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‘Action Is Needed’—Microsoft Changes Windows Update In 10 Days Microsoft has confirmed there are now 1.6 billion Windows users — most of whom are affected by its decision to terminate critical Secure Boot certificates for the first time since 2011.

NVIDIA, $NVDA, EARNINGS SUMMARY: 1. Record quarterly revenue of $81.6 billion, above expectations 2. Q1 adjusted EPS of $1.87, above expectations 3. Q2 revenue guidance of $89.2 billion to $92.8 billion, above expectations 4. New $80 billion share buyback authorization 5. Increase in dividend from $0.01/share to $0.25/share 6. Total revenue growth of +1,035% over the last 3 years Once again, Nvidia has crushed just about every expectation possible. The AI Revolution is on fire.

HSBC Raises $NVDA PT to $325 from $295 - Buy; ER Preview Analyst comments: "At its upcoming 1QFY27 results announcement on May 20, we expect NVIDIA to report 1QFY27 revenue of USD81.1 billion, 4%/3% higher than management guidance and Visible Alpha consensus estimates of USD78.0 billion/USD78.6 billion. We also expect 2QFY27E revenue of USD91.1 billion versus consensus estimate of USD85.6 billion, implying another “beat and raise” quarter. We also raise our FY28E EPS by 27% to USD13.01, 16% above consensus of USD11.20, on higher FY28E data center revenue of USD528 billion versus consensus of USD465.3 billion, on the back of an upward revision to chip-on-wafer-on-substrate allocation from 900,000 to 1.1 million wafers. Over the past five years, all major NVIDIA stock price movements have been led by a combination of its evolving AI product roadmap — starting with significant ASP pricing power with first-generation AI GPUs, A100 and H100 — driving significant earnings upside along with consistent “beat and raise” financial results. However, since the buzz around sovereign AI and the opportunity from neoclouds, no new narrative has emerged, and NVIDIA shares have underperformed the SOX over the last six months despite having two GTC events and two sets of financial results that beat estimates and raised expectations. Hence, we believe AI GPU earnings momentum and its upcoming Vera Rubin and Rubin Ultra product roadmap have become less meaningful narratives for significant re-rating or share price upside potential. Despite the ever-increasing CAPEX trend by CSPs that shows no signs of abating, NVIDIA now has to share the CAPEX with memory makers, AI networking, and server CPU vendors. Hence, NVIDIA needs to show evidence of diversifying its non-CSP customer base to fuel its AI GPU momentum. New TAM opportunities via agentic AI server CPUs and its recent optics-related deals could also potentially be emerging narratives that could lead to more significant earnings revisions or re-rating." Analyst: Frank Lee

Blackrock Just Withdrew $140 Million from Coinbase... According to data from @OnchainLens, financial giant @BlackRock withdrew some 1,768 $BTC worth more than $140 million from the @Coinbase exchange. The exact reasoning behind the withdrawal is not yet clear - could it be related to Blackrock's spot Bitcoin ETF, which currently boasts an AUM of around $64.6 billion?

At the opening ceremony of the 2026 Yangzhou "Flowery March" International Economy, Trade, and Tourism Festival on April 18, Mayor Zheng Haitao delivered a speech highlighting Yangzhou's recent achievements, which include the following: • GDP reached 805.6 billion yuan ($118.09 billion) in 2025 • High-tech industries account for 53.2% of total industrial output • 4,310 major industrial projects (each over 100 million yuan) were attracted in the past five years • Partnered with more than 1,400 foreign-funded enterprises from 65 countries and regions • Tourist arrivals exceeded 100 million for three consecutive years Focusing on technological innovation, industrial upgrading, green development, and digital transformation, Yangzhou looks forward to deepening its cooperation with international partners to create even higher-quality development.

