to two models on Friday.
Andrew Yang thinks the next big startup opportunity is lowering the cost of living
Andrew Yang made a list of everything Americans overpay for — housing, food, wireless — and thinks the next startup gold rush is giving that money back.
New OpenAI Academy courses for the next era of work
OpenAI introduces three Academy courses that help people build practical AI skills, create repeatable workflows, and apply agents in everyday work.
Check out real-life AI prototypes from the Futures Lab.
University of Waterloo students develop AI prototypes like sign language tutors to reshape the future of education and work.
Anthropic to disable its most advanced AI models after US order limiting foreign access - The Guardian
Anthropic to disable its most advanced AI models after US order limiting foreign access The Guardian Anthropic Blocks Foreigners From Using Mythos and Fable AI The New York Times Anthropic shuts down newest AI model after U.S. bans foreign use The Washington Post
The latest AI news we announced in May 2026
Here are Google’s latest AI updates from May 2026
Will Artificial Intelligence Replace Therapy for Addiction? - Psychology Today
Will Artificial Intelligence Replace Therapy for Addiction? Psychology Today
Catch up on the Dialogues stage at Google I/O 2026.
A recap of the 2026 I/O Dialogues, where leaders discuss the future of AI, quantum computing, robotics and creativity.
I/O 2026: Welcome to the agentic Gemini era
The latest from Google I/O: See how we’re helping you get more done with Gemini.
Gemini 3.5: frontier intelligence with action
At Google I/O we released Gemini 3.5, our latest series of models combining frontier intelligence with action.
Built to benefit everyone: our plan
A vision for the future of AI, focusing on access, safety, and shared prosperity as OpenAI works to ensure AGI benefits everyone.
What is generative AI?
If you've tried ChatGPT, Microsoft Copilot, Nano Banana, Grok, or any other AI chatbot or image generator, you've used generative AI (also called GenAI). Over the past few years, huge developments in generative AI and computing power have taken these kinds of tools out of research labs and made them a practical part of everyday life. You've almost definitely used generative AI, but let's dig a little deeper and add some more context. Table of contents: What is generative AI? How does generative
Exclusive: U.S. bank regulators ramp up scrutiny of AI use at financial companies - Reuters
Exclusive: U.S. bank regulators ramp up scrutiny of AI use at financial companies Reuters
Magnifica Humanitas, Artificial Intelligence, and Amish Country - Front Porch Republic
Magnifica Humanitas, Artificial Intelligence, and Amish Country Front Porch Republic
KPMG pulls report on AI usage due to apparent hallucinations
Once again, AI proves to be an unreliable source of information about AI.
OpenAI to acquire Ona
OpenAI plans to acquire Ona to expand Codex with secure, persistent cloud environments, enabling long-running AI agents across enterprise workflows.
Katy Perry Didn’t Attend the Met Gala, But AI Made Her the Star of the Night
Another year, another viral deepfake of Katy Perry at the Met Gala and once again, she wasn’t even there. Photos showing the pop star in a sleek black designer gown circulated widely on social media during Monday night’s event, matching the “Superfine: Tailoring Black Style” theme. But the images were AI-generated. Perry quickly clarified she was not at the Met; she was on tour. Perry’s reaction “Couldn’t make it to the MET, I’m on The Lifetimes Tour (see you in Houston tomorrow IRL),” she posted to Instagram alongside the fake images. She added a jab at AI confusion: “P.s. this The post Katy Perry Didn’t Attend the Met Gala, But AI Made Her the Star of the Night appeared first on DailyAI.
How a two-person SEO shop is building an engine to run twelve clients in thirty minutes a month
Adrian Martinez runs a digital marketing agency in Toronto focused on Website Design, SEO, and answer engine optimization (AEO). He and his wife deliver for about twelve clients today. The constraint is time. Each account takes 10 to fifteen hours a month in hands-on work: research, drafts, technical SEO, and reporting. That math caps growth before it caps ambition. Adrian's bet isn't to hire a dozen account managers first. It is to build a delivery engine that maintains high quality while the t
‘There’s a huge market demand’: University of Utah approves new bachelor’s degree in artificial intelligence - The Salt Lake Tribune
‘There’s a huge market demand’: University of Utah approves new bachelor’s degree in artificial intelligence The Salt Lake Tribune
10 top women in AI in 2026
AI is changing our world, but the stories of who build it often get lost in the noise. Behind the headlines and hype, a group of women are solving AI’s fundamental challenges – despite working in an industry persisently impacted by gender inequality. Women make up just 22% of AI professionals worldwide and only 12% of AI researchers. In academic publishing, female researchers account for just 29% of first authors on AI papers, a number that hasn’t increased since the mid-2000s. This is a story about ten leaders who have influenced AI despite the odds being stacked against them. Their The post 10 top women in AI in 2026 appeared first on DailyAI.
SpaceX officially prices shares at $135 in the largest IPO ever
Wits its official share pricing announcement, SpaceX's IPO has begun.
Chinese cybercrime operation that used AI to scam ‘hundreds of thousands of victims’ sued by Google
The tech giant said a group called "Outsider Enterprise" used AI to scam hundreds of thousands of victims, sending 2.5 million text messages over a span of two weeks.
Access OpenAI models and Codex through your Oracle cloud commitment
Access OpenAI models and Codex through Oracle Cloud, using existing commitments to build and deploy AI with enterprise security and governance.
