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A dozen years after its launch, fintech company Chime rang the bell this morning at the Nasdaq MarketSite in Times Square to celebrate its initial public offering. One of the most anticipated IPOs of the year, Chime is seemingly already basking in listing-day glory. After announcing that shares would hit the exchanges at a price of $27, investors were champing at the bit, and shares opened much higher, at around $43. That gave the company a valuation of around $18.4 billion. I never imagined when Chris [Britt, Chimes other cofounder and current CEO] and I met that wed be standing here today, Chime cofounder Ryan King tells Fast Company. Even if Chime does manage to reach that lofty $17 billion valuation, that is still a far cry from the $25 billion valuation it lassoed in 2021. Notably, another fintech company, eToro Group, also recently went public and has seen shares grow by 18% in the weeks since. Circle Internet Group, a stablecoin issuer, experienced a similar post-IPO boost in recent weeks. While we dont know where Chimes stock is headed, the companys leadership is happy to mark the milestone. Its a special moment, and Im feeling a lot of gratitude, King says. I figured itd be fun, wed learn some stuff, and build something new. But odds were, it wouldnt work out. That was the extent of it. But after building the company into one of the biggest fintechs on the market over the last 12 years, King says its a culmination of everything he and Britt have worked toward. Chime offers online banking products and services, many of which don’t add on additional fees or charges that may be associated with the same services at traditional banks. It has roughly 8.6 million members, and during 2014, generated $1.7 billion in revenue, a 31% increase over 2023. King says that the IPO timing was right for the company, despite volatility in the markets and political space. We have been preparing for this moment for a while. Were not trying to time the market, he says. We felt like the time was right. As for whats ahead, King says theres still more work to do. Two-thirds of the country are not well served by the big, traditional banks, and we feel like weve created a better model, he says. Were just getting started. Theres a lot more [work] in front of us than behind us.
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Welcome to AI Decoded, Fast Companys weekly newsletter that breaks down the most important news in the world of AI. You can sign up to receive this newsletter every week here. A cooler-headed look at Apples troubled AI story Apple said at its WWDC developer conference Monday that the much-hyped Apple Intelligence features it announced at last years event are still not ready to shipand likely wont be until 2026. Now, the company is living with a media narrative that its failed, fallen behind, stuck in neutral, or in retreat on AI. To be clear, Apples various operating systems already include AI-powered features, from photo cleanup to call screening. And the company did deliver some of the Apple Intelligence tools it promised last year. But those were first-generation large language model functions like text message summarization and writing assistanceunambitious by generative AI standards and not especially useful. What got everyone excited at WWDC 2024 were the advanced, personalized AI features that allow Siri to leverage a users personal data and carry out actions on their behalf. Some examples: A user could say, Hey Siri, when does Moms flight land? and Sirihaving access to the users email, contacts, calendar, and fileswould know the answer. Siri would also be able to see whats on the users screen, so it could, for example, read an address from a text message and add it to a contact card. A user could tell Siri to take action within an app, such as: Siri, take the red-eye out of this photo, or Send the email I drafted to Judy and Carmen. Cool stuff, useful, and things Apple is uniquely positioned to offer. So what happened? Apple software chief Craig Federighi and head of marketing Greg “Joz” Joswiak offered a fuller explanation during a round of media interviews Tuesday. According to them, Apple had a working version one of a new Siri architecture before announcing the personal AI features at WWDC 2024. (In other words: No, the demos werent vaporware.) The Siri team believed they could continue polishing the product until it performed reliably enough for a late 2024 release. But Federighi explained in an interview with TechRadar that the system continued to generate too many unreliable results in internal testing. When a year-end release became unrealistic, the team aimed for spring 2025. But by then, the system was still inconsistent. Thats when the Siri team concluded it would need to create a version two of the new Siri architectureone that extends across the entire Siri experience. Until that reworked Siri is performing reliably in-house, Federighi said, the company wont speculate on a public release date. Federighi also argued that Apple is trying to do something with personalized AI that nobody else has really done. Theres some truth to thatespecially if we limit the field to mobile-based AI assistants, and when were talking about doing it at Apples scale. Apple may look like an AI laggard now, but it would look far worse if it shipped something that didnt work or wasnt