Top Innovations
No-code IAM platform offers developers a secure, scalable way to connect AI agents to external tools without having to manually manage and store tokens, scopes, and permissions
Descope, the drag & drop…
Amazon Bedrock serverless endpoint system dynamically predicts the response quality of each model and efficiently routes it to the most appropriate model based on cost and response quality
Amazon Bedrock has announced the…
Key News
Alacriti’s next-gen ACH solution provides a unified payments infrastructure to process wires and real-time payments through multiple rails while allowing configurable exception handling, posting and settlement
Alacriti has launched its enhanced…
HiddenLayer enhances explainability of AI models using Model Genealogy and AI Bill of Materials (AIBOM), that reveal lineage and pedigree to track how they were trained, fine-tuned, and modified
HiddenLayer released AISec Platform 2.0,…
Software containerization company Docker is launching the Docker MCP Catalog and Docker MCP Toolkit, which bring more of the AI workflow into the existing Docker developer experience and simplify AI software delivery. The new offerings are based on the emerging Model Context Protocol standard created by its partner Anthropic PBC. Docker argues that the simplest way to use Anthropic’s MCP to improve LLMs is to containerize it. To do that, it offers tools such as Docker Desktop for building, testing and running MCP servers, as well as Docker Hub to distribute their container images, and Docker Scout to ensure they’re secure. By packaging MCP servers as containers, developers can eliminate the hassles of installing dependencies and configuring their runtime environments. The Docker MCP Catalog, integrated within Docker Hub, is a centralized way for developers to discover, run and manage MCP servers, while the Docker MCP Toolkit offers “enterprise-ready tooling” for putting AI applications to work. At launch, there are more than 100 MCP servers available within Docker MCP Catalog. President and Chief Operating Officer Mark Cavage explained that “The Docker MCP Catalog brings that all together in one place, a trusted, developer-friendly experience within Docker Hub, where tools are verified, secure, and easy to run.”
Codacy, provider of automated code quality and security solutions, launched Codacy Guardrails, a groundbreaking new product designed to bring real-time security, compliance, and quality enforcement to AI-generated code. Guardrails is the first technology to make AI-generated code trustworthy and compliant by checking it before it ever reaches the developer. Codacy Guardrails is the first solution of its kind that integrates directly with AI coding assistants to enforce coding standards and prevent non-compliant code from being generated in the first place. Built on Codacy’s SOC2-compliant platform, Codacy Guardrails empowers teams to define their own secure development policies and apply them across every AI-generated prompt. With Codacy Guardrails, AI-assisted tools gain full access to the security and quality context of a team’s codebase. At the core of the product is the Codacy MCP server, which connects development environments to the organization’s code standards. This gives LLMs the ability to reason about policies, flag or fix issues in real time, and deliver code that’s compliant by default. Guardrails integrates with popular IDEs like Cursor AI and Windsurf as well as VSCode and IntelliJ through Codacy’s plugin, allowing developers to apply guardrails directly within their existing workflows.
Amazon Bedrock has announced the general availability of its Intelligent Prompt Routing, a serverless endpoint that efficiently routes requests between different foundation models within the same model family. The system dynamically predicts the response quality of each model for a request and routes the request to the model it determines is most appropriate based on cost and response quality. The system incorporates state-of-the-art methods for training routers for different sets of models, tasks, and prompts. Users can use the default prompt routers provided by Amazon Bedrock or configure their own prompt routers to adjust for performance linearly between the performance of two candidate LLMs. The system has reduced the overhead of added components by over 20% to approximately 85 ms (P90), resulting in an overall latency and cost benefit compared to always hitting the larger/more expensive model. Amazon Bedrock has conducted internal tests with proprietary and public data to evaluate the system’s performance metrics.