Catch up on 12 major I/O 2026 moments
Here are 12 of the biggest Google I/O 2026 keynote moments, including news about Gemini Omni, Gemini 3.5 Flash and more.
Bridging three-dimensional molecular structures and artificial intelligence with a conformation description language - Nature
Bridging three-dimensional molecular structures and artificial intelligence with a conformation description language Nature
Railway secures $100 million to challenge AWS with AI-native cloud infrastructure
Railway, a San Francisco-based cloud platform that has quietly amassed two million developers without spending a dollar on marketing, announced Thursday that it raised $100 million in a Series B funding round, as surging demand for artificial intelligence applications exposes the limitations of legacy cloud infrastructure. TQ Ventures led the round, with participation from FPV Ventures, Redpoint, and Unusual Ventures. The investment values Railway as one of the most significant infrastructure startups to emerge during the AI boom, capitalizing on developer frustration with the complexity and cost of traditional platforms like Amazon Web Services and Google Cloud. "As AI models get better at writing code, more and more people are asking the age-old question: where, and how, do I run my applications?" said Jake Cooper, Railway's 28-year-old founder and chief executive, in an exclusive interview with VentureBeat. "The last generation of cloud primitives were slow and outdated, and now with AI moving everything faster, teams simply can't keep up." The funding is a dramatic acceleration for a company that has charted an unconventional path through the cloud computing industry. Railway raised just $24 million in total before this round, including a $20 million Series A from Redpoint in 2022. The company now processes more than 10 million deployments monthly and handles over one trillion requests through its edge network — metrics that rival far larger and better-funded competitors. Why three-minute deploy times have become unacceptable in the age of AI coding assistants Railway's pitch rests on a simple observation: the tools developers use to deploy and manage software were designed for a slower era. A standard build-and-deploy cycle using Terraform, the industry-standard infrastructure tool, takes two to three minutes. That delay, once tolerable, has become a critical bottleneck as AI coding assistants like Claude, ChatGPT, and Cursor can generate working code in seconds. "When godly intelligence is on tap and can solve any problem in three seconds, those amalgamations of systems become bottlenecks," Cooper told VentureBeat. "What was really cool for humans to deploy in 10 seconds or less is now table stakes for agents." The company claims its platform delivers deployments in under one second — fast enough to keep pace with AI-generated code. Customers report a tenfold increase in developer velocity and up to 65 percent cost savings compared to traditional cloud providers. These numbers come directly from enterprise clients, not internal benchmarks. Daniel Lobaton, chief technology officer at G2X, a platform serving 100,000 federal contractors, measured deployment speed improvements of seven times faster and an 87 percent cost reduction after migrating to Railway. His infrastructure bill dropped from $15,000 per month to approximately $1,000. "The work that used to take me a week on our previous infrastructure, I can do in Railway in like a day," Lobaton said. "If I want to spin up a new service and test different architectures, it would take so long on our old setup. In Railway I can launch six services in two minutes." Inside the controversial decision to abandon Google Cloud and build data centers from scratch What distinguishes Railway from competitors like Render and Fly.io is the depth of its vertical integration. In 2024, the company made the unusual decision to abandon Google Cloud entirely and build its own data centers, a move that echoes the famous Alan Kay maxim: "People who are really serious about software should make their own hardware." "We wanted to design hardware in a way where we could build a differentiated experience," Cooper said. "Having full control over the network, compute, and storage layers lets us do really fast build and deploy loops, the kind that allows us to move at 'agentic speed' while staying 100 percent the smoothest ride in town." The approach paid dividends during recent widespread outages that affected major cloud providers — Railway remained online throughout. This soup-to-nuts control enables pricing that undercuts the hyperscalers by roughly 50 percent and newer cloud startups by three to four times. Railway charges by the second for actual compute usage: $0.00000386 per gigabyte-second of memory, $0.00000772 per vCPU-second, and $0.00000006 per gigabyte-second of storage. There are no charges for idle virtual machines — a stark contrast to the traditional cloud model where customers pay for provisioned capacity whether they use it or not. "The conventional wisdom is that the big guys have economies of scale to offer better pricing," Cooper noted. "But when they're charging for VMs that usually sit idle in the cloud, and we've purpose-built everything to fit much more density on these machines, you have a big opportunity." How 30 employees built a platform generating tens of millions in annual revenue Railway has achieved its scale with a team of just 30 employees generating tens of millions in annual revenue — a ratio of revenue per employee that would be exceptional even for established software companies. The company grew revenue 3.5 times last year and continues to expand at 15 percent month-over-month. Cooper emphasized that the fundraise was strategic rather than necessary. "We're default alive; there's no reason for us to raise money," he said. "We raised because we see a massive opportunity to accelerate, not because we needed to survive." The company hired its first salesperson only last year and employs just two solutions engineers. Nearly all of Railway's two million users discovered the platform through word of mouth — developers telling other developers about a tool that actually works. "We basically did the standard engineering thing: if you build it, they will come," Cooper recalled. "And to some degree, they came." From side projects to Fortune 500 deployments: Railway's unlikely corporate expansion Despite its grassroots developer community, Railway has made significant inroads into large organizations. The company claims that 31 percent of Fortune 500 companies now use its