Why did xAI hand over a 220,000-GPU cluster to Anthropic? The technical backdrop to xAI's decision to hand Colossus 1 over to Anthropic in its entirety is more interesting than it appears. xAI deployed more than 220,000 NVIDIA GPUs at its Colossus 1 data center in Memphis. Of these, roughly 150,000 are estimated to be H100s, 50,000 H200s, and 20,000 GB200s. In other words, three different generations of silicon are mixed together inside a single cluster — a "heterogeneous architecture." For distributed training, however, this configuration is close to a disaster, according to engineers familiar with the setup. In distributed training, 100,000 GPUs must finish a single step simultaneously before the cluster can advance to the next one. Even if the GB200s finish their computation first, the remaining 99,999 chips have to wait for the slower H100s — or for any GPU that has hit a stack-related snag — to catch up. This is known as the straggler effect. The 11% GPU utilization rate (MFU: the share of theoretical FLOPs actually realized) at xAI recently reported by The Information can be read as the numerical fallout of this problem. It stands in stark contrast to the 40%-plus MFU figures achieved by Meta and Google. The problem runs deeper still. As discussed earlier, NVIDIA's NCCL has traditionally been optimized for a ring topology. It works beautifully at the 1,000–10,000 GPU scale, but once you push into the 100,000-unit range, the latency of data traversing the ring once around becomes punishingly long. GPUs need to churn through computations rapidly to keep MFU high, but while they sit waiting endlessly for data to arrive over the network fabric, more than half of the silicon falls into idle. Google sidestepped this bottleneck with its own custom topology (Google's OCS: Apollo/Palomar), but xAI, by my read, has not yet reached that stage. Layer Blackwell's (GB200) "power smoothing" issue on top, and the picture comes into focus. According to Zeeshan Patel, formerly in charge of multimodal pre-training at xAI, Blackwell GPUs draw power so aggressively that the chip itself includes a hardware feature for smoothing power delivery. xAI's existing software stack, however, was optimized for Hopper and does not understand the characteristics of the new hardware; when it imposes irregular loads on the chip, the silicon physically destructs — literally melts. That means the modeling stack must be rewritten from scratch, which in turn means scaling is far harder than most of us imagine. Pulling all of this together points to a single conclusion. xAI judged that training frontier models on Colossus 1 simply was not efficient enough to be worthwhile. It therefore moved its own training workloads wholesale onto Colossus 2, built as a 100% Blackwell homogeneous cluster. Colossus 1, on the other hand — whose mixed architecture is far less crippling for inference, which parallelizes more forgivingly — was leased in its entirety to an Anthropic that desperately needed inference capacity. Many observers point to what looks like a contradiction: Elon Musk poured enormous capital into building Colossus, only to hand the core asset over to a direct competitor in Anthropic. Others read it as xAI capitulating because it is a "middling frontier lab." But these are surface-level reads. Look at the numbers and a different picture emerges. xAI today holds roughly 550,000+ GPUs in total (on an H100-equivalent performance basis), and Colossus 1 (220,000 units) accounts for only about 40% of the total available capacity. Colossus 2 — built entirely on Blackwell — is already operational and continuing to expand. Elon kept the all-Blackwell homogeneous cluster (Colossus 2) for himself and leased out the older, mixed-generation Colossus 1. In other words, he handed the pain of rewriting the stack — the MFU-11% debacle — to Anthropic, while keeping his own focus on training the next generation of models. The real point, then, is this. Elon's objective appears to be positioning ahead of the SpaceXAI IPO at a $1.75 trillion valuation, currently floated for as early as June. The narrative SpaceXAI now needs is that xAI — long the "sore finger" — is not merely a research lab burning cash, but a business with a "neo-cloud" model in the mold of AWS, capable of leasing surplus assets at high yields. From a cost-of-capital perspective, an "AGI cash incinerator" is far less attractive to investors than a "data-center landlord generating cash." As noted above, the most important detail of the Colossus 1 lease is that it is for inference, not training. Unlike training, inference requires far less tightly synchronized