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1 May 2026, 22:09

Crypto, tech, and software stocks rose as the S&P 500 and Nasdaq closed at record highs

Crypto, tech, and software stocks are rallying today because traders are buying growth names again while the S&P 500 and Nasdaq Composite sit at record levels. The S&P 500 rose 0.29% to 7,230.12 after touching a fresh all-time intraday high. The Nasdaq Composite gained 0.89% and closed at 25,114.44, also at a record. The Dow Jones Industrial Average went the other way, falling 0.31%, or 152.87 points, to 49,499.27. Apple ( AAPL ) helped push the wider market higher, while lower oil prices gave traders one less headache at the start of the new trading month. Donald Trump had said on Truth Social that he would raise tariffs on European cars and trucks: “Based on the fact the European Union is not complying with our fully agreed to Trade Deal, next week I will be increasing Tariffs charged to the European Union for Cars and Trucks coming into the United States. The Tariff will be increased to 25%.” Trump also wrote, “It is fully understood and agreed that, if they produce Cars and Trucks in the U.S.A. Plants, there will be NO TARIFF.” Stellantis (STLA) fell more than 2% after the post, while Ferrari (RACE) lost nearly 1.5%. Tech traders buy software stocks as the sector beats the S&P 500 across every major period The technology sector gained 1.57% on the day, while the S&P 500 rose 0.29%. That is why the rally looks so concentrated. Traders are not treating every corner of the market the same. They are buying tech, AI-linked names, cloud companies, security firms, and software stocks tied to infrastructure. The longer-term numbers also show why money keeps chasing tech. The sector is up 8.34% year-to-date, while the S&P 500 is up 4.84%. Over one year, tech has gained 42.67%, compared with 29.83% for the index. Over three years, tech is up 122.43%, while the S&P 500 has gained 73.41%. Over five years, tech has returned 125.84%, compared with 72.45% for the index. The software stocks rally is also coming from infrastructure names. This group includes companies that build system software, operating systems, networking tools, cloud storage, web services, and related tech services. Microsoft (MSFT) traded at $414.44 and rose 1.63%. Oracle (ORCL) traded at $171.83 and jumped 6.47%. Palantir (PLTR) traded at $144.12 and gained 3.60%. Palo Alto Networks (PANW) traded at $181.08 and rose 0.98%. Cybersecurity and cloud names also joined the rally. CrowdStrike (CRWD) traded at $455.64 and gained 2.22%. Synopsys (SNPS) traded at $489.02 and rose 1.33%. Cloudflare (NET) traded at $217.50 and jumped 6.11%. Fortinet (FTNT) traded at $86.29 and gained 2.35%. CoreWeave (CRWV) traded at $119.01 and rose 6.64%. Block (XYZ) traded at $71.81 and gained 1.84%. Crypto stocks climb as Bitcoin gains in April, while futures drive most of the buying Crypto-linked stocks also traded higher, especially names tied to exchanges, payments, Bitcoin holdings, and mining. Robinhood (HOOD) traded at $73.69 and gained 1.1%. Coinbase (COIN) traded at $191.21 and rose 1.83%. Strategy ( MSTR ) traded at $177.28 and jumped 7.15%. PayPal (PYPL) traded at $50.43 and gained 0.58%. Block (XYZ) traded at $71.82 and rose 1.86%. Circle (CRCL) traded at $99.89 and surged 9.91%. The mining and crypto treasury board looked messier, because crypto stocks rarely behave like polite adults. IREN (IREN) traded at $45.65 and gained 0.31%. Bitmine Immersion Technologies (BMNR) traded at $21.87 and rose 2.2%. Galaxy Digital (GLXY) traded at $28.11 and gained 2.44%. Riot Platforms ( RIOT ) traded at $18.50 and jumped 7.31%. Hut 8 (HUT) traded at $76.94 and rose 1.53%. Bullish (BLSH) traded at $39.29 and gained 4.13%. Core Scientific (CORZ) traded at $20.34 and rose 1.68%. Some crypto names fell despite the wider bid. MARA Holdings (MARA) traded at $11.45 and fell 4.5%. Alliance Resource Partners (ARLP) traded at $26.15 and lost 1.73%. CleanSpark (CLSK) traded at $12.15 and fell 3.03%. Rumble (RUM) traded at $7.33 and lost 2.69%. Bitcoin gained 12.7% in April after rising nearly 2% in March. That gave it two straight winning months after five monthly losses. Ether gained 8% in April, also its second monthly gain in a row. CryptoQuant said perpetual futures were the “sole driver” of Bitcoin’s rally. Its apparent demand gauge, which tracks the 30-day change in direct Bitcoin purchases, stayed negative through April while futures demand increased. The smartest crypto minds already read our newsletter. Want in? Join them .