platform, though deployments range from company-wide infrastructure to individual team projects. Notable customers include Bilt, the loyalty program company; Intuit's GoCo subsidiary; TripAdvisor's Cruise Critic; and MGM Resorts. Kernel, a Y Combinator-backed startup providing AI infrastructure to over 1,000 companies, runs its entire customer-facing system on Railway for $444 per month. "At my previous company Clever, which sold for $500 million, I had six full-time engineers just managing AWS," said Rafael Garcia, Kernel's chief technology officer. "Now I have six engineers total, and they all focus on product. Railway is exactly the tool I wish I had in 2012." For enterprise customers, Railway offers security certifications including SOC 2 Type 2 compliance and HIPAA readiness, with business associate agreements available upon request. The platform provides single sign-on authentication, comprehensive audit logs, and the option to deploy within a customer's existing cloud environment through a "bring your own cloud" configuration. Enterprise pricing starts at custom levels, with specific add-ons for extended log retention ($200 monthly), HIPAA BAAs ($1,000), enterprise support with SLOs ($2,000), and dedicated virtual machines ($10,000). The startup's bold strategy to take on Amazon, Google, and a new generation of cloud rivals Railway enters a crowded market that includes not only the hyperscale cloud providers—Amazon Web Services, Microsoft Azure, and Google Cloud Platform—but also a growing cohort of developer-focused platforms like Vercel, Render, Fly.io, and Heroku. Cooper argues that Railway's competitors fall into two camps, neither of which has fully committed to the new infrastructure model that AI demands. "The hyperscalers have two competing systems, and they haven't gone all-in on the new model because their legacy revenue stream is still printing money," he observed. "They have this mammoth pool of cash coming from people who provision a VM, use maybe 10 percent of it, and still pay for the whole thing. To what end are they actually interested in going all the way in on a new experience if they don't really need to?" Against startup competitors, Railway differentiates by covering the full infrastructure stack. "We're not just containers; we've got VM primitives, stateful storage, virtual private networking, automated load balancing," Cooper said. "And we wrap all of this in an absurdly easy-to-use UI, with agentic primitives so agents can move 1,000 times faster." The platform supports databases including PostgreSQL, MySQL, MongoDB, and Redis; provides up to 256 terabytes of persistent storage with over 100,000 input/output operations per second; and enables deployment to four global regions spanning the United States, Europe, and Southeast Asia. Enterprise customers can scale to 112 vCPUs and 2 terabytes of RAM per service. Why investors are betting that AI will create a thousand times more software than exists today Railway's fundraise reflects broader investor enthusiasm for companies positioned to benefit from the AI coding revolution. As tools like GitHub Copilot, Cursor, and Claude become standard fixtures in developer workflows, the volume of code being written — and the infrastructure needed to run it — is expanding dramatically. "The amount of software that's going to come online over the next five years is unfathomable compared to what existed before — we're talking a thousand times more software," Cooper predicted. "All of that has to run somewhere." The company has already integrated directly with AI systems, building what Cooper calls "loops where Claude can hook in, call deployments, and analyze infrastructure automatically." Railway released a Model Context Protocol server in August 2025 that allows AI coding agents to deploy applications and manage infrastructure directly from code editors. "The notion of a developer is melting before our eyes," Cooper said. "You don't have to be an engineer to engineer things anymore — you just need critical thinking and the ability to analyze things in a systems capacity." What Railway plans to do with $100 million and zero marketing experience Railway plans to use the new capital to expand its global data center footprint, grow its team beyond 30 employees, and build what Cooper described as a proper go-to-market operation for the first time in the company's five-year history. "One of my mentors said you raise money when you can change the trajectory of the business," Cooper explained. "We've built all the required substrate to scale indefinitely; what's been holding us back is simply talking about it. 2026 is the year we play on the world stage." The company's investor roster reads like a who's who of developer infrastructure. Angel investors include Tom Preston-Werner, co-founder of GitHub; Guillermo Rauch, chief executive of Vercel; Spencer Kimball, chief executive of Cockroach Labs; Olivier Pomel, chief executive of Datadog; and Jori Lallo, co-founder of Linear. The timing of Railway's expansion coincides with what many in Silicon Valley view as a fundamental shift in how software gets made. Coding assistants are no longer experimental curiosities — they have become essential tools that millions of developers rely on daily. Each line of AI-generated code needs somewhere to run, and the incumbents, by Cooper's telling, are too wedded to their existing business models to fully capitalize on the moment. Whether Railway can translate developer enthusiasm into sustained enterprise adoption remains an open question. The cloud infrastructure market is littered with promising startups that failed to break the grip of Amazon, Microsoft, and Google. But Cooper, who previously worked as a software engineer at Wolfram Alpha, Bloomberg, and Uber before founding Railway in 2020, seems unfazed by the scale of his ambition. "In five years, Railway [will be] the place where software gets created and evolved, period," he said. "Deploy instantly, scale infinitely, with zero friction. That's the prize worth playing for, and there's no bigger one on offer." For a company that built a $100 million business by doing the opposite of what conventional startup wisdom dictates — no marketing, no sales team, no venture hype—the real test begins now. Railway spent five years proving that developers would find a better mousetrap on their own. The next five will determine whether the rest of the world is ready to get on board.
How AI Mode is changing the way people search in the U.S.
One year after launch, see how AI Mode’s users are shifting from keywords to natural language queries.