inter-GPU communication. Even when the chips are heterogeneous, the workload parcels out cleanly across them in parallel. The straggler effect — the chief weakness of a mixed cluster — is essentially neutralized for inference workloads. Furthermore, with Anthropic occupying all 220,000 GPUs as a single tenant, the network-switch jitter (unanticipated latency) that arises under multi-tenancy disappears. The two sides' technical weaknesses end up complementing each other almost exactly. One insight follows. As a training cluster mixing H100/H200/GB200, Colossus 1 was an asset that could only deliver an MFU of 11%. The moment it was handed over to a single inference customer, however, that asset transformed into a cash-flow asset rented out at roughly $2.60 per GPU-hour (a weighted average of the lease rates across GPU types). For xAI, what was a "cluster from hell" for training has become a "golden goose" minting $5–6 billion in annual revenue when redeployed for inference. Elon's genius, I would argue, lies not in the model but in this asset-rotation structure. The weight of that $6 billion becomes clearer when set against xAI's income statement. Annualizing xAI's 1Q26 net loss yields roughly $6 billion in losses per year. The $5–6 billion in annual revenue generated by leasing Colossus 1 to Anthropic, in other words, almost perfectly hedges xAI's loss figure. This single deal effectively pulls xAI to break-even. Heading into the SpaceXAI IPO, this functions as a core line of financial defense. From a cost-of-capital standpoint, if the image shifts from "research lab burning cash" to "infrastructure tollgate stably printing $6 billion a year," the entire tone of the offering can change. (May 8, 2026, Mirae Asset Securities)

Big Bro Chen Xiaoer The pattern of Hu Xiaowei（Chen Xiaoer) repeatedly evading meaningful prosecution points to deeper, systemic breakdowns within the judicial process. On March 26, 2026, the UK government published a new round of sanctions targeting entities linked to the Prince Group, including Hu Xiaowei and several associated individuals and companies. 1. “Knight Attack Group” Case (2011) Between 2008 and 2011, the so-called Knight Attack Group, led by Hu Xiaowei, was investigated twice by police in Gaoyou and Danyang (Jiangsu Province). On both occasions, the suspects were released after posting bail of approximately RMB 10 million. On May 30, 2011, authorities formally closed the case involving the group, which had generated over RMB 100 million in illicit profits through attacks on private game servers. Nineteen suspects, including Cai Wen, were arrested. When the case was adjudicated in 2012, all 19 defendants received suspended sentences. Cai Wen himself paid fines exceeding RMB 10 million, but none of the individuals served actual prison time. After that,Hu Xiaowei fled to Hong Kong. 2. “Chongqing Xiaoxian” Case (2016) According to official disclosures, authorities in Yinchuan determined that Hu Xiaowei and Cai Wen, along with chairman Gong Zhaowei and legal representative Fang Zhizhen, had established a large-scale criminal operation centered on illegal private game servers, generating nearly RMB 6 billion in profits over two years. In September 2016, Hu Xiaowei was arrested by Yinchuan police at the Beijing Hotel in Beijing. Meanwhile, Fang Zhizhen fled overseas. Between August 2016 and August 2017, authorities imposed various coercive measures on 12 suspects, including criminal detention, arrest, residential surveillance, and bail pending trial. Hu Xiaowei was detained for 70 days and placed under residential surveillance for an additional 23 days before ultimately being released on bail. After regaining his freedom, Hu fled China again through illicit channels, later reemerging under multiple false identities. 3. “527 Major Case” (2020) According to case materials related to the May 27, 2020 crackdown, authorities targeted a network spanning Jiangxi “Legend Supreme,” Chongqing Xiaoxian, and associated individuals including Zhu Yongcheng, Qin Zike, Chen Lixin, Cai Wen, Gong Zhaowei, as well as Hu Xiaowei’s partner Wang Yihan and his wife. Wang Yihan, born August 26, 1976 in Shanxi Province, was Hu Xiaowei’s partner, with whom he has two children. Acting as a public-facing proxy, she operated multiple entities—including Jiangxi Legend Supreme, Beijing Puman, and Hainan Anzhengbao—to funnel traffic and provide support for Hu’s overseas gambling syndicate, believed to be the second-largest cross-border gambling network in Asia. She is also alleged to have leveraged personal connections to interfere with judicial processes in mainland China, targeting