Stablecoins provide retailers with a means to execute transactions instantaneously, lessen dependency on conventional banks and payment intermediaries, and potentially forge more direct connections with consumers. The e-commerce giant Shopify, for example, already allows merchants to accept USDC, through integrations with Coinbase and Stripe. Walmart is reportedly investigating similar options. Concurrently, Fiserv and other companies are creating stablecoin infrastructure designed for smaller banks and fintech firms. There are additional advantages as well. Through programmable features, merchants can create customizable rewards, cashback in tokens, or temporary discounts triggered by wallet activities. Without a clear value proposition for consumers during checkout, even well-crafted systems could find it hard to gain traction. Nevertheless, there is ample opportunity for innovation. If merchants can reinvest even a small portion of the $187 billion they save on swipe fees into customer rewards and incentives, that formula might begin to shift. Consider Shopify, which now offers 1% cashback in USDC when customers use stablecoins for payment. Coinbase is reportedly developing systems that would support loyalty programs, credit products, and more, which are linked to stablecoin wallets. If retailers can combine stablecoin payments with enticing rewards, faster refunds, personalized offers, and increased privacy, the value proposition becomes much more attractive.
Financial services marketers must adopt a whole-brain approach that blends emotional storytelling with strategic insight by using traditional AI for data and analytics and gen AI for crafting personalized campaigns and customer journeys through scripting ads, designing banners, and optimizing for SEO
Until recently, marketers could only use the calculative or predictive capabilities of AI. However, gen AI allows marketers to create content and do research at a scale and speed we’ve never seen before. This opens new opportunities to drive innovation. Gen AI is supercharging both the analytical and creative dimensions of marketing. This means marketers now have the tools to automate important but tedious tasks like content production, freeing up time to focus on strategic decisions. Imagine having your AI assistant develop the first draft of an entire campaign — scripting ads, designing banners, and optimizing for SEO — while you, the marketer, focus on the bigger picture: refining the message, shaping the narrative and connecting with your audience emotionally. For financial services marketers, gen AI can streamline campaign personalization across highly regulated products, draft copy that adheres to compliance guardrails and surface insights from vast pools of transaction and behavioral data — transforming what used to be bottlenecks into creative springboards. For example, a bank marketer can use gen AI to create personalized financial wellness journeys for customers, based on predictive models drawn from transaction data.
Beep launches NAVI fully autonomous public transit system in Florida; transit-integrated AV mobility services at scale, can operate and maintain fully autonomous public transportation systems
Beep, provider of autonomous shared mobility solutions, has begun operating the Jacksonville Transportation Authority’s (JTA) autonomous vehicle service, called NAVI (Neighborhood Autonomous Vehicle Innovation). NAVI is the first fully autonomous public transportation system in the U.S. Backed by a five-year O&M contract, Beep will operate and maintain the NAVI service, which is part of the JTA’s groundbreaking Ultimate Urban Circulator (U2C) program. In this first phase of the JTA’s U2C program, Beep will operate and maintain a customized fleet of fourteen electric, autonomous, Buy America and ADA compliant Ford E-Transit vehicles integrated with automated driving system (ADS) capability from Oxa. Beep’s proprietary technology will facilitate both vehicle deployment and command center operations from the JTA’s new Autonomous Innovation Center (AIC). The company says that the AIC is a first-of-its-kind for autonomous transit. Beep’s AV-agnostic supervision and management platform (AutonomOS™) enables operators and agencies to deliver transit-integrated AV mobility services at scale, providing for vehicle and cabin supervision, fleet orchestration, and integrated workflow management. The vehicles will be deployed along the Bay Street Innovation Corridor, a 3.5-mile route in downtown Jacksonville with twelve stops from the Central Business Core to the Sports and Entertainment District. The routing aims to encourage downtown revitalization.
UiPath CTO details “Controlled agency” concept that can help delegate work across different AI services by embedding AI agents within structured workflows, where deterministic tasks are handled by robots and non-deterministic tasks are delegated to agents
An approach known as “controlled agency” can help us determine who (or what) does what in the workplace. In AI world, controlled agency is becoming a key mechanism for delegating work across different automation and intelligence services in much the same way. Working to apply this precise principle across his firm’s estate of technologies is Raghu Malpani in his role as chief technology officer at agentic automation company UiPath. “UiPath approaches controlled agency by embedding AI agents within structured workflows, where deterministic tasks are handled by [software] robots and only non-deterministic tasks are delegated to agents. This ensures agents are used where adaptive decision-making is actually needed. Agents are designed to be single-minded i.e. focused on narrow, well-scoped objectives. Complex workflows are composed of multiple such agents alongside deterministic automation, preserving clarity and modularity.” “We’ve built a platform that unifies AI, RPA and human decision making so companies can deliver smarter, more resilient workflows without added complexity. As models and chips commoditize, the value of AI moves up the stack to orchestration and intelligence.” UiPath Maestro is the orchestration layer that automates, models and optimizes complex business processes end-to-end with built-in process intelligence and key performance indicator monitoring to enable continuous optimization. Maestro provides the centralized oversight needed to scale AI-powered agents across systems and teams.
