Jack Minor

443 posts

Jack Minor

Jack Minor

@jackbminor

financializing compute @ornnexchange | @mit @harvardhbs

New York, NY Katılım Şubat 2018
57 Takip Edilen183 Takipçiler
Jack Minor
Jack Minor@jackbminor·
good thing he has solid data @OrnnExchange
TBPN@tbpn

Kalshi CEO @mansourtarek_ thinks compute could become the largest commodity on the planet, creating the largest derivatives market alongside it. He says companies already spend roughly $1 trillion a year on compute, with that figure potentially growing 10x by 2030. As prices become more volatile and compute becomes a larger corporate expense, producers and buyers will increasingly want to hedge their exposure. “Historically, if that market has any degree of success, it ends up being at least 10 to 15 times the underlying market.”

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Ornn
Ornn@OrnnExchange·
Anthropic is reportedly in early talks to lease around $10 billion of compute from Meta over two years. A single lab renting capacity from a hyperscaler, at a scale that used to describe entire acquisitions. What it tells you is that the biggest players in AI have stopped trying to build to meet their compute needs, and started trading it.
The New York Times@nytimes

Breaking News: Meta is in talks to lease computing power from its data centers to Anthropic in a potential $10 billion deal. nyti.ms/4yrmMVN

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Jack Minor
Jack Minor@jackbminor·
excited to partner
Tarek Mansour@mansourtarek_

Today, we launched GPU compute forward curves derived from our prediction market prices. Forward curves are now available on Nvidia B200. H200, and A100 chips. Forward curves track implied future prices. They are how mature commodity markets form expectations, allocate capital, and manage risk. Energy, interest rates/SOFR, FX, metals, and agricultural markets all rely on market-implied forward prices. Despite becoming one of the key inputs in the global economy, compute has lacked that market-derived infrastructure. Compute right now is where oil was before NYMEX — traded only via OTC deals, just like oil used to trade OTC between producers and refiners. As compute becomes as fundamental to the economy as energy, the industry will need a similar derivative market to promote efficient price discovery. Prediction markets are uniquely suited to this problem. Compute is not one uniform commodity and spans many chips, grades, tenors, locations, and contract structures. A live prediction market can aggregate those dispersed views into transparent prices that reflect market expectations for different maturities. The opportunity is big. Hyperscalers are spending over $700B on compute this year and the market is expected to grow to $7-10T by 2030. If this market behaves like traditional commodity markets, a liquid derivative market could be 10-20x bigger than the underlying spot market. Compute is still not uniform enough, but this is a step towards standardization as forward curves will help us see the rise and fall of different model prices and how they correlate. The forward curve is a first step. Up next: futures and perps.

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Kush Bavaria
Kush Bavaria@bavaria_kush·
The largest and most useful byproduct of compute markets will be the futures curve. A credible forward curve turns fragmented GPU quotes into a shared view of future scarcity. @Kalshi has built the first forward curve in the market. bloomberg.com/news/articles/…
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Jack Minor
Jack Minor@jackbminor·
@willdepue been waiting for this. next I'll wear a ring to record all of my conversations for more context
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will depue
will depue@willdepue·
on this note, i built a PersonalOS by exporting all data from every app i've ever used main purpose was building a 300k tok context pack about my life. embedded all iMessage/Apple Notes/Docs/etc, summarized, retrieved across. having models read every text you've ever sent is a very effective way to teach them about who you are also cool to see every Uber, flight, or photo i've ever taken
will depue@willdepue

dear claude code & codex teams, please, for the love of god, where is my executive super assistant that has: (1) a deep understanding of me via great memory, just pack 200k context with every chat. you can build this personal store from past chats, but also i'll just give you all my data, respond to 100 different personal questions, give you all my Apple Notes and iMessage (2) a no-chat interface. i don't want something that forgets me everytime, that i have to skip to the right chat. just ditch chats! its all one chat history, same as a real assistant. compaction and retrieval cannot be that hard (3) i can trust to reasonable certainty with my credit cards, info, email access, etc. and i can delegate to go do things for me. i just want something that can book a flight, hotel, refill my runpod credits, update all my old passwords, cancel all unused subscriptions (4) it is good at pinging me by sending me a text when i need to answer something for it or do something it can't. good back and forth communication. it also can't die all the time. i do think the idea of 'responses' is wrong, it should just always be 'on' in idle mode, ready to act on scheduled tasks. it should have lots of webhooks/awaits, for example, maybe it should text me everytime i receive an important email that needs my attention. a small model can scan everything and then call a tool to wake up BIGGER ASKS: (5) please let it call people for me. go schedule drs. appointments, sit on hold for me, etc. same with access to imessage/email etc on just handling things for me that i don't want to care about. (6) computer use that doesn't interrupt my work (works on another desktop on my mac/its own windows?) and is faster than me at filling forms and doing things. like how are models slower than me at typing and filling out forms than me still! i will pay you $5k a month minimum for this. if you need me to buy some physical hardware or rent some cloud thing that's fine. i'm fine with taking on some security risk, it's that valuable, you take risk with a human EA too chatgpt almost feels deprecated as a product compared to codex now. like models in codex are significantly less helpless and more capable than chatgpt, such that i don't want to use chat anymore (other than as a google replacement). plz fix or just build an integrated experience - will