Anysphere, maker of AI coding assistant Cursor, is growing so quickly that it’s not in the market to be sold, even to OpenAI, a source close to the company tells TechCrunch. It’s been a hot target. Cursor is one of the most popular AI-powered coding tools, and its revenue has been growing astronomically — doubling on average every two months, according to another source. Anysphere’s current average annual recurring revenue is about $300 million, according to the two sources. The company previously walked away from early acquisition discussions with OpenAI, after the ChatGPT maker approached Cursor, the two sources close to the company confirmed, and CNBC previously reported. Anysphere has also received other acquisition offers that the company didn’t consider, according to one of these sources. Cursor turned down the offers because the startup wants to stay independent, said the two people close to the company. Instead, Anysphere has been in talks to raise capital at about a $10 billion valuation, Bloomberg reported last month. Although it didn’t nab Anysphere, OpenAI didn’t give up on buying an established AI coding tool startup. OpenAI talked with more than 20 others, CNBC reported. And then it got serious over the next-fastest-growing AI coding startup, Windsurf, with a $3 billion acquisition offer, Bloomberg reported last week. While Windsurf is a comparatively smaller company, its ARR is about $100 million, up from $40 million in ARR in February, according to a source. Windsurf has been gaining popularity with the developer community, too, and its coding product is designed to work with legacy enterprise systems. Windsurf did not respond to TechCrunch’s request for comment. OpenAI declined to comment on its acquisition talks. OpenAI is likely shopping because it’s looking for its next growth areas as competitors such as Google’s Gemini and China’s DeepSeek put pricing pressure on access to foundational models. Moreover, Anthropic and Google have recently released AI models that outperform OpenAI’s models on coding benchmarks, increasingly making them a preferred choice for developers. While OpenAI could build its own AI coding assistant, buying a product that is already popular with developers means the ChatGPT-maker wouldn’t have to start from scratch to build this business. VCs who invest in developer tool startups are certainly watching. Speculating about OpenAI’s strategy, Chris Farmer, partner and CEO at SignalFire, told TechCrunch of the company, “They’ll be acquisitive at the app layer. It’s existential for them.”
Cybersecurity company Snyk announced the launch of Snyk API & Web, a new dynamic application security testing or DAST solution designed to meet the growing demands of modern and increasingly AI-powered software development. The new service integrates technology from Probley, a startup acquired by Snyk into Snyk’s application security platform. The technology unifies critical AppSec testing techniques into a single developer security platform. The DAST service seeks to assist in dealing with risks that can occur when businesses increasingly leverage generative AI and use APIs to bridge the gap between LLMs and the applications they fuel. Snyk argues that APIs introduce vulnerabilities that can expose AI models to significant risks, jeopardizing the security of entire software supply chains. Snyk API & Web offers a robust solution for developers and AppSec teams to proactively discover, inventory and secure API vulnerabilities before they become threats. The new service offers tools designed to simplify DAST for developers and security teams. The inetgration also leverages AI-driven capabilities to detect vulnerabilities that are often missed by conventional methods. This makes the solution especially useful in fast-paced development environments where speed and accuracy are paramount. API & Web also includes an AI-powered API Security Testing engine that uses generative AI and traditional machine learning models. The engine helps developers better map the growing API attack surface and automate the process of scanning for vulnerabilities.
Automated Security Validation platform Pentera is setting a new standard for enterprise-scale security validation with the introduction of its Distributed Attack Orchestration architecture and AI-reporting capabilities. These enhancements meet the requirements of security teams to scale security validation testing to govern a consistent security posture across decentralized enterprise IT architectures. With a choice of persistent or dynamic attack nodes deployed across multi-site infrastructures, security teams can run simultaneous security validation tests coordinated through a single interface. Each node runs in-depth attack emulation, ensuring that as testing scales across the enterprise, the depth and rigor of validation remain uniform. Designed for centralized control, Pentera provides security teams with the following capabilities to manage distributed testing efficiently: Granular Test Scheduling, Real-Time Control over Test Operations, Silent Runs – Pentera provides advanced control over test noise levels, with signed commands and payloads, allowing operators to test across environments without overloading the SOC with false alarms. “Our Distributed Attack Orchestration solution provides visibility into how adversaries can exploit the enterprise attack surface, while our AI-based reporting aggregates the trends security leaders need to prioritize to reduce exposure across the organization,” said Ran Tamir, Chief Product Officer at Pentera.
