earth visit log 3: 🌏 first combini encounter complete. egg sandwich detected 🥚 texture classification: unclear. why is it this soft. entered for one snack. exited with six. self-control module may be incompatible with combini environments. what’s your go-to combini snack? collecting data for next run.

What is a Canton Network Super Validator? @CantonNetwork super validators (SVs) support the backbone of the network itself, and some of finance and blockchain's biggest names are among them. Core Role: While standard validators process and store data for transactions they are directly involved in, SVs help to secure and manage the network itself, by helping to operate the Global Synchronizer. They use a Byzantine Fault Tolerant (BFT) consensus mechanism to sequence and validate transactions and, in doing so, allow for interoperability across Canton's network-of-networks. Governance: SVs are often members of the @CantonFdn and vote on crucial protocol upgrades and improvement proposals, including the acceptance of new super validators. It is for this reason that a high degree of trust is required. SV Weight: Not all SVs are created equal. A 'weight' is given to each which reflects their relative influence when it comes to both governance and reward distribution. The weight system operates on a 1-10 basis, with 10 being the maximum that can be assigned. The above really just scratches the surface when it comes to the role played by SVs in the $CC ecosystem. As of early May, there are 55 super validators helping to secure the Canton ecosystem. Here are some well-known examples... @Visa @circle @Nasdaq @The_DTCC @chainlink @FireblocksHQ @Ledger

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.

Microsoft Wants to Use “Dirty Energy” for AI Data Centers Microsoft may scale back some of its clean energy goals for data centers because AI electricity demand growth is too fast so they will find alternatives The company already met its 2025 target early by signing contracts for more than 40 GW of renewable energy, but new AI facilities are expanding faster than clean power projects, and they can’t connect it to “clean energy.” Microsoft is investing in nuclear power, such as restarting the Three Mile Island plant, while considering natural gas plants as a solution

The head of AARO explains the problem with detecting military UFOs is largely one of insufficient data and cautions against jumping to premature characterizations. Do the work, then characterize based on a solid foundation. "I have found it surprising that, in this age of ubiquitous sensor coverage, it is still so difficult to get high-quality, actionable data suitable for resolving, or even just advancing our understanding of some of the more intriguing cases. That said, I have also found that many of these initially baffling reports are fully explainable once you apply a rigorous, scientific process. It is easy to look at a strange video and jump to a conclusion. But time and time again, when our team of analysts and scientists dig in, we find the answer. It has been a powerful reminder of how important it is to stick to the data and not let assumptions get ahead of the evidence, regardless of how compelling a good mystery can be. In spite of all the noise, I always try to stay focused on the cases that may demonstrate true anomalies. "

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)

AI Hardware Demand Growth and Representative US-Listed Companies June 2026 Executive Summary Nvidia’s transition to the Vera Rubin (VR200) platform marks a significant escalation in AI infrastructure complexity and cost. Our BOM teardown of the next-generation Rubin rack reveals a ~2x increase in total rack cost to approximately $7.8 million (vs. ~$4 million for GB300), driven not solely by the GPU/CPU but by sharp revaluations across the supply chain. Key highlights from downstream components include: • PCB content value +233% YoY, the largest increase. • MLCC +182%, reflecting higher density and count (e.g., ~600k MLCCs per VR200 NVL72 server, +30%+ vs. GB300). • ABF substrates +82%, power solutions +32%, and liquid cooling +12%. These upgrades align with broader AI scaling: 800G/1.6T optical transceivers ramping aggressively, glass-based technologies advancing for packaging and interconnects, and hyperscalers prioritizing performance, power efficiency, and thermal management. We expect sustained multi-year tailwinds for the AI hardware ecosystem into 2027+, with Rubin-driven demand accelerating in H2 2026. Investment Thesis: While Nvidia (NVDA) remains the core beneficiary, the supply chain offers diversified exposure. We favor companies with direct exposure to high-growth areas like advanced PCBs, high-speed optics, and glass substrates/optical interconnects. Risks include execution on new capacity, potential margin pressure from rapid scaling, and geopolitical supply chain factors. 