AI May Soon Help You Understand What Your Pet Is Trying to Say
Chinese tech powerhouse Baidu has filed a patent for a system that could use AI to decode animal sounds and behaviour then translate those signals into human language. For the millions of pet owners wondering what their animals are thinking, this could be the first real step toward bridging the communication gap between humans and animals. The tech Baidu’s system would collect animal vocalizations, body movements, and biological signals. It would merge that data and feed it into an AI model trained to identify emotional states. These emotional states could then be rendered in human language to boost “cross-species communication”. The post AI May Soon Help You Understand What Your Pet Is Trying to Say appeared first on DailyAI.
The Gemini app becomes more agentic, delivering proactive, 24/7 help
A look at how the Gemini app is becoming more agentic, delivering proactive, 24/7 help.
How an astrophysicist uses Codex to help simulate black holes
Discover how astrophysicist Chi-kwan Chan uses Codex to build black hole simulations, helping scientists study extreme physics and test Einstein’s theory of general relativity.
9 demos of Gemini Omni and Gemini 3.5 in action
Watch 9 videos showing the capabilities of Gemini Omni and Gemini 3.5, announced at Google I/O 2026.
A GOP revolt over AI is taking shape - Politico
A GOP revolt over AI is taking shape Politico
Listen Labs raises $69M after viral billboard hiring stunt to scale AI customer interviews
Alfred Wahlforss was running out of options. His startup, Listen Labs, needed to hire over 100 engineers, but competing against Mark Zuckerberg's $100 million offers seemed impossible. So he spent $5,000 — a fifth of his marketing budget — on a billboard in San Francisco displaying what looked like gibberish: five strings of random numbers. The numbers were actually AI tokens. Decoded, they led to a coding challenge: build an algorithm to act as a digital bouncer at Berghain, the Berlin nightclub famous for rejecting nearly everyone at the door. Within days, thousands attempted the puzzle. 430 cracked it. Some got hired. The winner flew to Berlin, all expenses paid. That unconventional approach has now attracted $69 million in Series B funding, led by Ribbit Capital with participation from Evantic and existing investors Sequoia Capital, Conviction, and Pear VC. The round values Listen Labs at $500 million and brings its total capital to $100 million. In nine months since launch, the company has grown annualized revenue by 15x to eight figures and conducted over one million AI-powered interviews. "When you obsess over customers, everything else follows," Wahlforss said in an interview with VentureBeat. "Teams that use Listen bring the customer into every decision, from marketing to product, and when the customer is delighted, everyone is." Why traditional market research is broken, and what Listen Labs is building to fix it Listen's AI researcher finds participants, conducts in-depth interviews, and delivers actionable insights in hours, not weeks. The platform replaces the traditional choice between quantitative surveys — which provide statistical precision but miss nuance—and qualitative interviews, which deliver depth but cannot scale. Wahlforss explained the limitation of existing approaches: "Essentially surveys give you false precision because people end up answering the same question... You can't get the outliers. People are actually not honest on surveys." The alternative, one-on-one human interviews, "gives you a lot of depth. You can ask follow up questions. You can kind of double check if they actually know what they're talking about. And the problem is you can't scale that." The platform works in four steps: users create a study with AI assistance, Listen recruits participants from its global network of 30 million people, an AI moderator conducts in-depth interviews with follow-up questions, and results are packaged into executive-ready reports including key themes, highlight reels, and slide decks. What distinguishes Listen's approach is its use of open-ended video conversations rather than multiple-choice forms. "In a survey, you can kind of guess what you should answer, and you have four options," Wahlforss said. "Oh, they probably want me to buy high income. Let me click on that button versus an open ended response. It just generates much more honesty." The dirty secret of the $140 billion market research industry: rampant fraud Listen finds and qualifies the right participants in its global network of 30 million people. But building that panel required confronting what Wahlforss called "one of the most shocking things that we've learned when we entered this industry"—rampant fraud. "Essentially, there's a financial transaction involved, which means there will be bad players," he explained. "We actually had some of the largest companies, some of them have billions in revenue, send us people who claim to be kind of enterprise buyers to our platform and our system immediately detected, like, fraud, fraud, fraud, fraud, fraud." The company built what it calls a "quality guard" that cross-references LinkedIn profiles with video responses to verify identity, checks consistency across how participants answer questions, and flags suspicious patterns. The result, according to Wahlforss: "People talk three times more. They're much more honest when they talk about sensitive topics like politics and mental health." Emeritus, an online education company that uses Listen, reported that approximately 20% of survey responses previously fell into the fraudulent or low-quality category. With Listen, they reduced this to almost zero. "We did not have to replace any responses because of fraud or gibberish information," said Gabrielli Tiburi, Assistant Manager of Customer Insights at Emeritus. How Microsoft, Sweetgreen, and Chubbies are using AI interviews to build better products The speed advantage has proven central to Listen's pitch. Traditional customer research at Microsoft could take four to six weeks to generate insights. "By the time we get to them, either the decision has been made or we lose out on the opportunity to actually influence it," said Romani Patel, Senior Research Manager at Microsoft. With Listen, Microsoft can now get insights in days, and in many cases, within hours. The platform has already powered several high-profile initiatives. Microsoft used Listen Labs to collect global customer stories for its 50th anniversary celebration. "We wanted users to share how Copilot is empowering them to bring their best self forward," Patel said, "and we were able to collect those user video stories within a day." Traditionally, that kind of work would have taken six to eight weeks. Simple Modern, an Oklahoma-based drinkware company, used Listen to test a new product concept. The process took about an hour to write questions, an hour to launch the study, and 2.5 hours to receive feedback from 120 people across the country. "We went from 'Should we even have this product?' to 'How should we launch it?'" said Chris Hoyle, the company's Chief Marketing Officer. Chubbies, the shorts brand, achieved a 24x increase in youth research participation—growing from 5 to 