Virtual outreach event on FSB sound practices for financial institutions’ responsible adoption of artificial intelligence - Financial Stability Board
Virtual outreach event on FSB sound practices for financial institutions’ responsible adoption of artificial intelligence Financial Stability Board
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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 "The last generation of cloud primitives were slow and outdated, and now with AI moving everything faster, teams simply can't keep up."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.
Gemini’s personalized AI image generation is now free for US users
Google is expanding Gemini’s personalized AI image generation to eligible free users in the U.S., allowing the chatbot to create images based on your interests and data from connected Google apps.
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Using AI to help physicians diagnose rare genetic diseases affecting children
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Unlocking Britain’s next era of productivity: Building a nation of AI trailblazers
Google UK shares its latest Economic Impact Report and how to enable more people to unlock the benefits of AI-powered technologies.
Salesforce rolls out new Slackbot AI agent as it battles Microsoft and Google in workplace AI
Salesforce on Tuesday launched an entirely rebuilt version of Slackbot, the company's workplace assistant, transforming it from a simple notification tool into what executives describe as a fully powered AI agent capable of searching enterprise data, drafting documents, and taking action on behalf of employees. The new Slackbot, now generally available to Business+ and Enterprise+ customers, is Salesforce's most aggressive move yet to position Slack at the center of the emerging "agentic AI" movement — where software agents work alongside humans to complete complex tasks. The launch comes as Salesforce attempts to convince investors that artificial intelligence will bolster its products rather than render them obsolete. "Slackbot isn't just another copilot or AI assistant," said Parker Harris, Salesforce co-founder and Slack's chief technology officer, in an exclusive interview with Salesforce. "It's the front door to the agentic enterprise, powered by Salesforce." From tricycle to Porsche: Salesforce rebuilt Slackbot from the ground up Harris was blunt about what distinguishes the new Slackbot from its predecessor: "The old Slackbot was, you know, a little tricycle, and the new Slackbot is like, you know, a Porsche." The original Slackbot, which has existed since Slack's early days, performed basic algorithmic tasks — reminding users to add colleagues to documents, suggesting channel archives, and delivering simple notifications. The new version runs on an entirely different architecture built around a large language model and sophisticated search capabilities that can access Salesforce records, Google Drive files, calendar data, and years of Slack conversations. "It's two different things," Harris explained. "The old Slackbot was algorithmic and fairly simple. The new Slackbot is brand new — it's based around an LLM and a very robust search engine, and connections to third-party search engines, third-party enterprise data." Salesforce chose to retain the Slackbot brand despite the fundamental technical overhaul. "People know what Slackbot is, and so we wanted to carry that forward," Harris said. Why Anthropic's Claude powers the new Slackbot — and which AI models could come next The new Slackbot runs on Claude, Anthropic's large language model, a choice driven partly by compliance requirements. Slack's commercial service operates under FedRAMP Moderate certification to serve U.S. federal government customers, and Harris said Anthropic was "the only provider that could give us a compliant LLM" when Slack began building the new system. But that exclusivity won't last. "We are, this year, going to support additional providers," Harris said. "We have a great relationship with Google. Gemini is incredible — performance is great, cost is great. So we're going to use Gemini for some things." He added that OpenAI remains a possibility as well. Harris echoed Salesforce CEO Marc Benioff's view that large language models are becoming commoditized: "You've heard Marc talk about LLMs are commodities, that they're democratized. I call them CPUs." On the sensitive question of training data, Harris was unequivocal: Salesforce does not train any models on customer data. "Models don't have any sort of security," he explained. "If we trained it on some confidential conversation that you and I have, I don't want Carolyn to know — if I train it into the LLM, there is no way for me to say you get to see the answer, but Carolyn doesn't." Inside Salesforce's internal experiment: 80,000 employees tested Slackbot with striking results Salesforce has been testing the new Slackbot internally for months, rolling it out to all 80,000 employees. According to Ryan Gavin, Slack's chief marketing officer, the results have been striking: "It's the fastest adopted product in Salesforce history." Internal data shows that two-thirds of Salesforce employees have tried the new Slackbot, with 80% of those users continuing to use it regularly. Internal satisfaction rates reached 96% — the highest for any AI feature Slack has shipped. Employees report saving between two and 20 hours per week. The adoption happened largely organically. "I think it was about five days, and a Canvas was developed by our employees called 'The Most Stealable Slackbot Prompts,'" Gavin said. "People just