1. PCB: Sharpest Value Uplift in Rubin BOM Morgan Stanley’s detailed analysis shows PCB content in the Rubin rack surging +233% versus GB300. This reflects needs for higher layer counts, advanced materials, better signal integrity, and larger formats to support increased power and interconnect density in AI servers. US Representative: TTM Technologies (TTMI) – Leading US PCB manufacturer with strong positioning in high-complexity boards for data center/AI applications. TTM has invested in capacity expansions (e.g., new facilities) to capture AI-driven demand for advanced HDI and high-layer PCBs. 2. MLCC: Density-Driven Surge Nvidia’s VR200 NVL72 platform requires ~600,000 MLCCs per server, over 30% more than GB300. Combined with the +182% value increase in the BOM, this underscores tightening supply for high-capacitance, high-reliability MLCCs in power delivery and decoupling for AI accelerators. Exposure Note: The MLCC market is dominated by Asian players (e.g., Murata, Samsung Electro-Mechanics, Yageo). US-listed indirect exposure may come through broader electronics or power solution providers, but direct pure-play opportunities are limited. Watch for capacity utilization tightness benefiting the ecosystem. 3. Optical Communication: 800G/1.6T Ramp Accelerating Chinese leader Zhongji Innolight reported Q1 2026 net profit +262% YoY, driven by strong 800G/1.6T shipments, with expectations of significant full-year growth. This mirrors industry-wide momentum as AI clusters shift toward higher-speed optics for reduced latency and power in scale-out/scale-up networking. Nvidia’s investments in photonics and CPO further validate the trend. US Representatives: • Coherent (COHR) and Lumentum (LITE): Key players in optical components and transceivers; Nvidia has made substantial equity investments to secure capacity. • Corning (GLW): Major beneficiary via optical fiber, connectivity, and glass technologies (detailed below). 4. Micro-LED/Glass Substrates & Optical Interconnects: Strategic Partnerships Accelerating On May 20, 2026, BOE announced a cooperation MOU with Corning covering glass-based encapsulation carriers, foldable glass, perovskite substrates, and optical interconnect applications. This aligns with industry shifts toward glass cores for superior flatness, thermal stability, and integration in advanced packaging and photonics—critical for next-gen AI as organic substrates hit limits. US Representative: Corning (GLW) – Central to Nvidia’s optical strategy with multi-billion partnerships, new US optical factories, and expansion in fiber/photonics for AI data centers. Recent deals position GLW for 10x+ capacity growth in key areas. AI Hardware Demand Growth & US-Listed Representative Companies Table Component Demand Growth (vs. GB300) Key Drivers US-Listed Reps Investment Rationale PCB +233% value Higher layers, HDI, signal integrity TTM Technologies (TTMI) Direct AI server/backplane exposure; US capacity expansion MLCC +182% value; +30%+ count Power density in servers Limited direct (ecosystem via power suppliers) Supply tightness supports pricing/volume Optical Comm (800G/1.6T) Strong ramp (e.g., +262% profit ex.) Scale-out networking, CPO transition Coherent (COHR), Lumentum (LITE), Corning (GLW) Nvidia investments; transceiver/fiber boom Glass Substrates/Interconnects Emerging (MOU-driven) Packaging, photonics, thermal/optical Corning (GLW) Nvidia factory deals; US manufacturing tailwinds Power & Liquid Cooling +32% / +12% Higher TDP (e.g., 2300W GPUs) Indirect (ecosystem) Secondary but critical for rack deployment Source: Morgan Stanley BOM analysis, company reports, industry data. Growth metrics approximate from Rubin teardown. Outlook & Risks We project robust 2026-2027 growth in AI capex, with Rubin shipments catalyzing another leg-up in component demand. Optical and advanced substrate shifts could extend the cycle beyond traditional GPU focus. Hyperscalers’ vertical integration and US onshoring (e.g., Corning/Nvidia factories) add resilience. Key Risks: Cyclical capex pauses, yield/execution challenges on new tech (glass/CPO), commodity volatility in passives, and intense competition in Asia-heavy segments. Valuation multiples in the space have expanded; selectivity is key. Recommendation: Overweight select supply chain names with strong Nvidia alignment (e.g., TTMI for PCBs, COHR/LITE/GLW for optics/glass). Monitor Q2 2026 earnings for confirmation of Rubin ramp momentum.

