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Why Most CIOs Are Quietly Praying for Retirement — And the Few Who Aren’t Are About to Get Very Rich I had a moment this week where I was sitting across from a Director of IT and it hit me — this poor bastard has the toughest job in the entire company. The business folks get to be full-time dreamers: “Hey, can we automate this? Can the AI just know what to do? Can it walk my dog while I’m in this meeting?” Meanwhile he’s over there thinking about data security, system reliability, whether some employee is gonna click on an email that says “You’ve won a $1,000 Walmart gift card!”, whether Ukrainian hackers are going to steal their customer data at 2 a.m., and whether his entire team is about to get replaced by three interns and ChatGPT — all while knowing none of this stuff actually works the way the brochures promised. And here’s the part that makes me feel for the guy — for his entire career he’s been rewarded for keeping the machines running and not getting fired. Now we’re asking him to suddenly become a profit center, to be out over his skis with AI initiatives. It’s like telling the hall monitor he’s now responsible for running the company’s underground poker game. Did I just compare our AI software to an underground poker game? Yeah, probably not the best analogy, but hang with me here, I’m rolling. Meanwhile the C-suite is over there wondering why nothing’s happened yet, completely oblivious to the fact that they’ve spent twenty years brutally punishing IT for not playing defense. Hell, I know CIOs who got fired because Windows 95 sucked. The real kicker is how to even get started. Our philosophy has always been to start small — automate one workflow, prove it works, and then compound fast. Smart in theory. In practice, with a big organization, that feels like bringing a birthday candle to a forest fire. The C-suite doesn’t get excited about incremental. They want to see something that actually moves the needle. So you’re stuck trying to thread this ridiculous gap: build something small enough to actually work, get real user adoption, and make sure the vendor isn’t full of shit. Honestly, I don’t envy that seat one bit. At Collide, we’re committed to being real partners with the folks actually doing the building. I’ve got serious scar tissue from getting fired for not being “openly collaborative” with other oil and gas companies on well spacing back in the shale days, and I’m never making that mistake again. We’re gonna share what we learn, educate when we can, and actually listen — God knows we have a lot to learn too. Truth is, my tech guys are dying to find some partners in crime — and I really gotta stop with the crime analogies, I swear that’s not what we’re doing here — because they get all excited explaining the latest and greatest AI breakthrough and I respond with the technical sophistication of a man asking if his rotary phone has Bluetooth. Sip slowly, my friends.

Nicolai Tangen, CEO of Norges Bank Investment Management pressed IBM CEO Arvind Krishna directly on whether AI is a bubble (Save this). And Krishna responded with what has become known inside financial circles as the $8 trillion math problem. A single gigawatt of AI data center capacity filled with accelerators, liquid cooling, and power infrastructure costs roughly $60 to $80 billion to build and populate. The industry has committed to more than 100 gigawatts of buildout globally. That is $6 to $8 trillion in capital expenditure and because AI grade hardware depreciates on a five-year cycle, that entire sum must be effectively replaced and refreshed every five years. To service the interest on $8 trillion in capital at a conservative 10% borrowing rate, the AI ecosystem would need to generate approximately $800 billion in annual profit, a number that currently exceeds the combined net income of every large technology company in the world. Goldman Sachs estimates $7.6 trillion in aggregate AI CapEx between 2026 and 2031 alone, and Reuters Breakingviews has flagged that even if the capital is available, physical bottlenecks power permits, land, cooling infrastructure, and electrical grid connections mean that half of the planned data center projects are being cancelled or delayed