1 May 2026, 20:25

Scout AI Raises $100 Million to Train Autonomous Military Vehicles for War – Exclusive Bootcamp Visit

BitcoinWorld Scout AI Raises $100 Million to Train Autonomous Military Vehicles for War – Exclusive Bootcamp Visit Scout AI, a defense-focused startup, has raised $100 million in Series A funding to train its AI models for military operations. Bitcoin World visited its bootcamp at a US military base in central California. The company builds autonomous military vehicles using advanced AI. Its goal is to deploy these systems in conflict zones for logistics and combat support. Scout AI Raises $100 Million for Military AI Training Scout AI announced the $100 million Series A round on Wednesday. The funding is led by Align Ventures and Draper Associates. This follows a $15 million seed round in January 2025. The company calls itself a “frontier lab for defense.” It focuses on creating AI models that can operate military assets autonomously. The startup was founded in 2024 by Coby Adcock and Collin Otis. Adcock is also on the board of Figure AI, a humanoid robot company led by his brother Brett. Otis previously worked at autonomous trucking firm Kodiak. He says the limitations of existing autonomous systems in unpredictable environments drove him to start Scout. Scout AI’s core product is an AI model named “Fury.” This model controls and commands military vehicles. Initially, it will handle logistical support. However, the company plans to expand into autonomous weapons systems. The technology builds on existing large language models (LLMs). Inside Scout AI’s Bootcamp: Training AI for War Bitcoin World received an exclusive tour of Scout AI’s training operations. The company asked us not to name the military base. At the site, four-seater all-terrain vehicles (ATVs) roam hillside trails. These vehicles are part of a training exercise. The goal is to train AI models to navigate conflict zones. The training is led by former soldiers. They put the ATVs through simulated missions. Drivers work eight-hour shifts. They log moments when they had to take control from the AI. This data is used to improve the model through reinforcement learning. The company has been training for six weeks. Scout AI uses Vision Language Action models (VLAs). These are based on LLMs and control robots. Google DeepMind first released this technology in 2023. VLAs allow the AI to connect prior knowledge to new tasks quickly. Otis compares this to a human learning to fly a drone with a headset. It is not a big leap. Autonomous ATVs in Action I drove one of Scout’s ATVs on the rutty trails. The terrain was challenging: steep hills, loose sand, and confusing intersections. I am not an experienced driver but managed a fair attempt. This shows the kind of general intelligence the company wants in its models. I also rode in the ATV under autonomous control. The vehicle accelerated faster than a human might. It hugged the right on wider trails but stayed in the middle on narrow ones. When confused, it slowed down to think. This happened several times during a 6.5 km loop. The AI is still learning, but it shows promise. Military Contracts and Applications Scout AI has secured $11 million in military technology development contracts. Clients include DARPA, the Army Applications Laboratory, and other Department of Defense customers. It is one of 20 autonomy companies used by the US Army’s 1st Cavalry Division. The division uses these technologies during regular training cycles at Fort Hood in Texas. The expectation is that proven products will deploy with the unit in 2027. The first applications of ground autonomy will be automated resupply. This includes carrying water or ammunition to distant observation posts. In convoys, a crewed truck might be followed by six to ten autonomous vehicles. This saves human labor for more important tasks. Brian Mathwich, an active duty infantry officer, recalled a recent exercise in Alaska. He led a resupply convoy in total darkness and wished for autonomous vehicles. The Technology Behind Fury: VLAs and LLMs Scout AI’s approach differs from traditional autonomous systems. Most autonomous cars operate in structured environments with rules. Operating off-road on unmarked trails is a different challenge. Otis realized this when he saw that his system at Kodiak was not intelligent enough for a war zone. VLAs offer a solution. They are based on LLMs and used to control robots. Scout AI uses existing LLMs as a base but declined to name which ones. Otis says the company has agreements with “very well known hyperscalers.” It also uses deterministic systems and other AI flavors to round out capabilities. The company expects to build its own model from the ground up in the future. Much of the new capital will go into training and compute costs. Otis believes Scout could beat existing leaders to AGI because its model interacts with the real world constantly. Scout AI’s First Product: Ox Scout AI sees