Salt Security announced the launch of the Salt Model Context Protocol (MCP) Server, giving enterprise teams a novel access point of interaction with their API infrastructure, leveraging natural language and artificial intelligence (AI). Built on the open MCP standard, Salt’s MCP Server enables AI agents to discover, understand, and analyze API behavior with contextual awareness and enterprise-grade precision. Salt’s MCP server creates a personal ChatGPT experience for our customers with new capabilities: Contextual API Search: Contextual search across their entire API inventory. API Explainer: Explain the use and functionality of each API in your environment so security teams can be more independent. Posture Gap Contextual Search and Analysis: Allow security teams to identify API posture gaps and misconfigurations in a free-form search. Remediation Guidance: Offers AI-driven, actionable recommendations to mitigate high-risk vulnerabilities.
Stripe deploys over 60 AI and stablecoin products as a hedge against geopolitical and tariff-related cross-border payment risks
Stripe this week deployed more than 60 products covering a range of artificial intelligence and distributed ledger uses as it competed with legacy card networks and fintechs such as Block and PayPal. These deployments come as the entire payments industry assesses the impact of President Donald Trump’s tariffs, which are pressuring firms to support rapid changes in cross-border strategy. Geographic flexibility is important for a payment tech firm operating in cross-border payments and affects both financial institutions and the global corporations they serve, Sean Viergutz, PwC principal and financial services transformation leader, told. “Being present or easily able to operate in multiple regions allows firms to hedge geopolitical and regulatory risks by not being overly reliant on a specific country or region,” Viergutz said. “Additionally, it allows them to maintain operations in alternative markets when one becomes unstable.” Taking advantage of shifts in supply-chain strategies amid the trade war is a revenue opportunity for cross-border payment companies, according to FXC Intelligence, which reports these adjustments and currency hedges can help offset tariff-related cross-border payment risks, such as a decline in credit for exposed businesses and price volatility. “This is a big opportunity for international commerce,” Tony DeSanctis, a senior director at Cornerstone Advisors, told. “Because the time, currency risk, and cost of international transactions can be mitigated using dollar-denominated stablecoins, this represents a sizable opportunity for international transactions.” Stripe, which provides payment portals for businesses, among other products, has a roster of clients that includes software companies, AI developers , financial institutions and more traditional businesses. The tariffs don’t affect digital businesses because they don’t ship tangible items, Paul Harapin, head of Asia Pacific and Japan at Stripe said, “but that could change.” Stripe has seen an increase in clients that are seeking to pivot their cross-border businesses to deemphasize the U.S. in response to the trade war. “We’ve been told ‘We’re tripling down in the U.K.,'” Harapin said. “They’re already there but are now moving faster.” To deepen its presence in Asia, Stripe recently partnered with Luckin Coffee, a large China-based chain that is expanding elsewhere in the region. The payment company hopes the Luckin deal can raise Stripe’s profile, drawing attention to Stripe services that can address compliance and currency differences within the region. “Asia Pacific is positioned very well for growth,” Harapin said. “The problem is the complex regulations and managing fraud. There are also different payment methods in different countries.”
New York state to establish a supervision framework for BNPL- disclosures, dispute resolution, limits on fee, data privacy; requires disclosure when a price was set by an algorithm using personal data
Governor Kathy Hochul signed a new legislation as part of the FY26 Enacted Budget that will protect consumers across New York and fight back against scams or exploitative practices. From simplifying the process of cancelling recurring online subscriptions to cracking down on overdraft fees that target low-income consumers, these new laws will help New Yorkers fight back against unfair corporate practices. The FY26 budget includes legislation requiring businesses to notify consumers of upcoming renewals and price changes as well as provide clear instructions on how to cancel subscriptions. Under this legislation, cancellation processes must be simple, transparent, and fair – ensuring that it is just as easy to cancel a subscription as it was to sign up. With e-commerce sales rising and returns accounting for billions of dollars annually, New Yorkers deserve stronger consumer protections. The FY26 Budget also includes legislation to require online retail sellers to post return and refund policies in a way that is easily accessible for consumers; and a legislation to establish a licensing and supervision framework for BNPL providers. This legislation will introduce safeguards, such as disclosure requirements, dispute resolution standards, limits on all charges and fees, and data privacy protections to ensure consumers are better protected when using these financial products. The FY26 Budget includes first-in-the-nation legislation that requires businesses to disclose clearly to consumers when a price was set by an algorithm using their personal data, subject to certain exceptions.
