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Bobs Discount Furniture, a Connecticut-based furniture retailer backed by Bain Capital, is putting it all on the table. The company is going public, with shares expected to begin trading on Thursday, February 5, after being priced at $17. The retailer raised $331 million in its initial public offering (IPO). Shares will trade on the New York Stock Exchange (NYSE) under the symbol BOBS. The IPO was originally announced last month. The company’s retail operations are expansiveit has more than 200 locations in 26 states as of September of last year, but the East Coast is its stronghold. Data from Renaissance Capital shows that 61% of its revenue came from stores in New England, New York, and the Mid-Atlantic. Retail headwinds and risk factors Bob’s listing will test investor appetite for traditional retail businesses as the space has faced headwinds. Many brick-and-mortar chains are pushing through a difficult environment and market conditions, particularly as consumers have struggled with increased prices in recent years. Home furnishings retailers, in particular, have had a rough run lately. Chains including Circle Furniture, American Signature, and At Home have all filed for bankruptcy, along with adjacent retailers such as Bed Bath & Beyond and Big Lots. Another important detail: Bobs sources a lot of its furniture stock from Vietnam, Thailand, Malaysia, and Cambodia, which are subject to (or could become further targeted by) the Trump administrations tariffs. It’s a risk factor that stands out in the company’s filing with the Securities and Exchange Commission (SEC). We may not be able to fully or substantially mitigate the impact of these or future tariffs, pass price increases on to our customers or secure adequate alternative sources of products, which would have a material adverse effect on our business, operating results and financial performance,” the filing reads. The company has been performing well, however. It reported net revenue of more than $1.7 billion for the nine months ended September 28, 2025, which was an increase of more than 20% during that same period the year prior, per the filing. During that same period, net income rose 64%.
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E-Commerce
The Trump administration announced Wednesday that it wants to create a critical minerals trading bloc with its allies and partners, using tariffs to maintain minimum prices and defend against China’s stranglehold on the key elements needed for everything from fighter jets to smartphones.Vice President JD Vance said the U.S.-China trade war over the past year exposed how dependent most countries are on the critical minerals that Beijing largely dominates, so collective action is needed now to give the West self-reliance.“We want members to form a trading bloc among allies and partners, one that guarantees American access to American industrial might while also expanding production across the entire zone,” Vance said at the opening of a meeting that Secretary of State Marco Rubio hosted with officials from several dozen European, Asian and African nations.The Republican administration is making bold moves to shore up supplies of critical minerals needed for electric vehicles, missiles and other high-tech products after China choked off their flow in response to President Donald Trump’s sweeping tariffs last year. While the two global powers reached a truce to pull back on the high import taxes and stepped-up rare earth restrictions, China’s limits remain tighter than they were before Trump took office.The critical minerals meeting comes at a time of significant tensions between Washington and major allies over President Donald Trump’s territorial ambitions, including Greenland, and his moves to exert control over Venezuela and other nations. His bellicose and insulting rhetoric directed at U.S. partners has led to frustration and anger.The conference, however, is an indication that the United States is seeking to build relationships when it comes to issues it deems key national security priorities.While major allies like France and the United Kingdom attended the meeting in Washington, Greenland and Denmark, the NATO ally with oversight of the mineral-rich Arctic island, did not. A new approach to countering China on critical minerals Vance said some countries have signed on to the trading bloc, which is designed to ensure stable prices and will provide members access to financing and the critical minerals. Administration officials said the plan will help the West move beyond complaining about the problem of access to critical minerals to actually solving it.“Everyone here has a role to play, and that’s why we’re so grateful for you coming and being a part of this gathering that I hope will lead to not just more gatherings, but action,” Rubio said.Vance said that for too long, China has used the tactic of unloading materials at cheap prices to undermine potential competitors, then ratcheting up prices later after keeping new mines from being built in other countries.Prices within the preferential trade zone will remain consistent over time, the vice president said.“Our goal within that zone is to create diverse centers of production, stable investment conditions and supply chains that are immune to the kind of external disruptions that we’ve already talked about,” he said.In Beijing, Chinese Foreign Ministry spokesperson Lin Jian said in response to a question about the trading bloc that “we oppose any country undermining the international economic and trade order through rules set by small cliques.”To make the new trading group work, it will be important to have ways to keep countries from buying cheap Chinese materials on the side and to encourage companies from getting the critical minerals they need from China, said Ian Lange, an economics professor who focuses on rare earths at the Colorado School of Mines.