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AI coding agents have become one of the fastest-growing categories in enterprise software. In the span of just a few years, these development tools have evolved from simple autocomplete assistants into autonomous systems capable of taking over the complete software development cycle, all via natural language prompts. As vibe-coding takes off, tools from startups like Cursor and Anthropics Claude Code have quickly reached multibilliondollar revenue run rates. Cursor reportedly crossed $1 billion in annual recurring revenue (ARR) in 2025 and has since approached $2 billion in Q1 of 2026. Anthropics Claude Code has scaled even faster, reaching an estimated $2.5 billion annualized run rate within its first year, making it one of the fastestgrowing products in the category that accounts for a large share of Anthropics $14 billion ARR. Yet inside large enterprises, writing code is rarely the hardest part of the job. Data scientists, engineers, and analysts spend much of their time maintaining and augmenting pipelines rather than building new ones. The real bottleneck in enterprise AI, therefore, is not software development itself, but operating complex data systems in production. Databricks CEO and co-founder Ali Ghodsi believes that the gap represents the next frontier for AI automation. In his view, the next generation of AI agents wont just write software, but operate the data systems that modern businesses depend on. That strategic bet is behind Genie Code, a system of autonomous AI agents unveiled today, designed for data engineering, data science, and analytics operations. The system extends the companys existing Genie platform ecosystem, which allows knowledge workers to ask questions about enterprise data in natural language. (More than 20,000 organizations already used Databrickss data management and analytics tools; the companys ARR surpassed $5.4 billion annual revenue in February.) Instead of functioning merely as a coding assistant or helping generate code faster, these agents actually understand the structure of the data and existing data problems, Ali Ghodsi says. It can automatically set up pipelines, analyze why something is failing, and understand issues like when a dataset schema changes or when permissions are modified. For instance, Genie Code can help determine how a dataset should be prepared for modelingrandomizing the data, separating part of it into a test set, or training a model on the remaining portion. After training, the system can aid in evaluating the results using metrics such as F1 scores or the area under the curve, and then analyzing them to determine whether the model is performing well or requires improvement.It can suggest trying different approachesmaybe retraining the model or generating plots and graphs to visualize performance, and uncover reasoning about what changes might improve the results, Ghodsi explains. Its not about just generating random code snippets, but understanding the entire structure of the data problem and working through the modeling workflow the same way a data scientist or engineer would. Databricks and Enterprise Context A major reason many AI coding agents struggle in enterprise data environments is context. Most developer tools train primarily on public code repositories and general programming examples. Enterprise data systems, however, add another layer of complexity. Data carries business semantics, governance rules, and access policies that determine how information can be used. Without that context, an AI agent may generate technically correct code that fails once deployed in production. Genie Code attempts to address that problem by integrating directly with Unity Catalog, Databricks governance framework for enterprise data. This integration allows the system to understand data lineage, access permissions, and organizational policies across an enterprises entire data estate. Maintaining pipelines and making sure they are reliable and always running is a big part of a data engineers job, and this is where Genie Code can augment them significantly, Ghodsi says. It can monitor systems continuously and respond immediately when something breaks, even in the middle of the night, analyzing complex traces and diagnosing what happened so that the pipeline can be fixed and kept running reliably. The architecture relies on a multi-agent architecture powered by multiple AI models. Ghodsi explains that the system combines LLMs from providers including Anthropic, OpenAI, and Google, alongside smaller open-source models optimized for specific tasks. There are many things inside a workflow where you dont need a huge modelyou just need something fast that can perform a very specific operation reliably. The larger models provide the reasoning capabilities necessary for complex problem-solving and planning. Smaller open-source models are trained to handle more routine operations quickly and efficiently. Moreover, the architecture is built around multiple collaborating agents rather than a single monolithic AI system. Each agent specializes in particular functions, such as diagnosing pipeline failures or analyzing data patterns. These agents share context, memory, and skills, allowing them to coordinate their actions and execute complex workflows across the data stack. Databricks describes this approach as agentic data work. Rather than prompting an AI assistant for small pieces of code, users can delegate entire objectives to the system. Another challenge with autonomous AI systems is maintaining reliable performance in production environments over time, as agents often encounter unfamiliar scenarios that degrade performance. To address that issue, Databricks has acquired Quotient AI, a startup specializing in evaluation and reinforcement learning for AI agents. The companys technology helps evaluate agent behavior, continuously measuring output quality and detecting regressions before they cause production failures. Quotient AIs founders previously worked on improving the quality of GitHub Copilot, giving them deep expertise in evaluating AI coding systems. Vibe-coding for data systems The rise of vibe-coding has created a new battleground for agentic AI-powered coding tools and reshaped the competitive landscape in software infrastructure. Databricks is approaching the market from a different direction. Ghodsi says the AI coding market and the enterprise data automation market are evolving in parallel but distinct directions. While tools like Cursor and Anthropics coding agents are reshaping how developers write software, Databricks is focused on transforming how companies manage and operate their data systems. Even