By Jeff Overley ( February 4, 2025, 11:21 PM EST) — February is off to a rip-roaring start in several circuits, which have already heard more major cases than we can count on one hand. There’s plenty more ahead, including a moment of truth for judiciary policymaking that has managed to anger both the defense and plaintiffs bars. We’ll explore all that in this edition of Wheeling & Appealing, which also includes a quiz pegged to recent presidential news…. Law360 is on it, so you are, too. A Law360 subscription puts you at the center of fast-moving legal issues, trends and developments so you can act with speed and confidence. Over 200 articles are published daily across more than 60 topics, industries, practice areas and jurisdictions. A Law360 subscription includes features such as Daily newsletters Expert analysis Mobile app Advanced search Judge information Real-time alerts 450K+ searchable archived articles And more! Experience Law360 today with a free 7-day trial. source

Apple has reportedly scrapped a project to create new augmented reality glasses that would fit closely within the rest of its device ecosystem. The team working on the project was informed of the decision in the last week of January, according to sources familiar with the decision and reported by Bloomberg. What would Apple’s AR glasses have been like? Unlike bulky virtual reality headsets, the glasses — code-named N107 — were designed to resemble standard eyewear, including conventional earpieces instead of a heavy band. Apple had originally planned for the glasses to launch in 2027. The glasses were expected to feature “microLED-type screens,” allowing users to see text, images, and videos directly in their field of vision. Engineers reportedly tested integrating the device with both an iPhone and a Mac, but technical hurdles prevented the progress. Apple’s AR glasses significantly impacted the iPhone’s battery life, and the team struggled to match the device’s processing power to the glasses’ demands. When paired with a Mac, the product failed to meet executive expectations due to performance issues. These challenges ultimately led Apple to abandon the project, according to Bloomberg. However, the development of a next-generation Apple Vision Pro remains unaffected. SEE: Make sure Apple users work smoothly in a Microsoft-based environment with our guide for IT professionals. What is Apple’s place in the augmented reality market? Apple’s strategy with emerging technologies such as AI has been to wait out competitors and release a version with a firm position among Apple’s existing products. The company followed this approach with Apple Intelligence, which enhanced its existing chatbot, Siri. Apple appeared to be applying the same strategy to AR glasses. Rivals like Meta and Google have gone ahead with Ray-Bans, the Meta Quest, and Android XR. Meta’s standalone augmented reality glasses, code-named Orion, are intended to launch for consumers in 2027. Apple’s now-shelved glasses would also have competed with similar offerings from XReal and Lenovo, both of which manufacture consumer smart glasses. Must-read Apple coverage In terms of size and shape, Apple’s Vision Pro line stands on the line between augmented reality glasses and virtual reality headsets. The semitransparent headset is meant to bring “spatial computing” in a home or office setting, but it does not serve  the same function as the smaller augmented reality glasses. According to Bloomberg, the Apple Vision Pro has struggled to gain traction with customers due to its large size and the $3,499 price tag. Some consumers reported headaches and eye strain. Instead of floating information onto real-world surroundings, the “spatial computing” device projects a virtual reality screen. source

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Y Combinator-backed startup Martin AI announced today it has raised $2 million in seed funding to develop what it claims is a more intuitive and personalized AI assistant that could rival upcoming offerings from OpenAI and Google. The funding round included Pioneer Fund, FoundersX Ventures, Eight Capital, SV Tech Ventures, Sandhill Markets, Splash Capital and notable angel investors including DoorDash cofounder Andy Fang. Founded by 19-year-old college dropouts Dawson Chen and Ethan Hou, who left Yale and Berkeley respectively, Martin AI has developed an AI assistant that can be reached through multiple channels including phone calls, text messages, email and Slack. The assistant manages calendars, email inboxes and to-do lists, and can even make calls or send texts on users’ behalf. A next-generation AI assistant to rival Big Tech “Consumer AI requires a whole new interface, and we’re going to build that up from the ground up, from first principles,” said Chen, CEO of Martin AI, in an exclusive