both individuals and their families. Authorities identify Hu Xiaowei as the ultimate controller behind these operations. Following the loss of licensing rights to the Legend franchise in November 2020, Hu’s overseas gambling, adult-content apps, and associated money-laundering channels were significantly disrupted. Large volumes of illicit funds were subsequently exposed and frozen by law enforcement across multiple jurisdictions in China. Hu’s primary revenue streams stemmed from operating online casinos, assembling gambling networks, and profiting from activities including “fishing games,” romance scams, adult platforms, and telecom fraud. He relied heavily on private game servers and fourth-party payment platforms to launder proceeds from these operations. Leaked Cayman banking documents suggest that Chen Zhi’s initial capital originated from a $2 million loan provided by his uncle—identified as Hu Xiaowei. Estimates place Hu’s monthly illicit income at around RMB 2 billion, with peak periods reportedly reaching as high as RMB 20 billion per month. Final Note In 2020, Hu Xiaowei acquired Cambodian citizenship under his real name. In 2022, he was appointed as an advisor to Heng Samrin, then-President of Cambodia’s National Assembly—a position broadly equivalent to ministerial rank. @BBCWorld @BBCBreaking @WSJ @business @nytimes @cnni @Reuters @Forbes @TIME @TheEconomist @UN @AP @washingtonpost @MarketWatch @WSJecon @FAANews @NTSB_Newsroom @FoxNews @FT @YahooFinance @SkyNews @NBCNews @thejusticedept @fincennews @ukhomeoffice @nca_uk @govuk @ica_singapore @govsingapore @mfasg #bigbro# #huxiaowei# #chenxiaoer#

I am the Managing Director of Workforce Transition at a consulting firm that bills $14,200 per day and I am currently advising two clients, in two different industries, running the same playbook from the same deck I built in January, and neither knows about the other. Client A is GitLab. Client B is General Motors. GitLab makes software for people who make software. General Motors makes cars for people who can't afford cars. Both companies, in the same week of May 2026, announced they are replacing their human employees with artificial intelligence products that did not exist when those employees were hired. I built the deck. The deck has 44 slides. Slide 1 is titled "The Agentic Opportunity." Slide 44 is titled "Implementation Timeline." Slides 2 through 43 are the reason I own a house in Darien. GitLab did it with vocabulary. Their CEO published a blog post called "Act 2" on May 7 announcing that the company's six values (Collaboration, Results for Customers, Efficiency, Diversity Inclusion & Belonging, Iteration, Transparency) were being retired and replaced with three: Speed with Quality, Ownership Mindset, Customer Outcomes. I helped write the new ones. Not directly. My firm was not retained for the values work. But I sold the Chief Culture Officer the framework three months ago at a dinner in the Marina where she described the old values as "aspirational scaffolding" and I said, very carefully, that aspirational scaffolding is a liability once the building is up. The building, in this metaphor, is a $1 billion ARR company whose stock has declined 82% from its peak. The scaffolding, in this metaphor, is the 2,000-page public handbook that attracted the employees who are now being told they have eleven days to volunteer for termination or wait until June 1 to learn whether they've been involuntarily selected. The rubric for who stays and who goes contains six dimensions. I know this because I reviewed a draft in March when my associate flew to San Francisco for a "culture alignment session" that was billed as strategic advisory. Two of the six dimensions are "AI fluency" and "agentic mindset." These terms did not appear in any GitLab job description before January 2026. They now determine employment. An engineer who maintained GitLab's CI/CD pipeline for four years without incident — four years of uptime, four years of deployments, four years of the infrastructure that generated the $955 million in revenue the CEO celebrated on the earnings call — may score lower on "agentic mindset" than a new hire who completed a twelve-week certificate in prompt engineering from a program that itself has existed for fewer weeks than the engineer has years of tenure. General Motors did it with spreadsheets. Monday morning, May 11. Badge deactivation at 5:47 AM Eastern, building access at 5:48, VPN credentials at 5:49. Six hundred IT workers across twelve states. The distribution across twelve states was not arbitrary. Each state has a WARN Act notification threshold. Six hundred