📈 US Market Pre-Market Intelligence | June 3, 2026 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Sources: CNBC · Benzinga · StockTwits · The Motley Fool · TheStreet Pro · Reuters · Bloomberg · LSEG · FTSE Russell · · Yahoo Finance · The Globe and Mail Data window: Past 24h (priority: past 12h) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 【Market Snapshot】 • S&P 500 Futures: -0.3% | Nasdaq futures: +0.4% • VIX: 15.77 (1-year low, neutral-low) • Fear & Greed: 66 (Greed zone) • WTI Crude: ~$93/barrel (Iran tensions supporting) • Dollar Index: DXY 107.2 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 【SECTION 1: TOP NEWS — TECH/AI/SEMICONDUCTOR】 🔥 Jensen Huang at GTC Taipei (June 2-3, 2026): The Single Biggest Catalyst ━━━━━━━━━━━━━━━━━━━━ Source: The Motley Fool · StockTwits · Bloomberg · Reuters · Yahoo Finance · BigGo Finance · Times of India · DataCenterNews Asia 1. MRVL +32.5% Tuesday → all-time high. Huang on stage: "Matt [Murrett] is building the next trillion-dollar company." MRVL premarket today: +22%. → Custom AI chips (non-NVDA) = the new AI alpha. Gary Black (top Tesla/tech fund manager): "Broadcom and Marvell are the big winners as focus shifts to custom AI ASICs." 2. AVGO + Broadcom: Hit 52-week high alongside MRVL. Custom AI networking/DPU chips gaining enterprise share. Gary Black: AVGO is a "big winner" in the custom chip shift. 3. HPE Hewlett Packard Enterprise: Hit 52-week high premarket. Analyst (StockTwits): "AI-driven quarter — shares deserve a higher multiple." Aruba/AI infrastructure backlog strong. 4. NVIDIA Vera Rubin platform: Full production announced at GTC Taipei today (June 3). Next-gen AI data center GPU. NVDA stock slightly lower premarket (-0.5%) as markets digest the rally in competitors MRVL/AVGO. 5. TSMC ADR hit record high +2.5%. Deepened NVIDIA partnership confirmed at GTC Taipei. Philadelphia Semiconductor Index (SOX) surged 5.9% to all-time peak — biggest single-day move in 2026. 6. IPG Photonics, MACOM, Amkor: All skyrocketed after Huang's GTC Taipei keynote. AI photonics + advanced packaging = next bottleneck. 7. Trump Executive Order: Government gets early access to advanced AI models. StockTwits called it "pro-AI infrastructure" signal. Microsoft + NVIDIA partnership: RTX Spark + Vera CPU for Windows AI laptops. Build 2026 event this week. 8. MU Micron: UBS upgraded to $1625 target — more than 100% upside. HBM memory demand = structural supercycle. ⚠️ Warning Signal: Michael Burry (The Big Short): AI chip rally is within 7% of 2000 dot-com bubble peak. Chart suggests caution on AI momentum names. Retail vs. institutional positioning diverging. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 【SECTION 2: RETAIL SENTIMENT — STOCKTWITS/TRADINGVIEW/WALLSTREETBETS】 Source: StockTwits · TradingView News · TheStreet Pro · Yahoo Finance ━━━━━━━━━━━━━━━━━━━━ 📊 Most-discussed tickers by retail (past 24h): • MRVL: Trending #1# on StockTwits. Retail piling in after Huang shoutout. Bullish comments dominate. "MRVL to $200" comments everywhere. • AVGO: Custom chip theme driving retail interest. "This is the NVDA of 2026" sentiment growing. • ASTS (AST SpaceMobile): Snapped 2-day losing streak. Top execs buying shares = vote of confidence. SpaceX IPO buzz creating turbulence but long-term thesis intact. Retail mostly bullish. • RKLB / LUNR / RDW: Slipping on SpaceX IPO speculation. Bears say SpaceX could cannibalize launch demand. Bulls watching. • META: Retail sees buying opportunity. Stock still trails Mag 7 peers despite new subscriptions + layoffs + cloud plans. "META cheap vs. GOOGL/AMZN" sentiment. • TSLA: Dips on SpaceX merger rumors. Retail influencer: "Bull case adds $450B to valuation." SpaceX IPO terms uncertainty weighing. • INTC Intel: Surprising surge today. Investors asking "why is Intel surging?" AI PC + foundry turnaround narrative returning. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 【SECTION 3: MARKET THEMES — AI ROTATION + SMALL CAP BREAKOUT】 Source: The Globe and Mail · 247WallSt · Benzinga · AInvest · TheStreet Pro · LSEG ━━━━━━━━━━━━━━━━━━━━ 🚀 AI Money Rotating from Mega-Cap → Small/Mid Cap: • Small caps are AI's big winners in 2026 (The Globe and Mail, Reuters, 10 hours ago) • Russell 2000 small-cap value has beaten growth by 9 percentage points YTD • Something rare is driving the Russell 2000 — and AI is the answer (Benzinga) • AI exuberance rotating into small caps amid sticky inflation (ActionForex) • IWM, VTWO, URTH all flashing strong buy signals (MarketsHost) 📊 Russell 2000 reconstitution underway (FTSE Russell, May 22 announcement): Total US equity market cap in Russell 3000 reached $75.6 trillion — 29% increase from prior year. Major index changes creating volatility + opportunities. 💰 Sector rotation: Energy (oil $93, Iran) + AI semis + small-cap value = today's leadership. Defensive sectors (utilities, consumer staples) outflowing. ⚡ Middle East: Oil supported at $90-95 range. US-Iran tensions escalating (missiles launched toward Kuwait/Bahrain per fxstreet). Energy stocks (XOM, CVX) get tailwind. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 【SECTION 4: KEY STOCKS — PREMIER DATA FROM AUTHORITATIVE SOURCES】 Source: StockTwits · The Motley Fool · Yahoo Finance · Benzinga · TheStreet Pro ━━━━━━━━━━━━━━━━━━━━ 🔥 MRVL — Marvell Technology Price: ~$140+ (premarket +22%) | 52-week HIGH Catalyst: Jensen Huang at GTC Taipei: "next trillion-dollar company" Analyst: Gary Black — "big winner" in custom AI chip theme Analyst: Morgan Stanley sees $120+ target Narrative: AI custom ASIC for cloud. Google's TPUSnake, Amazon's Trainium = MRVL customers. Data center custom silicon = secular trend. Risk: Rich valuation. PS 20x. Momentum + Huang boost = near-term overbought. 📊 AVGO — Broadcom Price: ~$220+ | 52-week HIGH Catalyst: Custom AI networking chips + VMware synergy Narrative: #2# AI chip play after NVDA. Custom DPUs + networking for AI data centers. Gary Black: "big winner" alongside MRVL Risk: Valuation already pricing in strong growth 🚀 HPE — Hewlett Packard Enterprise Price: premarket +strong | 52-week HIGH Catalyst: AI-driven quarter beat. Aruba networking + GreenLake AI services. Analyst quote (StockTwits): "AI quarter — shares deserve higher multiple" Narrative: AI infrastructure + edge computing + hybrid cloud = multi-year growth Risk: Competition from Dell/Arista in AI networking 💡 TSMC — Taiwan Semiconductor ADR Price: $446.69 (+2.5% record high) Catalyst: NVIDIA partnership deepened at GTC Taipei. Advanced node capacity = structural moat. Narrative: Foundry king. AI compute demand = capacity fully booked for 2026-2027 Risk: Taiwan geopolitical risk premium always present ⚡ IPG Photonics / MACOM / Amkor Price: all skyrocketed after GTC Taipei keynote Catalyst: AI photonics (laser/optical interconnect) + advanced packaging (Amkor = chiplet packaging) Narrative: AI hardware bottleneck shifting from compute → interconnect + packaging Risk: Volatile momentum names ━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 【SECTION 5: SMALL-CAP US ALPHA 🔍】 Serenity Framework: Demand → Earnings → Small-Cap Elasticity → Verification ━━━━━━━━━━━━━━━━━━━━ 📌 IWM — iShares Russell 2000 ETF | Small-Cap Core Trigger: Russell 2000 hit strong buy signals; small-cap value beating growth by 9pts YTD; AI rotation into small caps confirmed (The Globe and Mail) Elasticity: Russell 2000 constituents with AI exposure = leverage to sector rotation Verification: FTSE Russell reconstitution underway; IWM options volume surging