interview with VentureBeat. “We’re going to iterate really fast. As you can see, none of these big companies have launched. They’ve been working on agents for a while. We were not afraid to launch really fast.” The startup has been rapidly iterating on its product since launching last summer, recently introducing a web dashboard and new mobile interface. Unlike traditional AI assistants that rely on voice commands, Martin employs what Chen calls a “custom memory architecture” that allows it to better understand user preferences and context over time. “We think there are really three phases to building [something like Google’s] Jarvis or a personal agent,” Chen told VentureBeat. “Phase one is letting it follow direct orders…Phase two is following continuous orders over time…Phase three is proactively inferring orders.” Martin AI can handle complex tasks like scheduling, making calls and composing emails across multiple platforms, demonstrating its versatility as a personal assistant. (Credit: Martin AI) How Martin’s custom memory architecture powers proactive AI assistance The funding comes as tech giants prepare to launch their own AI agents. OpenAI recently announced an assistant called Operator while Google is developing Jarvis. However, Chen believes Martin’s focus on user experience and rapid iteration gives it an advantage. “While they have lots of resources, OpenAI and Google are distracted and risk-averse,” Chen explained. “We’re scrappy, we ship fast, and are laser focused on the consumer.” Martin AI has attracted over 10,000 early users to its platform, with a portion subscribing to its paid service. The company plans to use the new funding to expand its engineering team and accelerate product development, particularly around its personalization and proactive assistance capabilities. The Martin AI dashboard integrates calendar, email and task management into a unified web interface, showing the assistant’s daily briefing feature. (Credit: Martin AI) Silicon Valley veterans bet on AI assistants as the next consumer platform The startup’s vision extends beyond simple task execution. “I’m a big believer in the future of agents — I think every person will have like five to 10 agents in their life five years from now,” Chen predicted. “We want Martin to be the one that’s closest to the consumer.” The investment round also included participation from industry veterans like JJ Fliegelman and former Uber executive Manik Gupta, suggesting growing confidence in consumer AI applications despite an otherwise cooling venture capital environment. Martin’s approach represents a bold bet that consumers will pay for AI assistance that goes beyond basic voice commands. The company faces significant challenges, not least competition from tech giants and questions around data privacy and security. However, its early traction and focus on user experience suggest there may be room for nimble startups to carve out a space in the emerging AI assistant market. The service is available now through the company’s iOS app and web interface at trymartin.com, with a seven-day free trial for new users. source

Research has found that data centres can reduce their energy usage by up to 30% simply by altering around 30 lines of code in the Linux kernel’s network stack. Scientists from the University of Waterloo in Canada identified inefficiencies in the way servers process incoming network traffic. The breakthrough comes from interrupt request suspension, a technique that optimises CPU power efficiency by reducing unnecessary interruptions during high-traffic conditions. Typically, when a new data packet enters the network, it triggers an interrupt request, causing the CPU core to pause its current task to process the data, slowing things down.  The new code reduces interrupt requests by allowing the system to actively check the network for new data packets when needed instead of waiting for each individual interrupt. However, since this approach is power-intensive, the system reverts to interrupt handling when traffic slows. By refining how the kernel handles IRQs, data throughput improves by up to 45% while ensuring tail latency remains low. In other words, the system can handle more traffic without delays for the most time-sensitive operations. The modification has been incorporated into Linux kernel version 6.13. “We didn’t add anything,” said Cheriton School of Computer Science Professor Martin Karsten in a press release. “We just rearranged what is done when, which leads to a much better usage of the data centre’s CPU caches. It’s kind of like rearranging the pipeline at a manufacturing plant, so that you don’t have people running around all the time.” Data centres will be responsible for up to 4% of global power demand by 2030, driven by AI, at least in part. Training