itself as a software company. It builds an intelligence layer for military machines. It does not intend to make the vehicles themselves. Its first product is called “Ox.” This is a command and control software bundled on hardened computer hardware. It includes GPUs, communications, and cameras. Ox allows individual soldiers to orchestrate multiple drones and autonomous ground vehicles. Commands are prompt-like: “Go to this waypoint and watch for enemy forces.” This makes it easy for soldiers to use the technology in the field. Autonomous Weapons: A Controversial Path Scout AI is also working on autonomous weapons. It is testing drones for reconnaissance and as weapons. The drones use vision language models for intelligence. One system involves groups of munition drones flying with a larger “quarterback” platform. This platform provides more compute resources to command them. In one mission, drones would search for hidden enemy tanks and attack them. This could happen without human intervention. Otis argues that this is more precise than indirect artillery fire. Autonomous weapons are a flash point in defense tech politics. Experts note that the concept is old: heat-seeking missiles and mines have been in use for decades. Jay Adams, a retired US Army Captain who leads Scout’s operations team, says the key is how weapons are controlled. The company’s munition drones can be programmed to attack only threats in a specific geographic area. They can also require human confirmation. Adams says autonomous weapons are unlikely to fire because they are scared, unlike a young soldier. VLAs for Better Targeting VLAs offer promise for better targeting. Scout says its models are pretrained on military data. This prepares them for scenarios like running into an enemy tank during a resupply mission. Lt. Col Nick Rinaldi, who supervises Scout’s work for the Army Applications Laboratory, says automated targeting is hard. It is unlikely to be used outside constrained environments in the near term. However, the potential of VLAs to reason about threats makes them a promising technology to investigate. Scout AI’s Mission and Challenges Scout AI wears its mission on its sleeve. Executives criticize companies that are reluctant to work with the government. Google reportedly pulled out of a Pentagon contest for autonomous drone swarm control. Scout is working on similar capabilities. Otis says, “The AI people don’t want to work with the military.” He references Anthropic’s spat with the Pentagon over terms of service. He adds, “None of them are open to running agents on one-way attack drones.” Despite this, Scout uses existing LLMs as a base. It declined to say if it uses open-weight models from Chinese companies. The company expects to address this by building its own model. The founders say much of the capital will go into training and compute costs. Otis wonders if Scout will beat existing leaders to AGI because its model constantly interacts with the real world. Conclusion Scout AI’s $100 million raise marks a significant step in military AI development. The company is training its Fury model to operate autonomous vehicles in conflict zones. Its bootcamp at a US military base shows the practical challenges of this work. The technology uses VLAs and LLMs to provide general intelligence for military assets. The first applications will focus on logistics and resupply. However, the company is also working on autonomous weapons. This raises ethical and practical questions. Scout AI’s approach is controversial but driven by a belief that autonomous systems are necessary for future warfare. The company’s success will depend on its ability to train models that are reliable, safe, and effective in unpredictable environments. FAQs Q1: What is Scout AI? Scout AI is a defense startup that builds AI models for autonomous military vehicles. It was founded in 2024 by Coby Adcock and Collin Otis. Q2: How much funding has Scout AI raised? Scout AI raised a $100 million Series A round in 2025, led by Align Ventures and Draper Associates. It also raised a $15 million seed round earlier in the year. Q3: What is the Fury AI model? Fury is Scout AI’s AI model that operates and commands military assets. It is built on Vision Language Action models and large language models. Q4: What are the first applications of Scout AI’s technology? The first applications are automated resupply missions. This includes carrying water or ammunition to distant observation posts. The technology will also be used in convoys with autonomous vehicles. Q5: Is Scout AI working on autonomous weapons? Yes, Scout AI is developing autonomous weapons systems. It is testing drones for reconnaissance and attacks. The company says these systems can be programmed to require human confirmation or operate within specific geographic areas. This post Scout AI Raises $100 Million to Train Autonomous Military Vehicles for War – Exclusive Bootcamp Visit first appeared on BitcoinWorld .

