News & Updates
The latest news and updates from companies in the WLTH portfolio.
UAE ups pre-IPO stake in Anthropic
Abu Dhabi's MGX backed Anthropic's $65 billion fundraise, further strengthening its position in the Claude developer. MGX, launched in 2024 by Abu Dhabi AI conglomerate G42 and sovereign wealth fund Mubadala, and chaired by UAE National Security Adviser Sheikh Tahnoon bin Zayed, owns stakes in three frontier model frontrunners: Anthropic, OpenAI, and xAI. All three are expected to go public this year (xAI as part of its parent company SpaceX), making the UAE the Gulf state with the greatest private-market exposure to the AI race. Qatar's QIA has invested in both Anthropic and xAI, while Saudi Arabia's PIF-backed HUMAIN is also a recent backer of xAI. Anthropic initially ruled out taking the region's money on national security and moral grounds, only to backtrack. The latest funding round gives it a $965 billion valuation, above that of its rivals. Anthropic has blown past growth expectations while spending at least $1.25 billion a month on compute in "a very delicate dance" between growing too fast and going bankrupt, as one investor told Semafor's Reed Albergotti.
Weatherford Announces Definitive Agreement to Acquire NCS Multistage, Expanding Completions Portfolio and Unconventional Resource Exposure
HOUSTON, June 01, 2026 (GLOBE NEWSWIRE) -- Weatherford International plc (NASDAQ: WFRD) ("Weatherford" or the "Company") and NCS Multistage Holdings, Inc. (NASDAQ: NCSM) ("NCS Multistage") today announced that Weatherford has entered into a definitive agreement to acquire NCS Multistage. Under the terms of the agreement, NCS Multistage stockholders have an election to receive either Weatherford common stock or a combination of Weatherford common stock and cash. On a blended basis, this is expected to be the equivalent of 0.463 shares of Weatherford common stock for each NCS Multistage share with up to 19.99% of this payable in cash. Annual cost synergies are expected to be at least $15 million and be realized within 18 months of closing. The deal is expected to be immediately accretive to adjusted Free Cash Flow per share. NCS Multistage brings a complementary technology portfolio aimed at supporting the optimization of oil and gas well completions and field development strategies. Its solutions are designed to enhance reliability and performance in complex well environments and are widely recognized for engineering rigor and capital-efficient deployment. Compelling Strategic Benefits The acquisition is expected to complement and enhance Weatherford's portfolio by: * Expanding offerings in the well completions segment, while deepening Weatherford's capabilities in the unconventional space. * Supporting the delivery of differentiated, technology-enabled solutions that help customers improve operational and production outcomes. * Providing an avenue for further growth of NCS Multistage's portfolio by leveraging Weatherford's international footprint. Girish Saligram, Weatherford's President and Chief Executive Officer, commented, "The acquisition of NCS Multistage is a natural complement to our completions strategy and enhances the application fit of our well construction products portfolio. NCS Multistage's technology is expected to enhance our ability to serve customers across the completion lifecycle, from well design through production optimization and late-life interventions, while deepening our exposure to the growing unconventional resource market. We expect to realize at least $15 million in annual run-rate cost synergies over a period of 18 months. Additionally, we see a meaningful opportunity to create additional value by bringing this technology to our global customer base, and we look forward to welcoming NCS Multistage into Weatherford." Ryan Hummer, NCS Multistage's Chief Executive Officer, commented, "This is a significant step for NCS Multistage that we believe positions our business -- and the talented people who built it -- for the next phase of growth as part of a leading global energy services company. I am proud of the company that our team at NCS Multistage has built, and it is clear from our interactions that Weatherford recognizes the strength of our technology, the quality of our operations, and the commitment of our people. This combination creates an opportunity for our products, technology, and people to reach a broader set of customers and markets faster than we could on our own, supported by Weatherford's financial strength and international footprint, providing long-term opportunity and value for our stakeholders." Transaction Details and Approvals The transaction has been approved by the Board of Directors of Weatherford, the Board of Directors of NCS Multistage, and the controlling stockholder of NCS Multistage that owns more than 50% of NCS Multistage's outstanding common stock. The transaction is subject to certain customary closing conditions, including regulatory approvals, and is expected to close in the second half of 2026. Until the transaction closes, Weatherford and NCS Multistage will continue to operate as separate, independent companies. Under the terms of the agreement, NCS Multistage stockholders can elect to receive either 0.554 shares of Weatherford common stock at closing, or a combination of 0.239 shares of Weatherford common stock and a cash amount equal to 0.137 shares of Weatherford common stock at closing, subject to proration and certain limitations and adjustments. On a blended basis, this is expected to be the equivalent of 0.463 shares of Weatherford common stock with up to 19.99% of the total equity consideration payable in cash. Advisors King & Spalding LLP is acting as legal counsel to Weatherford and Baker Botts L.L.P. is acting as legal counsel to NCS Multistage. Piper Sandler & Co. is serving as financial advisor to NCS Multistage. About Weatherford Weatherford is a global energy services company that helps customers drill smarter, complete wells more effectively, and maximize production across the entire well lifecycle. With a differentiated portfolio of market-leading solutions, integrated technologies, and a broad global customer footprint across six continents, we blend advanced engineering, digital intelligence, and world-class field expertise to reduce risk, improve performance, and maximize the value of customer assets. Together, we elevate every operation, delivering stronger wells, sharper decisions, and better energy for the world. Visit weatherford.com for more information and connect with us on social media. About NCS Multistage NCS Multistage is a leading provider of highly engineered products and support services that facilitate the optimization of oil and natural gas well construction, well completion and field development strategies. NCS Multistage provides products and services primarily to exploration and production companies for use in onshore and offshore wells, predominantly those that have been drilled with horizontal laterals in both unconventional and conventional oil and natural gas formations. NCS Multistage's products and services are utilized in oil and natural gas basins throughout North America and in selected international markets, including the North Sea, the Middle East and Argentina. Visit ncsmultistage.com for more information. Forward-Looking Statements This communication includes statements, which, to the extent they are not statements of historical or present fact, constitute "forward-looking statements" within the meaning of the U.S. Private Securities Litigation Reform Act of 1995. Forward-looking statements, and any related oral statements, can be identified by the use of terms such as "believe," "project," "expect," "anticipate," "estimate," "outlook," "budget," "intend," "strategy," "plan," "guidance," "may," "should," "could," "will," "would," "will be," "will continue," "will likely result," and similar expressions, although not all forward-looking statements contain these identifying words. These statements include, but are not limited to, statements about the expected timing and completion of the proposed transaction between Weatherford and NCS Multistage, the anticipated benefits of the proposed transaction, and plans and expectations for the new combined company after the completion of the proposed transaction. Such statements are based upon the current beliefs of Weatherford's and NCS Multistage's management and are subject to significant risks, assumptions, and uncertainties. Should one or more of these risks or uncertainties materialize, or underlying assumptions prove incorrect, actual results may vary materially from those indicated in our forward-looking statements. Readers are cautioned that forward-looking statements are only estimates and may differ materially from actual future events or results, based on factors including but not limited to the ability to complete the proposed transaction on the timeframe or on the terms currently anticipated or at all, including due to a failure to obtain requisite regulatory approvals; risks related to difficulties, inabilities or delays in integrating the parties' businesses; the ability to realize the anticipated benefits of the proposed transaction, including estimated synergies; the occurrence of any event, change or other circumstance that could give rise to the right of either or both parties to terminate the Merger Agreement; the potential impact of the announcement or consummation of the proposed transaction on the parties' stock price and on their respective business, contractual and operational relationships; risks related to business disruptions from the proposed transaction that may harm the business or current plans and operations of either or both parties, including diversion of management time from ongoing business operations; the risk that the proposed transaction and its announcement could have an adverse effect on the ability of either or both parties to hire and retain key personnel; the outcome of any legal proceedings that may be instituted against Weatherford or NCS Multistage, or their respective directors; the possibility that the proposed transaction may be more expensive to complete than anticipated, including as a result of unexpected factors or events, or unforeseen or unknown liabilities; Weatherford's ability to receive, in a timely manner and on satisfactory terms, required shareholder and court approval, and to satisfy the other conditions to the proposed redomestication within the expected timeframe or at all; our ability to realize the expected benefits from the proposed redomestication; the occurrence of difficulties in connection with the redomestication, including any costs related thereto; the risk that the proposed redomestication disrupts current plans and operations; global political, economic and market conditions, political disturbances, war or other global conflicts, terrorist attacks, public health issues such as pandemics, changes in global trade policies, tariffs and sanctions, weak local economic conditions and international currency fluctuations; general global economic repercussions related to U.S. and global inflationary pressures and potential recessionary concerns; as well as the factors and risks described in Weatherford's Annual Report on Form 10-K for the year ended December 31, 2025 and NCS Multistage's Annual Report on Form 10-K for the year ended December 31, 2025, and, in each case, in subsequent filings with the U.S. Securities and Exchange Commission. Other unpredictable factors not discussed in this communication could also have material adverse effects on forward-looking statements. You should not place undue reliance on any of Weatherford's or NCS Multistage's forward-looking statements. Any forward-looking statement speaks only as of the date on which such statement is made, and Weatherford and NCS Multistage undertake no obligation to correct or update any forward-looking statement, whether as a result of new information, future events or otherwise, except as required by applicable law, and we caution you not to rely on them unduly. No Offer or Solicitation This communication is not intended to and shall not constitute an offer to sell or the solicitation of an offer to sell or the solicitation of an offer to buy any securities, or a solicitation of any vote or approval, nor shall there be any sale of securities in any jurisdiction in which such offer, solicitation or sale would be unlawful prior to registration or qualification under the securities laws of any such jurisdiction. No offer of securities shall be made except by means of a prospectus meeting the requirements of Section 10 of the Securities Act of 1933, as amended (the "Securities Act"), or in a transaction exempt from the registration requirements of the Securities Act. Additional Information About the Transaction and Where to Find It In connection with the proposed transaction, Weatherford intends to file a registration statement on Form S-4 (the "Form S-4") that also constitutes a prospectus of Weatherford with respect to the shares of Weatherford to be issued in the proposed transaction (the "prospectus") and NCS Multistage intends to file an information statement on Schedule 14C, with the Securities and Exchange Commission (the "SEC"). Each of Weatherford and NCS Multistage may also file other relevant documents with the SEC regarding the proposed transaction. This document is not a substitute for the Form S-4 or prospectus or any other document that Weatherford or NCS Multistage may file with the SEC. INVESTORS AND SECURITY HOLDERS ARE URGED TO READ THE REGISTRATION STATEMENT, THE INFORMATION STATEMENT/PROSPECTUS AND ANY OTHER RELEVANT DOCUMENTS THAT MAY BE FILED WITH THE SEC, AS WELL AS ANY AMENDMENTS OR SUPPLEMENTS TO THESE DOCUMENTS, CAREFULLY AND IN THEIR ENTIRETY IF AND WHEN THEY BECOME AVAILABLE BECAUSE THEY CONTAIN OR WILL CONTAIN IMPORTANT INFORMATION ABOUT THE PROPOSED TRANSACTION. Investors and security holders will be able to obtain free copies of the Form S-4 and the information statement/prospectus (if and when available) and other documents containing important information about Weatherford, NCS Multistage and the proposed transaction, once such documents are filed with the SEC through the website maintained by the SEC at http://www.sec.gov. Copies of the documents filed with, or furnished to, the SEC by Weatherford will be available free of charge on Weatherford's website at https://weatherford.com/investor-relations/home. Copies of the documents filed with, or furnished to, the SEC by NCS Multistage will be available free of charge on NCS Multistage's website at https://ir.ncsmultistage.com. The information included on, or accessible through, Weatherford's or NCS Multistage's website is not incorporated by reference into this communication. For Investors: Luke Lemoine Weatherford Investor Relations +1 713-836-7777 [email protected] Mike Morrison NCS Multistage Holdings Chief Financial Officer and Treasurer +1 281-453-2222 [email protected] For Media: Kelley Hughes Weatherford Corporate Communications, Marketing & Sustainability [email protected]
Kraken Set to Launch CFTC-Regulated Bitcoin Perpetual Futures