OpenAI’s GPT-4, with 1.76 trillion parameters, consumed an amount of energy equivalent to the annual power usage of 5,000 U.S. households. This figure doesn’t even include the electricity required for inference, which is the process in which the AI generates outputs based on new data. SEE: Sending One Email With ChatGPT is the Equivalent of Consuming One Bottle of Water Data center operators arguably have a responsibility to reduce their carbon footprint, yet it does not appear to be a priority. A report from the Uptime Institute found that fewer than half of data center owners and operators even track key sustainability metrics such as renewable energy consumption and water usage. Individual businesses don’t appear to be motivated to take a stand against their data centers’ energy-intensive practices, either. In fact, recent research found that nearly half of businesses are relaxing sustainability goals to allow for their AI expansions. Tech giants have also come under scrutiny. In July, Google came under fire after its annual environmental report revealed that its emissions had increased by 48% in four years, largely due to the expansion of its data centres to support AI developments. More about data centers Aoife Foley, senior member of the Institute of Electrical and Electronics Engineers and engineering professor at Queen’s University Belfast, told TechRepublic in an email: “Modern enterprises continuously generate and accumulate vast amounts of data. This includes routine activities across enterprise systems, machines, sensors, and demand-side digitalisation. “All of this data comes in multiple forms – whether redundant or critical. However, the majority is unstructured and inert content, commonly referred to as ‘dark data’ which is becoming more prevalent. The result is a large volume of digital data that needs to be stored, most of which will not even be accessed later. “Those managing data centres and server rooms must strive for a high standard of energy efficiency, demonstrated through aggressive power use effectiveness targets. Achieving sustainability means addressing environmental considerations during solution design as well as during the build. Solutions must meet pre-defined and agreed environmental sustainability criteria. This includes filtering dark data, removing unnecessary information from storage and relying upon ‘greener’ energy sources.” source

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More The AI industry is witnessing a seismic shift with the introduction of DeepSeek-R1, a cutting-edge open-source reasoning model developed by the eponymous Chinese startup DeepSeek. Released on January 20, this model is challenging OpenAI’s o1 — a flagship AI system — by delivering comparable performance at a fraction of the cost. But how do these models stack up in real-world applications? And what does this mean for enterprises and developers? In this article, we dive deep into hands-on testing, practical implications and actionable insights to help technical decision-makers understand which model best suits their needs. Real-world implications: Why this comparison matters The competition between DeepSeek-R1 and OpenAI o1 isn’t just about benchmarks — it’s about real-world impact. Enterprises are increasingly relying on AI for tasks like data analysis, customer service automation, decision-making and coding assistance. The choice between these models can significantly affect cost efficiency, workflow optimization and innovation potential. Key Questions for Enterprises: Can DeepSeek-R1’s cost savings justify its adoption over OpenAI o1? How do these models perform in real-world scenarios like mathematical computation, reasoning based analysis, financial modeling or software development? What are the trade-offs between open-source flexibility (DeepSeek-R1) and proprietary robustness (OpenAI o1)? To answer these questions, we conducted hands-on testing across reasoning, mathematical problem-solving, coding tasks and decision-making scenarios. Here’s what we found. Hands-on testing: How DeepSeek and OpenAI o1 perform Question 1: Logical inference If A = B, B = C, and C ≠ D, what definitive conclusion can be drawn about A and D? Analysis: OpenAI o1: Well-structured reasoning with formal statements. DeepSeek-R1: Equally accurate, more concise presentation. Processing time: DeepSeek (0.5s) versus OpenAI (2s). Winner: DeepSeek-R1 (equal accuracy, 4X faster, more concise). Metrics: Tokens: DeepSeek (20) vs OpenAI (42). Cost: DeepSeek ($0.00004) vs OpenAI ($0.0008). Key Insight: DeepSeek-R1 achieves the same logical clarity with better efficiency, making it ideal for high-volume, real-time applications. Question 2: Set theory problem In a room of 50 people, 30 like coffee, 25 like tea and 15 like both. How many people like neither coffee nor