Genesis AI building a general-purpose model to enable robots to automate a wide range of repetitive tasks from lab work to housekeeping by using synthetic data capable of accurately modeling the physical world
Genesis AI, a startup that aims to build a foundational model for powering all kinds of robots, has emerged from stealth with a giant $105 million seed round co-led by Eclipse Ventures and Khosla Ventures. The startup wants to build a general-purpose model that will enable robots to automate a wide range of repetitive tasks, from lab work to housekeeping. Genesis is turning to synthetic data, which it generates using a proprietary physics engine that, it says, is capable of accurately modeling the physical world. Genesis’ synthetic data engine originated from an academic project that Xian led in collaboration with researchers from 18 universities. Several participants from that project have since joined Genesis, making up its current staff of over 20 researchers who specialize in robotics, machine learning, and graphics. Genesis claims its proprietary simulation engine allows it to develop models faster, a distinct advantage over competitors who rely on Nvidia’s software. Genesis is developing its synthetic data and building the foundational model across two offices, in Silicon Valley and Paris. As the next milestone, Genesis plans to release its model to the robotics community by the end of the year.
Visa and Mastercard casting themselves as the connective tissue and playing to their strengths of global scale, trusted rails, built-in fraud protection, and tokenization tech to gain from the shift in card swipe fees to stablecoin payments
A major turf war is heating up in the payments worldand Visa and Mastercard suddenly find themselves on defense. Stablecoins like USDC are gaining traction, with companies like Shopify, Coinbase, and Stripe quietly rerouting payments around traditional card networks. For merchants, the pitch is irresistible: faster settlement, fewer fees, and no middlemen. With U.S. businesses spending roughly $187 billion a year on card swipe fees, even a small shift could redraw the map. Treasury Secretary Scott Bessent has hinted the stablecoin marketnow at $253 billioncould reach $2 trillion in the next few years. That’s not a side bet. That’s a direct hit. Visa and Mastercard aren’t sitting still. They’re flipping the narrativecasting themselves as the connective tissue for all things digital, stablecoins included. Visa is letting banks issue digital tokens and pilot stablecoin settlement directly on its network. Mastercard, meanwhile, just teamed up with Paxos to mint and redeem USDG, its fiat-backed stablecoin. The two networks are leaning into their edge: global scale, trusted rails, built-in fraud protection, and tokenization tech that masks sensitive data at checkout. That’s not just defense. It’s a strategic pivot.
Survey finds Gen Z and Millennials are the most likely to double-check AI-generated responses, with fact-checking rates of 47% and 44%, respectively indicating trust is a challenge when using gen AI
Coveo Solutions Inc.‘s latest Employee Experience Relevance Report found that nearly half of the employees feel frustrated when they don’t have access to the right tools or information. Frustration related to unhelpful tools has only increased over the past few years, from 28% in 2022 to 40% in 2025. Meanwhile, confidence has dropped. These frustrations are part of a broader problem. Information is scattered across too many systems. The same tools that organizations have put in place to fix disconnected systems are still ineffective in many cases according to the employee feedback from the survey: including intranets (31%), enterprise search tools (24%), and generative AI (15%). When using gen AI, trust is a challenge. 42% of employees fact-checked answers provided by AI tools in 2025, up from 36% in 2024. Trust is only slightly higher in enterprise-approved tools than in open-source tools like ChatGPT, with just 17% of employees fully trusting responses from internal systems and 14% trusting open-source tools. Gen Z and Millennials are the most likely to double-check AI-generated responses, with fact-checking rates of 47% and 44%, respectively. Nearly half of the respondents experienced a hallucination when it comes to using AI, with 22% saying it happened during work. These aren’t limited to minor issues either. They’re happening in core business functions such as software development, IT and executive leadership. Weekly hallucinations were reported by 60% or more in each group. Hallucinations are also prevalent in industries with quick decision-making, such as software, IT, finance and accounting. AI hasn’t delivered on the promise of reducing the time it takes to find information. Employees are still moving among multiple systems and sources to search for information needed to do their job. Those in tech roles navigate five or six systems on average. The findings suggest that many AI deployments are still more customer-focused than employee-facing. Employee productivity is lower on the list at 26%. Across large organizations, looking for information amounts to millions of hours wasted each year. It’s clear from the report findings that current systems aren’t helping people focus as they should. The hope is that gen AI could turn things around. According to 42% of the employees surveyed, their organizations have already invested in gen AI tools and training aimed at improving information search in the workplace.