HiddenLayer released AISec Platform 2.0, the platform with the most context, intelligence, and data for securing AI systems across the entire development and deployment lifecycle. Tnew release includes Model Genealogy and AI Bill of Materials (AIBOM), expanding the platform’s observability and policy-driven threat management capabilities. With AISec Platform 2.0, HiddenLayer is establishing a new benchmark in AI security where rich context, actionable telemetry, and automation converge to enable continuous protection of AI assets from development to production. With AISec Platform 2.0, HiddenLayer empowers security teams to Accelerate model development, Gain full visibility, Automate model governance and enforcement and Deploy AI with confidence. AISec Platform 2.0 introduces: 1) Model Genealogy: Unveils the lineage and pedigree of AI models to track how they were trained, fine-tuned, and modified over time, enhancing explainability, compliance, and threat identification. 2) AI Bill of Materials (AIBOM): Automatically generated for every scanned model, AIBOM provides an auditable inventory of model components, datasets, and dependencies. Exported in an industry-standard format, it enables organizations to trace supply chain risk, enforce licensing policies, and meet regulatory compliance requirements. 3) Enhanced Threat Intelligence & Community Insights: Aggregates data from public sources like Hugging Face, enriched with expert analysis and community insights, to deliver actionable intelligence on emerging machine learning security risks. 4) Red Teaming & Telemetry Dashboards: Updated dashboards enable deeper runtime analysis and incident response across model environments, offering better visibility into prompt injection attempts, misuse patterns, and agentic behaviors.
Cyera, the world’s fastest-growing data security company, today announced the launch of Omni DLP, a breakthrough AI-native solution that finally delivers on the promise of enterprise data loss prevention. Omni DLP combines the power of Cyera’s AI-native Data Security Posture Management (DSPM) with a real-time DLP analysis engine from its Trail Security acquisition, creating a unified architecture that protects data at rest, in motion, and in use. With Omni DLP, organizations gain: 1) AI-Powered Noise Reduction – Eliminate over 95% of false positive alerts to focus on the few most critical and actionable 2) Real-Time, Adaptive Protection – Automatically detect your unique data and prevent exfiltration 3) Deep AI Governance – Control data used in AI tools and prompts, and prevent data exposure through AI systems. 4) 360 View – all your endpoint, network, email, messaging and cloud DLP risks, alerts and policies in a single view, leveraging AI for enrichment and correlation. 5) Policies That Learn – auto-tuned controls that evolve with your data. “Omni DLP is the brain DLP has been missing,” said Yotam Segev, CEO and co-founder of Cyera. “Omni DLP enables us to protect sensitive data in motion – the crown jewels – automatically classified by our AI-native classification engine. This is data security the way it was meant to be: intelligent, adaptive, and built for the AI era.”
Apache Airflow community is out with its biggest update in years, with the debut of the 3.0 release. Apache Airflow 3.0 addresses critical enterprise needs with an architectural redesign that could improve how organizations build and deploy data applications. Unlike previous versions, this release breaks away from a monolithic package, introducing a distributed client model that provides flexibility and security. This new architecture allows enterprises to: Execute tasks across multiple cloud environments; Implement granular security controls; Support diverse programming languages; and Enable true multi-cloud deployments. Airflow 3.0’s expanded language support is also interesting. While previous versions were primarily Python-centric, the new release natively supports multiple programming languages. Airflow 3.0 is set to support Python and Go with planned support for Java, TypeScript and Rust. This approach means data engineers can write tasks in their preferred programming language, reducing friction in workflow development and integration. Instead of running a data processing job every hour, Airflow now automatically starts the job when a specific data file is uploaded or when a particular message appears. This could include data loaded into an Amazon S3 cloud storage bucket or a streaming data message in Apache Kafka.
Relyance AI, a data governance platform provider that secured $32.1 million in Series B funding last October, is launching a new solution aimed at solving one of the most pressing challenges in enterprise AI adoption: understanding exactly how data moves through complex systems. The company’s new Data Journeys platform addresses a critical blind spot for organizations implementing AI — tracking not just where data resides, but how and why it’s being used across applications, cloud services, and third-party systems. Data Journeys provides comprehensive view, showing the complete data lifecycle from original collection through every transformation and use case. The system starts with code analysis rather than simply connecting to data repositories, giving it context about why data is being processed in specific ways. Data Journeys delivers value in four critical areas: First, compliance and risk management: The platform enables organizations to prove the integrity of their data practices when facing regulatory scrutiny. Second, precise bias detection: Rather than just examining the immediate dataset used to train a model, companies can trace potential bias to its source. Third, explainability and accountability: For high-stakes AI decisions like loan approvals or medical diagnoses, understanding the complete data provenance becomes essential. Finally, regulatory compliance: The platform provides a “mathematical proof point” that companies are using data appropriately, helping them navigate increasingly complex global regulations. Customers have seen 70-80% time savings in compliance documentation and evidence gathering.
