Sumer Sao
618 posts

Sumer Sao
@sumersao
meditating to keep my heart rate down so I can drink more coffee // prev @kalshi @robinhoodapp @stanford

Just in; we are joining OpenAI ✌️openai.com/blog/openai-ac…






Mistakes are unavoidable, fixing them correctly isn't. @Kalshi demonstrating once again why regulation is at the core of making prediction markets useful and safe for all.


As an exchange, we resolve the market according to the rules, even when there is disagreement with the resolution. I understand many of you are frustrated about the Khamenei market, and I want to clear up a few things along with steps we have taken to improve: The market rules were not changed. The death carveout and settlement based on last-traded-price were part of the published market rules from the outset (see the screenshot from the rules, which can also be found in our official filing from 2025 on the CFTC website). The death carveout was also always shown on the market page (see market page below). Once the strikes on Iran started, we added the “Green Box” to further highlight the death carveout in the UI (this is not a rule change, just a highlight of the rules). We settled according to the rules. Traders were paid based on the last traded price. Some traders who held YES feel like they should have won (as in the market should have settled to YES). But the rules clearly stated that the market would not settle to YES in the event of death: traders expect us to settle the market based on the rules and we have to apply the rules consistently for both YES and NO holders; changing settlement because one side is unhappy would break trust in the exchange (imagine you held NO and you read the rules, but then we decided to settle contrary to the rules). Death carveouts are important; as a federally regulated prediction market, we are required and feel it is important not to enable direct profiting from war, assassination, terrorism, or other violent outcomes. No trader lost money on this market. While the rules were clear and we tried our best to highlight them, traders vocalized they were not prominent enough. We heard you, and we decided to reimburse out of pocket for all fees and all net losses from trading in the market: 1. If you sold for a net loss before settlement, we reimbursed you for that net loss and 2. if you didn’t recoup your position cost during settlement, we paid you the difference so you get your full position cost back. You can find reimbursements under "Your activity" in the app and website. No trader ended net-negative after our reimbursements. Kalshi did not profit on this market. We do not stand to benefit from one resolution or the other. We earn fees from facilitating trades. For this market, we reimbursed all fees back to users. We also reimbursed net losses users incurred on the market out of pocket, so no trader ended net negative. As a result, Kalshi incurred a substantial loss to make users whole. We will improve. We learned a lot from this market. We are updating how we present similar markets (e.g., those with a death carveout or where a death might be a likely scenario) so traders can see the exception more clearly before they trade. We will surface the exception in the title and at the top of the market page. We also recognize that the first iteration of the Green Box warning created confusion; we revised it and reimbursed net losses for the market. Going forward, we are implementing a tighter review process for UI highlights. The best part about Kalshi is you all and I'm sorry for the disappointment. We'll improve, thank you for bearing with us.

The wait is over: Combos are officially live for everyone on Kalshi! Last week alone they did over $100m in volume. And it's compounding: our app grew 50% in November and we hit another volume record yesterday ~$340m. In traditional financial markets, traders bundle multi-leg options instead of buying separately to get better prices. Combos offer the same price improvement. Like with our other markets, there is no house: when you submit a Combo, traders compete in the open market to take the other side. We keep marching.

AI scaling will be energy-limited at the global level in the next 3-4 years. Conventional computing is reaching its limits. It’s time to stop simulating neural networks on digital logic and start building hardware that actually behaves like them. We are Unconventional AI. Learn more about our mission: unconv.ai/introducing-un…


Kalshi raised $1B at an $11B valuation. A decade ago, only a few thousand people knew what a prediction market was. Eighteen months ago, most prediction markets were banned - until we overcame the government to set them free. Over the past seven years, our community has opened up an entirely new category. Today, Kalshi is trusted, used, and loved by millions of people. It’s a part of everyday culture, and it’s driving one of the most important shifts in consumer behavior in recent history. The time has finally come for prediction markets to achieve their full potential and we are intent on making that happen. To all the believers and the early adopters: thank you.



Generalists are useful, but it’s not enough to be smart. Advances come from specialists, whether human or machine. To have an edge, agents need specific expertise, within specific companies, built on models trained on specific data. We call this Specific Intelligence. It's what we're building at Applied Compute. We unlock the latent knowledge inside a company, use it to train custom models, and deploy an in-house agent workforce that reports to your team. We work with sophisticated companies that have already captured early gains from general models, like @cognition, @DoorDash, and @mercor_ai. They’re pulling even further ahead with proprietary in-house agents that don’t need to wait for the next public model release. Together, we are building and validating models and agents in days instead of months, achieving state-of-the-art performance on customer evals. Our team has high density and low latency. Our founders all worked on different parts of this problem while they were researchers at OpenAI — @ypatil125 as a key member on the agentic software engineer effort (Codex), @rhythmrg as a core contributor to the first RL-trained reasoning model (o1), and @lindensli as a core contributor on ML systems and infrastructure for RL training. Two-thirds of the team are former founders, and everyone brings a deep technical background, from top AI researchers to Math Olympiad winners. We are backed by $80M in funding from Benchmark, Sequoia, Lux, Elad Gil, Victor Lazarte, Omri Casspi, and others. With their support, we are growing the team, scaling deployments, and bringing to market the first generation of agent workforces built on specific models. In short: 1. We are building Specific Intelligence for specific work at specific companies. 2. That will power in-house agent workforces to support their human bosses. 3. That in turn will unlock AI’s full potential through humanity’s greatest engine of progress: thriving corporations in a free market.



Kalshi recently raised $300M+ at $5B from Sequoia, a16z, Paradigm and others. Since then, we've grown over 3x, hit $50B of annualized volume, and became the largest prediction market in the world. And today…Kalshi goes global. 140+ countries. 1 liquidity pool.