“Let’s just say it’s standard economics or standard behavior. If I can cheat and get away with it, I will,” he said.At least for defense contractors, Lange said the Pentagon can enforce where those companies get their critical minerals, but it may be harder with electric vehicle makers and other manufacturers. US turns to a strategic stockpile and investments Trump this week also announced Project Vault, a plan for a strategic U.S. stockpile of rare earth elements to be funded with a $10 billion loan from the U.S. Export-Import Bank and nearly $1.67 billion in private capital.In addition, the government recently made its fourth direct investment in an American critical minerals producer, extending $1.6 billion to USA Rare Earth in exchange for stock and a repayment deal. The Pentagon has shelled out nearly $5 billion over the past year to spur mining.The administration has prioritized the moves because China controls 70% of the world’s rare earths mining and 90% of the processing. Trump and Chinese President Xi Jinping spoke by phone Wednesday, including about trade. A social media post from Trump did not specifically mention critical minerals.Heidi Crebo-Rediker, a senior fellow in the Center for Geoeconomic Studies at the Council on Foreign Relations, said the meeting was “the most ambitious multilateral gathering of the Trump administration.”“The rocks are where the rocks are, so when it comes to securing supply chains for both defense and commercial industries, we need trusted partners,” she said.Japan’s minister of state for foreign affairs, Iwao Horii, said Tokyo was fully on board with the U.S. initiative and would work with as many countries as possible to ensure its success.“Critical minerals and (their) stable supply is indispensable to the sustainable development of the global economy,” he said. Agreements and legislation move forward The European Union and Japan together as well as Mexico announced agreements to work with the United States to develop coordinated trade policies and price floors to support the development of a critical minerals supply chain outside of China. The countries said they would develop an agreement about what steps they will take and explore ways to expand the effort to include additional like-minded nations.Also Wednesday, the Republican-controlled House approved a bill to accelerate mining on federal land despite objections from Democrats and conservation groups that it amounted to a blank check to foreign-owned mining corporations.The bill, which next heads to the Senate, would codify Trump’s executive orders to boost domestic mining and processing of minerals important to energy, defense and other applications. Associated Press writers Matthew Daly and Ken Moritsugu in Beijing contributed to this report. Didi Tang, Josh Funk and Matthew Lee, Associated Press
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E-Commerce
AI coding agents are suddenly everywhere, the latest thing Silicon Valley cannot stop talking about. From venture-backed startups to splashy big tech keynotes, the promise sounds the same: just describe what you want, and the AI will build it for you. It is a seductive idea, especially in a world where software projects are notorious for moving slowly. But inside large companies, that vision is already starting to unravel. What looks impressive in a demo often falls apart in the real world. As soon as AI-generated code runs into actual enterprise data, the problems show up. Schemas clash, governance breaks down, and a supposed breakthrough can quickly turn into a liability. Coding agents tend to break down when theyre introduced to complex enterprise constraints like regulated data, fine-grained access controls, and audit requirements, Sridhar Ramaswamy, CEO of Snowflake, tells Fast Company. He says most coding agents are built for speed and independence in open environments, not for reliability inside tightly governed systems. As a result, they often assume they can access anything, break down when controls are strict, and cannot clearly explain why they ran a certain query or touched a specific dataset. This gap between what AI can write and what it actually understands is becoming one of the most expensive problems in enterprise AI. Gartner predicts that 40% of agentic AI projects will be canceled by 2027 because they lack proper governance, and only 5% of custom enterprise AI tools will ever make it into production. Ramaswamy says the core issue in enterprise AI is writing functional code in a way that is secure, transparent, and compliant from the start. He argues that companies need to put trust, accuracy, and accountability ahead of unchecked automation, and that most coding agents today sit outside existing data governance systems instead of being built into them. Snowflakes answer is Cortex Code, a data-native AI coding agent designed to work directly inside governed enterprise data, not as a layer sitting on top of it. It comes alongside with a newly announced $200 million partnership with OpenAI. Together, they reflect a contrarian bet that the real battle for enterprise AI will be won at the data layer. AI Coding Agents Dont Understand Enterprise Context Most AI coding agents are great at writing code on their own, but they struggle once that code has to run inside a real company. Large organizations live with constant constraints, from security rules and uptime demands to shared business logic that evolves over time. Agents trained mostly on public code and synthetic examples rarely absorb those realities, and the disconnect shows up almost right away. Enterprise data also lives across data warehouses, third-party platforms, and