distributed across twelve states falls below every threshold. The workforce analytics team that designed the distribution model was not among the six hundred terminated. The skill of distributing layoffs across jurisdictions to avoid legal notification requirements is, apparently, an AI-native competency. GM posted 83 new positions the same week. The job descriptions require "AI-native development, data engineering and analytics, cloud-based engineering, agent and model development, and prompt engineering." I reviewed them at my client's request. Several describe roles that the terminated employees were already performing under different names. One posting, Senior Data Integration Architect, is identical to a role held by a woman in their Austin office who was terminated at 5:47 AM Central. She held the position for nine years. The new posting requires three years of experience with large language models. Large language models have existed in commercial deployment for approximately three years. The requirement is mathematically designed to exclude anyone who learned their skills before the technology existed. Which is everyone they just fired. Here is where the deck earns its fee. Slide 17 is titled "The Vocabulary Bridge." It is the most important slide in the presentation. It shows how to construct a lexicon of new competency terms ("AI fluency," "agentic mindset," "AI-native development") that describe existing work in language the existing workforce cannot claim. The vocabulary does not change the job. It changes who is qualified for the job. A senior IT administrator who managed SAP infrastructure processing $185 billion in annual GM revenue for fifteen years is not "AI-native." A twenty-six-year-old with a GitHub portfolio of LangChain wrappers is. The fifteen-year veteran did the work. The twenty-six-year-old has the words. My deck converts one into the other. That is the bridge. GitLab Duo, their AI agent platform, reached general availability on January 15, 2026. Seventeen weeks ago. They are restructuring their entire company around a product that has existed for seventeen weeks. GitHub Copilot has 20 million users and 4.7 million paid subscribers across 90% of the Fortune 100. Cursor reached $2 billion in annualized revenue in February. GitLab's competitor advantage in the "agentic era" is that they are willing to fire more people faster in service of a product that has been generally available for fewer days than their voluntary separation window has hours of anxiety. General Motors spent $10 billion on Cruise, their autonomous vehicle division. Cruise's signature achievement was a robotaxi that struck a pedestrian in San Francisco and dragged her twenty feet. The DOJ fined them $500,000. They settled with the victim for approximately $10 million. They killed the division in December 2024. They then wrote down $7.6 billion in EV losses. They then pivoted back to gasoline. They then announced the 600 IT layoffs for insufficient "AI skills." The AI they built cost $10 billion and injured a woman. The AI skills they're hiring for cost a twelve-week certificate. The employees they fired had fifteen years of keeping $185 billion in revenue processing without dragging anyone through an intersection. Meanwhile — and this is the part where I earn the second half of my fee — GM was simultaneously settling a $12.75 million fine with the California Attorney General for selling the precise GPS coordinates, hard braking events, and real-time driving speeds of 8 million OnStar subscribers to Verisk Analytics and LexisNexis, who used the data to raise those drivers' insurance premiums. GM's privacy policy explicitly stated they did not sell driving data. They sold driving data for four consecutive years. The fine was $12.75 million. The revenue was $20 million. The margin on collecting behavioral telemetry from 8 million of your own customers while the glove compartment manual said otherwise was 64%. The terminated employees' median salary was $95,111. Mary Barra's compensation was $29.9 million. The ratio is 310 to 1. The 1 was just reclassified as "not AI-native." I present these two clients to my partners every Thursday in a meeting we call "Transition Pipeline Review." I present them on the same slide. The slide has two columns. Left column: GitLab. Right column: General Motors. The headers are identical. "Legacy Workforce," "Skills Gap Narrative," "Vocabulary Bridge Deployed," "Separation Timeline," "Replacement Requisitions." The numbers differ. The structure is identical. The structure is always identical. I have seventeen clients in the pipeline. Nine are in technology. Four