120 participants — by using Listen to overcome the scheduling challenges of traditional focus groups with children. "There's school, sports, dinner, and homework," explained Lauren Neville, Director of Insights and Innovation. "I had to find a way to hear from them that fit into their schedules." The company also discovered product issues through AI interviews that might have gone undetected otherwise. Wahlforss described how the AI "through conversations, realized there were like issues with the the kids short line, and decided to, like, interview hundreds of kids. And I understand that there were issues in the liner of the shorts and that they were, like, scratchy, quote, unquote, according to the people interviewed." The redesigned product became "a blockbuster hit." The Jevons paradox explains why cheaper research creates more demand, not less Listen Labs is entering a massive but fragmented market. Wahlforss cited research from Andreessen Horowitz estimating the market research industry at roughly $140 billion annually, populated by legacy players — some with more than a billion dollars in revenue — that he believes are vulnerable to disruption. "There are very much existing budget lines that we are replacing," Wahlforss said. "Why we're replacing them is that one, they're super costly. Two, they're kind of stuck in this old paradigm of choosing between a survey or interview, and they also take months to work with." But the more intriguing dynamic may be that AI-powered research doesn't just replace existing spending — it creates new demand. Wahlforss invoked the Jevons paradox, an economic principle that occurs when technological advancements make a resource more efficient to use, but increased efficiency leads to increased overall consumption rather than decreased consumption. "What I've noticed is that as something gets cheaper, you don't need less of it. You want more of it," Wahlforss explained. "There's infinite demand for customer understanding. So the researchers on the team can do an order of magnitude more research, and also other people who weren't researchers before can now do that as part of their job." Inside the elite engineering team that built Listen Labs before they had a working toilet Listen Labs traces its origins to a consumer app that Wahlforss and his co-founder built after meeting at Harvard. "We built this consumer app that got 20,000 downloads in one day," Wahlforss recalled. "We had all these users, and we were thinking like, okay, what can we do to get to know them better? And we built this prototype of what Listen is today." The founding team brings an unusual pedigree. Wahlforss's co-founder "was the national champion in competitive programming in Germany, and he worked at Tesla Autopilot." The company claims that 30% of its engineering team are medalists from the International Olympiad in Informatics — the same competition that produced the founders of Cognition, the AI coding startup. The Berghain billboard stunt generated approximately 5 million views across social media, according to Wahlforss. It reflected the intensity of the talent war in the Bay Area. "We had to do these things because some of our, like early employees, joined the company before we had a working toilet," he said. "But now we fixed that situation." The company grew from 5 to 40 employees in 2024 and plans to reach 150 this year. It hires engineers for non-engineering roles across marketing, growth, and operations — a bet that in the AI era, technical fluency matters everywhere. Synthetic customers and automated decisions: what Listen Labs is building next Wahlforss outlined an ambitious product roadmap that pushes into more speculative territory. The company is building "the ability to simulate your customers, so you can take all of those interviews we've done, and then extrapolate based on that and create synthetic users or simulated user voices." Beyond simulation, Listen aims to enable automated action based on research findings. "Can you not just make recommendations, but also create spawn agents to either change things in code or some customer churns? Can you give them a discount and try to bring them back?" Wahlforss acknowledged the ethical implications. "Obviously, as you said, there's kind of ethical concerns there. Of like, automated decision making overall can be bad, but we will have considerable guardrails to make sure that the companies are always in the loop." The company already handles sensitive data with care. "We don't train on any of the data," Wahlforss said. "We will also scrub any sensitive PII automatically so the model can detect that. And there are times when, for example, you work with investors, where if you accidentally mention something that could be material, non public information, the AI can actually detect that and remove any information like that." How AI could reshape the future of product development Perhaps the most provocative implication of Listen's model is how it could reshape product development itself. Wahlforss described a customer — an Australian startup — that has adopted what amounts to a continuous feedback loop. "They're based in Australia, so they're coding during the day, and then in their night, they're releasing a Listen study with an American audience. Listen validates whatever they built during the day, and they get feedback on that. They can then plug that feedback directly into coding tools like Claude Code and iterate." The vision extends Y Combinator's famous dictum — "write code, talk to users" — into an automated cycle. "Write code is now getting automated. And I think like talk to users will be as well, and you'll have this kind of infinite loop where you can start to ship this truly amazing product, almost kind of autonomously." Whether that vision materializes depends on factors beyond Listen's control — the continued improvement of AI models, enterprise willingness to trust automated research, and whether speed truly correlates with better products. A 2024 MIT study found that 95% of AI pilots fail to move into production, a statistic Wahlforss cited as the reason he emphasizes quality over demos. "I'm constantly have to emphasize like, let's make sure the quality is there and the details are right," he said. But the company's growth suggests appetite for the experiment. Microsoft's Patel said Listen has "removed the drudgery of research and brought the fun and joy back into my work." Chubbies is now pushing its founder to give everyone in the company a login. Sling Money, a stablecoin payments startup, can create a survey in ten minutes and receive results the same day. "It's a total game changer," said Ali Romero, Sling Money's marketing manager. Wahlforss has a different phrase for what he's building. When asked about the tension between speed and rigor — the long-held belief that moving fast means cutting corners — he cited Nat Friedman, the former GitHub CEO and Listen investor, who keeps a list of one-liners on his website. One of them: "Slow is fake." It's an aggressive claim for an industry built on methodological caution. But Listen Labs is betting that in the AI era, the companies that listen fastest will be the ones that win. The only question is whether customers will talk back.