tea? Analysis: OpenAI o1: Detailed mathematical notation. DeepSeek-R1: Direct solution with clear steps. Processing time: DeepSeek (1s) versus OpenAI (3s). Winner: DeepSeek-R1 (clearer presentation, 3x faster). Metrics: Tokens: DeepSeek (40) vs OpenAI (64). Cost: DeepSeek ($0.00008) vs OpenAI ($0.0013). Key Insight: DeepSeek-R1’s concise approach maintains clarity while improving speed. Question 3: Mathematical calculation Calculate the exact value of: √(144) + (15² ÷ 3) – 36. Analysis: OpenAI o1: Numbered steps with detailed breakdown. DeepSeek-R1: Clear line-by-line calculation. Processing time: DeepSeek (1s) versus OpenAI (2s). Winner: DeepSeek-R1 (equal clarity, 2X faster). Metrics: Tokens: DeepSeek (30) vs OpenAI (60). Cost: DeepSeek ($0.00006) vs OpenAI ($0.0012). Key Insight: Both models are accurate; DeepSeek-R1 is more efficient. Question 4: Advanced mathematics If x + y = 10 and x² + y² = 50, what are the precise values of x and y? Analysis: OpenAI o1: Comprehensive solution with detailed steps. DeepSeek-R1: Efficient solution with key steps highlighted. Processing time: DeepSeek (2s) versus OpenAI (5s). Winner: Tie (OpenAI better for learning; DeepSeek better for practice). Metrics: Tokens: DeepSeek (60) vs OpenAI (134). Cost: DeepSeek ($0.00012) vs OpenAI ($0.0027). Key Insight: Choice depends on use case — teaching versus practical application. DeepSeek-R1 excels in speed and accuracy for logical and mathematical tasks, making it ideal for industries like finance, engineering and data science. Question 5: Investment analysis A company has a $100,000 budget. Investment options: Option A yields a 7% return with 20% risk, while Option B yields a 5% return with 10% risk. Which option maximizes potential gain while minimizing risk? Analysis: OpenAI o1: Detailed risk-return analysis. DeepSeek-R1: Direct comparison with key metrics. Processing time: DeepSeek (1.5s) versus OpenAI (4s). Winner: DeepSeek-R1 (Sufficient analysis, 2.7X faster). Metrics: Tokens: DeepSeek (50) vs OpenAI (110). Cost: DeepSeek ($0.00010) vs OpenAI ($0.0022). Key insight: Both models perform well in decision-making tasks, but DeepSeek-R1’s concise and actionable outputs make it more suitable for time-sensitive applications. DeepSeek-R1 provides actionable insights more efficiently. Question 6: Efficiency calculation You have three delivery routes with different distances and time constraints: Route A: 120 km, 2 hours Route B: 90 km, 1.5 hours Route C: 150 km, 2.5 hours Which route is most efficient? Analysis: OpenAI o1: Structured analysis with methodology. DeepSeek-R1: Clear calculations with direct conclusion, Processing time: DeepSeek (1.5s) versus OpenAI (3s). Winner: DeepSeek-R1 (Equal accuracy, 2X faster). Metrics: Tokens: DeepSeek (50) vs OpenAI (112). Cost: DeepSeek ($0.00010) vs OpenAI ($0.0022). Key insight: Both are accurate; DeepSeek-R1 is more time-efficient.  Question 7: Coding task Write a function to find the most frequent element in an array with O(n) time complexity. Analysis: OpenAI o1: Well-documented code with explanations. DeepSeek-R1: Clean code with essential documentation. Processing time: DeepSeek (2s) versus OpenAI (4s). Winner: Depends on use case (DeepSeek for implementation, OpenAI for learning). Metrics: Tokens: DeepSeek (70) vs OpenAI (174). Cost: DeepSeek ($0.00014) vs OpenAI ($0.0035). Key insight: Both are effective, with different strengths for different needs. DeepSeek-R1’s coding proficiency and optimization capabilities make it a strong contender for software development and automation tasks. Question 8: Algorithm design Design an algorithm to check if a given number is a perfect palindrome without converting it to a string. Analysis: OpenAI o1: Comprehensive solution with detailed explanation. DeepSeek-R1: Efficient implementation with key points. Processing time: DeepSeek (2s) versus OpenAI (5s). Winner: Depends on context (DeepSeek for implementation, OpenAI for understanding). Metrics: Tokens: DeepSeek (70) vs OpenAI (220). Cost: DeepSeek ($0.00014) vs OpenAI ($0.0044). Key Insight: Choice depends on primary need — speed versus detail. Overall performance metrics Total processing time: DeepSeek (11.5s) vs OpenAI (28s). Total tokens: DeepSeek (390) versus OpenAI (916). Total cost: DeepSeek ($0.00078) versus OpenAI ($0.0183). Recommendations Production environment Primary: DeepSeek-R1. Benefits: Faster processing, lower costs, sufficient accuracy. Best for: APIs, high-volume processing, real-time applications. Educational/training Primary: OpenAI o1. Alternative: DeepSeek-R1 for practice exercises. Best for: Detailed explanations, learning new concepts. Enterprise development Primary: DeepSeek-R1