1 May 2026, 16:55

Pentagon AI Deals with Nvidia, Microsoft, and AWS Revolutionize Classified Military Operations

BitcoinWorld Pentagon AI Deals with Nvidia, Microsoft, and AWS Revolutionize Classified Military Operations The U.S. Defense Department has signed landmark agreements with Nvidia, Microsoft, Amazon Web Services (AWS), and Reflection AI to deploy their artificial intelligence technologies on classified networks. This move marks a significant step in transforming the United States military into an AI-first fighting force. The deals, announced on Friday, follow similar pacts with Google, SpaceX, and OpenAI. They authorize the Pentagon to use these companies’ AI hardware and models for lawful operational use on high-security systems. Pentagon AI Deals: A Strategic Shift Toward Military Modernization The Pentagon’s statement emphasizes that these agreements accelerate the transformation toward establishing the U.S. military as an AI-first fighting force. They strengthen warfighters’ ability to maintain decision superiority across all domains of warfare. The deals allow the Defense Department to integrate advanced AI tools directly into its classified networks, enabling faster data analysis and improved situational awareness. These pacts come amid the Pentagon’s accelerated diversification of AI vendors. This strategy follows a controversial dispute with Anthropic over usage terms. The Pentagon sought unrestricted use of Anthropic’s AI models, but the AI lab insisted on guardrails to prevent use for domestic mass surveillance and autonomous weapons. The two entities are currently in litigation, with Anthropic winning an injunction in March against the Pentagon’s move to brand the company a supply chain risk. Key companies involved: Nvidia – Provides AI hardware and chips for high-performance computing. Microsoft – Offers cloud infrastructure and AI models through Azure. Amazon Web Services (AWS) – Supplies cloud computing and AI services. Reflection AI – A newer AI firm specializing in secure AI deployments. Classified Networks and Security Classifications The DoD will deploy these AI systems on Impact Level 6 (IL6) and Impact Level 7 (IL7) environments. These are high-level security classifications for data and information systems deemed critical to national security. IL6 and IL7 require physical protection, strict access controls, and regular audits. The Pentagon aims to streamline data synthesis, elevate situational understanding, and augment warfighter decision-making using these advanced tools. This deployment ensures that sensitive military data remains secure while leveraging cutting-edge AI capabilities. The Pentagon’s statement highlights the importance of building an architecture that prevents AI vendor lock-in. It ensures long-term flexibility for the Joint Force by accessing a diverse suite of AI capabilities from across the resilient American technology stack. Background: The Anthropic Dispute and Vendor Diversification The Pentagon’s push for diverse AI vendors stems directly from its conflict with Anthropic. The Defense Department wanted unrestricted access to Anthropic’s AI models for military applications. However, Anthropic insisted on ethical guardrails to prevent misuse, particularly for domestic mass surveillance and autonomous weapons. This disagreement escalated into a legal battle, with Anthropic securing a court injunction against the Pentagon’s supply chain risk designation. This dispute prompted the Pentagon to seek alternative AI providers. By signing deals with Nvidia, Microsoft, AWS, and Reflection AI, the DoD reduces its reliance on any single vendor. This strategy aligns with the Pentagon’s goal of maintaining operational flexibility and avoiding dependency on companies that impose usage restrictions. GenAI.mil: The Pentagon’s Secure AI Platform The Pentagon has already deployed GenAI.mil, a secure enterprise platform for generative AI. More than 1.3 million DoD personnel have used this platform to access large language models (LLMs) and other AI tools within government-approved cloud environments. GenAI.mil primarily supports non-classified tasks such as research, document drafting, and data analysis. The new agreements extend these capabilities to classified networks, allowing warfighters to use AI for sensitive operations. This expansion marks a critical milestone in the military’s digital transformation. It enables real-time data synthesis and decision support in high-stakes environments. GenAI.mil usage statistics: 1.3 million+ DoD personnel have used the platform. Primary uses: Research, document