before they ever go live. Krishna also raised a second, structurally distinct concern that markets have largely ignored. He argued that the largest foundation models, GPT, Gemini, Claude, Llama are converging toward commodity status. When a product is a commodity, switching costs collapse. When switching costs collapse, pricing power evaporates and margins compress regardless of how much capital was spent building the capability. Morningstar's equity research team conducted a review of 132 technology companies in 2026 and found that AI had caused moat rating downgrades across roughly 40 major stocks concentrated in enterprise software, IT services, and SaaS with Adobe, Salesforce, Workday, and ADP among the companies whose competitive moats have materially weakened. The implication is that the companies spending the most on AI model development may be building an asset that is simultaneously the most expensive to produce and the most difficult to monetize with durable margins. This bear case is serious but it is also incomplete and that is what makes Krishna's framing so important to understand precisely. When pressed further, Krishna explicitly said he does not believe there is an AI bubble in the technology itself only in a subset of the infrastructure capital that is being deployed against speculative assumptions rather than proven demand. He draws the same analogy, the fiber optic overbuild of the late 1990s. Dozens of companies went bankrupt laying cable that nobody was using. And yet that exact "wasted" infrastructure became the physical backbone of every cloud company, every streaming service, every mobile network, and every modern AI training cluster that followed. The builders lost, the infrastructure won. And the companies that were built on top of it, Amazon, Google, Netflix, Salesforce compounded for two decades. The question, as Krishna framed it, is not whether AI is real. It is which capital deployment earns a return versus which gets stranded and crucially, whether you own the stranded assets or the companies built on top of them. On winners, Krishna was direct that distribution is the moat on the consumer side, and enterprise is wide open. The data supports this, Meta with 3.3 billion daily active users across Facebook, Instagram, and WhatsApp is building AI into a distribution network that no startup can replicate at any cost. Meanwhile, the productivity evidence arriving in real time is beginning to challenge the bear case's revenue projections. Jensen Huang just showed on stage at Computex that GitHub commits, the universal measure of global software output nearly tripled in the first months of 2026, effectively converting $3 trillion in developer salaries into $9 trillion in productive output. That is measurable, real time economic value already flowing through the system and it feeds directly back into token demand in a compounding loop that Krishna's static CapEx math does not fully capture.

The Ethereum Bull Thesis in 2026 (Why Now) with @Sharplink CEO @joechalom Timestamps 00:00 ETH Is Not Dead 01:16 Second Largest ETH Holder 02:33 Ethereum Wins The Scoreboard 04:20 Step Function Moment Coming 04:45 The Jeff Bezos Analogy 06:42 EFF Not The Marketing Arm 10:04 Private Sector Must Step Up 12:39 Warren Buffett Capitulation Thesis 13:22 BlackRock Doubled Down At FTX 15:44 WTI Crude Predicts Bitcoin 16:49 Best Entry Point Right Now 22:01 160M Agent Payments Q1 24:50 Institutions Need Finality Not Speed 25:02 BlackRock Tokenizing $8B Fund 30:32 Fidelity Now Largest Investor 33:19 First DeFi In Public Company 35:41 Retail Capitulation Best Entry

Some of my perspective on where the @ethereumfndn is going. First of all, this is only my own view. The board is not just me, and I have no extra special powers on the board that the other board members do not. @aerugoettinea is the one executing much of this transition. My input has been largely on technical questions. The board is in the process of expanding, and my own power within the org will continue to decrease, which is honestly what I want. The 2025 era brought many important improvements to EF and its ability to execute. Many issues were resolved, and EF continues to benefit from its improved efficiency and greater focus on concrete