started adding to it organically. I think it's up to 250-plus prompts that are in this Canvas right now." Kate Crotty, a principal UX researcher at Salesforce, found that 73% of internal adoption was driven by social sharing rather than top-down mandates. "Everybody is there to help each other learn and communicate hacks," she said. How Slackbot transforms scattered enterprise data into executive-ready insights During a product demonstration, Amy Bauer, Slack's product experience designer, showed how Slackbot can synthesize information across multiple sources. In one example, she asked Slackbot to analyze customer feedback from a pilot program, upload an image of a usage dashboard, and have Slackbot correlate the qualitative and quantitative data. "This is where Slackbot really earns its keep for me," Bauer explained. "What it's doing is not just simply reading the image — it's actually looking at the image and comparing it to the insight it just generated for me." Slackbot can then query Salesforce to find enterprise accounts with open deals that might be good candidates for early access, creating what Bauer called "a really great justification and plan to move forward." Finally, it can synthesize all that information into a Canvas — Slack's collaborative document format — and find calendar availability among stakeholders to schedule a review meeting. "Up until this point, we have been working in a one-to-one capacity with Slackbot," Bauer said. "But one of the benefits that I can do now is take this insight and have it generate this into a Canvas, a shared workspace where I can iterate on it, refine it with Slackbot, or share it out with my team." Rob Seaman, Slack's chief product officer, said the Canvas creation demonstrates where the product is heading: "This is making a tool call internally to Slack Canvas to actually write, effectively, a shared document. But it signals where we're going with Slackbot — we're eventually going to be adding in additional third-party tool calls." MrBeast's company became a Slackbot guinea pig—and employees say they're saving 90 minutes a day Among Salesforce's pilot customers is Beast Industries, the parent company of YouTube star MrBeast. Luis Madrigal, the company's chief information officer, joined the launch announcement to describe his experience. "As somebody who has rolled out enterprise technologies for over two decades now, this was practically one of the easiest," Madrigal said. "The plumbing is there. Slack as an implementation, Enterprise Tools — being able to turn on the Slackbot and the Slack AI functionality was as simple as having my team go in, review, do a quick security review." Madrigal said his security team signed off "rather quickly" — unusual for enterprise AI deployments — because Slackbot accesses only the information each individual user already has permission to view. "Given all the guardrails you guys have put into place for Slackbot to be unique and customized to only the information that each individual user has, only the conversations and the Slack rooms and Slack channels that they're part of—that made my security team sign off rather quickly." One Beast Industries employee, Sinan, the head of Beast Games marketing, reported saving "at bare minimum, 90 minutes a day." Another employee, Spencer, a creative supervisor, described it as "an assistant who's paying attention when I'm not." Other pilot customers include Slalom, reMarkable, Xero, Mercari, and Engine. Mollie Bodensteiner, SVP of Operations at Engine, called Slackbot "an absolute 'chaos tamer' for our team," estimating it saves her about 30 minutes daily "just by eliminating context switching." Slackbot vs. Microsoft Copilot vs. Google Gemini: The fight for enterprise AI dominance The launch puts Salesforce in direct competition with Microsoft's Copilot, which is integrated into Teams and the broader Microsoft 365 suite, as well as Google's Gemini integrations across Workspace. When asked what distinguishes Slackbot from these alternatives, Seaman pointed to context and convenience. "The thing that makes it most powerful for our customers and users is the proximity — it's just right there in your Slack," Seaman said. "There's a tremendous convenience affordance that's naturally built into it." The deeper advantage, executives argue, is that Slackbot already understands users' work without requiring setup or training. "Most AI tools sound the same no matter who is using them," the company's announcement stated. "They lack context, miss nuance, and force you to jump between tools to get anything done." Harris put it more directly: "If you've ever had that magic experience with AI — I think ChatGPT is a great example, it's a great experience from a consumer perspective — Slackbot is really what we're doing in the enterprise, to be this employee super agent that is loved, just like people love using Slack." Amy Bauer emphasized the frictionless nature of the experience. "Slackbot is inherently grounded in the context, in the data that you have in Slack," she said. "So as you continue working in Slack, Slackbot gets better because it's grounded in the work that you're doing there. There is no setup. There is no configuration for those end users." Salesforce's ambitious plan to make Slackbot the one 'super agent' that controls all the others Salesforce positions Slackbot as what Harris calls a "super agent" — a central hub that can eventually coordinate with other AI agents across an organization. "Every corporation is going to have an employee super agent," Harris said. "Slackbot is essentially taking the magic of what Slack does. We think that Slackbot, and