though our product name includes code, what it really focuses on is data work, Ghodsi says. Genie Code targets the workflows that occur after data enters an organizations platform. By focusing on the data layer, the company aims to address problems that general-purpose coding assistants are not designed to solve. The other tools in the market help software engineers write application code, which is great, says Ghodsi, But for us the end goal is the data: transforming data reliably, and helping organizations work with their data. Several organizations, including SiriusXM and Repsol, have already begun experimenting with the technology. SiriusXM uses Genie Code to help build and maintain internal data products, generate SQL queries, nd debug pipelines. According to Ghodsi, the company has reported around 20% productivity improvements in data engineering tasks. Genie Code assists engineers in creating data products with defined service-level agreements and reliability guarantees. Likewise, multinational energy and petrochemical company Repsol is using the technology to accelerate forecasting and production workflows. Instead of manually connecting notebooks, pipelines, and models across different systems, engineers can rely on Genie Code to orchestrate these processes automatically. Ghodsi added that thousands of other customers are already experimenting with the technology, although many deployments are still in early stages. The Future of Human Engineering Ghodsi does not expect autonomous agents to replace human engineers. Instead, engineers may spend less time writing code and more time designing architectures, supervising automated systems, and ensuring that AI-driven workflows operate reliably. The cost of automation is going down and the tools are becoming easier to use, so naturally the demand for automation increases. If you look at some of the numbers already, a huge percentage of activity on machines is actually agents operating in the background, he says. According to the companys recently released State of AI Agents report, AI agents now create 80% of databases and 97% of test and development environments on the Databricks platform. Just two years ago, agents barely registered in database activity, with human developers handling nearly all of that work. I wouldnt be surprised if that number goes from something like 80% to 99% in a short period of time. But that doesnt mean humans disappear from the process, Ghodsi explains. You also have to think about legal responsibility and quality guarantees. Those are areas where you still need a human in the loop.
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Each year, some of Americas greatest artists, thinkers, and business leaders have a chance to come together at SXSW in the spirit of creativity, innovation, and future-building. And with everything currently happening in technology and the workforce, this years gathering feels particularly timely. Of course, questions around AI will take center stage and remain our primary cultural fixation: How long until the next incredible breakthrough? Should Americans be fearful about an impending AI apocalypse or hopeful about the prospect of unlimited productivity gains? These topics are all valid, urgent, and deeply worthwhile to explore, but I also believe the most important workforce story unfolding in the U.S. today is less about what AI will do next, and more about what everyday Americans are doing right now in response to and in preparation for AIs growing impacts. If technological advancement is going to keep accelerating faster than our institutions can or are willing to adapt, the fact that workers have already begun adapting on their own in real time is a story of deep-rooted resilience within our culture and communities. It is also a story that seems to be signaling a pragmatic and optimistic reimagining of the American Dream. WORKFORCE DISRUPTION IS WELL UNDERWAY The speed of AI advancement is likely to continue to be astonishing. Although we can neither predict nor control the pace of innovation, we can acknowledge that AI is no longer a hypothetical but an economic force reshaping job security, hiring, and career planning. We also need to understand that while AI adoption has added pressure, workforce fragility in the U.S. was deepening long before generative models like ChatGPT entered the picture. Education costs have been compounding at an unhealthy rate in America for nearly half a century, with rising tuition costs significantly outpacing inflation since the 1980s. Meanwhile, the countrys student debt crisis also continues deepening, with total student loan debt in the U.S. exceeding $1.7 trillion in 2024, all while broader confidence in traditional education and career pathways has been gradually eroding. AI isnt causing workforce uncertainty but merely adding weight on top of existing cracks in the system. To focus solely on predicting the pace and extent of AI-driven job loss misses the real story: U.S. workers are already adapting, and its a process involving a bold reimagining of American values and stability. AMERICANS ARE CHOOSING DURABILITY Despite so much uncertainty, Americans dont appear to be giving in to fear as much as theyre leaning into resilience and practical decision-making. There are some strong cultural signals indicating a radical shift in the U.S. workforces strategic mindset, particularly in evolving views around traditional education and career pathways in this AI age. More specifically, a new survey of American workers we conducted at the Business For Good Foundation via the Harris Poll revealed a clear and widespread departure from most conventional ways of thinking about professional and economic fulfillment. For example, 75% of Americans shared that their views of a good job does not look the same now as five years ago, while 80% agreed more people are choosing trade training over four-year degree programs. Similarly, more than 78% said they believe long-lasting social and cultural stigmas around blue-collar work are beginning to dissipate in the U.S., with 76% saying they believe trade jobs are less likely to be replaced by AI. Rather than fearing widespread job loss and sustained unemployment, Americans are envisioning a future workforce defined by durability, where the workforces economic value is concentrated less in white-collar sectors and more by the durable, hands-on skills that have always played an indispensable role. It suggests an overall mood of pragmatic optimism, with Americans appearing to adjust to AI adoption much faster than our political and educational systems. GET AHEAD OF CHANGE While everyday Americans seem eager to get ahead of AIs inevitable