goals to this day. And so with those problems resolved, early this year, the largest remaining hole that I perceived was something different nagging at me: I would regularly spot people saying things like "vitalik says these beautiful things about ethereum needing to be decentralized, and have privacy, and be a sanctuary technology, but why do the EF's actions not reflect that?" Now, you may have been hearing something different. You may not have been sensing a feeling of crisis at all, and maybe were hearing people saying that finally we were taking execution and BD seriously and the main task for us is to keep going that way and be even better and faster. Then probably there is genuine difference between you and me, in what kinds of criticism I take most seriously, and what kinds of critics through their criticism are most able to make me feel pain. As an analogy, let's briefly switch over to a different domain. One belief you can have about Google is that it is a success story, and has brought a lot of good to humanity in organizing the world's information. Another belief you can have about Google is that they had a beautiful idealistic beginning, but at some point the corruption of mainstream corporate attitudes seeped in, and they slowly bit by bit completely abandoned the "don't be evil" slogan. My belief on Google specifically is probably somewhere between the two. BUT, if you had taken me back in time to ~2008, and offered me a button to press to make Google one or two standard deviations more "dogmatic", eg. give Richard Stallman permanent veto power over some key policies, I would immediately press it. Why? Because a choice for one company is not a choice for the world, or even one country. Google existed and exists in the context of a technology industry generally drifting away from early idealistic don't-be-evil roots and toward greed for financial gain, totalizing visions of accelerated superintelligence, infiltration by sociopaths, and craven capitulation to (or worse, active participation in) government pressure for ideological control, surveillance and war. And so *one company* doing something different, positioning itself to be what George Bernard Shaw calls the Unreasonable Man, resisting the trend of the times, would have been better for freedom, balance of power and stability of society as a whole, than *all* large companies bending to dominant trends. This is a part of my version of pluralism. This line of thinking is not just mine, but I also is not too far off from what Aya and others had in mind with the Mandate. Now how does this all get to the role of the EF? EF is not a "center of Ethereum", rather EF is "one node, with a defined purpose, alongside other nodes". We've always said that the EF should be the latter, but many in the Ethereum ecosystem (and even within the EF) wanted us to be the former. Now, we are taking action to ensure that we will be the latter. This is particularly important because EF is a limited organization, with limited resources and limited organizational capacity. The EF has only ~0.16% of all ETH (less than many other individual ETH holders), whereas among other blockchains it's common for "the central foundation" to have 10-50%. Fiscally, the EF was originally designed to fulfill a limited work scope defined in the token sale docs and other pre-launch materials (building the chain software; getting through Frontier, Homestead, Metropolis, Serenity), which was fully completed in 2022; it was not designed to be an eternal steward. And so today, the EF is choosing to use its remaining resources to pursue longevity over breadth (yes, this means we sell less ETH). The EF focuses *specifically* on those activities critical to the success of ethereum as a censorship/capture-resistant, open, private and secure system, that would not happen otherwise. This means making hard choices, and in some cases even activities that we highly approve of and people that we highly respect becoming outside of the EF. People of great technical talent, public respect and even alignment with the mission and CROPS being outside of the EF is in fact necessary if we want important tasks to be able to attract outside capital. This also means the EF taking opinionated stands culturally. This