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More ByteDance researchers have developed an AI system that transforms single photographs into realistic videos of people speaking, singing and moving naturally — a breakthrough that could reshape digital entertainment and communications. The new system, called OmniHuman, generates full-body videos that show people gesturing and moving in ways that match their speech, surpassing previous AI models that could only animate faces or upper bodies. How OmniHuman uses 18,700 hours of training data to create realistic motion “End-to-end human animation has undergone notable advancements in recent years,” the ByteDance researchers wrote in a paper published on arXiv. “However, existing methods still struggle to scale up as large general video generation models, limiting their potential in real applications,” The team trained OmniHuman on more than 18,700 hours of human video data using a novel approach that combines multiple types of inputs — text, audio and body movements. This “omni-conditions” training strategy allows the AI to learn from much larger and more diverse datasets than previous methods. Credit: ByteDance AI video generation breakthrough shows full-body movement and natural gestures “Our key insight is that incorporating multiple conditioning signals, such as text, audio and pose, during training can significantly reduce data wastage,” the research team explained. The technology marks a significant advance in AI-generated media, demonstrating capabilities that range from creating videos of people delivering speeches to depicting subjects playing musical instruments. In testing, OmniHuman outperformed existing systems across multiple quality benchmarks. Credit: ByteDance Tech giants race to develop next-generation video AI systems The development emerges amid intensifying competition in AI video generation, with companies like Google, Meta and Microsoft pursuing similar technologies. ByteDance’s breakthrough could give its TikTok parent company an advantage in this rapidly evolving field. Industry experts say such technology could transform entertainment production, educational content creation and digital communications. However, it also raises concerns about potential misuse in creating synthetic media for deceptive purposes. The researchers will present their findings at an upcoming computer vision conference, although they have not yet specified when or which one. source

IBM has had a very busy and prolific acquisition season. It acquired two Oracle consulting firms within the last five months. Applications Software Technology LLC (AST) On January 16, IBM announced its intent to acquire AST, a global Oracle consultancy. This strengthens its state, local, and education (SLED) presence, as AST specializes in delivering Oracle Cloud application implementation and digital transformation services, with a focus on the public sector, including local governments and K–12 education. Financial details of the transaction haven’t been provided, but it’s set to close in the first quarter of 2025. AST is a full-service enterprise systems integrator that has served clients in the public and commercial sectors for over 26 years. It focuses on implementations and transformation programs for Oracle Cloud applications: enterprise resource planning (ERP), human capital management, enterprise performance management (EPM), customer experience (CX), supply chain management (SCM), sales and service cloud, configure/price/quote (CPQ), and marketing. It also covers JD Edwards, NetSuite, and Salesforce solutions. Core implementation services are paired with cloud platform and technology support and managed services. Accelalpha In November 2024, IBM closed its acquisition of Accelalpha, a global Oracle services provider with deep expertise helping clients digitize core business operations and accelerate adoption of Oracle Cloud applications. This acquisition expands IBM’s Oracle consulting expertise in supply chain and logistics, finance, EPM, and customer transformation. Accelalpha was founded in 2009 and is a leading global provider of Oracle Cloud applications consulting across advisory, implementation, and managed services. It serves clients across North America, EMEA, APAC, and Latin America, with a focus on distribution, industrial, and financial services sectors. Accelalpha’s core expertise across Oracle Cloud applications includes Oracle SCM and Logistics, Oracle Cloud ERP, Oracle Cloud EPM, Oracle Cloud CX, and Oracle CPQ. Accelalpha has one of the largest Oracle logistics practices globally. Notable past acquisitions include Frontera Consulting, Key Performance Ideas, LogistiChange, and Prolog Partners. Both of these acquisitions will help IBM: Broaden and expand Oracle Cloud consulting expertise, particularly in high-demand areas such as supply chain management, logistics, and financial services. Address a growing demand for Oracle Cloud solutions. Increase