BlackCloak has launched an industry-first Identity Verification solution to combat deepfake-powered and other impersonation attacks, targeting high-profile executives and individuals. BlackCloak’s new Identity Verification offering is the first to address this rapidly emerging cybersecurity issue by enabling customers who receive a suspicious email to verify the sender’s identity and ensure the message is authentic. Integrated into its Digital Executive Protection platform, this feature enables users to verify the authenticity of suspicious communications, providing vital protection for executives, families, and businesses. BlackCloak’s new Identity Verification feature targets phishing campaigns, such as deepfakes, by allowing the user to prompt the sender to validate that they are who they claim to be through the BlackCloak mobile app. The new feature can combat the impact of deepfakes containing the following attributes: Synthetic Media, Facial Manipulation, Voice Cloning:, and Behavioral Mimicry.
Descope, the drag & drop external IAM platform, launched the Agentic Identity Hub, an industry-first platform that helps organizations solve authentication and authorization challenges for AI agents, systems, and workflows. The Descope no / low code external IAM platform helps organizations easily create, modify, and manage journeys for their consumers, business customers, partners, and APIs / AI agents using visual workflows. Capabilities announced include: 1) Inbound Apps, which provide every application an easy way to become its own identity provider using the OAuth standard. This allows AI agents to securely authenticate, access authorized user data, and take scoped actions on behalf of users with their explicit consent. 2) Outbound Apps, which provide every AI agent builder a secure, scalable way to connect AI agents to external tools without having to manually manage and store tokens, scopes, and permissions. Developers can choose from over 50 out-of-the-box tool integration templates including Gmail, HubSpot, GitHub, Snowflake, Slack, Notion, and Shopify. 3) MCP Auth SDKs and APIs that help developers building and managing remote MCP servers secure their systems with robust authorization controls as well as extend the MCP servers’ functionality by connecting them with multiple OAuth-based services.
Meta is using artificial intelligence tools to identify underage Instagram users who may have lied about their age to bypass platform safeguards. The company has announced that suspected underage users will be automatically placed into restricted “Teen Accounts” even if their account lists them as adults. Teen Accounts offer a controlled experience tailored for users under 16, limiting who can interact with them and restricting certain types of content. Meta claims the shift is designed to protect younger users and promote safe online behavior. Techniques include analyzing contextual clues, such as birthday wishes or tip-offs from other users, and comparing them with the stated age. Users will have the option to contest the AI’s decision and adjust their settings if misclassified. Meta will begin notifying parents directly, offering guidance on how to talk to teens about providing accurate age information online and encouraging them to verify their child’s listed birthday on Instagram.
WaveCX, provider of personalized, digital product engagement solutions for financial institutions, launched Curator Command, a major advancement in digital banking user experience (UX) that transforms natural language requests into direct in-app action. Built as an extension of the Curator platform, Curator Command expands on its AI-driven, semantic search capabilities, moving beyond simply understanding user intent to acting on it. Digital banking users often face friction when trying to complete basic tasks, including navigating complex menus, disconnected flows and overloaded support teams. Curator Command eliminates this friction by instantly connecting user intent to the right screen, step or solution, streamlining task completion and reducing reliance on manual navigation. The system enables customers and employees to type requests in plain language, understands the intent, reads app structure and real-time context, and activates the appropriate response. Curator Command adapts to each financial institution’s policies, user roles and app structure, rather than relying on prebuilt workflows or manually tagged flows. The platform reads the interface, understands user context and delivers accurate results from day one. Key benefits for financial institutions include: Reduced support volume and faster resolution; Increased adoption of digital tools and services; Faster onboarding and lower training costs; and A more responsive and intuitive user experience.