Ally Bank’s AI platform can pick the right external LLM depending on the use case or combine answers from several LLMs; it removes PII, tracks all transactions and rehydrates PII for context
In an era where differentiation in banking is increasingly difficult, Ally Bank has emerged as a leader in creating exceptional digital banking experiences. Sathish Muthukrishnan, chief information and technology officer at Ally Financial said, “The intent behind launching our technology strategy was to ensure that technology will continue to be relevant in an all-digital bank, but more importantly, to create differentiation and drive significant business outcomes. We categorized our strategy into six different pillars. The first is security. Our second pillar was driving tremendous experiences. The third pillar is how I know my experience is working. That’s when data analytics came in. Measure what consumers do, but more importantly, measure what they don’t do. Our operational pillar involved migrating to cloud, driving automation and consistency in how we develop and deploy code. And then we needed to preserve our culture and take care of our talent. These pillars laid the foundation for our transformation. We now have about 75% of our applications running on the cloud and about 95% of the enterprise data in the cloud. This allows us to learn from consumer behaviors, understand what they’re expecting and create experiences in real time so consumers think they are our only customer. We had our cloud strategy and data in the cloud warehouse. At the beginning of 2022, we redefined our network. As we were thinking about AI, we launched our chat assistant, Ally Assist. We created Ally AI because we knew technology was fast-evolving, but there were concerns about sending data to external LLMs. To address this, we built an AI platform that could connect to external LLMs but with added security — it removes PII, tracks all transactions and rehydrates PII for context. Our platform can connect to multiple LLMs — from GPT to FLAN to Bedrock. We can pick the right LLM depending on the use case or combine answers from several LLMs. Our content creation LLM is different from what we use for code generation or risk assessment. We have different models for different use cases. My advantage is that the product team, UI/UX team and technology team are all part of the same technology organization. We rolled out savings buckets — your deposit account with multiple savings buckets that you can name yourself. If you start questioning why roadblocks exist and how to solve them, your brand becomes more relevant to consumers. You become their next best experience, deepening relationships.”
Sakana’s Continuous Thought Machines (CTM) AI model architecture uses short-term memory of previous states and allows neural synchronization to mirror brain-like intelligence
AI startup Sakana has unveiled a new type of AI model architecture called Continuous Thought Machines (CTM). Rather than relying on fixed, parallel layers that process inputs all at once — as Transformer models do —CTMs unfold computation over steps within each input/output unit, known as an artificial “neuron.” Each neuron in the model retains a short history of its previous activity and uses that memory to decide when to activate again. This added internal state allows CTMs to adjust the depth and duration of their reasoning dynamically, depending on the complexity of the task. As such, each neuron is far more informationally dense and complex than in a typical Transformer model. CTMs allow each artificial neuron to operate on its own internal timeline, making activation decisions based on a short-term memory of its previous states. These decisions unfold over internal steps known as “ticks,” enabling the model to adjust its reasoning duration dynamically. This time-based architecture allows CTMs to reason progressively, adjusting how long and how deeply they compute — taking a different number of ticks based on the complexity of the input. The number of ticks changes according to the information inputted, and may be more or less even if the input information is identical, because each neuron is deciding how many ticks to undergo before providing an output (or not providing one at all). This represents both a technical and philosophical departure from conventional deep learning, moving toward a more biologically grounded model. Sakana has framed CTMs as a step toward more brain-like intelligence—systems that adapt over time, process information flexibly, and engage in deeper internal computation when needed. Sakana’s goal is to “to eventually achieve levels of competency that rival or surpass human brains.” The CTM is built around two key mechanisms. First, each neuron in the model maintains a short “history” or working memory of when it activated and why, and uses this history to make a decision of when to fire next. Second, neural synchronization — how and when groups of a model’s artificial neurons “fire,” or process information together — is allowed to happen organically. Groups of neurons decide when to fire together based on internal alignment, not external instructions or reward shaping. These synchronization events are used to modulate attention and produce outputs — that is, attention is directed toward those areas where more neurons are firing. The model isn’t just processing data, it’s timing its thinking to match the complexity of the task. Together, these mechanisms let CTMs reduce computational load on simpler tasks while applying deeper, prolonged reasoning where needed.