changes, this likely wont happen at scale without the appropriate support from organizations and U.S. business leaders. Recognizing this heightened need for more hands-on programs to increase access to skilled trade training, we at the Business for Good Foundation committed $100,000 to advancing workforce development in the first half of 2026. Of course, this will also require strategy and coordination, grounded in shared recognition that this shift away from traditional white-collar pathways is not an error but a process of economic regeneration. The growing emphasis on hands-on trades is not nostalgia, but necessary to strengthen the U.S. innovation infrastructure. Skilled work continues to underpin all non-negotiable aspects of American society, including access to housing and healthcare. At the same time, U.S. business owners are grappling with critical, pre-existing skilled labor shortages, meaning theyll increasingly need to depend on talent pipelines beyond traditional degree models. One recent example of what weve done at the Business for Good Foundation is a New York Capital Region pilot. As part of our commitment to workforce development, the foundation awarded a $25,000 grant to the Social Enterprise and Training (SEAT) Center to expand trade skills programming in the region and help bridge the gap between untapped talent and industry demand. Ive seen firsthand that simple, practical investments like in the SEAT Centerthose that better align workforce pathways with employer needs and expand access to education and career opportunities for motivated talent in underserved communitiescan go a long way toward creating a real and sustainable path to upward economic mobility. Im encouraging leaders across the country to take similar action, at any scale. However, such a model will largely remain limited without other like-minded business leaders and philanthropists willing to build on and replicate it at scale, and who are prepared to fully embrace a new American dream defined less by credentials and more by individual capabilities, determination, and human resilience. While this kind of change certainly wont happen overnight, I hope that those of us who attend SXSW this week might begin aligning our business priorities with the unique spirit of this event, working together to intentionally build a brighter, more prosperous, and innovative future for the U.S. workforce. Ed Mitzen is cofounder of Business for Good Foundation.
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Apples iOS 26 for iPhone got off to a rough start when it was finally released to the public in September of last year. Its new Liquid Glass design language remained unpolished in many areas, and the operating system harbored a fair amount of bugs. But since iOS 26.0 debuted, Apple has released three major updates for it, further polishing the interface and adding new features. And soon, Apple will update iOS 26 once again with the release of iOS 26.4. Its a release that is set to not just eliminate bugs and enhance the details of Liquid Glass, but is also set to add some significant new features to your iPhone. Heres whats coming, and when you can get iOS 26.4. What new features are coming to iOS 26.4? Apple has been beta testing iOS 26.4 since last month. Originally, the software update was rumored to include the companys revamped Siri, powered by Googles Gemini LLM. However, Siris AI revamp has been absent from all iOS 26.4 betas to date, so it looks like a truly useful Apple digital assistant is still a ways away. But that doesnt mean iOS 26.4 doesnt have any new features. Quite the contrary. Besides your normal user interface polishes and bug fixes, iOS 26.4 is set to include some major upgrades to its media apps, notes 9to5Mac. Those upgrades include: AI-powered music playlist creation: iOS 26.4 will add a feature to the Music app called Playlist Playground. The feature allows you to generate music playlists from natural-language text descriptions. So you could instruct the Playlist Playground feature to make a playlist of 80s rock ballads under five minutes long, and the Music app will generate a playlist based on your prompt. Podcasts app video overhaul: Apples Podcasts app has supported video podcasts for some time. But in iOS 26.4, its video capabilities are getting a major upgrade. Now you can quickly switch between the audio and video versions of a podcast. This feature will be great for those times when you are watching a video podcast, but then suddenly need to be on the movesoon youll be able to easily switch to the audio version of the podcast, ensuring you can still enjoy it when your eyes are needed on other things. Redesigned album and playlist interface: Also in the Music app, Apple has redesigned the look of the interface that you see when displaying playlists or albums in full screen. In iOS 26.4, the Music app will now tint the entire screen based on the album art color scheme, giving each playlist and album its own unique look. And thats not all: iOS 26.4 will add numerous small refinements and additions across the operating system, including new emojis, new Ambient Music widgets for your Home Screen, automatic activation of Stolen Device Protection, and more. iOS 26.4 beta: Download it now While Apple hasnt released iOS 26.4 to the general public yet, it has released four betas of the software to developers and public beta testers. And if you are in any of those two groups, you can download the latest beta of iOS 26.4 onto your iPhone today. To download the developer beta, youll need to be a member of the Apple Developer Program. If youre not a developer, but still want to try out the new software early, you can join the Apple Beta Software Program for free and get access to public betasincluding the iOS 26.4 betatoday. Of course, the usual warning applies: betas are buggy, and in rare cases, they can cause data loss or otherwise harm your phone. So always proceed with caution if you decide to download a beta. iOS 26.4 final release: Download it later this month If a beta isnt your thing, youll have to wait until Apple releases the final version of iOS 26.4 to the public. Thankfully, you probably wont have to wait too much longer. Apple generally has a 5-6 beta development cycle for iOS point upgrades like iOS 26.4. Apple released the first iOS 26.4 beta in mid-February, which means the final public version of the beta is highly likely to be released between mid-March and the end of the month. Once Apple releases the final version of the software, youll be able to download iOS 26.4 right to your iPhone using the devices Software Update feature in the Settings app.
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