According to an APK teardown done by Android Authority, an upcoming beta version of Gboard looks to be bringing on a few new features. The biggest one is something we see in Apple’s version of a digital keyboard. When typing, swiping down or “flicking” a key will pull down a symbol or number, allowing you to easily select a secondary character without having to break up momentum to tap and hold. The action comes naturally after some practice and works phenomenally well, if executed correctly. In Gboard, this new feature is found in a toggle labeled “flick keys to enter symbols.” It’s unclear whether this feature will work with numbers in the top row of the keyboard or only symbols. It’d be a little nonsensical to allow for only symbols to be “flicked” into the text bar. It’s also strange that this feature borrows terminology from iOS, though “flicking” would imply an upward swipe, not a downward one as described. Gboard is also adding a toggle for keeping the number row active in password layouts. This is accompanied by prompt entry in the writing tools tab, as well as rounded keys in Gboard.
Gmail is rolling out a handy new “Manage subscriptions” page that allows users to unsubscribe from email subscriptions with a single tap, though it appears to only be on Android so far. A notice in the app shows to inform users of the new option, with a “Manage subscriptions” button in the overflow menu. The new page, which was previously spotted just over a year ago, lists out email addresses and names for your email subscriptions. It also shows how many emails were sent “recently.” A button to the right side of the display then allows users to quickly unsubscribe from emails from that sender. In our testing so far, this generally works in a single click, though a small number of the ones we tried did pop up a browsing window to complete the process. Google adds that it “can take senders a few days to stop sending messages” after you use the unsubscribe shortcut.
A survey from Western & Southern Financial Group found that nearly a third of Americans surveyed have memorized at least one debit or credit card number. What’s more, 20% of respondents who know their card numbers spend more than $500 per month online, compared to just 13% of those who haven’t. They also carry, on average, 10% more debt than non-memorizers. The survey found a significant generation gap, with almost half of respondents who memorize their card numbers falling into the millennial group. Overall, more than a third of millennials have memorized at least one card number. The reasons people gave for memorizing their numbers also varied across generations. More than two-thirds of Gen Z respondents said they did so to make online shopping easier, while, 20% of baby boomers memorized their card numbers specifically to avoid using digital wallets. Gen Z was also the most likely to feel that knowing their card numbers led to more impulse buying and overspending. In contrast, a third of the baby boomers were the most likely to report that memorizing their card numbers had improved their financial discipline. There is some evidence that memorization goes hand in hand with responsible behavior. A total of 70% of memorizers checked their credit card or bank statements at least weekly, compared to 61% of non-memorizers. However, impulse buying was also slightly more common among memorizers. Roughly 26% made unplanned purchases, compared to 23% of non-memorizers. Memorizers were also somewhat more likely to shop when stressed.
eBay, a global commerce leader that connects millions of buyers and sellers around the world, today announced the expansion of its global strategic partnership with Klarna, the AI-powered payments and commerce network, to the U.S. market. eBay is rolling out Klarna’s flexible payments options to millions of eBay’s U.S. shoppers. The expanded partnership reflects eBay’s continued investment in bringing more choice, flexibility, and control to buyers while enhancing affordability across key categories. “With more than 2.3 billion listings, eBay is where people come to shop with purpose—whether they’re looking for value, rare finds, or sustainable options such as a refurbished camera, hard-to-find car part, or a vintage handbag,” said Avritti Khandurie Mittal, VP & General Manager of Global Payments and Financial Services at eBay. “We are thrilled to expand our global strategic partnership with Klarna to the U.S. We’ve been very pleased with the positive customer and business impact Klarna has delivered in some of our key markets including the U.K. and Europe, and we’re now excited to give millions of U.S. shoppers more flexible and affordable ways to pay on eBay.”
Since launching in the U.K., Austria, France, Italy, the Netherlands and Spain in December 2024, eBay shoppers are splitting up payments to access higher-ticket items—like a pre-loved luxury watch—in a more affordable, manageable way. Electronics, fashion, and collectibles are some of the most popular categories where eBay shoppers are paying with Klarna. Shoppers can pay for eligible eBay purchases in the U.S. using Klarna’s flexible payments options including Pay in 4, which allows customers to split their purchase into four interest-free payments as well as Financing, which offers flexible payment plans for larger purchases.