Microsoft finds API- based agents are generally more stable, less error-prone vis-à-vis GUI-based agents that require multiple actions to accomplish the same goal
Microsoft researchers have compared API-based and GUI-based AI agents, finding that each approach has distinct strengths and can work well together. API agents interact with software through programmable interfaces, while GUI agents mimic human use of software, navigating menus and clicking buttons. API agents are generally more stable and less error-prone, while GUI agents require multiple actions to accomplish the same goal. However, GUI agents can control almost any software with a visible interface, whether or not it offers an API. Microsoft outlines three strategies for combining both types of agents into hybrid systems: using API wrappers to hide GUI actions behind a programmable interface, using orchestration tools to coordinate both API and GUI steps in a workflow, and using low-code and no-code platforms for non-technical users to build automations using drag-and-drop interfaces. Recent advances in multimodal AI and new tools simplifying API development could lead to more flexible forms of automation that blur the line between front-end and back-end integration. Choosing the right agent for the job is crucial for long-term automation success. API agents are best for performance-critical tasks and security-sensitive environments, while GUI agents are better suited for legacy systems that lack APIs and mobile apps. Organizations can start with GUI agents and gradually switch to APIs as they become available.
Morgan Stanley forecasts the market for humanoids will grow to become materially larger than the global auto industry and reach approximately $4.7 trillion by the year 2050
Humanoid will arrive sooner than expected, says Morgan StanleyHumanoid will arrive sooner than expected, says Morgan Stanley. The investment banking firm projects that “the team set forth their proprietary humanoid TAM model that projects the global market for humanoid robots will grow to become materially larger than the global auto industry.” This accelerated timeline suggests a potentially disruptive force with broad economic and sector-wide implications. With the arrival of humanoids, the MS research estimates this potential impact by estimating that the market for humanoids could reach approximately $4.7 trillion by the year 2050. This provides the long-term investment opportunities and the transformative nature of humanoid technology for the investors. Within this AI-driven landscape, the development of humanoids, considered a subset of “Embodied AI,” is identified as a key area of focus. However, 2025 will be the year of Agentic AI, where companies can use Agentic AI tools to improve their businesses. Morgan Stanley says, “we believe the magnitude of benefits from AI adoption is vastly underestimated.” “AI spend is set to rise dramatically, but we see a $1.1 trillion revenue opportunity as early as 2028, with contribution margins of 34% in 2025, rising to 67% by 2028,” Morgan Stanley said. On the nuclear power front, Morgan Stanley believes that “nuclear renaissance will be worth $1.5 trillion through 2050 in the form of capital investment in new global nuclear capacity, which is based on the assumption that there will be 383.5GW of new nuclear capacity to be added globally, which is roughly equal to the current global nuclear capacity of 390GW.” Both AI and humanoids are seen as pivotal forces shaping investment strategies and offering significant opportunities for alpha generation within the evolving technological landscape.
TSB Bank offers access to safeguarding app for users who are fleeing or experiencing abuse- alerts can be sent to chosen emergency contact with a simple tap or shake of the smartphone
TSB will offer customers who are fleeing or experiencing abuse, free access to Hollie Guard Extra for a year1 – simply by downloading the app and using a unique activation code. Those wishing to claim can discuss their situation in branch, over the phone or via video banking. Once installed, Hollie Guard Extra transforms an everyday smart phone into a personal safety device. TSB has added this level of protection to its existing domestic abuse support – which includes its Emergency Flee Fund2 and Safe Spaces3. With a simple tap or shake of the device, the user can send alerts to chosen emergency contacts, including the police, and a 24/7 monitoring centre. The app allows for a user’s location to be shared every five seconds, alongside audio and video recordings, helping to keep people safe in a vulnerable or potentially dangerous situation. The app has already been downloaded by almost 500,000 people in the UK and Hollie Guard Extra is being used by police forces across England and Wales. In addition, it has led to numerous arrests and helped in more than 1,500 threatening and dangerous situations. TSB hopes that Hollie Guard Extra can provide further support to TSB’s Flee Fund – helping connect and protect victim-survivors having fled an abuser.