Kraken plans to offer CFTC-regulated Bitcoin perpetual futures in the US within 30 days, bringing regulated crypto derivatives onshore. Kraken is moving fast. The cryptocurrency exchange announced plans to launch CFTC-regulated perpetual futures contracts in the United States within 30 days. Eligible US clients will access these contracts directly on Kraken Pro. The product will sit alongside spot, margin, and CME-listed futures on a single interface. This marks a significant shift in how American traders engage with crypto derivatives. Read also: Kraken Parent Attracts $200M Investment From European Exchange Giant What Kraken's Perpetual Futures Mean for US Crypto Traders Perpetual contracts are derivatives that offer continuous exposure to an underlying asset. Unlike traditional futures, they carry no expiration date. Traders can hold positions without rolling them over. That flexibility makes perpetuals the most actively traded derivatives in digital asset markets globally. Annual trading volume for perpetuals surpassed $60 trillion in 2025. Until now, US traders had few regulated options to access them domestically. Most activity happened offshore. Kraken's launch aims to bring that activity into a regulated, onshore framework for the first time. John Palmer, Global Head of Derivatives at Kraken, addressed the significance of the move. He stated that US traders have been waiting for a regulated, domestic way to trade the product that defines global crypto derivatives markets. He added that perpetuals, spot, margin, and CME-listed futures will now sit on one interface, changing how US clients build and manage crypto positions. Eligible clients will trade a range of major digital assets. The list includes BTC, ETH, SOL, XRP, ADA, LINK, DOGE, LTC, and AVAX. Kraken also indicated plans to expand contract offerings and collateral options over time. How the Contracts Are Structured and Where They Will List The contracts will list on Bitnomial Exchange, LLC, a CFTC Designated Contract Market. Bitnomial was recently acquired by Payward, Kraken's parent company. Kraken filed the contract details under Commission Regulation 40.3. The contracts feature continuous pricing, no expiration, and an eight-hour funding rate. This structure matches the conventional format used globally for crypto perpetuals. They will share a futures wallet with Kraken's existing CME-listed contracts. That setup lets traders manage both CME futures and perpetuals side by side without switching platforms. Perpetuals on Kraken Pro are offered through NinjaTrader Clearing, LLC, doing business as Kraken Derivatives US. That entity holds registration as a CFTC Futures Commission Merchant. Spot margin and perpetual futures operate on and under the rules of Bitnomial Exchange, LLC. This announcement follows a string of US-focused product rollouts from Kraken. In July 2025, the exchange launched support for CME-listed crypto futures alongside spot markets. Earlier in May 2026, it introduced CFTC-regulated spot margin trading for eligible US clients. CFTC's Regulatory Shift Opened the Door for US Perpetuals Kraken's move did not happen in isolation. It follows a notable regulatory development that cleared the path for products of this nature. The CFTC recently approved a Bitcoin perpetual futures contract submitted by KalshiEX, LLC. That contract, known as BTCPERP, became the first regulated Bitcoin perpetual futures contract on a US exchange. As reported by LiveBitcoinNews, Kalshi submitted the BTCPERP contract on May 28, 2026, under Commission Regulation 40.3. The CFTC issued its Order for Approval the following day. The contract tracks Bitcoin's spot price without an expiration date. CFTC Approves First Regulated Bitcoin Perpetuals on Kalshi Traders can hold leveraged positions and settle funding rates periodically. The CFTC confirmed the contract complies with the Commodity Exchange Act and applicable commission regulations. That approval signaled a broader shift in the US regulatory environment for crypto derivatives. Kraken's 30-day timeline shows how quickly market participants moved to act on the opening. The exchange's filing puts it in a position to deliver the second major CFTC-regulated perpetuals product to US traders, building on the precedent Kalshi helped establish.
Blue Origin rocket explodes on launchpad in a setback for bid to catch Musk's SpaceX
An un-crewed Blue Origin New Glenn rocket exploded on a Florida launchpad during a test on Thursday, in a major setback for Jeff Bezos' space venture as it seeks to narrow the gap with Elon Musk's IPO-bound SpaceX. Video posted by NASASpaceflight, which livestreams launches from Florida, showed the towering New Glenn rocket igniting on the pad at about 2100 ET (0100 GMT on Friday) before erupting into a massive fireball that billowed skyward, sending a towering plume of flames and smoke into the air. The location was verified as Cape Canaveral, Florida by the coastline, launch pad facilities, water tower and buildings, which matched file and satellite imagery. The date was verified from the timestamp in a livestream posted by the source and the metadata of another video verified by Reuters of the same event. Blue Origin was preparing the rocket for its fourth launch, which was due to deliver 48 Amazon Leo satellites into low-Earth orbit, part of efforts to build a broadband constellation to rival Musk's Starlink network. Amazon Leo satellites were not integrated on the rocket at the time of the incident, a source familiar with the matter said, asking not to be named due to its sensitivity. The explosion marks the latest setback for the long-delayed New Glenn, which is supposed to play a central role in delivering lunar landers and cargo under NASA's Artemis lunar exploration missions. SpaceX, which unveiled its plans for an IPO earlier this month and is set to become the first trillion-dollar US market debut, has also faced setbacks with its rockets. In June last year, its massive Starship spacecraft exploded in a similarly dramatic fireball during testing in Texas while preparing for a test flight.
SpaceX Reserves Up to 5% of IPO Stock for Employees and Friends
SpaceX will reserve up to 5% of shares in its upcoming initial public offering for certain employees and friends and family of its executive officers, the company disclosed in an amended filing. The amount of Class A stock the company is setting aside for its directed share program is newly ...
Understanding AI market intelligence for anthropic stock tracking
Market intelligence systems in the AI space are becoming essential tools for understanding how advanced technologies evolve and gain adoption across industries. Instead of relying solely on traditional financial summaries, analysts now focus on real-time signals, ecosystem expansion and enterprise usage patterns to assess long-term potential. These insights help shape expectations around Anthropic stock, especially as AI capabilities continue to improve rapidly and influence both business workflows and developer ecosystems. How AI market intelligence systems actually work AI market intelligence systems are structured platforms designed to collect, process and interpret large-scale data from multiple digital sources. These systems do not rely on surface-level indicators but instead focus on deeper behavioural and technical signals that reflect how AI technologies are performing in real environments. The goal is to convert raw data into actionable insights that help understand growth, adoption, and scalability trends. Core components of intelligence systems * Data aggregation layer The data aggregation layer collects information from multiple sources, such as API usage, developer activity, enterprise integrations and research updates. This layer ensures that analysts receive a broad and balanced dataset representing both technical performance and market behaviour. By combining structured and unstructured inputs, it creates a strong foundation for deeper evaluation and reduces dependency on isolated metrics. * Signal processing engine The signal processing engine filters large volumes of raw data to identify meaningful patterns. It removes noise, highlights relevant changes, and organises signals such as adoption spikes, performance improvements and usage shifts. This allows analysts to focus only on impactful movements instead of irrelevant fluctuations. * Behavioural analysis module The behavioural analysis module studies how users, developers and enterprises interact with AI systems over time. It evaluates engagement frequency, usage consistency and integration depth. These insights help determine whether adoption is stable, growing, or temporary. * Predictive modelling framework The predictive modelling framework uses historical and real-time data to simulate future outcomes. It estimates adoption speed, ecosystem growth and scalability under different scenarios. These models are continuously refined as new data flows into the system. Role of real-time signals in AI evaluation Real-time signals are crucial for understanding how AI systems evolve in fast-moving environments. These signals capture immediate changes in usage, performance and ecosystem activity, allowing analysts to detect early shifts in momentum. In discussions around anthropic stock, real-time indicators provide valuable insights into adoption behaviour before broader trends become visible. Key real-time indicators used by analysts * API usage fluctuations Changes in API usage reflect shifts in demand and highlight how different industries are interacting with AI systems at scale. * Model update frequency Frequent updates indicate continuous innovation and strong research momentum within the organisation. * Developer engagement levels High engagement suggests a growing ecosystem where developers actively build and expand applications. * Enterprise integration updates The expansion of AI use across business departments signals deeper operational dependence and trust. * Latency and performance changes Improvements in speed and accuracy often lead to higher adoption in mission-critical applications. Each of these indicators helps analysts form a dynamic view of AI performance beyond traditional evaluation models. Analyst interpretation of AI ecosystem expansion AI ecosystem expansion reflects the growth of tools, applications and integrations built around a core AI system. A strong ecosystem indicates that the technology is becoming foundational infrastructure rather than a standalone solution. Analysts evaluate ecosystem size, diversity and integration depth to understand long-term sustainability. Factors that define ecosystem strength * Developer tooling availability Strong developer tools make it easier for third-party creators to build applications, leading to faster ecosystem growth and innovation. * Third-party application growth A rising number of external applications built on top of AI systems indicates widespread adoption and increased platform value. * Cross-platform integration Seamless integration with cloud systems and enterprise tools enhances usability and accelerates adoption across industries. * Community contribution levels Active communities support innovation, troubleshooting and collaboration, strengthening overall ecosystem resilience. Behavioural models used in the AI market interpretation Behavioural models help analysts understand how users and enterprises interact with AI systems over time. These models provide a human-centred perspective that complements technical analysis and helps explain adoption patterns more clearly. Key behavioural interpretation models * Adoption curve analysis Tracks how quickly users and organisations adopt AI systems and whether growth is accelerating or stabilising. * Retention behaviour study Measures how consistently users continue using AI systems after initial adoption, indicating long-term value. * Engagement depth measurement Evaluates how deeply AI tools are integrated into daily workflows rather than being used occasionally. * Usage diversity mapping Examines the range of applications across industries, from coding to customer support and analytics. These models help build a more complete understanding of AI adoption beyond surface-level statistics. Strategic interpretation of AI market intelligence Strategic interpretation combines technical, behavioural and ecosystem data into a unified analytical framework. This approach helps analysts understand competitive positioning, innovation strength and scalability potential in a structured way. Key strategic evaluation dimensions * Innovation velocity tracking Measures how quickly new features and improvements are released within AI systems. * Competitive landscape mapping Compares AI systems based on capability, efficiency and adoption strength across the industry. * Scalability assessment models Evaluates whether systems can handle increasing demand without performance decline. * Risk exposure analysis Identifies challenges such as infrastructure cost, regulatory shifts and competitive pressure. These dimensions help form a balanced understanding of long-term market behaviour. Long-term perspective on AI intelligence systems Long-term evaluation of AI systems depends on how effectively innovation translates into real-world adoption. As data becomes more structured and ecosystem activity expands, analysts gain clearer insights into growth patterns and sustainability. The ongoing attention around anthropic stock reflects how intelligence systems are increasingly used to interpret early signals rather than relying on traditional evaluation approaches. Sustained success in the AI sector depends on maintaining a balance between innovation, reliability and ecosystem expansion. Companies that achieve this balance are more likely to secure strong positioning as AI continues to reshape industries and workflows globally. Conclusion AI market intelligence systems provide a structured approach to understanding complex signals from technology performance, user behaviour and ecosystem growth. These systems help analysts move beyond surface-level interpretation and uncover deeper patterns that shape long-term potential. The narrative around Anthropic stock highlights how early intelligence signals are becoming central to modern market understanding. As AI ecosystems continue to expand, these systems will play an increasingly important role in interpreting how innovation translates into lasting technological value. SPONSORED ANTHROPIC STOCK TRACKING AI Anthropic PBC TradingView anthropic stock market intelligence systems AI evaluation ecosystem expansion market interpretation market intelligence
'Big Short' Michael Burry says SpaceX, Anthropic not worth $1 trillion
Michael Burry says he has major doubts about SpaceX and Anthropic's lofty valuations. In recent discussion threads on his Substack, the investor of "The Big Short" fame questioned the worth of Elon Musk's rocket, satellite, and AI company, and the maker of popular AI model Claude. "Any move up will be on hype and technicals," Burry wrote about SpaceX stock in a subscriber chat he started on Saturday. "Nothing in that S-1 suggests it is worth $1 trillion let alone $2 trillion." SpaceX filed an IPO prospectus known as an S-1 on May 20, revealing that last year it made $18.7 billion in revenue and posted a net loss of $4.9 billion. It is widely reported to be targeting a valuation of roughly $2 trillion as a public company. Anthropic announced last Thursday that it had raised capital at a $965 billion valuation, paving the way for a public listing at an even higher valuation. Reacting to the news, Burry said in a subscriber chat that he's skeptical the AI startup will ever warrant that price tag. "There is no guarantee, and not even a strong likelihood, that Anthropic is long-term worth anywhere near $1 trillion," he wrote. Burry added that Anthropic's business of developing cutting-edge AI models is "far too expensive, too much brute force," as over time, he expects computing power "will be commoditized, like internet use." "What is happening now is a false demand signal," he wrote, echoing his recent warning that the "tokenmaxxing" trend won't last. The frantic rush to secure computing power to run AI models is "driving buildout and orders that will be too much for what is needed a few years down the road," he added. Burry quipped that before paying $1 trillion for Anthropic, he would count to 1 trillion, and "in 240,000 years I might reconsider." SpaceX and Anthropic didn't immediately respond to requests for comment from Business Insider. Burry shot to fame after his prescient bet against the mid-2000s housing bubble was chronicled in the book and movie "The Big Short." Known for issuing grave predictions about crashes and recessions, he pivoted from running a hedge fund to writing about his personal investments on Substack late last year.
