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.
Fabi.ai’s feature addresses the challenges of static dashboards and restricted business workflows associated with legacy BI by automating the delivery of personalized, AI-enhanced insights directly to the tools data teams use daily
Fabi.ai announced the launch of Workflows, a revolutionary data insights pipeline feature that enables data and product teams to build automated, intelligent workflows that deliver personalized insights directly to stakeholders’ preferred tools. Unlike legacy BI platforms that create “dashboard graveyards,” Workflows meets business users where they actually work—in Slack, email, and Google Sheets—while leveraging AI in the data analysis process to generate meaningful summaries and actionable recommendations. The product addresses three critical failures of legacy BI: restricted data access that ignores real business workflows, misaligned incentives that prioritize seat sales over insight sharing, and the creation of static dashboards that users ultimately abandon for spreadsheets. Workflows transforms this paradigm by automating the delivery of fresh, AI-enhanced insights directly to the tools teams use daily, without forcing data experts to an advanced degree in the vendor’s tooling. Key capabilities of Workflows include: Universal Data Connectivity: Connect to any data source including Snowflake, Databricks, MotherDuck, Google Sheets, Airtable, and more; Integrated Processing Tools: SQL for querying, Python for advanced analysis, and AI for natural language processing and insight generation working seamlessly together; Smart Distribution: Automatically push AI-generated, customized insights via email, Slack, or Google Sheets on configurable schedules; AI-Powered Analysis: Leverage AI to process unstructured data, extract insights from notes and comments, and generate executive summaries; Python-Native Architecture: Enterprise-grade security with scalable AI processing capabilities
Vertically integrated DeFi chain Katana focuses on real yield generation for institutions by deploying bridged assets like ETH and USDC on Ethereum and redirecting sequencer fees back into the network
Katana, a vertically integrated DeFi chain, has launched its mainnet with over $240 million in “productive TVL,” capital that is actively deployed into lending and trading strategies. Incubated by Polygon Labs and GSR, Katana is designed to concentrate liquidity, generate real yield, and route value back to users. The blockchain functions more like a coordinated financial venue than an open playground, avoiding liquidity fragmentation that has plagued DeFi for years. Katana is positioning itself to solve structural liquidity challenges that have long limited institutional participation in DeFi. By concentrating liquidity across chains and protocols into fewer, more accessible pools, Katana can support high-volume, capital-efficient transactions. Institutional appeal is central to Katana’s strategy, with features like real-time rewards, transparent APY breakdowns, and sequencer fee recycling designed to meet the demands of firms that need yield, efficiency, and accountability. At the core of this system is VaultBridge, a mechanism that deploys bridged assets like ETH, USDC, and wBTC into yield-generating strategies on Ethereum. Alongside VaultBridge, Katana introduces chain-owned liquidity, a system that redirects sequencer fees back into the network. This creates a self-reinforcing loop: as activity on the chain increases, so does the pool of capital available to users, which in turn improves trading execution, reduces slippage, and boosts overall yield. Katana’s token design reflects a broader shift in how DeFi infrastructure is being built. It is centered on turning every layer of infrastructure into a yield engine, focusing on transparency yield sources, sustainable incentives, and a clear link between usage and revenue. Katana is built using Polygon’s CDK framework and the OP Stack, with finality provided by Succinct’s SP1 zk prover. However, the long-term test will be whether the ecosystem can continue delivering competitive yields without overrelying on emissions.
Membrane Labs’ risk engine offers crypto institutional lenders real-time VaR analysis, position stress testing, portfolio exposure modeling, and other tools to aid underwriting and quantitative risk management
Membrane Labs has launched an institutional-grade risk engine purpose-built for lenders operating in digital asset markets. Powered by Bitpulse, a leader in crypto underwriting and quantitative risk infrastructure, the system delivers real-time VaR analysis, position stress testing, portfolio exposure modeling, and other tools to empower institutions to thrive in the open economy. “Institutions can’t afford blind spots when billions move at blockchain speed,” said Carson Cook, Founder & CEO of Membrane Labs. “Our risk engine brings clarity and actionability to risk management—without requiring clients to build a quant team from scratch.” The risk engine equips institutional credit and risk teams with the tools to quantify VaR, simulate stress scenarios, and analyze exposures with greater speed and precision. Fully integrated into Membrane’s loan and collateral management infrastructure, these capabilities enhance real-time visibility across the entire credit lifecycle, streamlining decision-making and improving operational efficiency and visibility. In partnership with Bitpulse, Membrane’s risk engine is powered by Bitpulse’s Risk Engine API suite, a leader in quantitative risk infrastructure, bringing advanced analytics directly into Membrane’s institutional workflows.
Ripple taps OpenPayd’s global fiat infrastructure, including real-time payment rails, multicurrency accounts and virtual IBANs to offer a rail-agnostic and fully interoperable cross-border payments solution through a unified platform; applies for a national banking charter
Financial services infrastructure provider OpenPayd launched a partnership with blockchain company Ripple. The collaboration will see OpenPayd’s global fiat infrastructure, including real-time payment rails, multicurrency accounts and virtual IBANs, support Ripple Payments into euros and British pounds. “By combining Ripple Payments with OpenPayd’s rail-agnostic and fully interoperable fiat infrastructure, we are delivering a unified platform that bridges traditional finance and blockchain,” OpenPayd CEO Iana Dimitrova said. “This partnership enables businesses to move and manage money globally, access stablecoin liquidity at scale, and simplify cross-border payments, treasury flows and dollar-based operations.” Ripple Payments is Ripple’s cross-border payment solution, employing blockchain, digital assets and a network of payout partners to deliver cross-border payments and on/off ramps for banks, FinTechs and cryptocurrency firms. The partnership is part of OpenPayd’s efforts to expand its newly launched stablecoin infrastructure, with the company providing direct minting and burning capabilities for Ripple USD (RLUSD). Businesses will be able to convert between fiat and RLUSD, accessing OpenPayd’s suite of services using a single API.
Penn State study shows diffusion-based approach to automatically generate valid quantum circuits achieves 100% output validity by learning the patterns of circuit structure directly from graph-structured data, offering a scalable alternative to LLM-based approaches
A recent study from Penn State researchers introduces a diffusion-based approach to automatically generate valid quantum circuits—offering a scalable alternative to today’s labor-intensive quantum programming methods. The proposed framework, dubbed Q-Fusion, achieved 100% output validity and demonstrates promise for accelerating progress in quantum machine learning and quantum software development. Unlike LLM-based approaches that treat circuit generation like language modeling, or reinforcement learning that requires trial-and-error with human-defined rules, Q-Fusion learns the patterns of circuit structure directly from data. This bypasses the need for hand-crafted heuristics and enables the model to discover novel circuit layouts. Q-Fusion points toward a more scalable future, where models can rapidly explore vast design spaces and generate circuits that are physically viable on actual quantum hardware. The authors note that diffusion models offer advantages over generative adversarial networks (GANs) and other common generative techniques due to their stability and flexibility with graph-structured data. Q-Fusion also incorporates hardware-specific constraints such as limited qubit connectivity and native gate sets, ensuring that generated circuits can potentially be deployed on real quantum devices without extensive post-processing. As quantum computing continues to mature, tools like Q-Fusion could play an essential role in making the technology more accessible and productive. Automating the generation of valid, deployable quantum circuits will reduce the workload on quantum software engineers and accelerate the pace of experimentation. The model’s diffusion-based approach is not only a strong alternative to other QAS methods but also opens new possibilities for combining machine learning with quantum program synthesis. It also aligns with trends in AI where graph-based diffusion models are showing strong performance across domains ranging from drug discovery to chip design.