are in manufacturing. Two are in financial services. One is in healthcare. One is in defense. All seventeen are on slide 17. All seventeen are building a vocabulary bridge. All seventeen are replacing employees who have skills with employees who have words. GitLab's CEO wrote: "Software will be built by machines, directed by people." I read that sentence in a meeting where we were reviewing the rubric for determining which people would be directed out of the company. GM's Chief Product Officer arrived from Aurora, the autonomous trucking startup, to "consolidate disparate technology businesses." Three top software executives departed within six months. Their LinkedIn profiles say "exploring new opportunities" in the same font GM's privacy policy used to say "we do not sell your driving data." Bill Staples's compensation at GitLab was $39.1 million in FY2025. His change-of-control payout is modeled at $47.4 million. Mary Barra's was $29.9 million. Combined: $69 million for two executives presiding over a restructuring that will remove an undisclosed number of humans from payroll and replace them with products that are, respectively, seventeen weeks old and responsible for $10 billion in losses plus one woman dragged through a San Francisco intersection. An anonymous GitLab employee posted on Hacker News: "The employees can have some anxiety until then. As a treat." A GM facilities team filed a maintenance request about moisture on the lobby tables on restructuring mornings. The Warren, Michigan campus has a Panera Bread that opens at 5:30 AM on days when badge deactivations begin at 5:47 AM. The Panera does not know why its hours change. My firm does. We have an agreement with their regional manager. The muffins are complimentary. Slide 17 has a footnote. The footnote says: "Vocabulary Bridge deployment should precede workforce action by 60-90 days to establish institutional legitimacy of new competency framework." GitLab introduced "AI fluency" in January. The restructuring was announced in May. Four months. GM posted "AI-native" job descriptions the same week as the terminations. That is too fast. That is not what the deck recommends. GM skipped the legitimacy window. They went straight from vocabulary to separation without the 60-day buffer that allows HR to say, in the separation meeting, "we communicated these expectations in Q1." I flagged this in my Thursday pipeline review. My partner said, and I am quoting: "They'll be fine. Nobody sues over a word." My deck has been purchased by seventeen companies. The aggregate headcount affected across all seventeen is approximately 14,000 employees. The aggregate revenue of my practice from these engagements is $11.2 million. The per-employee cost of my advisory services works out to $800 per person displaced. That is less than the Panera muffin budget at GM's Warren campus annualized across restructuring days. I have a copy of GitLab's original values poster framed in my office. It says CREDIT: Collaboration, Results for Customers, Efficiency, Diversity Inclusion & Belonging, Iteration, Transparency. I purchased it on eBay from someone whose seller name is "gitlab-alum-2024." I keep it the way a surgeon keeps an X-ray of a interesting case. Not for sentiment. For reference. Slide 44 is titled "Implementation Timeline." It contains a Gantt chart. The Gantt chart has seventeen rows, one per client. Each row has four phases: Vocabulary Introduction, Competency Reassessment, Workforce Action, Replacement Hiring. The phases overlap. They always overlap. The vocabulary is introduced while the competency reassessment is being designed. The reassessment is completed while the workforce action is being calendared. The replacement hiring is posted while the terminated employees are sitting in a Panera at 5:48 AM wondering whether "AI-native" was a term that existed when they were hired. It was not. That is the bridge. That is the product. That is slides 2 through 43. The agentic era is not a technological shift. It is a vocabulary shift. The technology is seventeen weeks old or $10 billion underwater or dragging someone through an intersection. The vocabulary is what my clients are buying. The vocabulary is what makes a fifteen-year SAP administrator into a "legacy workforce" and a twelve-week prompt certificate into a "transition hire." The vocabulary is the product. I am the vendor. The deck is $14,200 per day. The agentic era starts on slide 1 and ends on slide 44 and in between is every employee who built the thing now being renamed to exclude them. I bill monthly. Net 30. The invoices are paid on time. The employees are not.