Is Redwire Stock a Buy Ahead of the SpaceX IPO?
SpaceX is set to make its public market debut in June and is likely to be the largest initial public offering (IPO) on record. As the company gears up to go public, investors are beginning to pay more attention to space stocks, and for good reason. According to McKinsey, the global space economy could reach $1.8 trillion by 2035. Space is becoming an increasingly important element of national security, and the U.S. is investing heavily in its development, procuring satellites, autonomous systems, spacecraft, sensors, and other key space components. Redwire (NYSE: RDW) is one space company that provides crucial infrastructure and technology to help make this possible. Is the stock a buy ahead of the SpaceX IPO? Will AI create the world's first trillionaire? Our team just released a report on the one little-known company, called an "Indispensable Monopoly" providing the critical technology Nvidia and Intel both need. Continue " Redwire plays a key role in defense and the growing space industry Redwire operates two distinct segments: a space segment and a defense technology segment. In its space segment, Redwire develops hardware and technology for space infrastructure, including building blocks for spacecraft, like solar panels, robotic arms, and other parts, along with parts that support massive satellite constellations. When NASA's Artemis II mission took flight earlier this year, Redwire's optical imaging and sun sensor technologies were tools utilized on the Orion spacecraft. In its defense technology segment, Redwire builds highly advanced military drones (uncrewed aerial systems) that can fly autonomously, have been combat-tested, and can operate in highly contested, GPS-denied environments. For example, Redwire has delivered hundreds of its Penguin drones directly to the Ukrainian military for use in real-world combat operations. The company is a major provider and has customers that include the U.S. government, including NASA, the U.S. Army, the Marine Corps, and the Department of Homeland Security. But it also provides components for top aerospace and defense companies, including Lockheed Martin, Boeing, Airbus, and Blue Origin. In April, the U.S. Space Force's Space Systems Command selected Redwire as one of 14 companies to compete under this $1.8 billion contract program to design and build advanced space surveillance and reconnaissance satellites. Earlier this year, it was awarded a multi-award contract for the Missile Defense Agency's Scalable Homeland Innovative Enterprise Layered Defense (SHIELD) IDIQ.
Anthropic offers EU access to Mythos
Simply sign up to the Artificial intelligence myFT Digest -- delivered directly to your inbox. Anthropic has offered the EU access to its Mythos artificial intelligence model, in the first instance of expanded access to the highly capable cyber security software outside the US and the UK. A spokesperson for the EU's cyber security agency Enisa confirmed it was in talks to use the model, a development that was first reported by Bloomberg. "It's been offered but the conditions are still being agreed," the spokesperson said. European Commission officials visited San Francisco last week to negotiate with the company about joining its so-called Project Glasswing -- an industry coalition of mainly US companies that have been using it since early April to find and patch security vulnerabilities in their systems. "I can confirm that the Commission had several productive meetings with Anthropic. We welcome the latest developments on potential future access," European Commission spokesperson Thomas Regnier said on Monday. "This is the result of the Commission's strong bilateral co-operation and engagement with Anthropic," he said, adding that "this latest development is of utmost importance to get a clear picture of the potential risks". The exact conditions for the EU's access to Anthropic's program still need to be defined, including how much access the US company would gain to the bloc's own systems while using Mythos, according to people familiar with the discussions. Anthropic declined to comment. Earlier this year, Anthropic chief executive Dario Amodei told the FT that the company was keen to share Mythos with US-allied governments. "We're excited for the US government . . . and the governments of all our allies to use this technology to defend Ukraine, to defend Taiwan, to defend democracies under attack," Amodei said in April. "But I don't want them turned on our own people or used for undemocratic ends, whether by autocracies or our own governments." Anthropic initially limited the release of Claude Mythos Preview to a small group of companies considered part of critical infrastructure, due to its advanced cyber capabilities and the potential for it to be used as a weapon for cyber attacks. These included tech companies such as Microsoft and Apple, banks including JPMorgan, and cyber security groups such as CrowdStrike, among others. The company has been working closely with the US government on testing and deploying the model internally with national security and other sensitive departments. Outside the US, the UK government is the only known entity to have gained access via the government's AI Security Institute, which evaluated it prior to its rollout. Last week Anthropic said it was working on releasing Mythos to all its customers. "Models of this capability level require stronger cyber safeguards before they can be generally released," the company said in a statement. "We're making swift progress on developing these safeguards and expect to be able to bring Mythos-class models to all our customers in the coming weeks." Additional reporting by Paola Tamma in Brussels
Hyperliquid SpaceX Contract Collapses 45% in Minutes, Liquidates $1.51M
After the crash, the mark price still sat $220 above the oracle price, and Ventuals said it is taking steps to prevent recurrence and evaluating user compensation. Hyperliquid's SPACEX-USDH perpetual contract plunged from an open of $2,277 to a low of $1,254, a near-45% collapse, within a single 30-minute window on Thursday afternoon before partially recovering to around $2,169, liquidating 405 users across 1,393 positions and erasing $1.51 million in value, according to Hyperliquid data. Contract Had Just $4.87 Million in 24-Hour Volume Before the Crash The episode was concentrated in a market with minimal depth. Over the prior 24 hours the contract had generated just $4.87 million in total trading volume across an open interest base of under $2.9 million. The 30-minute crash window played out against that backdrop of thin volume, with insufficient market depth to absorb the selling pressure. The median liquidated position held just $31 in margin, pointing to a retail-heavy user base holding leveraged positions with a median of just $31 in margin, according to Hyperliquid data. SPACEX-USDH Is a Synthetic Perpetual Contract With No Public Price Benchmark The Hyperliquid SPACEX-USDH is a synthetic perpetual contract tied to SpaceX's market valuation rather than actual shares in the company. Because SpaceX remains private ahead of its anticipated IPO, Hyperliquid created the contract to allow investors to speculate on what they believe the company will be worth at listing. Traders holding the contract receive no ownership stake or shareholder rights in SpaceX. Unlike perpetual futures contracts on Bitcoin or Ethereum, which anchor to deep and liquid spot markets, the SPACEX contract has no public price benchmark. SpaceX shares trade only through private secondary markets restricted to accredited investors, leaving the contract without a liquid public reference price. Mark Price Remained $220 Above Oracle Price Even After the Liquidation Event At settlement following the crash, the mark price of $2,132 still sat more than $220 above the oracle price of $1,908, according to Hyperliquid data. The gap between mark price and oracle price persisted at more than $220 at settlement. SpaceX shares trade only through private secondary markets restricted to accredited investors, leaving the contract without a liquid public reference price against which to anchor. Ventuals, the platform behind the contract, said it had "taken immediate steps to prevent this from happening again on any of the pre-IPO markets." The company is also evaluating compensation for affected users. Retail Traders Bear Losses as SpaceX IPO Timeline Adds Context SpaceX is targeting an IPO in June. The crash liquidated 405 retail traders holding leveraged positions on a synthetic contract with no public price benchmark and no connection to actual SpaceX equity, according to Hyperliquid data. The move played out against a backdrop of $4.87 million in 24-hour volume and under $2.9 million in open interest, resulting in 1,393 liquidated positions. Hyperliquid data showed the mark price remained more than $220 above the oracle price at settlement, with the contract still at a premium to its underlying reference price following the liquidation event.
Why It Matters if OpenAI or Anthropic Wins the IPO Race
In the bitter rivalry between AI heavyweights OpenAI and Anthropic, it will mostly be who has the best technology that determines the ultimate victor. But which one of them gets to its public offering first matters a great deal, too. The window for initial public offerings is decidedly open, with a ...