drafting, data analysis. Environment: Government-approved cloud systems. Impact on Warfighter Decision-Making The integration of AI into classified networks promises to revolutionize how military personnel process information. By streamlining data synthesis, AI tools can provide warfighters with actionable insights faster than traditional methods. This capability is crucial for maintaining decision superiority in complex, rapidly evolving combat scenarios. Experts note that AI can analyze vast amounts of intelligence data, identify patterns, and recommend courses of action. This augmentation of human decision-making reduces cognitive load and improves response times. The Pentagon’s statement underscores that these tools will give warfighters the confidence to act and safeguard the nation against any threat. Real-World Context: AI in Modern Warfare Other nations, including China and Russia, are also investing heavily in military AI. The United States aims to maintain its technological edge by integrating AI across all domains of warfare. These deals with leading tech companies ensure that the U.S. military has access to the most advanced AI capabilities available. The Pentagon’s focus on AI-first transformation reflects a broader trend in defense strategy. Autonomous systems, predictive analytics, and machine learning are becoming central to modern military operations. The new agreements position the U.S. military to leverage these technologies securely and effectively. Timeline of Pentagon AI Initiatives The Pentagon’s AI journey has accelerated rapidly in recent years: 2023: Launch of GenAI.mil for non-classified tasks. 2024: Initial agreements with Google, SpaceX, and OpenAI. 2025: Deals with Nvidia, Microsoft, AWS, and Reflection AI for classified networks. Ongoing: Legal dispute with Anthropic over usage terms. This timeline shows the Pentagon’s increasing reliance on commercial AI technologies. It also highlights the challenges of balancing military needs with ethical considerations raised by AI developers. Expert Insights on AI Vendor Lock-In Defense analysts emphasize the importance of avoiding vendor lock-in. By diversifying its AI suppliers, the Pentagon ensures it can adapt to changing technological landscapes and avoid being constrained by any single company’s policies. This approach also fosters competition, potentially driving innovation and reducing costs. The Pentagon’s statement explicitly mentions building an architecture that prevents AI vendor lock-in. This strategy ensures long-term flexibility for the Joint Force. Access to a diverse suite of AI capabilities from across the resilient American technology stack will give warfighters the tools they need. Conclusion The Pentagon’s AI deals with Nvidia, Microsoft, AWS, and Reflection AI represent a major leap forward in military technology. By deploying these advanced systems on classified networks, the U.S. Defense Department aims to transform its forces into an AI-first fighting force. These agreements follow earlier pacts with Google, SpaceX, and OpenAI, and come amid a legal dispute with Anthropic over usage restrictions. The Pentagon’s focus on vendor diversification ensures long-term flexibility and operational superiority. As AI continues to reshape modern warfare, these partnerships will play a critical role in maintaining national security and decision superiority across all domains. FAQs Q1: What companies are involved in the Pentagon’s new AI deals? The Pentagon signed agreements with Nvidia, Microsoft, Amazon Web Services (AWS), and Reflection AI to deploy AI on classified networks. Q2: What security levels are these AI systems deployed on? The AI systems will be deployed on Impact Level 6 (IL6) and Impact Level 7 (IL7) environments, which are high-security classifications for national security data. Q3: Why did the Pentagon diversify its AI vendors? The Pentagon diversified its AI vendors after a dispute with Anthropic over usage restrictions. This strategy prevents vendor lock-in and ensures long-term flexibility. Q4: What is GenAI.mil? GenAI.mil is the Pentagon’s secure enterprise platform for generative AI. Over 1.3 million DoD personnel use it for non-classified tasks like research and data analysis. Q5: How will these AI deals impact warfighter decision-making? The AI tools will streamline data synthesis, elevate situational understanding, and augment decision-making, giving warfighters faster and more accurate insights. This post Pentagon AI Deals with Nvidia, Microsoft, and AWS Revolutionize Classified Military Operations first appeared on BitcoinWorld .