Plaid’s ID verification tech can now counter deepfakes, synthetic media and facial duplicates; and includes age estimation to flag impersonation risks
Plaid has updated its identity verification (IDV) product to counter the threat posed by fraudsters who use Gen AI. New features added to the product include deepfake and synthetic media detection, facial duplicate detection to catch repeat fraud attempts, age estimation to flag impersonation risks, and risk-based flows that adapt in real time to streamline trusted users and escalate high-risk ones. The upgrades are now available to all Plaid IDV customers, with no extra integration required. Powered by our Trust Index, these enhancements help you streamline verification for trusted users while tightening controls for higher-risk cases: Risk-Based Escalations; Selfie Re-Authentication; Trust Index Risk Check. By combining rich data sources and high-fidelity signals with the scale of our network, Plaid gives companies a faster, more secure way to verify identity and stop fraud at the front door. These enhancements are available to all Plaid Identity Verification (IDV) customers out of the box, with no additional integration required.
Pagaya’s platform for second-look personal loans offers potential for a mid-sized bank of over $1.5 billion of personal-loan origination in less than nine months; can help lenders “bid better” on Credit Karma or Experian
Alternative lending fintech Pagaya Technologies has its sights set on expanding its personal loan offering to regional and super-regional banks while it also builds out its marketing acquisition engine. Pagaya currently partners with banks such as U.S. Bank and neobanks such as SoFi to offer artificial intelligence-powered second-look personal loans to consumers who might not otherwise qualify. Pagaya integrates with lenders’ loan origination systems and buys the loans it originates from the lenders and sells those loans on the secondary market. It is also active in auto lending and point-of-sale buy now/pay later lending. All in, Pagaya counts 31 lenders as partners. Pagaya is in talks with four or five regional banks to help build out or expand their personal-loan offerings, co-founder and CEO Gal Krubiner told. “There is a new era where people are starting to look at growth, and for the regional banks, personal loan is a good way to grow the franchise and to give solutions and products to their customers,” he said. Many regional banks look to personal loans to help secure deposit inflows, a trend that Pagaya is hoping to capitalize on when bringing new partner banks into the fold, Krubiner said. “From our perspective … working with Pagaya could generate for a mid-sized bank over $1.5 billion of personal-loan origination in less than nine months,” Krubiner said, citing U.S. Bank’s performance on the platform. Pagaya is also using its integration into lenders’ underwriting platforms to offer pre-screened loans to potential customers in another avenue that it hopes will lead to growth, said Sanjiv Das, president of Pagaya. “Think about our total market opportunity. We have 31 lending partners. Those 31 lending partners have about 60 million consumers as existing customers. We’ve only scratched the surface right now with the 3% [penetration],” Das, told. Pagaya is also working to help lenders “bid better” for leads from data aggregators, such as Credit Karma or Experian, Das said. The push toward regional banks comes on the heels of solid first-quarter earnings results that beat analysts’ estimates across nearly every metric. Revenue jumped 18% year over year to $290 million, ahead of analysts’ expected $285 million. Net income landed at $8 million, or 10 cents per share, compared with a $21 million loss in the same reporting period last year and eclipsing analysts’ estimate of a $10 million, or 15 cent per diluted share, loss. Shares of Pagaya have risen about 26% since the company reported earnings on May 7, according to a research note from David Scharf at Citizens. Scharf attributes the gains to Pagaya hitting positive GAAP net income ahead of schedule. KBW analyst Sanjay Sakhrani bumped his price target for Pagaya’s stock following the earnings report, pointing to pre-screen and affiliate channels as “growth drivers.” “We believe PGY is well-positioned to shift toward revenue growth across its three loan markets — personal, auto, and POS and deliver profitability. While macroeconomic volatility may introduce risks to funding costs and underwriting capabilities, management’s disciplined risk approach and measured appetite provide confidence,” Sakhrani said.