Genesis AI’s AI models for smart robotics adopt a data-centric, full-stack approach to physical AI by developing a scalable universal data engine for physics simulation to build robots that can work around people, adapt and overcome complex spaces and even understand new situations
Genesis AI, a global physical artificial intelligence research lab that develops AI models for smart robotics, has launched after raising $105 million in funding. Genesis AI said that it brings a data-centric, full-stack approach to physical AI by building a scalable universal data engine for physics simulation and using large-scale robotics data collection. Robots driven by physical AI robotics foundation models, or RFMs, can work around people, adapt and overcome complex spaces and work alongside people and even understand situations they were not originally introduced to. Genesis said it wants to deliver a platform that can bring human-level intelligence to robotics for different robots with an RFM that can be deployed no matter the type of robot. To approach the matter, the company forged an expert team of industry technical talent and academia from Mistral AI SAS, Nvidia Corp., Google LLC, Carnegie Mellon University, Massachusetts Institute of Technology, Stanford University, Columbia University and the University of Maryland. Genesis said its core engineering team has deep expertise in simulation, graphics, robots and large-scale AI model training and deployment. Genesis believes there’s a clear opportunity for general-purpose robotics across factory floors, warehouses, healthcare and agriculture. All these scenarios require precise tool use and close proximity with human counterparts, which cannot be easily programmed with the current software stacks employed by modern solutions.
Regulatory acceptance of digital currencies similar to MiCA-compliant stablecoins could create interoperable frameworks that can preserve monetary sovereignty while benefiting from more liquid, efficient settlement rails
Observers across financial, regulatory, and crypto communities saw in MiCA the early contours of a potential digital euro—and a precedent that could influence global standards. Now, with the rules officially in effect for stablecoins as of June 2024, their regulatory acceptance is no longer theoretical. Across the EU, MiCA-compliant stablecoins are circulating in volume. These include Dutch-issued euro stablecoins, U.S.-based euro tokens, decentralized versions like Euro Tether, and dollar stablecoins restructured to meet MiCA’s requirements. Because global liquidity is incredibly helpful—especially when it’s programmable, transparent, and accessible across borders. In the six months following MiCA’s implementation, regulators in several jurisdictions have taken concrete steps to define their own approaches to stablecoins. In July 2024, Singapore finalized its Stablecoin Regulatory Framework, which includes capital and redemption safeguards, similar to MiCA’s EMT structure. The United States remains slower to move. The Clarity for Payment Stablecoins Act of 2023 is still stalled in Congress, though the New York Department of Financial Services has taken the lead by issuing individual approvals for USD-backed stablecoins. Meanwhile, Hong Kong concluded its stablecoin consultation in early 2025, signaling a shift toward regulatory recognition in Asia. If countries recognize each other’s regulated digital currencies—or better yet, create interoperable frameworks—they can preserve monetary sovereignty while benefiting from more liquid, efficient settlement rails.
Modernizing existing REST APIs requires identifying APIs with OpenAPI specs, evaluating which APIs you want AI agents to have access to, configuring the MCP server, handling authentication and authorization, and deploying using the sidecar pattern
This blog discusses how organizations can modernize existing REST APIs with minimal effort by exposing them as tools via the Model Context Protocol (MCP). MCP is a universal standard that allows AI models to interact with external tools, data sources, and applications, effectively serving as a bridge between AI systems and the broader digital ecosystem. To modernize existing REST APIs, follow a 4-step process: Understand your application’s API surface: Use a well-documented REST API with an OpenAPI specification; Wrap your API with FastMCP: FastMCP is a lightweight server that automatically converts API endpoints into tools that AI agents can understand and call; Deploy both your existing application and the MCP server together using Amazon Elastic Container Service (Amazon ECS) and AWS Fargate with a sidecar pattern; Build the AI agent with Strands Agents SDK: Strands provides a clean interface for connecting to MCP servers and integrating with Amazon Bedrock. To adapt this pattern for your existing applications, identify APIs with OpenAPI specs, evaluate which APIs you want AI agents to have access to, configure the MCP server, handle authentication and authorization, deploy using the sidecar pattern, and test and iterate. Benefits of this approach include minimal changes to existing applications, incremental modernization without a complete rewrite, scalable architecture, security by design, and cost-effectiveness. To get started with modernizing your own applications, review the Strands Agents SDK documentation, explore Amazon Bedrock for available AI models, identify applications with well-documented REST APIs, start with a proof of concept using a non-critical application, consider authentication and security requirements for production deployment, and limit API endpoints the agent has access to through FastMCP configuration options.