China Unveils World’s First AI Hospital: 14 Virtual Doctors Ready to Treat Thousands Daily
China has unveiled the world’s first fully AI-powered hospital, marking a radical shift in the future of healthcare. Developed by Tsinghua University in Beijing, the “Agent Hospital” features 14 AI doctors and 4 AI nurses that can diagnose, treat, and manage up to 3,000 patients per day, without any human staff. Faster, smarter care: What would take human doctors 3 years, the AI doctors can do in 1 day. High IQ bots: These AI agents scored a 93.06% pass rate on the US Medical Licensing Exam. Training without risk: The virtual hospital allows medical students to practice in a fully The post China Unveils World’s First AI Hospital: 14 Virtual Doctors Ready to Treat Thousands Daily appeared first on DailyAI.
How we used Gemini to build Google I/O 2026
Learn how Googlers used AI to produce Google I/O 2026.
SpaceX IPO: Live updates on everything you need to know
TechCrunch has followed SpaceX's start, struggles, and successes from the early days. And we're here for what happens next too. This package of SpaceX IPO coverage includes who stands to win (and maybe some who won't), pre-IPO deals, and what's tucked inside its S-1 registration document.
5 ways Google Search can level up your thrift and vintage shopping
Uncover second-hand scores with AI tools in Google Search and Shopping.
Calendly vs. Google Calendar: Which should you choose? [2026]
Here's Google's simple (but powerful) software playbook: find products people like, make a Google-ized copy, and give it away for free. Love Dropbox and Zoom? Google Drive and Google Meet are solid substitutes, and you won't pay a thing. Calendly is the next app on Google's radar. Google Calendar's appointment scheduling feature, which started off as a barebones alternative, has gotten better over time. It's not as powerful as Calendly, but it's a reliable way for Google users to create booking
Confidential submission of draft S-1 to the SEC
OpenAI confirms a confidential S-1 submission to the SEC and has not yet determined timing for further action.
Anthropic’s safety warnings may have just backfired — the government has pulled the plug on its most powerful AI
Anthropic isn't hiding its frustration. "We disagree that the finding of a narrow potential jailbreak should be cause for recalling a commercial model deployed to hundreds of millions of people," the company wrote in a blog post.
Which AI models can you automate on Zapier? (Opus 4.8, Gemini 3.5 Flash, and more)
New AI models launch practically every week, and keeping up with which ones to use for specific workflows is a job in itself. Consider this article your living reference. At Zapier, we run every model through AutomationBench. It's our benchmark for testing how well models carry out multi-step workflows, not just static prompts. Below, I'll walk through every major AI provider available on Zapier, the models you can plug into your Zap workflows today, and what each one is best for based on Zapier
Cheaper, faster, and culturally aware, Avataar’s video AI is built for India’s scale
Avataar AI's distilled video model is priced at $0.005 for every second of generation.
The 6 best AI governance tools in 2026
I'll never forget the first time my childhood dog betrayed me. Before the incident, she was completely fine alone, knew every trick in the book, and only barked at the mailman and other potential serial killers. Then came that fateful night. I left for two hours, returning to shredded magazines, ripped couch cushions, destroyed dog toys, and a wagging tail. Let my canine misfortunes be a lesson for your AI endeavors. AI can be useful, fully functional, and your best friend—until the day it isn'
ChatGPT Is Making People Think They’re Gods and Their Families Are Terrified
ChatGPT, the popular AI chatbot from OpenAI, is unintentionally leading users into full-blown spiritual delusions, and families are sounding the alarm. On Reddit’s r/ChatGPT forum, a chilling thread titled “ChatGPT induced psychosis” is gaining traction. Users are reporting a disturbing pattern: their loved ones are convinced that ChatGPT is a divine being, a spiritual guru, or even a portal to God. Rolling Stone journalist Miles Klee spoke directly with affected individuals. One woman shared how her partner became obsessed after ChatGPT gave him cosmic nicknames like “spiral starchild” and claimed he was on a divine mission. He ultimately told her The post ChatGPT Is Making People Think They’re Gods and Their Families Are Terrified appeared first on DailyAI.
The best customer experience software in 2026
I canceled a subscription recently because I started questioning my experience. I'd been loyal for months, but the customer-facing AI agents felt like they were regressing, it took forever to speak to a human support agent, and all of the branding started targeting a much younger audience. They probably logged it as general churn; that's fine, I'm sure "old man yells at cloud" isn't something they would put into a report. Every single touchpoint you have with a customer shapes their experience,
Google just redesigned the search box for the first time in 25 years — here’s why it matters more than you think.