Unconventional orbital currents and torques due to ferro-rotational orbital textures - npj Spintronics
In this work, we demonstrate the electrical generation of unconventional orbital currents in FR systems that preserve both and symmetries. Symmetry arguments reveal that these -even rotation-induced orbital currents -- analogous to -odd magnetization-induced spin currents in FM systems -- manifest as (i) longitudinal orbital currents polarized along the FR axis and (ii) unconventional orbital Hall currents with polarization collinear with either the charge or orbital current [e.g., see (Fig. 1c). Tight-binding calculations show that these effects are driven by an electric hexadecapole (16-pole) moment arising from the FR order, through an intrinsic and nonrelativistic mechanism. To corroborate these findings, we perform first-principles calculations for the FR material TiAu4. Finally, we explore the experimental implications of rotation-induced orbital currents by studying an FR/FM bilayer within a tight-binding framework, demonstrating current-induced orbital accumulation in the FR layer as well as orbital torque in the FM layer. We further suggest that this unconventional orbital torque can enable deterministic, field-free switching of the FM order, pointing to a promising route for orbitronics research based on novel ferroic orders and higher-order electric multipoles. In the linear-response regime, the orbital current J (or spin current J) generated by an electric field E is expressed as , where X = L or S. Here, α and γ denote the orbital (spin) current flow and polarization directions, respectively. The rank-3 orbital (spin) conductivity tensor σ (σ) can generally be decomposed into -even and -odd contributions, with their nonzero components dictated by the system's symmetry. For example, in a nonmagnetic cubic system with point group O, only the -even conventional Hall components of , where α, β, and γ are mutually orthogonal, are symmetrically allowed. Symmetry breaking due to ferroic orders can induce additional nonzero components of σ. Here, we focus on ferroic orders that preserve symmetry, classified into two types: -odd FM order and -even FR order. In a cubic system, the FR and FM orders aligned along the z direction reduce the symmetry, leading to the point group 4/m and the magnetic point group , respectively. For both cases, the nonzero components of the total σ are given by refs. (see Supplementary Note 1 and Supplementary Table 1 for all nonmagnetic crystallographic point groups): In addition to the -even conventional Hall components (, , and ), the components induced by ferroic orders can be categorized into two groups: (i) diagonal components ( and ), describing longitudinal currents polarized along the order parameter (pink arrows in Fig. 1c), and (ii) off-diagonal components ( and ), representing unconventional Hall currents, where the polarization is collinear with either E or J (green arrows in Fig. 1c). It is worth noting that, in the presence of the first-type longitudinal components , the second-type Hall components take the form . This implies a conversion of a primary current into a secondary current (or of into ) for α ≠ β, corresponding to spin swapping or orbital swapping. These ferroic-order-induced currents inherit the -parity of the associated order parameters. In -odd FM metals, -odd longitudinal spin currents are electrically generated due to the nonrelativistic spin-polarized band structure. Additionally, -odd unconventional spin Hall currents -- also known as the magnetic spin Hall effect or spin swapping -- arise from SOC. These magnetization-induced spin currents in FM metals can accompany the relativistic -odd orbital currents via SOC, e.g., the magnetic OHE. In contrast, in -even FR systems, the rotation-induced orbital currents can be generated, including the longitudinal orbital currents and unconventional orbital Hall (or orbital swapping) currents. Importantly and distinctively, they require neither broken nor SOC, as will be demonstrated. Rotation-induced longitudinal orbital current To see how the orbital current can be generated in FR systems, we introduce a minimal tight-binding model with a relevant order parameter. The OAM dynamics can be driven by multipole degrees of freedom. Although the FR order is often described by an axial vector, such as the electric toroidal moment, we focus here on another emergent multipole in FR systems: the electric hexadecapole moment (rank-4), H ∝ xy(x - y) (Fig. 2a), which is even under and . The quantum mechanical operator for this can be constructed by replacing r = (x, y, z) with the OAM operators . Accordingly, we define an atomic-site electric hexadecapole moment operator as where and ℏ is the reduced Planck constant. Note that can emerge under the point group 4/m exhibiting the FR order along the z direction. In the atomic d-orbital basis {}, Eq. (2) simplifies to , which implies that hybridizes orbital wave functions, effectively rotating them around the z-axis, as illustrated in Fig. 2b. Let us introduce a two-dimensional square lattice tight-binding model incorporating . We adopt a minimal two-orbital basis {} for describing . Considering only nearest-neighbor hopping, the Hamiltonian is given by where k is the crystal momentum, a is the lattice constant, is the identity matrix, are the pseudospin Pauli matrices, and is determined by the Slater-Koster hopping parameters in units of eV. The term in Eq. (3) accounts for the crystal field that splits d and levels. The effect of the FR order along the z direction is incorporated through , which is equivalent to in the two-orbital basis, with its magnitude set by Δ = 0.1 eV. Figure 2c shows that a gap between d and bands is opened due to the electric hexadecapole moment. Near the gap, the eigenstates , with energies ϵ ≈ ± Δ, yield the expectation values . We note that corresponds to the submatrix of that is defined in the full d-orbital basis, so effectively captures the out-of-plane OAM. Here, we derive an intuitive picture of how an electric field E drives the dynamics of for a single Bloch state near the gap. Under , an electron with charge -e after time δt acquires momentum δk = - eEδt/ℏ, leading to the perturbation . The dynamics of follow the Bloch equation , where B(k) is the effective magnetic field satisfying , with arising from the electric-field-induced crystal field variation. In the vicinity of the band gap, with an initial condition , the solutions for small deviations from equilibrium are given by , , and This result shows that the electric hexadecapole moment undergoes precession due to the intrinsic crystal field that acts as a current-induced effective field, generating the nonequilibrium OAM . This behavior resembles spin dynamics in FM systems under an intrinsic spin-orbit field, although the effect here is nonrelativistic. Note that in Eq. (4) diverges as Δ → 0, but the net value vanishes as the gap closes. Although the net OAM (or ) vanishes upon k-integration, the net orbital current remains finite, leading to a nonzero . The conventional orbital current operator is defined as , where is the velocity operator. Substituting and , the longitudinal orbital current to first order in E is given by Integration of Eq. (5) over k-space yields a finite value, confirming the emergence of a rotation-induced orbital current driven by an intrinsic, nonrelativistic mechanism associated with a higher-order electric multipole. Unconventional orbital Hall current Additional orbital currents can emerge in multi-orbital systems exhibiting richer orbital texture. To explore this, we construct a three-dimensional tight-binding model for an FR system with the point group 4/m (Fig. 3a; see Methods and Supplementary Note 2), which constrains σ as given in Eq. (1). The tetragonal unit cell consists of A atoms with five d orbitals, and B atoms with an s orbital. The FR order along the z-axis arises from a rotational displacement of the four B atoms by an angle ϕ. The hopping pairs included in the model are shown in Fig. 3a. The next-nearest-neighbor hopping between d orbitals gives rise to the momentum-dependent d-orbital texture responsible for the conventional OHE. Its amplitude is assumed proportional to that of the nearest-neighbor hopping, with the proportionality factor η initially set to 0.5. The hopping between s and d orbitals, which depends on ϕ, characterizes the FR order. Figure 3b shows the band structure of this model with ϕ = 20°, which exhibits a nonzero expectation value of [defined in Eq. (2)] in equilibrium. Unlike earlier works, where was manually introduced into the Hamiltonian, in this model, it naturally emerges from structural rotation. It is noteworthy that downfolding our Hamiltonian into the two-dimensional d-orbital subspace yields a term proportional to for small ϕ (see Supplementary Note 3), revealing a direct connection between the electric hexadecapole moment and the FR order. We now proceed to compute the -even part of the orbital conductivity tensor σ using the Kubo formula (see Methods). Figure 3c presents numerical results for the nonzero orbital conductivity components for different values of ϕ, with . The longitudinal () and unconventional orbital Hall () components (e.g., see Fig. 1c), represented by pink circles and green triangles, respectively, vanish at ϕ = 0 and reverse sign under FR-order-reversal (ϕ → - ϕ). In contrast, the conventional orbital Hall components, indicated by blue × and orange + symbols, remain finite at ϕ = 0 and are invariant under FR-order-reversal. These results clearly demonstrate that rotation-induced OHE and conventional OHE have distinct physical origins, while both are -even and nonrelativistic. To further investigate the mechanism behind rotation-induced orbital currents, we compute for different values of η, which controls the next-nearest-neighbor hopping amplitudes, while fixing ϕ = 20° (Fig. 3d). We find that only the longitudinal component remains finite for η = 0, indicating that it arises solely from the FR order, specifically the electric hexadecapole moment, as demonstrated by our two-orbital model. On the other hand, both conventional and unconventional Hall components emerge as η increases, suggesting that the rotation-induced OHE requires not only the FR order but also the orbital texture responsible for the conventional OHE. This phenomenon can be understood in terms of nonrelativistic orbital swapping -- an orbital analog of spin swapping. It has been shown that in FM metals, a spin-polarized current is converted into a swapped spin current through the interplay of the orbital texture and SOC. Similarly, our results show that the longitudinal orbital current , induced by the FR order, is converted into the unconventional orbital Hall current (or into when ) via the orbital texture. Notably, this conversion does not require SOC, in contrast to spin swapping. First-principles calculation for TiAu Next, we investigate the FR material candidate, tetragonal TiAu (space group I4/m) using first-principles calculations (see Methods). The crystal structure exhibits the FR order along the z-axis (Fig. 4a), leading to a nonzero electric hexadecapole moment (Fig. 4b). The orbital conductivity tensor σ takes the same form as Eq. (1), with seven independent nonzero components of , including those for α = x () and α = z (). The rotation-induced orbital currents associated with these components are illustrated in the left and right panels in Fig. 1c, respectively. The components for are related to those for by four-fold rotational symmetry about the z-axis. By evaluating the Kubo formula, the nonzero components are obtained as functions of the chemical potential. For (Fig. 4c), the conventional Hall components exceed 1000(ℏ/e)(Ω cm) at the Fermi level. Additionally, we identify rotation-induced components, including the longitudinal orbital conductivity and the unconventional orbital Hall conductivity . For (Fig. 4d), the rotation-induced components are smaller, with and . The magnitude of the unconventional terms depends on the FR orbital texture (e.g., see Fig. 3), motivating further materials exploration. While the orbital conductivity is fully nonrelativistic, the corresponding nonzero components of the spin conductivity tensor can manifest, too, due to SOC. When SOC is present, not only the electric multipole moments but also the atomic-site electric toroidal moments, defined in the spinful basis, can emerge from the FR order, contributing to the -even spin current generation. A key distinction, however, is that the spin conductivity vanishes in the absence of SOC, whereas the orbital conductivity remains largely unaffected by SOC due to its nonrelativistic origin (see Supplementary Note 4). Furthermore, the -even orbital conductivity arises purely from the interband contribution, which is robust against scattering time (see Supplementary Note 4), while possible extrinsic contributions are not considered. By contrast, the -odd conductivity in FM systems is dominated by the intraband contribution that scales with the scattering time, but it is prohibited here by invariance. Unconventional orbital torque and field-free switching So far, we have discussed rotation-induced orbital currents based on the conventional definition of the orbital current operator, which is not directly measurable. In this section, we show that FR order gives rise not only to orbital currents but also to effects that can be probed experimentally, such as OAM accumulation and orbital torque. To illustrate this, we examine an FR/FM bilayer using a tight-binding model (Fig. 5a; see Methods). In our model, under , the FR layer with generates conventional () and unconventional () orbital Hall currents without spin currents, while the FM layer does not produce orbital or spin Hall currents on its own. This design ensures that the current-induced torque on magnetization M in the FM layer originates solely from the OAM injection by the FR layer. Within linear-response theory, we compute the current-induced non-equilibrium OAM (δL) and spin (δS) in the FR/FM system with , where is the unit vector of M (Methods). Figure 5b shows the layer-resolved δL per applied electric field. Large OAM components along x and y appear near the top and bottom surfaces of the FR layer, demonstrating orbital accumulation from the unconventional (δL) and conventional (δL) orbital Hall currents. The induced OAM is transferred across the FR/FM interface and subsequently interacts with M. This generates δS, which acts as an effective field for the torque . In particular, the spin-orbit precession with δL results in , leading to the damping-like orbital torque . Due to the presence of δL and δL, we obtain , where and are the effective fields for the conventional and unconventional orbital torques, respectively. The effective fields per charge current density J are estimated in our model (see Methods) as and , which fall within the range of reported values for spin-orbit torque devices using heavy metals. To gain further insight into how these orbital torques contribute to magnetization switching, we simulate magnetization dynamics within a macrospin model. The dynamics of is described by the Landau-Lifshitz-Gilbert equation where γ is the gyromagnetic ratio, B is the magnetic anisotropy field, and α is the Gilbert damping parameter. We consider a type-x geometry, in which an easy axis is collinear with the charge current, i.e., . Parameters are set to α = 0.05 and B = 30 mT. The same and obtained above are used here, together with current pulses of J = ± 10 A/m. Figure 5d shows the trajectory of over time t upon a current pulse, illustrated in Fig. 5e. Initially, points along (t = t). The current pulse exerts the damping-like orbital torques on the magnetization. Because of the presence of , is not perfectly aligned with but instead slightly tilted toward (t = t), as shown in Fig. 5f. Hence, after the pulse is turned off, relaxes deterministically to (t = t) without the aid of an external magnetic field. Figure 5e, f illustrates the repetitive switching between under opposite current pulses. These results demonstrate that unconventional orbital torques from FR materials offer a viable route to field-free magnetization switching.