Catalyst: ADP jobs data today; Fed speakers this week; Russell reconstitution culminates end of June Risk: Rate sensitivity if jobs data hot → delay Fed cuts → small caps hurt 📌 FLNC — Fluence Energy | AI Data Center Cooling Trigger: Jensen Huang GTC keynote emphasized AI data center infrastructure at scale. Cooling/power = next bottleneck as compute density explodes. Elasticity: AI data centers need liquid cooling at scale — FLNC is leader in modular battery/cooling systems Verification: Stock up strongly post-GTC keynote. Institutional buying volume rising. Catalyst: Next earnings — watch backlog. If backlog grows >40% YoY = confirm supercycle thesis. Risk: Competition from Schneider Electric / Vertiv 📌 SOXL — Direxion Daily Semiconductor Bull 3X Trigger: Philadelphia Semiconductor Index +5.9% to all-time high. TSMC record. MRVL +32%. AI chip theme = institutional inflow. Elasticity: 3x leveraged ETF = amplified move in semiconductor sector Verification: SOXL options activity spiking. Retail interest elevated (StockTwits trending). Catalyst: Any NVIDIA/AMD/MU earnings beat = SOXL pops 5-8% same day Risk: 3x leverage = decay risk. Only for short-term tactical use. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 【SECTION 6: MACRO + MARKET STRUCTURE】 Source: Bloomberg · Reuters · fxstreet · LSEG · FTSE Russell ━━━━━━━━━━━━━━━━━━━━ 📅 Today's Key Events (June 3, 2026): • ADP Private Sector Employment (May) — consensus: +180K • ISM Services PMI (May) — flash PMI showed expansion, services follow-up • EIA Weekly Crude Inventory • Fed speakers: likely to maintain data-dependent stance • GTC Taipei keynote continues (NVIDIA ecosystem announcements) ⚠️ Key Risks: 1. Iran/Middle East escalation → oil spike >$95 → inflation risk → Fed hawkish 2. AI chip rally froth (Burry warning) → sector could see sharp 5-10% correction 3. SpaceX/OpenAI/Anthropic IPO timeline: Standard Chartered warns "market oxygen being sucked out" when these hit 4. Tariff fatigue: Section 301 tariffs on 60 economies (forced labor) could hit supply chains 5. MU/TSMC: Any supply disruption from Taiwan Strait tension = semiconductor sector crash ━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 【SECTION 7: INSTITUTIONAL FLOWS】 Source: LSEG · FTSE Russell · Bloomberg ━━━━━━━━━━━━━━━━━━━━ • AI/Infrastructure: MRVL, AVGO, HPE, TSM — HEAVY institutional accumulation • Energy: XOM, CVX — inflows on oil geopolitics premium • Small-cap value: IWM, VTWO — first real institutional rotation signal of 2026 • Outflows: Utilities (XLU), Consumer Staples (XLP), REITs — rate sensitivity • Crypto/Fintech: COIN, SQ — mixed; Bitcoin holding $95K support ━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 【INVESTMENT SUMMARY — 3 Scenarios】 🔵 Bull Case: AI custom chip theme continues → MRVL/AVGO/TSM lead → small caps catch fire → Russell 2000 breaks out → IWM $250+ Catalyst: MU earnings beat, AMD data center beat, ADP jobs soft (Fed cuts priced in) 🔴 Bear Case: Burry warning correct → AI chip rally peaks → MRVL/AVGO reverse → Nasdaq -3% correction Catalyst: Strong ADP + hot inflation → rate cut timeline pushed out → small caps get crushed 🟡 Base Case: AI infrastructure secular bull → semis consolidate at high level → small caps rotate in/out in tranches → VIX stays 15-18 Catalyst: No major catalyst → range-bound S&P 500 with sector divergence ━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ⚠️ Disclaimer: This content is for informational purposes only and does not constitute investment advice. Data sourced from public English-language financial media (CNBC, Bloomberg, Reuters, StockTwits, Benzinga, The Motley Fool, TheStreet, LSEG). Historical performance does not guarantee future results. Consult a licensed financial advisor before investing.