we're really excited about it, is going to be that." The vision extends to third-party agents already launching in Slack. Last month, Anthropic released a preview of Claude Code for Slack, allowing developers to interact with Claude's coding capabilities directly in chat threads. OpenAI, Google, Vercel, and others have also built agents for the platform. "Most of the net-new apps that are being deployed to Slack are agents," Seaman noted during the press conference. "This is proof of the promise of humans and agents coexisting and working together in Slack to solve problems." Harris described a future where Slackbot becomes an MCP (Model Context Protocol) client, able to leverage tools from across the software ecosystem — similar to how the developer tool Cursor works. "Slack can be an MCP client, and Slackbot will be the hub of that, leveraging all these tools out in the world, some of which will be these amazing agents," he said. But Harris also cautioned against over-promising on multi-agent coordination. "I still think we're in the single agent world," he said. "FY26 is going to be the year where we started to see more coordination. But we're going to do it with customer success in mind, and not demonstrate and talk about, like, 'I've got 1,000 agents working together,' because I think that's unrealistic." Slackbot costs nothing extra, but Salesforce's data access fees could squeeze some customers Slackbot is included at no additional cost for customers on Business+ and Enterprise+ plans. "There's no additional fees customers have to do," Gavin confirmed. "If they're on one of those plans, they're going to get Slackbot." However, some enterprise customers may face other cost pressures related to Salesforce's broader data strategy. CIOs may see price increases for third-party applications that work with Salesforce data, as effects of higher charges for API access ripple through the software supply chain. Fivetran CEO George Fraser has warned that Salesforce's shift in pricing policy for API access could have tangible consequences for enterprises relying on Salesforce as a system of record. "They might not be able to use Fivetran to replicate their data to Snowflake and instead have to use Salesforce Data Cloud. Or they might find that they are not able to interact with their data via ChatGPT, and instead have to use Agentforce," Fraser said in a recent CIO report. Salesforce has framed the pricing change as standard industry practice. What Slackbot can do today, what's coming in weeks, and what's still on the roadmap The new Slackbot begins rolling out today and will reach all eligible customers by the end of February. Mobile availability will complete by March 3, Bauer confirmed during her interview with VentureBeat. Some capabilities remain works in progress. Calendar reading and availability checking are available at launch, but the ability to actually book meetings is "coming a few weeks after," according to Seaman. Image generation is not currently supported, though Bauer said it's "something that we are looking at in the future." When asked about integration with competing CRM systems like HubSpot and Microsoft Dynamics, Salesforce representatives declined to provide specifics during the interview, though they acknowledged the question touched on key competitive differentiators. Salesforce is betting the future of work looks like a chat window—and it's not alone The Slackbot launch is Salesforce's bet that the future of enterprise work is conversational — that employees will increasingly prefer to interact with AI through natural language rather than navigating traditional software interfaces. Harris described Slack's product philosophy using principles like "don't make me think" and "be a great host." The goal, he said, is for Slackbot to surface information proactively rather than requiring users to hunt for it. "One of the revelations for me is LLMs applied to unstructured information are incredible," Harris said. "And the amount of value you have if you're a Slack user, if your corporation uses Slack — the amount of value in Slack is unbelievable. Because you're talking about work, you're sharing documents, you're making decisions, but you can't as a human go through that and really get the same value that an LLM can do." Looking ahead, Harris expects the interfaces themselves to evolve beyond pure conversation. "We're kind of saturating what we can do with purely conversational UIs," he said. "I think we'll start to see agents building an interface that best suits your intent, as opposed to trying to surface something within a conversational interface that matches your intent." Microsoft, Google, and a growing roster of AI startups are placing similar bets — that the winning enterprise AI will be the one embedded in the tools workers already use, not another application to learn. The race to become that invisible layer of workplace intelligence is now fully underway. For Salesforce, the stakes extend beyond a single product launch. After a bruising year on Wall Street and persistent questions about whether AI threatens its core business, the company is wagering that Slackbot can prove the opposite — that the tens of millions of people already chatting in Slack every day is not a vulnerability, but an unassailable advantage. Haley Gault, the Salesforce account executive in Pittsburgh who stumbled upon the new Slackbot on a snowy morning, captured the shift in a single sentence: "I honestly can't imagine working for another company not having access to these types of tools. This is just how I work now." That's precisely what Salesforce is counting on.