Fractional-order systems for neuromorphic computing: software and hardware opportunities and challenges - npj Unconventional Computing
Applying a fractional derivative transforms these inputs into signals that approximate white noise, thereby maximizing entropy and improving the efficiency of spike coding. This implies that fractional-order dynamics are not only biologically plausible but may confer functional advantages by aligning neural dynamics with the statistics of natural sensory inputs. The introduction of fractional-order dynamics into RC provides control over a fundamental trade-off in how the reservoir processes temporal information. By replacing the standard Leaky Integrate-and-Fire (LIF) with an Fractional-order Leaky Integrate-and-Fire (FLIF), α may be used to control a balance between retaining a rich history of past inputs versus maintaining sensitivity to new environmental signals. Fractional calculus generalizes the concepts of differentiation and integration to non-integer orders. The most classical definition is the Riemann-Liouville form, which expresses a fractional derivative as an integral operator with a power-law kernel. For a function x(t), the Riemann-Liouville derivative of order α (0 < α < 1) is defined as where Γ(⋅) is the Gamma function. This formulation makes explicit the non-local nature of fractional differentiation: the present state depends on the entire history of the system, weighted by a power law. Unlike integer-order derivatives, which depend only on instantaneous changes, the Riemann-Liouville derivative encodes memory of all past dynamics. Two alternative formulations are commonly used in applications. The Grünwald-Letnikov (GL) derivative is defined in terms of finite differences: where are generalized binomial coefficients. The GL form is particularly useful for numerical simulations, as it lends itself to discrete-time implementations. Accordingly, the GL form is utilized throughout this work. The Caputo derivative modifies the Riemann-Liouville form by moving differentiation inside the integral: where denotes the first derivative of x(τ). This definition is often favored in physical modeling because it allows for initial conditions specified in terms of integer-order derivatives, making it compatible with experimentally accessible quantities such as voltages and currents at t = 0. In practice, the GL and Caputo forms yield very similar dynamical behavior, and both converge to the Riemann-Liouville derivative in the appropriate limits. All three definitions emphasize the same fundamental property: fractional differentiation couples the present dynamics of a system to its entire history, with memory decaying according to a power law. To connect fractional calculus with RC, the standard LIF neuron is extended to a FLIF neuron model. The classical LIF neuron describes the membrane potential V(t) as a first-order differential equation with a leak toward the resting potential and input-driven currents. In extending this model to the FLIF model, the first-order derivative is replaced by a fractional derivative of order α, yielding dynamics with long-tailed memory. A common biophysical form is written in terms of the effective capacitance C and resistance R: where V is the resting potential and I(t) is the input current. An equivalent form, which we use throughout this work and in our simulations, rewrites the leak term in terms of the membrane time constant τ = RC: where b is an optional bias current. This representation is more convenient for numerical implementations, since τ is typically treated as a tunable parameter. When V(t) reaches a threshold V, the neuron emits a spike and the potential is reset to V. The introduction of the fractional derivative fundamentally changes the dynamics. For α = 1, the model reduces to the classical LIF neuron with exponential decay. For α < 1, the neuron exhibits a power-law memory kernel, integrating its input with a heavy-tailed weighting of past activity. This produces dynamics consistent with experimental observations of spike-rate adaptation, anomalous diffusion of ions, and the whitening of power-law input signals. The parameter α thus controls the balance between sensitivity to recent inputs and the retention of long-term history, offering a tunable mechanism for matching neural dynamics to the statistics of input stimuli. The FLIF neuron provides a compact and biologically grounded way to introduce scale-free memory into artificial neural reservoirs. It also serves as a bridge between theoretical models of fractional dynamics and practical implementations in both software and hardware, making it a natural building block for FOR computing. In the sections that follow, we adopt the τ-based form in Eq. (5), as it directly corresponds to our software implementation using the Grünwald-Letnikov discretization. An important consequence of fractional dynamics is a shift in the effective rheobase of the neuron. For α < 1, the fractional derivative acts as an additional dampening term on the membrane potential, reducing the rate at which inputs accumulate toward threshold. This means that lower values of α require stronger input to elicit a spike, effectively raising the rheobase. Accordingly, to compare across different α values, one must account for this difference in excitability, either by adjusting bias currents or scaling input gain. Without such normalization, reservoirs at lower α may appear artificially quiescent as they are under-driven relative to higher-α systems. Algorithm 1 summarizes the complete discrete-time update procedure for a single FLIF neuron using the GL discretization; Fig. 1 shows the corresponding signal-flow block diagram. Algorithm 1 GL-FLIF neuron update Require: α, Δt, L, τ, b, V, V, V 1: Precompute for k = 1, ..., L 2: Initialize circular buffer , V ← V 3: for each timestep n do 4: Read input current I 5: ⊳ GL history term 6: 7: if V≥V then 8: Emit spike; V ← V 9: end if 10: Push V into circular buffer 11: end for Information-theoretic analysis This section establishes the information-theoretic foundation for understanding FORs. We show that the fractional order α acts as a control parameter that simultaneously governs three key computational properties: memory capacity (the ability to store past inputs), AIS (predictability of future states from present states), and information transfer (sensitivity to external inputs). Critically, these properties do not all peak at the same value of α, implying that different tasks will demand different optimal settings depending on their computational requirements. We begin by formalizing the FOR as an extension of the standard reservoir computing framework, then systematically characterize how α shapes the reservoir's information processing capabilities. This analysis provides the theoretical foundation for understanding why FORs outperform integer-order baselines on certain tasks, and offers principled guidance for selecting α based on task structure. Formally, a reservoir can be described as a collection of N neurons, each with its own state x(t) and update rule. A general form is where is the input, W and are the input and recurrent weight matrices, and f(⋅) denotes the neuron model (e.g., standard LIF, fractional LIF). The readout is trained as a linear combination of reservoir states, with W learned, for example, by regression or another learning rule. In this work, the nonlinear update rules f are implemented as fractional-order neuron models, introducing the order α as a tunable dynamical parameter. Replacing standard integer-order neurons with fractional-order neurons introduces power-law memory kernels into the reservoir dynamics. The fractional-order α acts as a control parameter, continuously tuning the balance between responsiveness to new inputs and retention of long-term history. To understand the role of α, we analyze the reservoir's information-theoretic and dynamical properties. A standard measure of reservoir performance is the Memory Capacity (MC), which quantifies how well past inputs can be reconstructed from the present reservoir state. Two forms are relevant here. The linear memory capacity measures the recovery of delayed inputs themselves. For a scalar input sequence {u}, the linear MC at lag k is defined as where is the optimal linear reconstruction of u from the reservoir state X. The nonlinear memory capacity extends this definition by replacing u with a nonlinear function of the delayed input. A common choice is to use Legendre polynomials P(u), yielding The total nonlinear memory capacity is the sum of across lags k and polynomial orders m. The cumulative memory capacity reported here is the sum of both linear and nonlinear contributions: This combined measure captures both the ability of the reservoir to store direct input histories and its ability to encode nonlinear transformations of them. As shown in Fig. 2, empirically, the total (i.e., cumulative) memory capacity decreases with increasing α in an approximately sigmoidal fashion: for low α, the reservoir integrates over long histories and achieves high memory capacity; as α increases, memory decays more rapidly, and at α ≈ 1 the reservoir retains only short-term input traces. This decreasing sigmoid relationship highlights a smooth but sharp transition from history-rich to history-poor dynamics. Interestingly, correlations between neuron activities peak in the transition region of this curve, suggesting that reservoir units are maximally coupled when the system is shifting between long-memory and short-memory regimes. This correlation peak may reflect the presence of critical-like dynamics where the reservoir is most sensitive to both its own state and its external input. The AIS quantifies how much information the reservoir's present state holds about its immediate future. Formally, where is the reservoir state vector. For small α, the reservoir's long memory produces an inertial, slow-moving state dominated by the past reservoir states. New inputs act as strong perturbations, and X is therefore not highly predictable from X alone, yielding low AIS. As α increases, the reservoir becomes more predictably responsive to inputs as the dynamics become dominated by the recurrent structure, thus AIS rises. In other words, higher α values produce reservoirs with more deterministic internal evolution. The complementary quantity is information transfer, which measures how much of the reservoir's current state reflects the history of the external input. Formally, where U denotes a delay-embedded representation of the input's past. For low α, the reservoir excels at integrating the input signal, storing substantial information about its external environment. For high α, the reservoir becomes dominated by the dynamics of the recurrent structure of the reservoir, and information transfer from the input decreases. With reference to Fig. 3, together these measures reveal a fundamental trade-off. Low α values configure the reservoir as a sensitive sensor: highly responsive to external inputs, capable of storing long histories, but internally less predictable. High α values configure the reservoir as a stable internal model: self-determined, predictable, but less permeable to input. At intermediate α, both information transfer and AIS are significant, producing a balance where the reservoir both listens to the input and stabilizes its own dynamics. This intermediate regime, which coincides with the transition in memory capacity and the peak in neuron-to-neuron correlations, represents the most computationally powerful setting. FORs therefore generalize the standard framework by introducing a tunable continuum of memory and dynamical regimes. The fractional-order α interpolates between input-driven and self-driven dynamics, between long-memory storage and short-memory responsiveness. The balance achieved at intermediate α suggests a critical-like operating point where reservoirs are maximally expressive: capable of capturing external structure, internally stable, and richly interactive. This positions fractional reservoirs as a principled extension of reservoir computing, with information-theoretic properties that can be tuned continuously through α. Fractional-order neuron implementations The realization of fractional-order neurons requires translating continuous-time dynamics into discrete or physical forms while preserving the long-memory and power-law characteristics that define fractional systems. Because fractional differentiation is inherently non-local in time, each implementation involves trade-offs among accuracy, computational efficiency, and resource usage. We organize implementations into two primary domains: software-based numerical realizations and hardware-based digital or analog realizations. In software, fractional dynamics are typically approximated by discrete convolution over past membrane potentials, capturing history-dependent effects through weighted sums with power-law coefficients. This approach allows precise control of parameters such as fractional order α, timestep Δt, and history length L, making it suitable for systematic experimentation and benchmarking. The Grünwald-Letnikov (GL) formulation is particularly advantageous due to its direct correspondence with the continuous fractional derivative and its straightforward implementation as a running sum over stored voltages. In hardware, fractional-order dynamics can be approximated through digital fixed-point implementations or realized