is all intended in cooperation with all other parts of ethereum. We recognize that many other parts of the ethereum world highly respect CROPS and related values. But highly respecting is not the same as choosing to specialize and totally dedicate to a domain (Compare in a different domain: I think reducing animal cruelty is important, and I like vegan food, but am not full unconditional vegan myself) EF is still in a transition period, and we expect its new long-term form to stabilize over the next few months. What are the guiding principles of this new form? Again, I am only one person, but I can give my answer from a technical perspective (there are also critical non-technical aspects). At the core, *Ethereum must be impressive*. We are living in an age of highly intelligent AI and all kinds of other technological acceleration. "Status quo EVM, with a hard fork or two a year to optimize for short-term needs of users" is not interesting. To some, "impressive" means: 250ms latency and 1M TPS. I think Ethereum trying to go that route is a mistake. Being as fast and as scalable as possible, and only a small epsilon more decentralized than the others, is a route to mediocrity, and if we try it we will lose. I think Ethereum should scale. But I think Ethereum should strive the hardest to be deeply impressive in a different dimension: the CROPS dimension. This means things like: * Provably bug-free Ethereum. This is a goal that all cybersecurity researchers would have thought is absurd and impossible, up until roughly 6 months ago. Now, it's on the cusp of being possible, thanks to AI-assisted formal verification. So we should be frontrunners in doing this. * Available chain consensus. Ethereum is, and with lean consensus will cotninue to be, the ONLY chain that has both (i) traditional-BFT style properties that it's safe under asynchrony up to a high level of fault tolerance, and (ii) the bitcoin PoW-style property that under synchrony it's safe up to 49% attackers. As far as I can tell, literally no other chain has this or is planning for it; bitcoin goes for (ii) only and most other chains go for (i) only. Some will remember I fought hard for this, Unreasonably insisting that it is not OK for ethereum to rely on social consensus and hard forks to rescue ethereum from 34% of nodes going offline. It's OK for chains like hyperledger, bnb, solana, tempo, etc. It's not OK for bitcoin or ethereum or eg. zcash. * Intermediary minimization. The fact that smart contract wallets, protocols like railgun, etc have to send transactions through intermediaries to get included onchain is honestly embarrassing, and it's a constant point of fragility. Hence the work on FOCIL and EIP-8141 (and 7701 and years of work before) to make transaction sending intermediary-minimized with public mempool and strong inclusion properties, in a truly general-purpose way, that covers not just eg. secp256r1, but also privacy protocols and much more. Kohaku is pushing intermediary minimization at the user layer, pulling Ethereum away from the dystopian status quo world where our wallets don't even verify the chain, send our private data out to a dozen third-party servers, and toward a brighter CROPS future. Some of these goals are Unreasonable - maybe Ethereum would be "fine" getting only 50% of the way - what if we depend on intermediaries, but make it easy to switch? But going 50% of the way would not make Ethereum Deeply Impressive in the CROPS way. So we push for 100%. Fortunately all these goals are compatible with high TPS, this is a major focus of research (esp. on scaling the state). Well-designed L2s can also help, especially L2s optimized for specific applications (eg. high-volume trading, privacy...). These goals are even compatible with significantly lower slot times, thanks to Raul's work on erasure-coded P2P, and many other optimizations. The most high-value "product" of the ethereum blockchain, financially speaking, is ETH the asset. Ethereum secures $250 billion of ETH. The types of properties of Ethereum that I mentioned above are very good for ETH the asset. Nearly 90% of my net worth is in ETH, and most of the remainder is ~$40m of onchain fiat of which every dollar has already been allocated for some open-source biotech