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Jay Yu 🐟
Jay Yu 🐟@0xfishylosopher·
Lots of buzz recently on compute capital markets. But what might these markets actually look like? A few thoughts on its market structure from first principles: > First, almost everyone agrees that compute has a nonfungibility quality. It behaves closer to electricity (temporal, nonfungible) than corn, oil, and gold. > This nonfungibility creates several downstream corollaries: (1) Reservations/capacity forwards are almost always bilateral OTC trades on particular SKUs and params (I want X hours of H200s in us-east-1 running Y model at 12pm on 8/1/2026) (2) There is no transparent "one-size-fits-all" pricing model for "generic H200s" like there is for corn/oil/gold, hence no proper futures market used for hedging (3) Most of the teams building in the space (eg. Silicon Data, Ornn, Compute Desk) are focusing on "standardization" indices/benchmarks, in preparation to create a liquid futures market. > The short-side of compute markets fundamentally comes from neoclouds (Coreweave, Nebius, Lambda) and indepedent data centers (people with GPUs), while the long-side of compute markets comes from inference dev platforms (Fireworks, Modal, Baseten) and the agentic applayer (Cursor, Perplexity, Suno, Rime) that do not run datacenter fleets > But these principals will never directly trade on general compute exchanges (eg. an H200 basket) because they require specific SKUs. Instead, they'll make their reservations/capacity forwards for specific SKUs with OTC dealers. > These dealers in turn can "hedge" particular SKUs with exposure to the underlying generalized basket exchanges. So the folks actually using compute futures exchanges are going to be MMs/OTC desks/compute dealers on both sides. This creates an endgame market structure like below:
Jay Yu 🐟 tweet media
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Dave Blundin
Dave Blundin@DaveBlundin·
REDUX: If you didn’t yet, check out my epic interview with Kush Bavaria (@bavaria_kush), CEO and Co-Founder of @OrnnExchange! Fresh off the $33M raise, and now backed by @a16z, @Link_Ventures, and others, the Ornn team went from idea to $100M+ valuation within 8 months - possibly the fastest for an MIT company ever. Plus, Larry Fink publicly calls for exactly what Kush & team are already building. The desperation for Ornn’s product is growing daily. My agents are literally grinding to a halt. The foundation labs need an ocean of compute hours that doesn’t exist. Ornn provides hyperscalers the stability to rationalize that investment, while creating an entirely new asset class for investors who want to back the AI revolution. Link below - tune in!
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Vikram Singh
Vikram Singh@vikramcantsingh·
I first met @jackbminor in my sophomore year of high school at a summer camp at Stanford. Last summer – seven years down the line – I randomly bumped into Jack and two of his friends while wandering through a flea market in Hanoi, Vietnam during my grad trip. These two friends were @bavaria_kush and @wayne_nelmz. Six months later, Jack called me into the Ornn office (aka their apartment) to chat about tokenization. A 30-min chat ended up lasting for two hours and those two hours turned into months. Today I’m pleased to announce Galaxy Ventures’ participation in @OrnnExchange’s seed round.
Kush Bavaria@bavaria_kush

Today, we're announcing our Series Seed funding of $33M led by @a16zcrypto

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Kush Bavaria
Kush Bavaria@bavaria_kush·
Today, we're announcing our Series Seed funding of $33M led by @a16zcrypto
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Autopilot
Autopilot@joinautopilot·
President Trump just committed $17,500,000,000 to build 10 new nuclear reactors Here are the stocks that benefit: $CCJ: Cameco *Owns nearly half of the private company building all 10 reactors, and sells the uranium to fuel them $BAM: Brookfield *Owns the other half of that same company $BWXT: BWX Technologies *Builds the actual reactor parts inside each plant $CW: Curtiss-Wright *Makes the pumps and valves that keep reactors running safely $LEU: Centrus Energy *The only U.S. company that enriches uranium domestically
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Jack Minor
Jack Minor@jackbminor·
does anyone even use AMD MI300x anymore
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Ornn
Ornn@OrnnExchange·
Understanding inference token spend is the clearest insight into AI demand from the enterprise level. With the launch of OpenAI, Anthropic, and Google tokens benchmarks, the Ornn Token Price Indices are live on the Ornn Data.
Dr. Alex Wissner-Gross@alexwg

x.com/i/article/2066…

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