geographic reach and industry depth. Accelalpha’s global team and strong presence in industries like distribution and finance will help IBM expand its reach in key markets and sectors. Plus, AST’s presence in Canada, India, North America, and the UK complements IBM’s global delivery. Increase breadth and depth in the public sector. AST has a strong track record in the public sector, especially in local governments and educational institutions. This puts IBM’s capabilities in an industry with unique challenges that’s still stuck in legacy on-premises systems. Moving forward, IBM can better position itself when competing with several other leading Oracle service providers, but it should be noted that, since neither of these acquisition transactions were officially closed prior to the end of October 2024, Forrester was unable to consider and add capabilities and quantitative/qualitative data from either Accelalpha or AST during its recent evaluation of IBM for the Forrester Wave™ on Oracle services. For more insights, clients can book time with me (via an inquiry or guidance session). source

As the IT landscape continually evolves, CIOs and IT decision makers face an increasingly complex array of challenges. The most pressing involve decisions concerning when to adopt new technologies. For example, consider the decisions VMware customers are now being forced to make after its recent acquisition by Broadcom. Many organizations with perpetual VMware licenses are facing significant annual fee increases demanded by VMware. In fact, according to Device42, “some VMware customers are experiencing cost increases of as much as 1,200%.” VMware products that were once available for license purchase are now only being offered as part of subscription bundles, leading to steep price hikes over multi-year terms. Why get locked into a costly contract when you have alternatives? Start by keeping these three considerations in mind as you build out your VMware roadmap strategy. 1. Weigh your virtualization options VMware’s shift in licensing strategy has left many organizations in a state of uncertainty, and potentially locked into multi-year terms. Perpetual licensees that have made substantial investments in their existing VMware infrastructure now find themselves under pressure to either accept the new terms or explore alternative solutions. This situation creates an immediate need for adequate time to make optimal decisions, and explore all options, including VMware alternatives such as commercial and open-source hypervisors and virtualization solutions, public and private clouds, and container platforms. With renewals fast approaching, enterprises lack the time necessary to evaluate and shift to alternative virtualization environments (this includes hypervisors, management systems and myriad ancillary tools for things like high availability and virtual machine backup to name a few). For many, these are business-critical systems, and exploring self-support options is too risky. Therefore, the focus must shift toward strategies that allow organizations to “buy time” and find certainty during uncertain times. 2. Plan for long-term success The concept of buying time is not merely about delaying decisions but also about creating a buffer to thoroughly analyze all available options without feeling forced to make a quick move. And this is exactly where Rimini Street can help. Rimini Street offers VMware customers an alternative, comprehensive annual support program that can enable them to continue using their perpetual licenses to run critical business applications while maximizing the value of their VMware investment. With  Rimini Protect™, Rimini Consult™ and Rimini Support™ for VMware, organizations have time to make these and other critical infrastructure decisions while receiving Rimini Street award-winning premium support, continued security protection, and expert guidance to help clients optimize their current VMware investments while building an infrastructure roadmap for the future. The rapid pace of technological change often means vendors roll out innovations faster than IT leaders can assess or implement them. You need to evaluate virtualization solutions not just for your current needs but also for their capability to support cloud computing, artificial intelligence (AI), internet of things (IoT), and future emerging tech. Buying time can help enable you to better mitigate and minimize risks while planning for long-term success. 