1 May 2026, 13:26

Stakeholders bemoan data center development hurdles as Japan plays catch up

Japan is eager to build more data centers. But finding enough electricity to power them while maintaining efficiency and global competitiveness is a delicate balancing act. Data center capacity will dictate how quickly AI rolls out and which industries benefit first. At Japan’s largest technology expo, SusHi Tech Tokyo 2026, industry leaders drew attention to increased bidding competition for electricity between households and AI data centers. Will AI drive up electricity bills? Rocky Lee of Zettabyte, an AI infrastructure company based in Taiwan, said that tackling latency is a major factor behind electricity volume. “If you ask an AI a question and get a response 40 seconds later, that’s not an ideal customer or enterprise experience. Power has to be transferred to GPUs, which is where we see the shortage.” He warned that households in Japan will likely bear the brunt of rising electricity costs. “AI is competing with you. If somebody is willing to pay a little bit more than you, then you have a problem,” said Rocky Lee of Zettabyte, an AI infrastructure company based in Taiwan. Wholesale electricity prices have already soared in U.S. cities with a high concentration of data centers , such as Virginia, Texas, and Silicon Valley. What is regional Japan’s role? The need for low-latency AI services is prompting companies to build data centers around big cities such as Tokyo and Osaka. However, the Japanese government is trying to buck this trend. Japan is home to an estimated 256 operational data centers. The U.S. , on the other hand, operates a whopping 5,400 facilities, followed by approx. 520 in Germany, 500 in the UK and roughly 450 in China. On April 24, it announced an expansion of its GX strategy with the aim of creating industrial clusters around renewable energy sources in regional Japan. The designated regions have not been made public, but likely include Hokkaido, Tohoku, and Kyushu. GMI Cloud is one AI cloud startup that is poised to build Japan’s largest data center in the southern city of Kagoshima. The massive $12 billion gigawatt-scale (GW) project is expected to be completed by 2030. Japan is a safe haven for data GMI Cloud Founder and CEO, Alex Yeh, explained that ample availability of nuclear power is just one reason for the location. “Japan is a huge hub for fiber optic internet access from the U.S. to Asia, such as South Korea, Taiwan, Singapore and the rest of Southeast Asia. That’s why Google, Amazon, Microsoft Azure are located in Japan.” Its data protection policy is an added advantage. Alex Yeh said Japan is the best choice when it comes to building highly sought-after sovereign data centers. “Data is sensitive. There’s government data, military data, and enterprise data. You don’t want data situated in geopolitically sensitive areas such as the U.S. and Korea. That’s why Japan matters.” Corporate giants bet on AI infrastructure Japan’s legacy industrial giants are pivoting toward data centers and power infrastructure in an effort to reinvent their business model and generate new avenues of growth. Japanese telecommunication giant NTT is expanding R&D into AI-native infrastructure. It currently holds the largest market share of data centers in Japan. It has more than 160 sites across all 47 prefectures. On April 27, it announced the AI x OWN initiative. It’s NTT’s effort to redesign the internet around real-time AI use. In a statement, NTT President Akira Shimada said “NTT’s AI infrastructure must shift from conventional ICT infrastructure to infrastructure for a new market premised on AI utilization.” NTT also plans to triple its domestic power capacity from approximately 300 MW today to around 1 gigawatt by fiscal 2033. Can data center deregulation boost AI competition? At SusHi Tech Tokyo 2026 , Alex Yeh of GMI Cloud said top-down deregulation could make Japan globally competitive in AI data centers. He criticized legacy businesses for stifling innovation as well as the government’s preference for traditional, concrete-built data centers. “In the U.S. and Taiwan, data centers are built modularly. These are 40-foot container units that can be shipped and deployed quickly. They’re essentially pre-built data centers, with all wiring integrated, that can be dropped on-site. So why can’t we do that in Japan?” Yeh hopes Japan will turn to modular data centers, slashing construction timelines to six to eight months instead of the 18 to 24 months needed for conventional concrete facilities. There’s a middle ground between leaving money in the bank and rolling the dice in crypto. Start with this free video on decentralized finance .