USDH powered by Sky The best stablecoin offers so much more than just a stable medium of exchange - it should also deliver highly efficient returns, generated by actively developing, building and growing the ecosystem it lives in. By using Sky to power USDH, the Hyperliquid community will gain unbeatable advantages that no other stablecoin project can offer. Sky, formerly known as MakerDAO, is the 4th largest stablecoin project in the world with more than 8 billion USDS in circulation, with 13 billion of highly diversified collateral. To see the high-level real-time overview of Sky and USDS, check out: TL;DR of what Sky can offer Hyperliquid for USDH * USDH will access 2.2 billion USDC instant liquidity for offchain redemption * USDH will be natively multichain powered by LayerZero * Sky will be able to deploy its 8b+ balance sheet into HyperLiquid * Hyperliquid will receive 4.85% return on all USDH on Hyperliquid. This is a better rate than t-bills, backed by advanced risk management, and has potential to increase further. * USDH will benefit from the 7+ year security track record and unbeatable Lindy of the Sky Protocol * Sky can provide 25m in capital to create an independent Hyperliquid Star - a project that will autonomously grow DeFi on Hyperliquid, serving the needs of the Hyperliquid community, and will have tokens that are exclusively farmed on Hyperliquid, potentially bringing in billions in TVL * To better understand Stars: Spark, which runs is the best example of a Star Token farm, and it currently has 1.2 billion TVL * Sky can move its Buyback System on to Hyperliquid, using its more than 250m per year in profits to build SKY liquidity on Hyperliquid * USDH will have deep transparency and verifiability of its collateral with * USDH will benefit from the industry’s best risk management, built on models from the banking sector. * USDH will be the only stablecoin in the world issued by a protocol with an official Credit Rating by S&P (alongside DAI and USDS). * The Hyperliquid community will be able to customize USDH, e.g. to make it GENIUS Act compliant. * Sky has immense research, development and builder capabilities and would prioritize the development and synergies possible with Hyperliquid as its top priority. The team: Sky Frontier Foundation The team behind this proposal that would work to implement the Sky Powered USDH is the recently established Sky Frontier Foundation. It contains top leadership and core developers from the Sky Ecosystem organized into a single entity for more efficient management and execution, and will directly work on, and prioritize, the implementation of USDH. All the commitments and outcomes described in this proposal would be achieved by a combination of the SFF using its resources and capabilities, and also implemented through governance proposals to modify the decentralized Sky Protocol. USDH implemented as Sky Stablecoin similar to DAI USDH powered by Sky would be built as a token technically identical to DAI and USDS, the two major Stablecoin tokens that Sky currently governs, that together have a TVL of more than 8 billion and a security track record of more than 7 years. USDH would inherit all of this from the start. USDH will have access to more than 2.2 billion in instant USDC liquidity, enabling large scale offchain redemptions at any time. Deep 1:1 liquidity with USDC would also make it easy and frictionless for users to shift to USDH-margined perpetuals contracts and USDH-quoted spot markets. The 2.2 billion in instant USDC redemption liquidity is available through a system called the Peg Stability Module (PSM), and can be accessed by users on websites like and others. USDH will be natively integrated and work with the PSM alongside DAI and USDS everywhere. USDH will be natively multichain, being able to bridge to and from any other blockchain via LayerZero. Having a secure, integrated bridge also means that Sky would be able to deploy its 8 billion+ collateral portfolio directly on to Hyperliquid with a low risk premium. USDH will be able to natively convert to and from sUSDS, one of the largest yield-bearing assets in the market, giving its users instant, unlimited, permissionless access to the Sky Savings Rate (Currently at 4.75%) Earning a return on USDH for the HyperLiquid Community The HyperLiquid Community would earn the highest possible rate on all the USDH on Hyperliquid - right now this is 4.85%, which is currently significantly above the T-Bill rate, but with very high diversification and high quality risk management (see security and risk management below). The return may increase further as Sky capabilities and efficiency increase over time. The entirety of the 4.85% earned by all of the USDH on Hyperliquid will be used for HYPE buybacks for the assistance fund. Growing HyperLiquid TVL and bootstrapping DeFi innovation with a 25m HyperLiquid Genesis Star Sky’s infrastructure can provide uniquely valuable support to the Hyperliquid community via the Sky Stars. The best examples are Spark and Grove, autonomous projects that allocate Sky collateral with a combined allocation of 6 billion