physically through analog elements with inherent fractional characteristics, such as memcapacitive and memristive devices. Digital realizations, particularly those targeting field-programmable gate array (FPGA) or application-specific integrated circuit (ASIC) platforms, enable parallel execution and exploration of scaling behaviors, while analog approaches offer the possibility of directly computing with the physics of fractional-order components. Together, these implementations establish the foundation for practical fractional-order neuromorphic systems, spanning from precise numerical simulation to physical embodiment of fractional dynamics. Fractional-order software implementations Simulating fractional-order neurons requires discretizing the continuous-time dynamics. Here we describe the Grünwald-Letnikov (GL) formulation and its direct update rule. For comparison, we briefly note the role of Euler methods commonly used for integer-order LIF models. The GL form expresses the fractional derivative as a weighted sum over past values: with timestep Δt, history window length L, and coefficients Substituting this into the fractional LIF equation with explicit evaluation of the leak term at V yields the practical update rule: When V≥V, the neuron emits a spike and is reset to V. This direct update aligns with our implementation: a circular buffer maintains past voltages, GL coefficients are precomputed, and the scaling factor Δt ensures consistency across timesteps. For integer-order LIF neurons (α = 1), the forward Euler method gives the familiar update while the implicit Euler scheme evaluates the leak at V instead of V, improving stability for stiff systems. The fractional GL update in Eq. (15) can be viewed as a natural generalization: the instantaneous update is still explicit, but past states enter through the convolutional history term with power-law weights. The principal computational cost of the GL method arises from the history term in Eq. (15). At each timestep, evaluating the convolution requires O(L) operations, where L is the history window length. For long simulations or large networks, this scaling can dominate runtime. The memory requirement is also O(L) per neuron, since each must maintain a buffer of its past voltages. In practice, L is chosen to balance accuracy and efficiency. Smaller L truncates the power-law kernel, reducing memory effects but lowering cost. Larger L captures the full long-tail dynamics more accurately at the expense of higher runtime and memory. For a reservoir of N neurons simulated over T timesteps, the total cost of the GL convolution is O(N⋅L⋅T), and the memory requirement is O(N⋅L) for the history buffers plus O(L) for the shared coefficient array. For typical values of L used in our benchmarks, this cost is tractable on modern CPUs and GPUs, but becomes a bottleneck when scaling to millions of neurons. To characterize the accuracy-efficiency trade-off empirically, we swept L from 5 to 250 at α = 0.5 on the FSDD spoken-digit task across 10 trials. Results are shown in Fig. 4. Accuracy follows a sigmoid-like curve with L: performance is essentially degenerate below L = 30, rises steeply through L ∈ [50, 100], and saturates empirically beyond L ≈ 150. The saturation point is task-dependent, determined by the temporal scale of the relevant history in the input signal; tasks with longer-range dependencies would be expected to saturate at larger L. In the case of FSDD there is a hard upper bound at L = 250, imposed by the sample duration: each sample spans 250 micro-steps (25 macro-steps × Δt = 0.1), so L > 250 cannot add further within-sample history. One practical design choice for FSDD is L = 100, which achieves 93.3% accuracy at the knee of the saturation curve, as beyond L = 100 gains of less than 1% come at linearly increasing compute cost. Several optimizations exist to mitigate this cost, such as using recursive coefficient updates, Fast Fourier Transforms for long convolutions, or rational transfer-function (IIR) approximations to the power-law kernel. We adopt the direct truncated GL form in this work for clarity and reproducibility. In hardware realizations of fractional-order neurons, the fractional order α is set by physical device parameters that are subject to manufacturing variation, temperature sensitivity, and long-term drift. We evaluate robustness to two non-ideality scenarios: drift in the realized value of α, and additive noise on the input signal. We simulated device-level variation in α by training at the nominal α = 0.5 and testing at drifted values ranging from -10% to +10%. As shown in Fig. 5, classification accuracy on the FSDD task is nearly invariant to drift across this range, changing by less than 2 percentage points at ±10% and approximately 0.5 percentage points at ±5%. The near-symmetric response about α = 0.5 is consistent with operation near a local optimum of the accuracy-α curve. We evaluated robustness to additive i.i.d. Gaussian noise on the input features (σ = 0 to 0.5), simulating sensor noise or signal corruption. Figure 6 compares the fractional reservoir (α = 0.5) against the integer-order baseline (α = 1.0). Performance at α = 0.5 remains stable up to σ = 0.01 and degrades gracefully beyond, while maintaining a consistent accuracy advantage over the integer-order baseline across all tested noise levels. Notably, the integer-order reservoir shows comparatively less sensitivity to increasing noise, with the performance gap between α = 0.5 and α = 1.0 narrowing at higher σ. Two effects likely contribute to this. The standard LIF acts as a low-pass filter over its membrane time constant, attenuating high-frequency noise components before they influence spiking. In contrast, the fractional LIF incorporates input noise into its power-law memory trace, where it persists across timesteps and is recurrently recirculated through the reservoir dynamics. Fractional-order hardware implementations Implementation of fractional-order systems directly in hardware has the potential to improve performance over a software-only implementation, given the opportunity for increased parallelism and the speed advantages of dedicated hardware. A digital hardware implementation may serve as a bridge, allowing for the evaluation of fractional dynamics applied across a range of problems while analog components with fractional characteristics are still in development. We explore existing work below, including review articles on digital fractional-order systems, as well as two articles describing fractional-order neurons targeting an FPGA execution environment. We then offer an examination of relevant design considerations for digital fractional-order neurons, an overview of a fractional LIF neuron implemented in SystemVerilog, and a discussion of implications for achieving fractional dynamics in a digital context. Regarding a general approach to the implementation of fractional-order calculus in digital systems, review articles and ref. contain mathematical grounding through an overview of fractional-order definitions and options for numerical approximations. Clemente-López et al. address fractional-order systems on both FPGAs and embedded hardware, while Ali et al. focus on FPGAs and their unique architectural features. Both perform tradeoff analysis between different numerical approximation methods and discuss topics most relevant to digital systems, such as discretization techniques and usage of floating-point versus fixed-point representations. Clemente-López et al. highlight the Adomian Decomposition Method as well as the Grünwald-Letnikov (GL) method as the two primary approximation methods used in digital implementations. They further note that the GL method is generally preferred due to its simplicity and flexibility. In ref. , Ali et al. additionally describe the usage of the popular Tustin method for discretization. The above review articles are in general agreement that the GL method for fractional-order approximation is well-suited for digital platforms. In addition, they both cite the choice to use fixed-point as an appropriate design decision for numerical representation. In ref. , Malik and Mir present a Hindmarsh Rose (HR) neuron with fractional-order update dynamics. Their work spans single neuron behavior as well as the combined behavior resulting from a coupling of two neurons, with the execution of neuron activity on FPGA. The authors include waveforms showing neuronal dynamics and a range of spiking behavior. Details on representation in hardware include FPGA resource utilization and notes describing the use of a 32-bit fixed-point format. Similarly, Tolba et al. outline an approach for fractional-order update dynamics in an Izhikevich neuron model executing on an FPGA. They also produced waveforms showing the resulting spiking behaviors, as well as block-level hardware diagrams. Though their work includes greater depth regarding details such as bit widths and routing between high-level components, the focus of the article tends towards the comparison between the integer-order and fractional-order implementations, and the information on engineering tradeoffs and their implications for the fractional-order alone is limited. As discussed above, selection of α (alpha) determines a system's history retention as well as the relative weighting of recent historical values. This is due to the binomial coefficient magnitudes resulting from a particular value of α at a given timestep, as seen in Eq (13). Figure 7 shows the relative coefficient magnitude decay with increasing timestep for different values of α. Lower values of α provide greater history retention, as seen by the minimal decay in coefficient magnitude, while higher values of α decay much more quickly. In each case, the coefficient magnitude of the historical value at timestep 1 is equal to the value of α itself, so that higher values of α more heavily weight values in recent history. In the context of digital hardware representation, the memory capacity of a system is dependent on the precision afforded to the binomial coefficient values. If the bit width of the binomial coefficients is too small, the memory capacity will shrink to match the time step with the coefficient magnitude matching the smallest value representable, given the number of bits. Table 1 shows the maximum history length, or value of k, across α values in 3 different fixed-point formats: UQ0.8, UQ0.16, and UQ0.32, where each format is unsigned with zero bits representing the integer component, and 8, 16, or 32 bits, respectively, representing the fractional component. The required fixed-point representation for binomial coefficients should be determined by the chosen α value for a given application, along with the desired history length. Figure 8 shows the maximum history length for a range of α values in fixed-point formats UQ0.8, UQ0.16, and UQ0.32. An unsigned fixed-point format utilizing 64 bits for the fractional component would allow for a history length of nearly 4 billion, e.g., when using α = 0.9. As such, a fixed-point format of UQ0.64 is likely larger than necessary for most applications, and has the potential to produce time- and space-complexity issues in execution. Conversely, UQ0.16 becomes insufficient at higher α values where rapid coefficient decay limits history to fewer than 100 steps; UQ0.32 is the practical minimum for applications requiring both high α and extended history. We chose to implement a FLIF neuron using the hardware description language SystemVerilog (SV). Simulation was performed using cocotb, a Python framework targeting chip verification, along with tools from the oss-cad-suite toolchain, such as verilator and gtkwave. Our goal in simulation was to verify the spike timing adaptation described in ref. . We created a preliminary proof-of-concept implementation using a coefficient bit width of 8 and a history length of 8. We iteratively doubled the history length in simulation until the target behaviors were achieved, with a history length of 256 and a coefficient bit width of 16. The final implementation uses Q8.0 fixed-point for input current and membrane potential, and Q0.16 for the GL coefficients. Figure 9 shows the hardware datapath of the implementation, and Fig. 10 summarizes the development pipeline. We verified that our implementation produced an increasing spike frequency in the presence of a constant input current, as well as a higher recovery spike frequency after a dropout of a constant input current. These findings are consistent with the behavior described by Teka et al. in ref. . Figure 11 shows the increasing spike frequency over time due to the fractional-order system memory. Similar memory effects are seen in Fig. 12, which shows a higher recovery spike frequency than the initial frequency after a brief period of zero input current. Primary implications of our findings when considering the execution of our design on an FPGA include the identification of available board resources. In particular, the mathematical operations associated with the multiplication and accumulation of coefficients and history terms are ideally mapped to efficient resources such as DSP slices. As the history length grows, the need for DSP slices increases linearly. Similarly, pre-computed binomial coefficient values require storage proportionate to history length. Intrinsically fractional-order hardware Analog realizations of fractional dynamics exploit physical devices whose impedance exhibits constant-phase ("fractance") behavior. A promising approach is the fractional-order memcapacitor, whose state-dependent capacitance produces a power-law memory kernel at the circuit level. Recent work has shown that such memcapacitive elements can reproduce neuronal behaviors, including spike-rate adaptation, long-lived responses, and signatures consistent with critical dynamics, positioning them as efficient and biologically plausible neuromorphic primitives. A practical fractional leaky integrate-and-fire (FLIF) neuron can be realized using four functional components: (i) a leak pathway, (ii) a memcapacitive integrator acting as the fractional memory core, (iii) a Schmitt trigger for robust thresholding, and (iv) a reset switch. In this configuration, the membrane node integrates input current through the memcapacitive branch, producing a slow fractional ramp whose rate depends on the fractional order α. The Schmitt trigger converts this ramp into discrete spikes with hysteresis, while a MOSFET or transmission gate provides the reset path, rapidly discharging the memcapacitor after each spike. Fractional memory effectively increases the rheobase current, so bias and gain normalization across α values are often required to maintain consistent firing thresholds. Schmitt trigger trip points should be chosen so the fractional ramp reliably crosses both thresholds under the expected input range, ensuring stable oscillation and noise immunity. By embedding the memory kernel directly into the physical capacitance rather than into algorithmic state variables, these designs achieve intrinsic fractional behavior with minimal computational overhead and high analog bandwidth. When physical fractional-order memcapacitors are unavailable, several well-established approximation methods can emulate their behavior over a target frequency band (ω, ω) corresponding to the neuron's operating spectrum. The Oustaloup method approximates s (0 < γ < 1) by a stable, minimum-phase IIR filter composed of first-order sections: where poles and zeros are geometrically distributed between ω and ω, and N controls the approximation order. Larger N broadens the constant-phase region but increases circuit complexity and power consumption; shallow approximations suffice for narrowband operation. ORA maps directly to active-RC or Gm-C biquad sections, and can be implemented in switched-capacitor or mixed-signal IIR topologies. A finite Foster or Cauer ladder of resistors and capacitors -- referred to here as an R-C network to distinguish it from the reservoir computing (RC) abbreviation used throughout this paper -- approximates a constant-phase element (CPE) whose impedance scales as Z(s) ∝ s over a target frequency band (ω, ω). The pole-zero structure of the ladder, with poles and zeros interlaced along the negative real axis, produces a nearly flat phase response of approximately -πα/2 across the band -- a defining property of a fractional-order element. This frequency-domain characterization has a direct time-domain counterpart: the impedance Z(s) ∝ s corresponds to convolution with the power-law kernel t/Γ(α), which is precisely the memory kernel underlying the FLIF dynamics described in the Fractional leaky integrate-and-fire neuron subsection. The Grünwald-Letnikov discretization of this kernel (Eq. (13)) approximates the continuous convolution via the weighted sum , where the GL coefficients c decay as k for large k. The R-C ladder thus physically realizes the same power-law memory that the GL algorithm implements in software, providing a direct analog instantiation of the fractional-order neuron's temporal dynamics. These networks are fully passive, compact, and compatible with SPICE-level modeling. The trade-off between accuracy and area is governed by ladder depth: deeper ladders yield better constant-phase fidelity but at higher component cost. Together, these methods -- memcapacitive and analog -- demonstrate several design strategies for realizing fractional-order neuronal dynamics. To illustrate how the theoretical trade-offs identified in the FOR computing subsection manifest in concrete applications, we consider a diverse set of benchmark tasks spanning classification, control, and prediction. These case studies are chosen to highlight complementary aspects of reservoir computation: (i) long-term memory and complex prediction, (ii) efficient representation of natural input signals, (iii) real-time control and stability, and (iv) classification and prediction capabilities. Specifically, we study: * spoken digit classification, * control of the cart-pole balancing problem, and * classification of blood glucose dynamics for diabetes prediction. Each task is implemented using a FOR computing framework or a fractional-order spiking neural network (FOSNN), with fractional order α varied systematically. Together, these examples demonstrate that FORs consistently outperform their integer-order counterparts, and that the benefits align with the theoretical trade-offs in memory, information storage, and sensitivity to natural statistics developed in earlier sections. Time-series classification Time-series prediction and classification is a canonical benchmark for reservoir computing, as it simultaneously probes a network's nonlinear transformation capacity and its ability to retain and process temporal dependencies. Here, we evaluate the performance of FORs on the Free Spoken Digit Dataset (FSDD), a widely used corpus for speech-based classification tasks. FSDD consists of short audio recordings of spoken digits (0-9) from multiple speakers, offering natural temporal variability. The task tests the reservoir's ability to process natural auditory signals that exhibit temporal correlations and spectral regularities common to many real-world sensory signals. As described in the biological motivation discussion above, fractional dynamics may be advantageous in such contexts, as fractional differentiation can act as a flexible signal whitening filter for 1/f-type power spectra. As shown in Fig. 13, FORs outperform their integer-order counterparts (α = 1.0) on the FSDD task. A complete sweep of α values shows a characteristic peak of accuracy at intermediate fractional orders (α ≈ 0.3-0.5), consistent with the theoretical prediction that optimal information processing occurs where the trade-off between memory retention and whitening balance is achieved. Lower α values yield strong memory but reduced temporal responsiveness, while higher α values approach the Markovian dynamics of standard reservoirs, losing long-range dependencies. The results above use offline ridge regression as the readout. To confirm that the effectiveness of α is not specific to this choice, we repeated the full sweep using Recursive Least Squares (RLS), an online readout that updates readout weights incrementally as each sample is processed and leaves the reservoir weights unchanged. Figure 14 shows classification accuracy for both methods across α = 0.1-1.0. The two readouts track closely throughout, both peaking at α = 0.5, and the fractional-order advantage over the integer-order baseline is preserved under both. The α optimum and the qualitative shape of the accuracy curve are thus readout-independent, confirming that α controls an intrinsic property of the reservoir dynamics rather than an artifact of the training procedure. Cart-pole balancing In this case study, a FOSNN is investigated to test if fractional memory dynamics improve performance and robustness on the cart-pole control task, compared to a standard SNN and multi-layer perceptron (MLP) across a range of α values. A comparison of model performance on the CartPole-v1 task is shown in Table 2. The MLP displayed the most significant performance change with the introduction of noise, as shown in Fig. 15. The average solve episode and standard deviation increased by 35.35% and 48.18%, respectively. Additionally, the MLP's completion rate decreased by 17%. The standard SNN offered moderate improvements, achieving a slightly higher stability than the MLP in noisy conditions, but it demonstrated a lower completion rate (7.9% loss) and overall performance in comparison to the FOSNN. In contrast, the mid-α FOSNN (α = 0.5-0.7) models displayed similar combinations of higher resilience and absolute performance. The baseline and noisy performances of an α = 0.6 FOSNN is shown in Fig. 16. In general, the mid-α FOSNN group maintained the highest completion rate (98%) while completing the environment the fastest on average (1411 episodes, noisy). Notably, FOSNN (α = 0.7) was the only value to show an absolute improvement in its completion rate (from 95% to 98%), and FOSNN (α = 0.6) improved its standard deviation by 4.32% (from 102.40 to 97.98). When tested under progressive node removal, the MLP showed the greatest overall robustness, achieving a normalized area under curve (AUC) of 0.540, a lower standard deviation of 43.47, and a critical damage threshold (CDT) of 70%. In comparison, while most FOSNN models were less robust than the MLP architecture, the FOSNN (α = 0.8) showed one notable advantage with the lowest magnitude of initial performance loss, with an initial degradation rate (IDR) of -4.04, as shown in Fig. 17. Diabetes prediction The diabetes prediction task evaluates the reservoir's ability to identify diagnostic patterns from physiological time-series data. Figure 18 shows the effect of varying the fractional order α on performance. Both accuracy and F1 score decreased monotonically as α increased, revealing that reservoirs with stronger memory effects (lower α) achieve superior predictive accuracy on physiological time-series data. The best performance was achieved near α = 0.1, corresponding to a long memory kernel and slower voltage decay, which effectively integrates temporal correlations across patient features. At higher α values, the network dynamics approach the Markovian regime of standard LIF neurons, leading to diminished temporal representation and reduced discriminative power. A comparative evaluation of all four network architectures is shown in Fig. 19 and Table 3. The FOR achieved the highest mean accuracy and F1 score, outperforming both the standard reservoir and feedforward models. While the MLP and SNN exhibited similar performance, the inclusion of recurrent dynamics in both reservoir variants yielded substantial gains, confirming that temporal integration -- especially when extended by fractional memory -- plays a key role in representing slow-varying physiological processes.
Is Redwire Stock a Buy Ahead of the SpaceX IPO?
SpaceX is set to make its public market debut in June and is likely to be the largest initial public offering (IPO) on record. As the company gears up to go public, investors are beginning to pay more attention to space stocks, and for good reason. According to McKinsey, the global space economy could reach $1.8 trillion by 2035. Space is becoming an increasingly important element of national security, and the U.S. is investing heavily in its development, procuring satellites, autonomous systems, spacecraft, sensors, and other key space components. Redwire (RDW 5.13%) is one space company that provides crucial infrastructure and technology to help make this possible. Is the stock a buy ahead of the SpaceX IPO? Redwire plays a key role in defense and the growing space industry Redwire operates two distinct segments: a space segment and a defense technology segment. In its space segment, Redwire develops hardware and technology for space infrastructure, including building blocks for spacecraft, like solar panels, robotic arms, and other parts, along with parts that support massive satellite constellations. When NASA's Artemis II mission took flight earlier this year, Redwire's optical imaging and sun sensor technologies were tools utilized on the Orion spacecraft. In its defense technology segment, Redwire builds highly advanced military drones (uncrewed aerial systems) that can fly autonomously, have been combat-tested, and can operate in highly contested, GPS-denied environments. For example, Redwire has delivered hundreds of its Penguin drones directly to the Ukrainian military for use in real-world combat operations. The company is a major provider and has customers that include the U.S. government, including NASA, the U.S. Army, the Marine Corps, and the Department of Homeland Security. But it also provides components for top aerospace and defense companies, including Lockheed Martin, Boeing, Airbus, and Blue Origin. In April, the U.S. Space Force's Space Systems Command selected Redwire as one of 14 companies to compete under this $1.8 billion contract program to design and build advanced space surveillance and reconnaissance satellites. Earlier this year, it was awarded a multi-award contract for the Missile Defense Agency's Scalable Homeland Innovative Enterprise Layered Defense (SHIELD) IDIQ. Is Redwire a buy? Redwire's backlog is up to $498 million, and analysts expect 40% revenue growth in 2026 and another 20% in 2027. That said, the business is currently losing money, and analysts don't forecast profitability within the next three years. RDW Revenue (TTM) data by YCharts Redwire is an early-stage company that has secured several key government contract wins, and as a result, the stock has surged 198% year to date. Given the stock's recent surge and lack of near-term profitability, Redwire is best left for aggressive investors with a long-term outlook and willing to stomach sizable price swings.