1 May 2026, 12:02

Ripple Prime Just Won Best Prime Broker: The Institution Era for XRP Is Here

Ripple Prime has won Best Prime Broker at the 2026 Hedge Fund Services Awards Europe. The award evaluates prime brokers on client service, product development, and sustainable business growth. Winning this category places Ripple Prime among traditional finance’s most respected institutions, and not just as a crypto-adjacent firm. This move reinforces Ripple Prime as a legitimate prime brokerage operation. Crypto commentator Xaif (@Xaif_Crypto) shared the news with his audience, stating that “the institution era for $XRP is here.” Ripple Prime just won Best Prime Broker at the 2026 Hedge Fund Services Awards Europe the institution era for $XRP is here pic.twitter.com/kq5ZXJYBoS — Xaif Crypto (@Xaif_Crypto) April 29, 2026 How Ripple Prime Got Here Ripple Prime’s rise has been rapid. In April 2025, Ripple acquired global prime brokerage firm Hidden Road for $1.25 billion . The deal made Ripple the first crypto company to own and operate a global, multi-asset prime broker. Hidden Road brought serious infrastructure. Its network has more than 300 institutional clients and facilitates $10 billion in daily trade volume. Hidden Road was rebranded to Ripple Prime. It subsequently launched US digital asset spot prime brokerage capabilities. This allows clients to execute OTC spot transactions across the most prominent digital assets and stablecoins, including XRP and RLUSD. The growth followed quickly. Ripple Prime recorded 3x growth in activity . What the Award Means for XRP Ripple Prime settles transactions via the XRP Ledger, giving the token a direct role in institutional activity. Every trade and every settlement cleared through Ripple Prime’s infrastructure creates utility demand for XRP, increasing its institutional adoption and potentially driving up its price. Ripple Prime has granted institutions direct access to settlement rails previously inaccessible to most blockchains. Winning a major European hedge fund award confirms that traditional finance recognizes this infrastructure as credible. What Comes Next? Ripple has not stopped building. Over the past year, the company spent nearly $4 billion acquiring key firms to accelerate its transformation, including Hidden Road for $1.25 billion and financial software provider GTreasury for $1 billion . Ripple also closed a $500 million strategic investment at a $40 billion valuation , led by Fortress Investment Group and Citadel Securities. The trajectory points toward continued institutional expansion. As Ripple Prime grows its European footprint and deepens relationships with hedge funds, XRP’s role as the settlement asset within that ecosystem becomes increasingly central. Xaif’s post captures the moment well. The institutional era for XRP is already in motion. Disclaimer : This content is meant to inform and should not be considered financial advice. The views expressed in this article may include the author’s personal opinions and do not represent Times Tabloid’s opinion. Readers are advised to conduct thorough research before making any investment decisions. Any action taken by the reader is strictly at their own risk. Times Tabloid is not responsible for any financial losses. Follow us on X , Facebook , Telegram , and Google News The post Ripple Prime Just Won Best Prime Broker: The Institution Era for XRP Is Here appeared first on Times Tabloid .