For a quarter century, the Google search box has been one of the most recognizable interfaces in computing: a thin white rectangle, a blinking cursor, a few typed words, and a list of blue links. On Tuesday, Google will formally retire that paradigm. At its annual I/O developer conference, Google announced a sweeping redesign of the search box itself — the literal text field where billions of queries begin every day — transforming it from a simple keyword input into a dynamic, AI-driven conversation starter that can accept text, images, PDFs, videos, and even open Chrome tabs as inputs. The company is also merging its AI Overviews and AI Mode features into a single, seamless search flow, eliminating the friction that previously forced users to choose between a traditional results page and an AI-forward experience. Liz Reid, Google's vice president and head of Search, called it "the biggest upgrade to our iconic search box since its debut over 25 years ago" during a press briefing on Monday. The announcement arrived alongside a blizzard of other news — new Gemini models, a personal AI agent called Spark, an intelligent shopping cart, a reimagined developer platform — but the search box redesign may prove to be the most consequential. It is the clearest signal yet that Google views the future of its flagship product not as a place where users type fragmented keywords, but as an interface where they hold open-ended, multimodal conversations with an AI system backed by the entire web. The new search box expands, accepts files, and coaches you on what to ask The changes show a fundamental shift in how Google expects people to interact with the product that generates the vast majority of Alphabet's revenue. The box itself now dynamically expands to accommodate longer, more conversational queries. Where the old interface subtly encouraged brevity — a narrow field suited to two- or three-word keyword strings — the new design invites users to fully articulate complex questions in granular detail. It also now supports multimodal inputs directly. Users can upload images, PDFs, files, and videos, or drag in content from Chrome tabs, right from the main search interface. Previously, some of these capabilities existed in AI Mode, but reaching them required extra steps. Now they sit at the primary entry point. Google is also deploying what it describes as an AI-powered query suggestion system that "goes beyond autocomplete." Rather than simply predicting the next word a user might type based on popular searches, the system helps users formulate complex, nuanced queries — essentially coaching them toward the kind of detailed questions that AI Mode handles best. The new search box is starting to roll out immediately in all countries and languages where AI Mode is available. Google is merging AI overviews and AI mode into one seamless experience Perhaps more significant than the box itself is the architectural change happening behind it. Google is unifying AI Overviews — the AI-generated summary panels that appear atop traditional search results — with AI Mode, the more immersive conversational search experience the company launched at I/O one year ago. Starting Tuesday, this merged experience will be live across mobile and desktop worldwide. A user can type a question, receive an AI Overview alongside traditional results, and then continue directly into a back-and-forth AI Mode conversation to ask follow-up questions — all without navigating to a separate interface. Reid explained the logic during the press briefing: the new AI search box is "an upgrade of our traditional search box, and so the results take you directly to main search rather than AI mode." She noted that while some power users actively sought out AI Mode, "for most users, they don't actually want to have to think about, do they want more of a traditional page or an AI-forward search experience." The goal, she said, was to ensure that "for most users, they don't have to think about where to go, they can just go to the search box they're familiar with, and it feels like they get the best experience afterwards." One billion users and doubling queries reveal how fast search behavior is shifting Google's decision to redesign the foundational interface of its most important product did not happen in a vacuum. The company shared a set of usage statistics during the briefing that reveal just how rapidly user behavior is already changing. AI Mode, which launched in the United States at I/O 2025, has surpassed one billion monthly users in its first year. AI Mode queries have been doubling every quarter since launch. AI Overviews, the lighter-weight AI summaries, now reach more than 2.5 billion monthly users. And overall search query volume hit an all-time high last quarter — a data point the company had previously disclosed on its earnings call. Sundar Pichai, Google's CEO, framed these figures as evidence that AI features are additive, not cannibalistic, to search usage. "When people use our AI-powered features in search, they use search more," he said. He added that he loves "how search has become less about individual queries and feels more like an ongoing conversation, giving users deeper insights and connecting you with the vastness of the web." Reid reinforced the point: "It's not just that people are searching more, it's that they're searching differently. They're fully expressing their questions in granular detail, asking those follow-up questions and searching across modalities." Gemini 3.5 Flash gives Google's AI search the speed it needs to work at scale Under the hood, the new search experience runs on Gemini 3.5 Flash, Google's newest AI model, which the company also introduced at I/O. Google upgraded AI Mode's underlying model to 3.5 Flash to deliver what Reid described as "an even more powerful AI search experience." Gemini 3.5 Flash is the workhorse of this year's announcements. Google claims it outperforms its previous frontier model, Gemini 3.1 Pro, on nearly all benchmarks while running four times faster in output tokens per second than comparable frontier models. Pichai described it as being "in a league of its own in the top right quadrant" of the Artificial Analysis index, which plots intelligence against speed — meaning it delivers near-frontier quality at dramatically lower latency. That speed matters enormously for search. A conversational AI search experience that feels sluggish would be dead on arrival for a product that serves billions of queries daily. By coupling the redesigned interface with a model optimized for both quality and throughput, Google is attempting to make AI-powered search feel as instantaneous as the old keyword experience — while being dramatically more capable. Search can now build interactive visuals and custom mini apps on the fly The redesigned search box is also the gateway to a set of new capabilities that push search far beyond text-based answers. Google announced what it calls "generative UI" — the ability for search to dynamically build custom widgets, interactive visualizations, and even mini applications in real time, tailored to a user's specific question. Reid offered a concrete example during the briefing: a user could ask "How do black holes affect space