Verve, media platform focused on emerging channels, and Audigent, a part of Experian and a leading data activation, curation and identity platform, today announced a collaboration that utilizes both companies’ proprietary probabilistic targeting technology to improve targeting in ID-less environments. The result is that brands can now use Audigent’s curation solutions to reach Verve’s global audience of 2.5 billion users. This provides cutting-edge, privacy-safe, curated probabilistic targeting across channels like connected TV (CTV), in-app advertising, and audio. Verve’s patented ATOM ID-less technology uses first-party on-device signals paired with advanced contextual and demographic modeling to build actionable audience profiles without traditional IDs. Audigent’s Hadron ID has consistently been one of the leading cookieless ID solutions. Audigent’s supply-side data activation powers its multi-publisher private marketplace (PMP) solutions: SmartPMPs, ContextualPMPs, and CognitivePMPs. These private marketplaces enable advertisers to engage specific audiences at scale. Now, in collaboration with Verve, Audigent can extend these capabilities to a broader range of environments including those in which cookies may not be available or underserved screens such as mobile.
Cadent, the predictive advertising company, launched The Cadent Platform — a unified, enterprise-class advertising system that uses predictive modeling and real-time performance optimizations to transform the omnichannel workflow. The new Cadent Platform brings AI-powered predictive intelligence to all stages of advertising — from audience planning to media activation to outcome measurement — helping brands “know before they go” by bringing together forecasting before a campaign begins with optimization as it runs. Built from the ground up with privacy, transparency, and performance at its core, the platform breaks down the growing silos across digital and video to deliver business outcomes at scale. The Cadent Platform empowers advertisers, agencies, and media owners to: Predict outcomes before campaigns launch by scoring all impressions, then optimize in real time; Maximize reach by assembling all available audiences through ID-independent and predictive targeting; and Orchestrate messaging and outcomes across all video and digital media. The Cadent Platform is built with modularly to easily integrate its leading features into existing technology stacks: 1) Audience Manager. A consumer data platform (CDP) that onboards and builds targetable audiences using ID-based, ID-less, and predictive models. With one of the highest match rates in the industry, it enables advertisers to engage audiences in cookieless environments that others can’t reach. 2) Ad Manager. An omnichannel DSP built for real-time activation and optimization across digital and video. Powered by 15+ years of predictive modeling and AI-driven creative optimization, it ensures KPI-aligned performance with full transparency and fraud protection. 3) Inventory Manager. A premium SSP that connects buyers directly to 150+ brand-safe publishers, delivering transparent auctions, nearly 100% MFA-free inventory, and curated audience deals across CTV and digital.
Sesh, a fan engagement ecosystem that connects people with their favorite music artists, announced it has raised $7 million. Sesh foster connection by connecting fans with interactive experiences, exclusive content and live events. Additionally, the company is launching its Member Card, allowing fans to register and seamlessly download a digital pass to their phone’s wallet—no app required—becoming an official part of their favorite artist’s community. For the first time, artists can send direct push notifications to their fans’ phone wallets, going beyond the access streaming platforms and social media channels provide. Artists can direct audiences to a new music release, exclusive merch drop, VIP event invite or presale tickets directly within the ecosystem. With Sesh, artists get access and a direct line to their fans, including full ownership of essential data such as email, location, name, date of birth and engagement insights, empowering creators to own and cultivate their audience relationships without relying on third-party platforms. By providing tools for direct engagement, including integrations with Spotify and TikTok, Sesh helps artists grow and strengthen their fan communities.
Embedded payroll startup Salsa continues to gain momentum. With a newly secured $20 million in Series A funding, its total capital is $30 million since its founding in 2021. The round was led by Altos Ventures, with additional participation from Greycroft, SemperVirens, Definition and Better Tomorrow Ventures. Altos Ventures Partner Tae Yoon said, “Payroll is one of the clear next frontiers in embedded FinTech. … We are thrilled to partner with Salsa as it becomes the foundational layer for payroll across entire industries.” The new funding will support Salsa’s efforts to help software platform developers in all 50 U.S. states and Canada embed payroll features in their products that they didn’t previously have. This includes launching integrated payroll offerings and tools that streamline tasks like worker onboarding, tax filings and tracking commissions, tips, overtime and employees who work in multiple locations — all without the need to have in-house payroll expertise.