legacy systems, and it carries layers of organizational meaning with it. Most coding agents treat that data like any other dataset, instead of the most tightly regulated asset a company has. The fallout shows up fast in production. Some enterprises say they spend weeks cleaning up AI-generated code that ignores internal data standards. In production, agents most often fail due to poor data integration, lax identity and security permissions, and hallucination for complex code workflows,” says Arun Chandrasekaran, vice president and analyst at Gartner. “Vendors often underestimate the gap because they assume that enterprises have centralized data and codified access policies, which isnt true in most large enterprises. Chandrasekaran adds that AI agents are embedded into developer IDEs without grounding in enterprise system semantics, which is the key reason why this issue persists. This can result in trust erosion and security exposure,” he says, “which can hinder production. According to a CodeRabbit study, AI-generated code introduces 1.7 times more issues than human-written code, including 75% more logic errors and up to twice the security vulnerabilities, conflicting with enterprise standards. Likewise, another study found that 45% of AI-generated code samples fail security tests, posing critical web application security risks. Ramaswamy says the most immediate consequence is slowed development. In some cases, teams quietly abandon agents altogether after early pilots fail governance checks. Even when the consequences are minor in nature, the perception of risk alone can cause organizations to roll back or freeze AI initiatives until stronger guardrails are in place, he says. According to Anahita Tafvizi, Snowflakes chief data analytics officer, this pattern points to a deeper design problem: Many coding agents can generate technically correct code, but they do not understand how business rules are applied, how access controls limit what is allowed, or how audit requirements determine whether a system can actually be trusted once it goes live. Meaningful enterprise innovation depends on context, she says. When an agent understands not just how to write code, but why certain controls exist and how decisions are governed, teams can build with confidence. Snowflakes Thesis: Context Beats Cleverness Snowflakes latest product, Cortex Code, is a data-native AI coding agent built directly into its governed data platform, rather than layered on top of it. That distinction matters. Instead of trying to guess enterprise rules from prompts, Cortex Code is designed with built-in awareness of schemas and operational constraints. The company says the goal is to make AI follow the same rules people already do. Ramaswamy says Cortex Code is not just about producing code faster than tools like Claude Code, but about understanding the realities of enterprise environments. Its value, he argues, comes from what he calls its deep awareness of the context and constraints that shape how large organizations operate, which allows a much wider range of employees to build solutions that are safe and reliable, even without advanced technical skills. Snowflake’s $200 million partnership with OpenAI further reinforces its architectural bet. Its a direct, first-party relationship that allows OpenAIs models to operate natively inside Snowflake, on top of enterprise data, Ramaswamy says. By bringing OpenAIs frontier model capabilities into Snowflake, we remove the operational friction of stitching together disparate tools and significantly lower the barrier to deploying advanced AI responsibly. An Inflection Point or a Higher Bar? Industry experts say that while Snowflake is making a big bet on a data-first approach with Cortex Code, it is far from alone. Rivals such as Databricks, Google BigQuery, and AWS Redshift are moving in the same direction, putting governance and auditability ahead of raw speed. Experts say Snowflakes main point of differentiation is how closely Cortex Code is tied to production data. As Doug Gourlay, CEO of data stoage company Qumulo, puts it, most companies have grafted increasingly capable agents onto developer tools and then tried to manage risk after the fact. Snowflake, he says, is flipping that model by treating governance and data semantics as the foundation on which AI operates. (While rivals excel in niche strengths like machine learning flexibility or platform scale, Cortex Code is built for teams that need governed, low-maintenance AI coding directly on live enterprise data.) Over time, this approach is likely to become table stakes. Enterprises will increasingly view AI that operates outside their governed data fabric as an unacceptable risk, regardless of how impressive its capabilities appear in isolation, says Gourlay. Coding tools such as Anthropics Claude Code, for instance, are largely optimized for developer-centric workflows, emphasizing controls like explicit change approvals and tight IDE integrations. Claude Code, in practice, requires being combined with additional governance layers or secure platforms for enterprise compliance. Snowflake and Anthropic recently partnered to enable the direct integration of Claude models into Snowflake Intelligence and Cortex AI, allowing its models to run inside Snowflakes governed data environment. Snowflake says its edge comes from working directly with enterprise metadata and semantic context. The company is betting that as organizations grow more cautious, they will turn away from agents that appear powerful but act unpredictably. If that proves true, those who ignore data context may define todays hype, while those who embrace it will shape what comes next.
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E-Commerce
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