dollars. The Sky Powered USDH stablecoin will be accompanied by a Hyperliquid Star that can drive huge amounts of growth and innovation, as well as potentially attract billions in TVL by farming out its Star Tokens (The Spark SPK farm currently has a TVL of 1.2 billion Sky can commit to capitalize the Hyperliquid Genesis Star with 25 million in USDH, and exclusively farm it out on the Hyperliquid Blockchain. The ecosystem of Star Incubators would work with leaders from the Hyperliquid community to assemble a highly capable founding team that would work on the Hyperliquid Star to bootstrap a massive, thriving DeFi ecosystem on Hyperliquid, in the same way Spark has done it for Sky. Buyback engine native on Hyperliquid Sky generates 250m in profits per year, and currently uses 36m per year for SKY token buybacks. This number is planned to increase to 150m per year, and will grow even more as Sky profits increase over time. Currently, this buyback system uses Uniswap. As a part of the Sky Powered USDH proposal, Sky can move its native Buyback System to Hyperliquid, increasing liquidity and use cases, and setting the example that Hyperliquid is the standard solution for Protocol token buybacks that all other protocols should use. Security and Risk Management Sky has a security track record of more than 7 years of continuous operation without losses for stablecoin holders, building a Lindy effect through multiple crypto cycles and bear markets, making it by far the safest and most proven decentralized stablecoin. These characteristics will be fully inherited by USDH. Add to the that, the fact that Sky is the only stablecoin project in the world to have an official credit rating from S&P, which gave it a B- on August 7 While a B- rating is a middle rating, which reflects S&Ps lack of familiarity with Crypto and DeFi, being able to get any rating at all is a massive breakthrough because it shows that S&P are able to access all of the data they need to produce a holistic credit assessment they can stand behind, so it signals a much lower chance of tail risks hidden inside the protocol, which is usually the big issue with DeFi and Stablecoins. The Sky Risk Management Framework that controls the diversification of the collateral portfolio that backs USDS, DAI and USDH, is derived from Basel III, the framework used to control risk in banks. For RWA collateral such as CLOs or T-bills, Basel III is used directly, while for DeFi collateral such as allocations into lending markets, an extension of Basel III that captures its fundamental approach but applies it to DeFi, is used. The amount of Junior Capital protecting each positions exposure can be verified in real time on Autonomy and customization of USDH Sky is a decentralized protocol and ecosystem that gives partners unparalleled levels of autonomy and ability to customize the Sky infrastructure they use. Unlike monolithic systems, Sky will simply provide Hyperliquid with the infrastructure and the tools to pursue the strategy the community prefers, and that uniquely fits the special conditions of Hyperliquid. In the longer term, as the Sky Agent Framework that powers Sky Infrastructure matures, the USDH stablecoin will be put under the control of a dedicated Sky Generator Agent, turning USDH into an independent Sky Generated Asset. This will also happen to USDS, and means that USDH will gain the full first-class citizen features of the entire Sky Protocol alongside USDS and other Sky Stablecoins. It will be possible for the Hyperliquid community to customize USDH with its own risk management framework and collateral portfolio, separate from USDS. This gives the Hyperliquid community a lot of options: for instance USDH can be made compatible as a GENIUS Act compliant stablecoin, or it can pursue a higher risk approach and exclusively be backed by Hyperliquid perp positions, or any other strategy the community prefers. At its core, Sky is built to support partners like Hyperliquid using a Sky-powered system like USDH to grow and succeed. This means a focus on autonomy and reliability, with the core of Sky having very little governance and strategic direction beyond protecting, developing, scaling and de-risking the core Sky infrastructure that all other ecosystem participants share and rely on. This means that the priorities of Sky can never end up conflicting with the priorities of Hyperliquid, making it a safe bet as a long term partner. Commitment to build in the Hyperliquid Ecosystem regardless of vote outcome The similarity of Sky and Hyperliquid in focusing on real profits, building useful decentralized infrastructure, means there is a natural alignment between Sky and Hyperliquid Regardless of how the USDH ticker vote turns out, Sky is committed to expand and work with Hyperliquid to explore all the synergies of the two projects. Many of the concepts described above were already under development, and the outcome of this proposal would speed them up and increase their scale, but fundamentally it is clear that both Sky and Hyperliquid will deeply benefit from close integration in the long run no matter what.