useful. Still, its not accurate to say Apple is the only one in the game. In May, Google announced a suite of personalized AI features that will begin rolling out this summer. Its Gemini assistant can use personal data in email to offer summaries, draft messages, provide reminders, and deliver context-aware insights. Gemini Live can analyze objects in the camera view and on the screen. And the agent mode in the Gemini app, built on Project Mariner, can perform multi-step tasks for users both online and within apps. Apples big mistake, of course, was announcing personal AI features in 2024 that it couldnt deliver in 2024or even 2025. That misstep might not have happened if this were just another set of traditional software updates. But were not in that era anymore. Apple is now operating in the realm of probabilistic computingthe domain of AI models. The usual rules for predicting software maturity and release timelines may no longer apply. Meta to rebrand itself MetaGPT (just kidding) Okay, forgive the Onion-esque headline, but it holds a seed of truth. Once again, Mark Zuckerberg is spending billions to push his company to the front of the line in the next big thing in consumer techthis time, its generative AI. (A few years ago, Facebook renamed itself Meta when it bet that the metaverse was the next big thing.) Now, The New York Times and Bloomberg report that the social media giantafter seeing its Llama models lag behind those from OpenAI, Google, and Anthropicis launching a new generative AI lab with ambitions of achieving superintelligence. (OpenAI, Anthropic, and others are pursuing artificial general intelligence, where AI matches or exceeds human abilities across a wide range of tasks. Superintelligence refers to AI that vastly outperforms humans at many tasks.) Zuckerberg has reportedly agreed to buy a 49% stake in Alexander Wangs Scale AIa company that specializes in human-labeled and synthetic training data for AI modelsfor nearly $15 billion. This appears to be another acquihire, where Metas primary interest is Wang himself. The 28-year-old will lead the new superintelligence group at Meta. (Microsoft made a similar move when it acquired Inflection AI and brought on its founder and CEO, Mustafa Suleyman.) Meta is also offering seven to nine figure salaries to attract top researchers to the new lab. The company already has a major AI figure in Yann LeCun, who leads its core AI research group. But The Times reports that Meta has experienced significant internal friction over how to approach the development and deployment of AI models. The companys focused AI efforts began in 2013 after it failed in a bid to acquire DeepMind, which ultimately went to Google. Unlike OpenAI and Anthropic, Meta doesnt rely on selling AI models or apps. Its primary business is advertising. Thanks to its massive ad revenue, Meta can afford to open-source its AI models, effectively giving developers free access in hopes of flooding the ecosystem and becoming dominant through ubiquity. But if Zuckerberg succeeds in getting Meta to the front of the superintelligence race, that strategy could shift. The company might begin locking down itsmodels and charging for accessjust like Google, OpenAI, and Anthropic. Hollywood is going after Midjourney, and it could be just the start Over the past couple of years, a range of content ownersnews organizations, authors, artists, comics, and record labelshave filed lawsuits against generative AI companies. Their claim: these companies used copyrighted content, usually scraped from the internet, to train AI models without permission or compensation. In response, AI firms often argue that their use of such material falls under the fair use provision of the Copyright Act. OpenAI and Microsoft, for example, have made that argument in an ongoing copyright case brought by The New York Times. Until now, the big Hollywood studios had mostly stayed on the sidelines. That changed today when the two largest, Universal and Disney, filed a joint copyright infringement lawsuit against Midjourney, one of the original AI image generators. (Midjourney is expected to roll out a new image-to-video feature this month, which may have prompted the legal action.) The studios have been under growing pressure from artists and writers to challenge AI labs over how training data is sourced. The Disney and Universal suit may be the opening salvo in a much broader conflict between Hollywood and the AI industry. Why start with Midjourney, a relatively small player, when far larger and wealthier AI companies have used the same kinds of scraped, copyrighted data to train their models? As The New York Times notes, the lawsuit appears to set the stage for something bigger. The language goes beyond a simple dispute between two companies. The plaintiffs argue that the use of copyrighted training data threatens to upend the bedrock incentives of U.S. copyright law that drive American leadership in movies, television and other creative arts. Stay tuned. More AI coverage from Fast Company: Databricks new One dashboard brings AI to the business class This corny conservative credit card ad signals a very scary future for AI OpenAI and Anthropic are getting cozier with government Teaching AI isnt enoughwe need to teach wisdom, too Want exclusive reporting and trend analysis on technology, business innovation, future of work, and design? Sign up for Fast Company Premium.