3. Engage a trusted IT advisor and partner As a VMware customer you do have options—you can diversify through different virtualization providers, or you can lift and shift all your workloads over to another provider or cloud. By engaging with Rimini Street, a trusted advisor and objective partner in the industry, we can help identify your best path forward, aligning technology strategy with your business objectives. You’ve likely made significant investments in your current VMware infrastructure, and we can share advice and counsel on how to both continue leveraging your mature and proven VMware infrastructure platform and get the premium, mission-critical quality support and services you need at a price point you can afford. Turning challenges into opportunities While the uncertainty introduced by VMware’s licensing changes may feel daunting, it’s actually a great opportunity for organizations to take control of their VMware strategy. By engaging a trusted IT advisor and partner in Rimini Street, organizations gain time and empower IT leaders to make strategic, informed decisions that align with their long-term goals. Learn more: Discover how Rimini Support for VMware, Rimini Protect and Rimini Consult, a trio of powerful, proven solutions immediately available for VMware products, can help you take control of your VMware strategy today. source

By Hope Patti ( February 4, 2025, 3:33 PM EST) — Meta asked a Delaware federal court to postpone ruling on its insurers’ request to remand a dispute over coverage for thousands of suits alleging harm from the company’s social media platforms, saying the action will likely soon be transferred to multidistrict litigation in California alongside the underlying claims…. Law360 is on it, so you are, too. A Law360 subscription puts you at the center of fast-moving legal issues, trends and developments so you can act with speed and confidence. Over 200 articles are published daily across more than 60 topics, industries, practice areas and jurisdictions. A Law360 subscription includes features such as Daily newsletters Expert analysis Mobile app Advanced search Judge information Real-time alerts 450K+ searchable archived articles And more! Experience Law360 today with a free 7-day trial. source

Accessibility helps your company increase revenue, resilience, and customer trust while lowering costs. It does this by helping you win more customers and deepen relationships with ones you already have. Recent actions by the US federal government haven’t changed this. Our research shows that: There are at least 17 major ways that accessibility helps companies improve their bottom line. To name just a few: It lessens the incidence of — and cost to solve — complaints from customers with disabilities. It avoids the need for legal support to address demand letters and lawsuits. It helps attract and retain employees who value accessibility. It helps service and technology providers win contracts with businesses and government entities globally that require accessibility. Accessibility is a billion-customer opportunity. If your experiences are not accessible, you’re turning away a population of 1.3 billion globally that spends $1.9 trillion annually. Add in that segment’s friends and family members who prefer to do business with brands that don’t shut out their loved ones, and you have an additional 3.3 billion people who prefer brands that prioritize accessibility. Accessibility also means better experiences for all customers (known as the curb-cut effect), as it leads to better usability and helps anyone who is experiencing a temporary or situational disability. You Can Still Get In Legal Trouble If Your Experiences Aren’t Accessible There are many laws protecting the rights of people with disabilities in the US and abroad, despite the US federal government’s movement away from it. Note that: You can still be sued under Title III of the Americans With Disabilities Act (ADA). In 2024 alone, there were over 4,000 digital accessibility lawsuits, according to an annual study from UsableNet. These lawsuits show no signs of slowing down, as court rulings increasingly interpret Title III of the ADA’s “place of public accommodation” terminology to mean digital experiences as well as physical locations. Accessibility shows up in other federal laws, such as the Individuals with Disabilities Education Act (IDEA) and the Air Carrier Access Act (ACAA). Many international jurisdictions remain committed to accessibility. Compliance deadlines for the EU’s European Accessibility Act — which requires companies that operate in the EU to create accessible experiences — arrive in June 2025. This will lead more companies to commit to accessibility. There are strict laws in other regions, too, such as the Accessibility for Ontarians with Disabilities Act (AODA) in Canada. If you’re a Forrester client and would like to ask me questions or work through your own business case for accessibility, you can set up a conversation with me. You can also follow or connect with me on LinkedIn if you’d like. A big thank you to my colleagues Christina McAllister and Judy Weader for their input on this post. source