or software or hardware initiative. That said, there are aspects of supporting ETH the asset - *necessary* aspects even - that are outside the scope of the EF. This is where we need other heroes (some of whom hold more ETH than the EF does) to step in and help. EF has been recently thinking more about how it will relate to other such organizations, and give them needed initial support. EF will be a smaller ship than in previous years, a more opinionated one - in some cases more opinionated in ways that might be difficult to comprehend - but a longer-lasting one, and one suited to making sure that ethereum brings something meaningful to the world. We are grateful to all those inside and outside the EF who are helping to make this happen.

AI数据中心电力上的关键环节：800VHDC，今天在这里 Ultra平台上得到大规模采用。其实800VHDC并不是全新概念，自从英伟达去年说要推直流供电架构后，市场对此的关注度其实挺高的，这个板块的相关的标的已经炒过一博预期了。但实际上800V直流电在下半年才真正大规模采用，值得关注。聊下几方面的问题： 1、800V HVDC架构是什么？ 传统高密度AI rack的路径大致是：电网中压AC → 变压器/UPS/PDU → 415/480VAC到机架 → 机架内PSU转54VDC/12VDC → GPU核心电压。 达子的800VDC愿景则是：在数据中心边界/电力室把中压AC集中转换成800VDC，用800V DC busway送到IT rack，再在靠近GPU的位置用高比率DC/DC转换。NVIDIA称，54V架构在200kW以上开始撞上物理限制；1MW rack如果继续用54V，单rack铜排最高可能需要约200kg铜，而800V架构通过减少电流、减少转换级数、减少机架内PSU，目标是提升效率、降低铜耗、释放机架空间。 它不是简单的电压升级，也不是“发明了直流供电”，而是AI数据中心供电架构的一次平台级切换，是对整个电力交付架构的系统性重构，旨在解决传统48V/54V机架电源的瓶颈（空间受限、铜缆过载、多级转换损耗高），支持单机架功率从数百kW跃升至1MW+，并为未来GW级AI工厂铺路。 2、800V HVDC的意义和革命性是什么？ 1）首先自然是效率和空间布局 效率提升：从电网到GPU的转换环节大幅减少，整体能效可提升从以前90%能大幅度提高到98.5%以上传输损耗显著降低，TCO（总拥有成本）降低可达30% 空间与密度优化：减少铜缆用量和电源单元体积，机架内计算空间利用率提升超80%，支持更高密度GPU集群 2）800V不是单一器件升级，而是生态重构：中央整流、800V DC busway、固态断路器、热插拔保护、sidecar/power rack、BBU/CBU、超容/电池储能、DC/DC、GaN/SiC、液冷都要协同。NVIDIA也明确说需要OCP等组织推动电压范围、连接器、安全标准。 如果大家有关注过新能源汽车产业链，应该有影响这两年国内电动车厂商都在推的“快充”基本上就是800V高压直流充电。现在达子正在把800VDC变成下一代AI rack标准化路线的一部分，所以一部分原来给新能源汽车充电产业链上的关键环节，又开始外溢到AI数据中心上了。 3、800V HVDC空间有多大？ 要看大背景，AI数据中心整体市场从2025年约3440亿美元增长至2032年超2万亿美元（CAGR 27.5%）。 功率基础设施将成为AI建设的核心瓶颈与增长点，NVIDIA的标准将加速 hyperscaler采用，带动固态变压器、GaN/SiC功率器件等子市场爆发。 2027年后，>300kW/rack、尤其是400kW-1MW rack的AI zones中，800VDC或类似HVDC架构渗透率快速提升。若未来新增AI容量中有30%-60%采用高压DC架构，并且每MW对应的核心800V电力链价值量在几十万到数百万美元区间，累计空间就会进入百亿美元到千亿美元级。 当然这个预测区间也很宽，因为真实取决于Kyber/Rubin Ultra出货节奏、超大云厂接受NVIDIA 800V的程度。 4、800V HVDC产业链构成 完全是英伟达参考设计主导资格认证，之前英伟达也公开列出的核心合作伙伴分为三类，竞争激烈，份额将取决于认证进度、量产能力和 hyperscaler合同。 1）硅片/功率半导体供应商（核心器件，如SiC/GaN MOSFET、控制器，用于高效转换）： 主要玩家：Texas Instruments（TI，已发布完整800V解决方案）、STMicroelectronics（ST，6-18kW功率板）、Infineon、ROHM（SiC器件）、Navitas（GaN/SiC）、Analog Devices、onsemi、Renesas、Innoscience、MPS、AOS、EPC等。 这些是NVIDIA“硅供应商”名单核心，TI/ST等已演示参考设 2）电源系统组件/模块供应商（电源架、Sidecar、DC-DC转换器等）： 主要玩家：Delta Electronics（与NVIDIA深度合作，发布800V解决方案）、Flex、LITEON、Megmeet、Lead Wealth、Bizlink等。 Delta等中国厂商优势明显，已有白皮书和技术落地；LITEON等股价因800V预期已经显著上涨。 3）数据中心电源系统/基础设施供应商（机架级配电、Sidecar、SST、母线等）： 主要玩家：Vertiv（Hopewind为其800V系统关键子供应商）、Schneider Electric（开发1.2MW Sidecar）、Eaton、ABB、GE Vernova、Siemens、Hitachi Energy、Mitsubishi Electric等。 这里面Vertiv、Schneider、Eaton等是传统强者。 个人角度看 1）Vertiv、IFFNY、Schneider、Eaton、Delta、ABB是最可能在早期800VDC相关收入中占到显著份额的几家公司； 2）LITEON、TI、ST、Infineon、onsemi是第二组确定性较强的受益者； 3）Navitas、Power Integrations、MPS、BizLink、Megmeet、Innoscience（英诺赛科）属于弹性更大但验证/量产/竞争风险也更高的一组。 个人角度当下比较看好的则是，当然这个还要动态迭代： nvts、IFNNY、英诺赛科、vicr 5、后续跟踪落地节奏的几个重要节点 1）NVIDIA Kyber / Rubin Ultra 2027节奏：是否明确把800VDC作为默认/主推rack电力架构，而且出货节奏也带动800V的落地节奏 2）OCP标准进展：800V连接器、安全、保护、PDB、BBU/CBU是否标准化。 3）看点电源管理系统组件，功率半导体供应商的点单披露，谁真正进入了进入backlog和量产socket；这个最关键决定了哪家供应商能吃到多大的份额 4）超大云厂路线：800V vs 400V/±400V vs 50V HPR是否分裂。决定了市场对800V hvdc的预期和想象空间。 5）单MW成本下降曲线：如果800VDC使每MW可部署GPU数量、能效和维护成本明显改善，它会从NVIDIA专用架构变成行业事实标准。

Let me lay out the unpleasant arithmetic of the replacement rate, and why a modern society finds it so hard to reach. A population of 100 women in an advanced economy needs 210 children to replace itself. Why? Absent sex-selective practices, roughly 105 boys are born for every 100 girls. Evolution overshoots male births because boys are more prone to early death from accidents and disease. Therefore, of 210 children, about 108 are boys and 102 are girls. Not all girls reach the midpoint of their fertile age: accidents, suicide, homicide, and illness take some. In an advanced economy, about 98% of them survive, leaving 100 women to replace the original 100. Now consider the distribution of children per woman. Imagine 15 women have no children. Five do so by choice, for