Zapier SDK: Connect your code files to thousands of actions
Right when I perfected my AI chatbot workflows, I found out all the cool kids had already migrated to building with AI coding agents. So I made the switch. And luckily for me, technical builders, and fellow vibe coders everywhere, Zapier SDK launched right on cue. Zapier SDK is a resource that gives AI coding agents access to pre-built app integrations in the Zapier directory. They all run through Zapier's governance layer, so you can build safely while you carry out more than 30,000 actions in
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Samsung Electronics brings ChatGPT and Codex to employees
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Therapists Too Expensive? Why Thousands of Women Are Spilling Their Deepest Secrets to ChatGPT
More women are turning to ChatGPT for emotional support, using the AI chatbot as a stand-in therapist as mental health systems buckle under pressure. With long wait times and soaring costs, AI is filling a growing gap. Mental health care is harder to access than ever. In the UK, NHS data shows patients are eight times more likely to wait over 18 months for mental health treatment than for physical health. Private therapy isn’t always an option either, with sessions costing £60 or more. In that vacuum, ChatGPT has become a surprising outlet. Real voices, real feelings Charly, 29, from The post Therapists Too Expensive? Why Thousands of Women Are Spilling Their Deepest Secrets to ChatGPT appeared first on DailyAI.
We’re strengthening our presence in Alabama through new investments and community support.
Google has announced a $1.5 billion investment for 2026 and 2027 to expand its data center campus in Jackson County, Alabama. Operating since 2019 on a repurposed former…
Cheaper AI is better: Soaring bills are reshaping how businesses choose models - Reuters
Cheaper AI is better: Soaring bills are reshaping how businesses choose models Reuters
Murder Victim Speaks from the Grave in Courtroom Through AI
Chris Pelkey was shot and killed in a road rage incident. At his killer’s sentencing, he forgave the man via AI. In a historic first for Arizona, and possibly the U.S., artificial intelligence was used in court to let a murder victim deliver his own victim impact statement. What happened Pelkey, a 37-year-old Army veteran, was gunned down at a red light in 2021. This month, a realistic AI version of him appeared in court to address his killer, Gabriel Horcasitas. “In another life, we probably could’ve been friends,” said AI Pelkey in the video. “I believe in forgiveness, and The post Murder Victim Speaks from the Grave in Courtroom Through AI appeared first on DailyAI.
Best Universities To Study AI in 2026
Artificial intelligence has made enormous strides in the past few years – with the introduction of a wide range of AI tools changing the landscape of how we assess data and operate within online spaces forever. This page ranks the 50 best universities to study AI around the world, based on scope, prestige, and the level of AI-related research each institution has released. Career prospects in AI There is a huge demand for individuals with a high degree of skills in artificial intelligence and machine learning, making AI a potential lucrative career prospect with countless opportunities as AI continues to The post Best Universities To Study AI in 2026 appeared first on DailyAI.
How agents are transforming work
A new OpenAI research paper shows how AI agents are transforming work, enabling longer, more complex tasks and expanding productivity across roles.
The AI jobs debate just got messier
A new report finds "high-intensity AI adopters” saw headcount increase 10.2%. Among those companies, entry-level headcount rose by 12%, countering the rhetoric that AI kills junior jobs.
How to improve AI agent performance
Trusting a new AI agent you just released can take time. You run it through your work data, watch it closely for days and weeks, always judging if it's working for you or against you. Just when you're starting to relax and enjoying the productivity boost, the AI provider launches a model update: the responses have now shifted, your instructions get interpreted differently, and you're back to zero trust again. So… progress? Improving AI agent performance is an ongoing process—not a one-time setup
Vibe coding platform Base44 launches own model as AI startups seek defensibility
Wix-owned vibe coding platform Base44 has started rolling out its own AI model — with hopes that it will eventually outperform frontier models.
What is AI agent orchestration?
You start with one AI agent to save time. A month later, you've got prompts in a doc, outputs in Slack, half-finished automations in three places, and the same request getting handled a dozen different ways depending on who saw it first. That's what happens when businesses try to "do AI" by building roughly 43 agents with no plan in place to coordinate them. AI agent orchestration solves this problem. Instead of relying on a single, general-purpose AI agent to do everything (which rarely works),
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.
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