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President Donald Trump plans to visit Ohio and Kentucky on Wednesday to argue that his policies can steady an economy facing shock waves from the war on Iran and to try to defeat one of the few congressional Republicans who has dared to defy him.In Cincinnati, the Republican president is touring Thermo Fisher Scientific, a pharmaceutical company. There, he’ll tout efforts to lower prescription drug prices, a key part of his attempts to show his administration is focused on making the cost of living more affordable for many Americans ahead of November’s midterm elections.After that, Trump will visit a logistics packing facility in nearby Hebron, Kentucky, part of the district of Rep. Thomas Massie. Trump is backing a primary challenger to Massie.The trip presents a test of Trump’s ability to cleanse his party of those who oppose him but also to try to stay on an economic message increasingly strained by the military action launched by the U.S. and Israel against Iran. He’ll be “talking about the economy, which is, of course, the utmost importance to him,” White House press secretary Karoline Leavitt said.Polls showed that Americans were increasingly wary of Trump’s handling of the economy even before the conflict with Iran began, and fighting there has derailed Trump’s messaging, as the low gas prices he once bragged about are now surging and stocks that had set record highs have slipped.Employers also cut an unexpectedly high 92,000 jobs in February, and revisions trimmed another 69,000 jobs from December and January payrolls which the White House had previously hailed as “blockbuster.”None of that has stopped Trump from continuing to insist the country is booming and blaming the Democrats for everything else.“They’re the one that caused the problem,” he told a House Republican meeting in Florida on Monday. “But we’re really bringing down prices big.”Democrats offer a sharp contrast to Trump’s depiction of the nation, arguing that costs remain high for many Americans more than a year into his second term and that families are still struggling under his policies. Trump’s affordability tour meets his opposition to Massie After Democrats won the Virginia and New Jersey governors’ races in November, the White House announced that Trump would travel the country to show that he’s taking kitchen table issues seriously and reassure voters nervous about still-rising prices and economic growth.Since then, the president has made stops in Pennsylvania, Georgia, Michigan, North Carolina and Texas though his speeches sometimes have been more focused on his own political grievances than his plans to try to help lower everyday costs around the country.This trip, however, marks the first time this primary cycle that Trump has sought to keep promises to punish members of his own party who oppose him on key issues. The president has endorsed Ed Gallrein, a farmer, businessman and retired Navy SEAL, who is running against Massie in Kentucky’s Republican primary on May 19. Trump and Gallrein will appear together on Wednesday.Massie is an outspoken Trump critic who opposed the White House-backed tax and spending measure and bucked Trump by pushing to have files related to the sex trafficking investigations into Jeffrey Epstein released. He’s also opposed the U.S. strike on Venezuela that toppled then-President Nicolás Maduro and, most recently, the war in Iran.“This isn’t America First,” Massie posted on X on Sunday, blaming the war for causing gas prices to jump. Will Weissert, Associated Press
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E-Commerce
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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E-Commerce
Canva’s new AI tool, launching today, is going to save time, money, and headaches for so many people. Called Magic Layers, it turns any flat bitmap image into a fully editable Canva project, extracting text, objects, and components into individual layers.This tool marks a fundamental shift in how we handle digital assets. Until now, a rendered image was basically a locked vault of pixels. If you wanted to change a typo or swap a background, you had four options: 1) Hunt down the original project file, 2) painstakingly change it in Photoshop, 3) accept a generative AI patch job, or 4) close the laptop and escape to live a real life somewhere by a nice beach. Magic Layers shatters the vault. By reverse-engineering a flat picture into its constituent parts, Canva cofounder and Chief Product Officer Cameron Adams tells me, Magic Layers empowers users to resurrect and tweak any image they have on their hard drive.[Image: Canva]Canva uses many models from OpenAI, Anthropic, and other developers, but the secret sauce behind this new layering capability is its proprietary AI design model, which the company unveiled last October. Think of it not just as a random design and image generator, but as a model that understands the elements of design. It looks at a picture and sees its skeletal structuredistinguishing the foreground subjects from the background scenery, and recognizing typography as actual text rather than just colored shapes. When you feed it an image, whether it was spat out by an AI prompt or dragged from an old folder, it dissects those elements perfectly. The new Canva multilayer tool is the implementation of those abilities.Most AI outputs are fixed, really flat things, and they’re not easy to edit. You either have to, like, live with an 80% solution or you have to spend time reprompting, trying to get that little bit of the image that you wanted to get fixed,” Adams says. But now, he adds, the model identifies everything in the frame and converts it into native Canva objects.So text isn’t just a cutout anymore. It becomes a live, editable text box. You can correct spelling errors, swap the font, adjust the size, or even translate the copy for international markets. The same goes for visual objects. Once separated, elements like a product bottle or a butterfly become completely independent actors on the canvas. You can move them, resize them, change their color, or banish them from the composition entirely without leaving a gaping hole behind, Adams explains.And since these extracted layers are treated exactly like standard Canva design elements, you can apply all of the platform’s existing tools to them, including upscaling or generative tweaks like Magic Edit. That’s the beauty of it, that it’s now a proper Canva design. So you can change any of those elements in any way,” Adams says. Because Canva operates in the cloud, this newly resurrected file is immediately ready for multiplayer collaboration. You and your team can jump into the project simultaneously and start moving things around. [Image: Canva]Its getting better all the timeThere is an interesting parallel here with Adobes recent launch of a new AI assistant for its web and mobile Photoshop apps. Both companies are trying to fix the fundamental flaw of current generative AI models like Google’s Nano Banana.When you ask a standard AI to remove a single item from a picture, the machine recalculates the whole picture from scratch, inevitably introducing random errors or hallucinations. Adobe tackles this problem by allowing users to point at or draw around an object. The AI then places these modifications on independent, clear overlays suspended above the base image, preserving the underlying raw pixels flawlessly. While Adobe’s method builds new, highly controlled editsincluding texton top of an existing foundation to guarantee precision, Canva’s Magic Layers takes the opposite route: It dismantles the foundation itself, breaking the flat image apart into discrete, fully interactive components.While these tools from both companies do, indeed, appear to be magical, to me they feel like features that are not going to stick around for too long. Theyre more like patches that solve generative AIs current problems with output uncertainty.Once engines like Nano Banana or Seedream can nail down every pixel, every text and typography, every single human, animal, tree, pair of jeans, or shampoo bottle everand it will happenwe will no longer be worrying about things being in layers. Objects, type, and components will simply exist in the reality of the image; the models will understand them just like humans do, allowing users to change anything they want instantly, and with precision. Everything will be liquid for you to touch and change. Software will follow your exacting and most complicated whims with perfection. But for now, Magic Layers is going to solve a lot of problems for a lot of people and companies all around the world.
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