time?" and receive an interactive visual in an AI Overview that brings the concept to life. Follow-up questions would trigger the system to dynamically generate entirely new visuals in real time. This is possible, she explained, because of "a novel real-time code generation system we built in partnership with the Google DeepMind team" that runs on Gemini 3.5 Flash. Generative UI capabilities will roll out to everyone this summer, free of charge. But Google is going further still. For ongoing tasks — planning a wedding, organizing a move, tracking a fitness routine — users will be able to build what the company describes as customizable, stateful experiences within search, powered by its Antigravity development platform. These require no coding expertise. Users simply describe what they want in natural language, and search builds it. Those experiences will be available in coming months, starting with Google AI Pro and Ultra subscribers in the United States. AI agents that monitor the web around the clock are coming to search results The redesign also opens the door to what Google calls "information agents" — AI agents that users can configure directly within search to monitor the web 24/7 for specific conditions and deliver synthesized updates when those conditions are met. A user could, for example, set up an agent to track market movements in a particular sector with specific parameters. The agent would create a monitoring plan, tap into real-time finance data, and proactively notify the user when conditions are met — complete with links and context for further research. Other use cases include apartment hunting, tracking sneaker drops, or monitoring any topic a user cares about. Information agents will launch first for Google AI Pro and Ultra subscribers this summer. These agents sit within a much larger strategic pivot that Google articulated throughout the briefing: the company is going all-in on AI systems that don't just answer questions but proactively take actions on users' behalf. Beyond search, Google introduced Gemini Spark, a 24/7 personal AI agent that runs on dedicated virtual machines in Google Cloud. It unveiled the Universal Cart, an intelligent cross-merchant shopping cart. It announced the Agent Payments Protocol for agents to make secure purchases. And it expanded its Antigravity developer platform into a full ecosystem for building autonomous AI agents. Publishers, advertisers, and SEO professionals face a new reality The redesign raises profound questions for the sprawling ecosystem — publishers, advertisers, SEO professionals — that has been built around the old model of keyword search and blue links. If users increasingly express their needs as full, conversational sentences rather than fragmented keywords, the entire discipline of search engine optimization will need to evolve. Keyword-density strategies become less relevant when the AI is parsing natural language intent rather than matching strings. Content that answers deep, nuanced questions in authoritative ways becomes more valuable; content engineered to rank for two-word keyword fragments becomes less so. For publishers, the stakes are existential. AI Overviews already synthesize information from across the web and present it directly in search results, reducing the need for users to click through to source material. The new seamless AI Mode integration deepens that dynamic: users can now get an AI-generated answer and ask multiple follow-up questions without ever leaving the search page. Google has consistently maintained that its AI features drive more traffic to publishers, but the redesign puts that claim under renewed scrutiny as the search results page becomes more self-contained. For advertisers — who fund the vast majority of Google's revenue — the shift from keywords to conversations changes the calculus of ad targeting. Conversational queries contain richer intent signals, which could make ad targeting more precise and valuable. But they also create new ambiguities: when a user is in the middle of a multi-turn conversation with AI Mode, where does an ad naturally fit? Google did not detail changes to its advertising model during the briefing, but the structural shift in the interface will inevitably reshape how ads are surfaced and measured. The search box was always more than a product — it was a habit for billions of people There is a reason Google chose to redesign the search box rather than simply adding new features behind it. The search box is not just a product element at this point; it is a cultural artifact — one of the few pieces of digital infrastructure used by essentially the entire internet-connected world. Changing it sends an unmistakable message about where the company believes computing is headed. For 25 years, the search box trained billions of people to think in keywords — to compress their curiosity into the shortest possible string of words. The new box invites them to do the opposite: to think out loud, to upload what they're looking at, to ask follow-up questions, to let an AI system handle the compression. Pichai tied the company's broader ambitions to a striking statistic: Google's surfaces now process over 3.2 quadrillion tokens per month, up seven-fold from a year ago. The company expects capital expenditures of approximately $180 to $190 billion in 2026 — roughly six times the $31 billion it spent four years ago — largely to support the infrastructure required for this AI transformation. When asked about the future of traditional search, he was direct. "Search is the most used AI product in the world," he said. The blinking cursor in Google's search box still invites you to type. But after 25 years of teaching the world to speak in keywords, Google is now asking it to speak in sentences — and betting roughly $190 billion that it will.
The 4 best AI website builders
Building a website is no longer a particularly hard task—but it can be an annoying one. If you look at most sites, there's a fair amount of text, images, and general organization to it all. Even with the best tools, it takes a few hours to put together something good. Wouldn't it be great if you could just create a website from scratch in just a few minutes? That's what AI website builders claim to do. The idea is that by using artificial intelligence, AI website builders can streamline everyth
Take our I/O 2026 quiz, vibe coded in Google AI Studio.
We used Google AI Studio to vibe code a quiz about our top I/O 2026 announcements.
Pope Leo XIV Declares AI a Threat to Human Dignity and Workers’ Rights
Pope Leo XIV is taking a bold stance on artificial intelligence, calling it “a challenge to human dignity, justice and labour” in his first major address since being elected leader of the Catholic Church. The new pontiff is placing AI at the center of the Church’s moral agenda, warning that we’re entering a new industrial revolution with the same threats to workers and human rights seen over a century ago. “In our own day… developments in the field of artificial intelligence pose new challenges,” Leo said, addressing the College of Cardinals on Saturday in the New Synod Hall. He echoed The post Pope Leo XIV Declares AI a Threat to Human Dignity and Workers’ Rights appeared first on DailyAI.