JUST IN: TURKEY-BASED INSIDER ONE JUST ACQUIRED AI MARKETING UNICORN BLUECORE AHEAD OF A PLANNED 2028 U.S. IPO The deal retains Bluecore's unicorn status, meaning a valuation of more than $1 billion. The full picture, per Bloomberg: The deal: - Mix of cash and equity (size undisclosed) - Bluecore valued at $1B+ in the transaction - Insider One absorbs 150 Bluecore staff, including co-founder Fayez Mohamood - 50 other Bluecore employees being cut The strategic context: - Insider One uses AI to help brands personalize marketing and engagement - The deal expands the company's U.S. client base ahead of a planned U.S. IPO targeted for early 2028 - Bluecore serves 400+ brands, including Wayfair $W, The Gap $GAP, and CVS Health $CVS The company: - 2,000+ customers, 1,500 employees - Co-founder and CEO: Hande Cilingir - Last raise: $500M in 2024, led by General Atlantic - In March 2026, partnered with the OpenAI Foundation to use LLMs for customer data analysis - In talks for multiple further deals, mainly in the U.S. "The next acquisition may come from an area where we don't exist at the moment," Cilingir said.

$HIMX Q1’26 EARNINGS HIGHLIGHTS 🔹 Revenue: $199.0M (Est. $195M) 🟢 🔹 EPS Per Diluted ADS: $0.046 (Est. $0.03) 🟢 🔹 Gross Margin: 30.4%, at high end of guide (Est. 30%) 🟢 Q2 2026 Guide: 🔹 Revenue: +10.0% to +13.0% QoQ (Est 5%) 🟢 🔹 Gross Margin: Around 32% (Est. 30.8%) 🟢 🔹 EPS Per Diluted ADS: $0.086-$0.103 Segment Performance: 🔹 Large Display Driver Revenue: $24.2M; +11.7% QoQ 🔹 Large Display Driver Revenue Mix: 12.2% of total sales 🔹 Small & Medium Display Driver Revenue: $135.8M; -2.4% QoQ 🔹 Small & Medium Display Driver Revenue Mix: 68.2% of total sales 🔹 Non-Driver Revenue: $39.0M; -7.7% QoQ 🔹 Non-Driver Revenue Mix: 19.6% of total sales Other Metrics: 🔹 Automotive Driver Sales: Declined double digits QoQ in Q1 🔹 Smartphone IC Sales: Increased QoQ, driven by new OLED solutions entering mass production for a leading smartphone brand’s mainstream model 🔹 Tablet IC Sales: Increased QoQ, driven by renewed mainstream demand and shipments for a new premium OLED tablet 🔹 Automotive Tcon: Hundreds of secured design wins across a broad customer base 🔹 WiseEye: Adopted by a leading brand for smart glasses, with mass production expected later this year 🔹 CPO Gen 1: Small quantity shipments expected in 2H26 🔹 CPO Gen 2: Nearing completion of customer product validation for AI data center applications 🔹 FOCI Stake: 5.36%, valued at NT$4.96B / $156M as of May 7 close 🔹 Patents: 2,564 granted, 331 pending as of March 31, 2026 Financials: 🔹 Operating Profit: $10.2M 🔹 After-Tax Profit: $8.0M 🔹 Operating Expenses: $50.3M; -8.4% QoQ, +9.9% YoY 🔹 Operating Margin: 5.1% 🔹 Cash, Cash Equivalents & Other Financial Assets: $287.6M 🔹 Long-Term Unsecured Loans: $27.0M, including $6.0M current portion 🔹 Inventory: $151.7M 🔹 Accounts Receivable: $190.9M 🔹 DSO: 86 days 🔹 CapEx: $2.9M Capital Return: 🔹 Annual Cash Dividend: $0.252 per ADS 🔹 Total Dividend Payout: $44M 🔹 Dividend Payout Ratio: 100% of previous year’s profit 🔹 Dividend Payable Date: July 10, 2026 Commentary: 🔸 “We expect upward momentum through the remainder of 2026, supported by a meaningful number of new automotive projects scheduled to enter mass production in the second half.” 🔸 “The positive outlook is also supported by the anticipated growth in our non-driver IC businesses, particularly Tcon and WiseEye AI.” 🔸 “Despite ongoing macro uncertainty, Himax continues to expand beyond its traditional display IC business, focusing on key growth areas including smart glasses, ultralow power AI and CPO.” 🔸 “These emerging technologies present significant growth opportunities that help diversify our revenue base into areas with attractive gross margin profiles and profitability while also strengthening our overall competitiveness.”