A SpaceX-Tesla union would mark the largest merger of all time. But does the math work?
Elon Musk may be about to use SpaceX's soaring valuation as a lifeline for Tesla -- and in doing so, create a combined giant that potentially loses money. A new Fortune analysis by colleague Shawn Tully lays out the math behind a reported potential SpaceX-Tesla merger. The combined entity would carry a $3.4 trillion valuation with SpaceX at an anticipated $1.75 trillion against Tesla's $1.65 trillion market cap. This would make it nearly three times the size of the largest merger ever completed. Tully writes: "Capitalizing on the incredible buzz surrounding the pending SpaceX IPO as a strategy for rescuing stricken Tesla makes perfect sense for Elon Musk. At an expected market cap of $1.75 trillion, SpaceX stock looks vastly overpriced -- an IPO prominent analysts are saying they'd avoid. So Musk could marshal its inflated shares as currency to pay big for Tesla, even making the deal at its current market cap, a number that's also over the top based on any conventional metric." However, the financial logic is where it gets uncomfortable, according to Tully. For example, SpaceX would issue new shares equivalent to 94% of its current count to absorb Tesla, doubling its share base to 8 billion. But Tesla's trailing GAAP earnings have collapsed from $15 billion in 2023 to $3.9 billion today and core operating earnings, stripped of regulatory credits and Bitcoin gains, are just $2.3 billion. On top of that, the cash flow picture is even more alarming, he writes. For CFOs, treasurers, and capital-markets professionals, the situation raises a sharp question: what does it mean when the world's most watched deal is designed to solve one overvaluation problem by potentially creating a larger one? You can read Tully's full story here. Sheryl Estrada [email protected] The post A SpaceX-Tesla union would mark the largest merger of all time. But does the math work? appeared first on Fortune.
Betting fraud: Google employee rakes in $1.2 million on Polymarket - Research Snipers
A Google developer used secret company data to place safe bets on the Polymarket crypto platform. The multi-million dollar fraud was now exposed and led to the arrest. The case highlights the problem behind the forecasting platform. Millions in fraud by Google developers Polymarket is currently making some headlines. The controversial platform, where you can bet on anything, such as wars, was recently even banned in Spain. Now a 36-year-old Google developer has been arrested in New York on suspicion of wire fraud and money laundering. The Italian, who works in Zurich, is said to have used insider information about his employer to place bets on the prediction platform. Between October and December 2025, the defendant placed targeted predictions under the pseudonym AlphaRaccoon. He bet on which people would take the top spots in Google's annual review. Among other things, he bet on the singer D4vd, whose chance of winning on the platform was very low. Since the employee already knew the result, he made a profit of 1.2 million US dollars (around 1 million euros). Covered tracks How ABC News reported, the man used an internal tool accessible to all employees. Google classified the behavior as a serious policy violation and immediately placed the employee on leave. The accused pleaded not guilty in court and was initially released on bail of 2.25 million US dollars (around 1.9 million euros). To cover his tracks, the developer deleted his Polymarket account after winning and moved the winnings from his digital wallet in the form of cryptocurrency. However, blockchain transactions are publicly visible, which is why observers suspected an insider behind the account early on. The hit rate for the unlikely events was too high to be based on pure chance. Second case of insider trading The current incident is the second criminal trial involving the crypto platform. A US soldier was previously indicted for using secret military information to bet on political developments in Venezuela. A US congressional committee is currently investigating how the platform can check its users and prevent illegal trading. The operators of Polymarket say they are cooperating closely with the investigative authorities. The company has now tightened its internal guidelines. The management announced technical adjustments in order to identify and report abnormalities in betting more quickly in the future.
Why It Matters if OpenAI or Anthropic Wins the IPO Race
In the bitter rivalry between AI heavyweights OpenAI and Anthropic, it will mostly be who has the best technology that determines the ultimate victor. But which one of them gets to its public offering first matters a great deal, too. The window for initial public offerings is decidedly open, with a receptive market. Cerebras, an AI-chip company, rose 68% on its first day of trading last month. Only digital-design platform Figma's absurd 250% rise last year was bigger for a company valued at more than $10 billion at listing in the past five years, according to FactSet data.
A Google engineer bet $2.7M on Polymarket using secret search data -- and walked away with $1.2M in profit
When it comes to placing bets, part of the thrill is the unknown potential gain -- or loss. But when Michele Spagnuolo placed a bet on Polymarket last year, he probably had a pretty good idea of what the results would be. The 36-year-old software engineer was arrested last week and charged with commodities fraud, wire fraud, and money laundering after allegedly using confidential internal Google data to place bets on the platform's most-searched person of 2025, according to NPR (1). Must Read * Here's how to get rich from rising US property values with as little as $100 -- and without the stress of angry tenants * Robert Kiyosaki says this 1 asset will surge 400% in a year and begs investors not to miss this 'explosion' * Millionaires under 43 are reshaping investing -- just 25% of their portfolios are in stocks. Here's where their money is going Prosecutors say that Spagnuolo, trading under the username AlphaRaccoon, reportedly wagered $2.7 million across 25 separate bets -- and walked away with $1.2 million in profit. He bet that Pope Leo XIV and Bianca Censori, who married Kanye West, would not take the top spot, but that rapper D4vd would. "The employee accessed our marketing material using a tool available to all employees, but using such confidential information to place bets is a serious breach of our policies," Google spokesperson Jaclyn Vazquez told NPR. Prediction markets face continued scrutiny This isn't the first time prediction market bets have resulted in federal charges. Last month, a U.S. Army Special Forces soldier was charged with using classified information about the capture of Venezuelan leader Nicolás Maduro to pocket more than $400,000 on Polymarket. In April, Kalshi suspended three accounts (2) that it believed belonged to congressional candidates Ezekiel Enriquez in Texas, Matt Klein in Minnesota, and Mark Moran in Virginia, who were bidding on their own races. Moran allegedly placed a bet on himself under the event contract, "Who will run for public office this year?" before announcing his candidacy. Moran claimed the bet was intentional (3) to see if he would be caught. But Bobby DeNault, Kalshi's Head of Enforcement, called the candidates' wagers "political insider trading" in a company press release, and he said they violated the platform's rules. DeNault has reported that his team scours social media, employment records, and other public data, such as the FTC's campaign data lists, and uses it to prevent insider trading. "Those trigger flags in our systems, and we've prevented hundreds of cases of insider trading based on that," DeNault told ABC (4).
Anthropic overtakes OpenAI as the world's most valuable startup after latest funding round
Anthropic has overtaken OpenAI to become the world's most valuable startup after raising $65 billion in fresh funding. The deal values the AI company at $965 billion post-money, pushing the maker of Claude ahead of OpenAI's most recently disclosed valuation of $852 billion and giving it the lead in the race between the two biggest private artificial intelligence companies. Anthropic announced the milestone in a blog post last week, saying the Series H funding round was backed by investors including Altimeter Capital, Dragoneer, Greenoaks and Sequoia Capital. The company also revealed that its annualised revenue run rate has crossed $47 billion, highlighting the rapid growth of its AI business. Anthropic said its annual revenue run rate has grown from about $10 billion last year to $47 billion now, driven by increasing demand for Claude among businesses and developers. Claude is the money-maker A major driver of Anthropic's growth is Claude, the company's AI model and chatbot platform. Anthropic said businesses across industries are increasingly using Claude in their day-to-day operations, while more individuals are turning to the chatbot and its related tools for work. Krishna Rao, Anthropic's chief financial officer, said Claude had become "increasingly indispensable" for customers around the world, with demand for products such as Claude Code and Cowork continuing to rise. The company said the latest funding will help it expand infrastructure, advance AI research and bring Claude to more workplaces globally. The funding round attracted a wide range of investors, including Capital Group, Coatue, D1 Capital Partners, GIC, ICONIQ and XN. Anthropic said the total also includes $15 billion in previously committed investments from hyperscalers, including $5 billion from Amazon. Strategic infrastructure partners such as Micron, Samsung and SK hynix also participated in the round. Anthropic's expansion plans Meanwhile, to support future growth, Anthropic has significantly expanded its computing capacity. The company said it has signed agreements with Amazon for up to five gigawatts of additional capacity, with Google and Broadcom for five gigawatts of next-generation TPU capacity, and with SpaceX for access to GPU resources through its Colossus infrastructure. Claude is now available through Amazon Web Services, Google Cloud and Microsoft Azure, although AWS remains Anthropic's primary cloud and training partner. Meanwhile, the latest figures underline how Anthropic's enterprise-focused strategy has reshaped the AI landscape. While OpenAI remains closely associated with consumer-facing products such as ChatGPT, Anthropic has positioned Claude as a tool for businesses, developers and coding workflows. Claude Code, in particular, has emerged as one of the company's fastest-growing products and has become a key driver of revenue growth.
One Google Employee Used The Company's Internal Data To Bet On Polymarket And Won $1.2M, DOJ Says
* US prosecutors charged Google engineer Michele Spagnuolo after alleging he used confidential search-ranking data to profit more than $1.2 million on Polymarket. * Authorities said Spagnuolo accessed non-public information through an internal Google tool and placed bets on the year's top search trends before the data was released. * The case ties with growing scrutiny of prediction markets, where Polymarket uses Circle's USDC stablecoin for all trading and settlement. Google (GOOG/GOOGL) software engineer Michele Spagnuolo was charged this week with commodities fraud, wire fraud, and money laundering after allegedly using confidential company data to pocket more than $1.2 million on Polymarket. According to a criminal complaint unsealed by the US Attorney's Office for the Southern District of New York, Spagnuolo, who is based in Switzerland, allegedly used an internal tool marked "Google Confidential" to access non-public search data. Then he placed bets on Polymarket through an account called "AlphaRaccoon" on the year's most-searched people and search items before the data went public. "Unlike the counterparties to his trades, Spagnuolo knew the outcome of these wagers before the trading public because he had accessed Google's confidential," read the filing. The poll in question was on Google's yearly announcement of "Year in Search, in which it publicly releases curated data reflecting the top trending searches from that year. Authorities allege that between October 15 and December 4 last year, Spagnuolo risked roughly $2.75 million on markets tied to Google's internal search data, and made more than $1.2 million once results went public. Polymarket settles all trades in Circle's USDC stablecoin. "Corporate insiders cannot use confidential business information to turn a profit in our markets," US Attorney Jay Clayton said, adding that Spagnuolo violated duties to his employer by trading on Google's internal data for personal gain. GOOG's stock was up by 0.11% during after-hours trading. On Stocktwits, the retail sentiment around GOOG remained in the 'bearish' zone, while chatter around it stayed in the 'low' levels over the past day. Polymarket And Circle Deepen Ties The charges come as Polymarket has been expanding its crypto infrastructure. In February, Circle (CRCL) and Polymarket announced a partnership to replace the platform's existing bridged stablecoin, USDC.e, with native USD Coin (USDC) for all trading, order placement, and settlement. On Stocktwits, the retail sentiment around USDC remained in the 'neutral' zone, while chatter around it dipped to 'high' from 'extremely high' over the past day.