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Many developers find that AI programming assistants have made writing code easier than ever. But maintaining the infrastructure that actually runs that code remains a challenge, requiring engineers to have detailed knowledge of complex cloud systems and how their companies use them. A startup called Antimetal is working to harness AI to guide engineers through resolving issues with software infrastructure in much the same way existing tools help them write code. “We’re going to investigate that by piecing this data together, telling you this really thorough narrative of what happened and why, and leading you to prescriptive actions about what you should do next,” says Antimetal cofounder and CTO Shreyas Iyer. The company began about two-and-a-half years ago, initially focusing on helping businesses optimize their spending on Amazon Web Services. The cloud platform is known for offering a vast range of infrastructure for startups and enterprises to run software online, but it’s also notoriously difficult to optimize for cost. Unexpected eventssuch as a surge in app popularity or a simple coding or configuration issuecan trigger sudden spikes in costs as metered resources are consumed more rapidly than anticipated. Antimetal’s software can automatically recommend ways to reduce monthly bills and quickly alert engineers to unexpected cost spikes before they cause financial strain. But, says cofounder and CEO Matthew Parkhurst, cost management was only the starting point for building a broader AI-driven infrastructure management platform. “Cost is still very important to us,” he says. “But now we’re going a bit more broad in terms of more holistic management.” [Image: Courtesy of Antimetal] Antimetal, which recently secured a $20 million Series A funding round, is now beta testing technology that helps engineers address infrastructure issues as they arise. The AI can offer suggestions or automate tasks such as rolling back code or restarting systems to fix problems. It also integrates with a wide range of platforms, including code repositories like GitHub, logging and monitoring tools, and major cloud providers like AWS, Google Cloud Platform, Microsoft Azure, and Oracle Cloud Infrastructure. This allows it to fetch relevant data and propose potential solutions. It can even tap into companies ticketing platforms to understand how previous issues were resolved. “What we’re doing to start is plugging into effectively every surface area of your infrastructure stack, your observability platform, your cloud, your ticketing system, your source code,” Iyer says. The software, expected to be publicly available later this year, is designed to learn how individual customers prefer to approach various issues, and which solutions tend to be most effective. Many customers begin by granting read-only access, allowing the software to analyze data and offer insights without making changes. As trust grows, they can assign more granular permissionssuch as letting it roll back problematic configuration changes on its own. “We’re not trying to jump to full automation out of the gate,” Iyer says. “We want to build this sort of incremental flow from being an autopilot and assisting you in your time of need to actually automating.” Looking ahead, Antimetal also aims to assist with new software deployments. The system could help teams anticipate potential issues and set up the necessary infrastructure to support new code from day one, Iyer says. The companys original pricing model for its cost optimization tool was based on a percentage of customers’ AWS spending. Parkhurst says pricing for the new AI capabilities will likely be usage-based, though specifics are still being finalized while the product is in beta. He sees strong demand for AI that can support the scaling and maintenance of software productsa task that for many companies is now more daunting than writing the code itself. “Writing code is not the hardest thing anymore,” Parkhurst says. “It’s maintaining it that is, by far, the biggest issue when building a company, or really anything, and serving it to the world.”
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