1 May 2026, 11:56

The $670 billion AI boom is delivering uneven results across the tech industry

The money big tech poured into artificial intelligence is starting to show results, but Wall Street remains nervous about the hundreds of billions being spent on chips and data centers, and not every company is winning. Reddit’s stock (NYSE: RDDT) rose 16% before the market opened on Friday, after the company furnished investors with a higher-than-expected revenue outlook for the coming quarter. The gains show how well Reddit’s AI-powered advertising solutions are doing. The company developed a system that inserts advertising into relevant discussion threads (interest-based communities known as subreddits) and utilizes AI to help advertisers write copy, manage campaigns, and automatically crop images to match different ad placements. Strong numbers set Reddit apart from tech rivals The numbers backing this up are hard to ignore. Reddit’s daily active visitors grew 17% to 126.8 million in the quarter, and the average revenue it made per user worldwide jumped 44%. That puts Reddit in a strong spot against much larger tech rivals like Meta’s Facebook and Instagram. Unlike those companies, Reddit is also still bringing people on board. “Reddit is still hiring and adding to our talent base,” Chief Operating Officer Jen Wong said. That’s a contrast to what Meta, Snap, and Pinterest have been doing. All three have cut thousands of jobs in the past year to cut costs and redirect money toward AI. Reddit’s content library has become valuable for another reason too. AI companies are competing to get their hands on text data to train their large language models, the systems behind tools like ChatGPT, and Reddit’s archive of discussions is a sought-after resource. Analysts at Morgan Stanley said that how well Reddit executes across these areas will be key to showing its value “even in a future GenAI enabled and agentic landscape.” Apple caught off guard as chip shortages bite On the hardware side, things look different. Apple (NASDAQ: AAPL) CEO Tim Cook said demand for Mac minis and Mac Studios has outpaced what the company expected, largely because developers are using them to run an AI agent platform called OpenClaw. The software lets users run AI agents locally on their own machines using their own data, and it caught on fast among developers. “The Mac Mini and the Mac Studio, both of these are amazing platforms for AI and agentic tools, and the customer recognition of that is happening faster than what we had predicted,” Cook said on the company’s Q2 earnings call. He added that reaching supply-demand balance for those products “may take several months.” The base model M4 Mac mini is already sold out on Apple’s website. On eBay, refurbished units are going for as much as $979. Demand has since spilled over to the Mac Studio, which is also sold out in several configurations. The shortage is costing Apple real money , even if the problem is one that other companies might envy. Cook also flagged a longer-term concern: memory chip costs. “Beyond the June quarter, we believe memory costs will drive an increasing impact on our business,” he said. Memory prices have risen sharply because so much of the global chip supply is being funneled into AI data centers. Research firm IDC expects PC shipments overall to fall 11.3% in 2026 because of this shortage. Apple’s MacBook Neo has also been hit. A shortage of A18 Pro chips has made the $599 laptop hard to find. The bigger picture is that the entire tech industry is feeling the pressure. Microsoft, Alphabet, Meta, and Amazon together spent $410 billion on infrastructure last year and are projected to spend more than $670 billion in 2026. “We’re seeing constraints across the board. The hyperscalers who are trying to get into the gold mine, they’re having to wait, or spend more to get in,” said Brent Thill, a tech analyst at Jefferies. “It’s good for the picks and shovels, but it’s not good for the people who are assembling all the pieces.” Overall, AI is creating clear winners in software while driving up costs and shortages across hardware. If you're reading this, you’re already ahead. Stay there with our newsletter .