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看到两个新闻来聊聊Parlay⬇️ 一个是最近 @Polymarket 开始支持Parlay（Combos/串关）。 另一个是昨天尼克斯队阵马刺的比赛中，Kalshi的做市商SIG录得体育市场上最大的亏损（作为参考，taker的总盈利是 $22.4M）。 而 @Kalshi 在今年早些时候就支持了Combos。今年前四个月里，Kalshi总共有8亿美金的Combos成交量，用户的损失在15%（由SIG和Kalshi一起米西😋） ❓为什么做Parlay的做市商不是一件容易的事情？ 1️⃣ Parlay的定价需要考虑的是各个事件的联合概率。而联合概率只有在事件独立的情况下才可以由各自概率推导出来P=P1 * P2 *... 所以从做市商的角度出发，如果直接按照独立事件定价，会给出过于乐观的价格。举个极端的例子，“尼克斯赢球”和“尼克斯 -5” 两个事件是包含关系，其实只有“尼克斯赢球”是有效的。 2️⃣ Parlay不像传统衍生品一样可以简单的对冲。这是因为可能结果的组合空间随着事件数量增多而爆炸。做市商需要lock的保证金也会是天文数字（当然SIG肯定和Kalshi有py协议） 3️⃣ 要对抗toxic flow。Kalshi的RFQ只藏在了APP端，并且简单的抓包是拿不到rfq的接口。文档中的combos endpoint跟Polymarket的API一样💩 但是还是有高手taker在今年3/24一天赚到 $750k ⚠️所以核心是在考验做市商的Risk Engine。如果你在做permisionless的parlay rfq，我觉得终极解决方案是通过一个类似 World Model 的东西来做 （@ylecun （不是 不然总会有这样那样的correlation被toxic taker给吃掉，特别当你是一家startup的时候（iykyk我在点名谁😼） 💡最近也有一些startup出来做这个问题，比如YC 26 Spring的 @totalistrading 。 创始人 @ericliu 提到过可以通过组合优化不同Parlay之间的事件进行对冲，从而达到类似Portfolio Margin的效果。 早期Pricing可以通过直接在independent prob上加一个discount；rfq也可以通过非常conservative的rule来ban掉包含同类型市场的组合。 特别是当Parlay扩展到其他非sportsbook的事件时，可能做insurance的玩家会更有优势 @UseCorgi @earthianai

@ScottPresler has done more for the conservative movement than anyone else of late. You’re a poor loser. Every Texan saw right through you which is why you were sent to pasture. Good riddance. What a disgraceful way to show your @$$ on the way out. @KenPaxtonTX @JohnCornyn @TeamCornyn

Every vote matters in this race. If you want strong conservative leadership that puts Las Vegas families first, now is the time to show up. Vote early or on Election Day. Vote Dr. Jeff Gunter for Congress.

🚨 EXCLUSIVE: ADA LLUCH BANNED FROM EUROPEAN PARLIAMENT 1) Conservative political commentator Ada Lluch has been barred from entering the European Parliament after publishing a video from inside the institution that featured members of The Left group. 2) According to a decision signed by Parliament Secretary-General Alessandro Chiocchetti, the incident took place during a March 26 visit to Brussels. 3) Parliament's document says the footage was recorded without a media permit and later posted on Lluch's public X account, making the video itself the central issue in the case. 4) Officials also cited procedural violations, arguing that Lluch was not continuously accompanied by her host MEP or an accredited parliamentary assistant during her visit, despite having been granted access to the building. 5) The ban takes effect immediately and could be extended further after review, meaning a journalist and commentator is now facing exclusion from the European Parliament over a video that Parliament says should not have been recorded.

No scripts. Just common sense, conservative values, and a commitment to fight for Las Vegas families. Lower taxes. Protect President Trump's agenda. Put more money back in your pocket. That's exactly what I'll do in Congress.

Some asked why I care about a low level race most Texans haven’t heard of. TX RRC makes decisions that affect me, my family and yours. I want smart, experienced “adults” for this job, not somebody just buying favors for their next run. Read daddy L.R. French Jr’s obituary. Quite the man in his day. But unlike his brothers, Bo fell so far from the family tree the only thing like his old man is the hair. His life’s been one long party interrupted by business and personal failures plus two losing runs for office in his home district. Not surprising for an egotistical bully who brags about destroying people, men and women. I know some of them. They’re why I’m here. With enormous family wealth Bo’s never had to actually work or worry about anything. He makes up fluffy resumes, stomps his foot and calls anybody who criticizes him a liar. He couldn’t relate to real people if there was a gun to his head. Texans deserve better. Bo’s fanboy team tells me this election is already a done deal. Apparently he doesn’t need my vote or yours. The big question is can they keep those skeletons in the closet cause Bo French wants to be your governor. Making it easy for you to label and judge me, I’m a conservative leaning Independent raised by East Texas Democrats. I see life from both sides.

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.