various reasons (professional, affective, religious). Ten face unfixable fertility problems, theirs or their partner’s. The 10% figure is conservative: the medical literature points to around 13%, and that does not even count male fertility problems. Of the remaining 85, 10 have one child, 60 have two, 10 have three, and 5 have four. I am stopping at four to keep the post concise; very few women in younger cohorts have five or more children, but I could adapt the example to account for them. Hence, the 100 women in this population have 180 children, for a completed fertility rate of 1.8. Interestingly, this is roughly the rate we saw in many advanced economies until the early 1990s, and in the U.S. until around 2008. But we are still 30 children short of replacement! Voluntary childlessness is only 5%. Three-quarters of women have two or more children. Look around: most of your friends will have two, plenty will have three or four. And yet, we are well below replacement. You would not look at this population and call it selfish (is having two kids hedonistic?) or accuse it of losing family values (only 5% of women are choosing voluntarily not to have children). The point is simpler. To reach 210 births, you need a substantial share of women to have three or more children. Two as the “normal” pattern will not get you there. And modern society makes three or more a costly proposition for most families. Of course, current fertility rates in most advanced economies are well below 1.8. But my point is that, under present social arrangements, we should not expect 2.1, even if (to humor last weekend’s debate) we banned smartphones and TikTok. We need many, many more families with three or four children. More pointedly, there is no self-regulating mechanism that pushes a society back to 2.1. The market-clearing analogy many economists use is flawed; scarcity feedback does not work the same way. (Another post on this another day.) And, as I often read, the claim that “nature” somehow regulates current overpopulation is just childish mumbo jumbo. So yes, the arithmetic of replacement rate is unpleasant.

【AIイラスト初心者向け：アートスタイル紹介✨】 自然言語版：第1弾 今回使うベースプロンプトはこちら👇 [STYLE]: [CHARACTER]: 1girl, solo, short white hair, beautiful girl, cute. [POSE]: dynamic pose, slight smirk. [OUTFIT]: elegant fantasy outfit, layered fabric, cropped ornate jacket, embroidered white tunic, detached sleeves, asymmetrical drapery, fitted shorts, black thighhighs, leather knee boots, celestial accessories, blue trim. [COMPOSITION]: medium full body, large character scale filling the entire frame. The composition uses a dynamic wide-angle lens effect and strong foreshortening, making her upper body and hand feel impactful and close to the viewer while keeping her entire figure visible within the frame. [BACKGROUND]: simple white background, high-contrast studio lighting, sharp focus. ---- ベースプロンプトの 【[STYLE]:】部分にプロンプトを差し込みます👍 ---- 【墨絵風・ラフ・インク】 The style is characterized by aggressive, raw ink-stroke textures with significant brush splatter and intentional rough smudges. It looks like a spontaneous creation on textured paper using bold, hand-drawn calligraphy strokes. Shading is minimal, applied with quick, dynamic ink washes, prioritizing movement and emotional intensity over polished details. ---- 【2Dイラスト・フラットグラフィック】 The artwork is a minimal, flat-design vector illustration. It rejects gradients and complex shading in favor of solid color blocks and clean, geometric shapes. The perspective effect is achieved solely through the character's form and bold, graphic lines. The color palette is limited but striking, creating a bold, modern graphic poster aesthetic. ---- 【90年代レトロ・セル画スタイル】 The artwork is rendered in a classic 90s anime cel-shaded style. It features imperfections like slight color bleeding and a distinct celluloid texture. The colors are muted yet deeply saturated, reminiscent of old anime film photography. There is a gentle, natural analog grain over the entire image, giving it a nostalgic and hand-painted feel. ---- ​【厚塗り・デジタルペイント】 The artwork is rendered in a rich digital painting style with visible brush textures and layered paint blending. Colors transition smoothly with soft edges and painterly strokes, creating depth and volume without relying on clean anime-style lineart. The image feels handcrafted, with expressive rendering and detailed surface texture throughout. ---- 自然言語の表現はとても自由で奥深いので、ぜひ色々組み合わせながら、自分だけのアートスタイルを探してみてください✨