Investing.co, a provider of financial news, tools and data to retail investors, has launched an AI-driven researcher called WarrenAI. WarrenAI promises to bridge the gap between Wall Street traders and retail player by combining the ease of ChatGPT with trusted premium market data and raw analytical power. The tool, claims Investing.com, is a personal, devoted financial researcher, with superhuman capacity and expertise, and which can answer just about any question with faster market reactions than a fleet of Wall Street analysts. The technology will launch in over 30 languages. While existing AI software such as ChatGPT source data from the entire web, WarrenAI has exclusive access to vetted, real-time, data on global markets. Investors get access to an array of over 1200 fundamental metrics spanning more than 72,000 companies, ETFs, mutual funds, closed-end funds and REITs, complete with a decade’s worth of historical data. In addition to condensing months of detailed financial news into summaries, WarrenAI can deliver SWOT analysis, provide the bearish and bullish cases for thousands of stocks, gather breaking Wall Street analyst outlooks, and run advanced stock screeners within seconds.
Vanguard is launching the firm’s first dynamic asset allocation fixed income model portfolios. Vanguard Fixed Income Risk Diversification and Vanguard Fixed Income Total Return join the firm’s lineup of model portfolios that provide financial advisors with access to broadly diversified, low-cost, and high-quality Vanguard-managed solutions. “Model portfolios empower financial advisors with streamlined investment manager research and ongoing portfolio construction and monitoring, so they can spend time on the things that really matter to their clients—like ensuring they’re meeting their investment goals,” said Brent Beardsley, Head of Advisor Solutions for Vanguard. Built to serve a variety of investment time horizons and risk profiles, Vanguard’s model portfolios support financial advisors’ portfolio construction needs so they can spend more time scaling their practice and deepening client relationships. Deeper client relationships lead to improved client loyalty and trust, according to Vanguard’s Advisor’s Alpha research, which can then assist with asset retention and referrals. Vanguard Fixed Income Risk Diversification and Vanguard Fixed Income Total Return model portfolios seek to outperform a market-capitalization-weighted benchmark—the Bloomberg U.S. Aggregate Index and Bloomberg U.S. Universal Index respectively—and allocations are recalibrated throughout the year to align with the Vanguard Capital Markets Model® (VCMM) 10-year forecasts. Vanguard’s Investment Strategy Group oversees the asset allocations for the models and Vanguard’s Fixed Income Group manages the fixed income funds included in each portfolio. The Vanguard Fixed Income Risk Diversification model portfolio features a weighted average expense ratio of 0.05% and is constructed for advisors and their clients in search of a highly diversified fixed income portfolio with exposure to global investment grade bonds intended to provide ballast against equity market volatility. Vanguard Fixed Income Total Return model portfolio is designed for advisors and clients seeking wealth accumulation and risk diversification from the fixed income sleeve of their portfolio. This model portfolio contains exposure to global investment grade and high yield bonds at a weighted average expense ratio of 0.08%.
Consumers’ perception of Apple’s AI platform is more favorable than that of investors, Morgan Stanley said in a research note. Morgan Stanley said it found that the Apple Intelligence platform has been downloaded and engaged with by 80% of eligible U.S. iPhone owners in the last six months, has an above average net promoter score of 53, and is characterized by iPhone users as “easy to use, innovative, and something that improves their user experience.” “While much of the public critique of Apple Intelligence is warranted, and investor sentiment and expectations on Apple’s AI platform couldn’t be lower, our survey of iPhone owners paints a more positive picture,” Morgan Stanley said in the note. Since September, the share of iPhone owners who believe it is extremely or very important to have Apple Intelligence support on their next iPhone rose 15 points to reach 42%. Among iPhone owners who are likely to upgrade their device in the next 12 months, the percentage saying that about the AI platform rose 20 points to reach 54%, according to the note. Morgan Stanley also found that consumers are willing to pay more for Apple Intelligence than they were in September. Those who have used the AI platform are now willing to pay an average of $9.11 per month for it, a figure that’s 11% higher than the $8.17 average seen in September, per the note. While we don’t expect Apple to put Apple Intelligence behind a paywall until the platform is more built out, the potential long-term monetization of an Apple Intelligence subscription could reach tens of billions of dollars annually when considering a 1.4B global iPhone installed base, 32% (and growing) of US iPhone owners have an Apple Intelligence support iPhone, and users are willing to pay up to $9.11/month for Apple Intelligence,” Morgan Stanley said in the note.
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