**Summary: Discussion between Jeff Liang and Quant Alex Wu on Optimizing Option Order Execution and Slippage Capture** The core topic of their conversation is: **The current option limit order execution is poor (high slippage, low fill rate), essentially due to the lack of professional high-frequency / algorithmic market-making capabilities. They need to upgrade from “cutting meat with a blunt knife” to a sophisticated Delta-hedging + options market-making system.** ### 1. Problem Diagnosis - Current order placement feels like **“cutting meat with a blunt knife”** — poor queue position, low fill probability, and severe slippage. - Jeff provided concrete data: **Average loss of approximately $5.2 per executed option contract** (slightly less than 1 bp), including fees and rebates — still unacceptable. - Even with perpetual futures maker fee rebates helping a bit, the situation “cannot be ignored.” - **Price checking and adjustment frequency is NOT the root cause.** The real drivers are **fill probability** and **queue position**. ### 2. Fundamental Solution Direction (Alex’s View) - A robust **Delta-hedging system** shares significant technical overlap with high-frequency market-making systems for spot, futures, and perpetual contracts. Without this foundation, one is essentially powerless against adverse selection. - Using **maker orders for Delta hedging** is conceptually the same as **Delta-1 market making for inventory risk management** — the analogy made everything “suddenly clear.” - Options market making and Delta-1 market making are **tightly coupled**: - The Delta-1 system handles the Delta exposure of options. - Options themselves can provide protection for Delta-1 positions. ### 3. Technical Difficulty and Implementation Path - This requires entering the realm of **algo trading / HFT**, involving substantial research and engineering resources. - **Language requirement**: Python is **not sufficient**. Must use **C++ and Rust**. - **Target clients**: Institutional clients and high-net-worth individuals engaging in on-exchange block trading. - **Detailed step-by-step roadmap from scratch (Alex’s plan)**: 1. Collect large volumes of **order book data** (snapshots, incremental updates, tick-by-tick trades) for perpetuals + futures + options. 2. Build **fill probability models + queue models**, including: - Limit order arrival intensity - Fill probability - Queue position - Latency modeling 3. First implement and validate on **Delta-1 products**, then extend the backtesting system to support these HFT primitives. 4. Expand from Delta-1 / single option contracts to **all option contracts** (requires major redesign and validation due to performance demands). 5. Develop specialized algorithms for **limit order posting + aggressive crossing** to reduce overall slippage. 6. Finally, conduct small-capital live trading validation. Alex repeatedly emphasized: **“This project is genuine heavy industry.”** ### 4. Consensus - Delta-One research is the foundation for studying option fill probabilities. - Options market making must be deeply integrated with the Delta-hedging system — they cannot be treated separately. - The current phase is **infrastructure building**, requiring patient and significant investment. **Overall Assessment**: Alex provided a highly professional and systematic optimization roadmap, covering data infrastructure, modeling, and execution layers. Jeff focused on the business pain point (real slippage costs). Both fully agree that a fundamental rebuild of the underlying high-frequency system is necessary. This is a classic **quantitative execution optimization** discussion — starting from a clear business problem and pointing directly toward building institutional-grade HFT-level capabilities.

Industrial inspection is a massive visual challenge. When robots like Spot from @BostonDynamics patrols a facility, it captures images of complex analog dials. Gemini Robotics-ER 1.6 is the upgrade that could process these, writing its own code to account for camera distortion and calculate exact tick marks.