👀 @demishassabis was right all along

Love this!!

Nvidia already backstopped Firmus' ~$15B order for 170k GPUs now the IPO's dead and Nvidia is writing the equity check too Seller, guarantor... and soon investor of the same customer ↺

One founder, zero employees... and $10M → $20M run rate in 6 weeks 🤯 Congrats @Bencera! Awesome to see @sapiom customers killing it 💪 @i_zerbib

Electricity is ~5% of a GPU-year in @VitalikButerin 's table... it's rounding error... and still the bottleneck. Miners hunt the cheapest kWh since it's a big chunk of their cost base. AI will pay whatever it takes for a MW that's live asap.

Remember when tranched financing was a red flag in VC? 10–15 years ago, tranches were about risk mitigation. VCs would commit capital in stages, contingent on revenue or product milestones. Founders hated it. As capital became abundant, tranches fell out of favor in competitive software deals. Single rounds became the norm. Now they're back... but as a mechanism for VCs to access the hottest deals. Founders are selling equity at different valuations, sometimes within days of each other. @gradypb recently shared that across 7 deals... @sequoia invested at ~$110M valuation as a building partner... followed within a month by VCs at ~$3.4B valuation 🤯 [I invest before all these rounds 😎] VCs went from tranching capital to mitigate risk to tranching valuations to access deals 🤑

. @nebiusai CRO @marcboroditsky: "We ran the cycle and discovered that we were pricing too low" .. so now it's auctions. ~2x short-term premium now has surge pricing

It’s been exciting to watch your vision come to life from the early days. Great to see @nvidia @SapphireVC @aranum joining the journey 🚀

based on reported monetization data from $SPCX $CRWV $NBIS $IREN Short-term: ~$40–50bn/GW Long-term: ~$20–25bn/GW Huge scarcity premium to get compute this quarter

demand is very strong. From last week's $CRWV conference: short-term deals pricing ~$40M/MW 70% of 2Q deals prepaid A100s from 2020 renewed into 2029 growing from 4.2 GW contracted -> 8 GW by 2030

Extraordinary growth (12x) and enormous bets ($518B in future infra obligations) in @AnthropicAI's S-1 👀 So.. for every $1 customers paid, it spent ~$2.75 ($4.6B of revenue in 2025, $12.65B of operating costs) This is not only the cost of serving each customer dollar... it also includes building next-gen models, capacity for future demand, etc... but someone needs to finance the gap Private markets financed this so far.. now it's public markets' turn 🤑 Extraordinary growth doesn't come cheap.

Tech analysts expect the sector's operating cash flow to double to ~$2.4T by 2028. Analysts covering tech's customers see their cash flow growing far less. Torsten Slok: who will write the checks to cover the gap? See @apolloglobal chart below 👇 Don't think the chart necessarily shows an inconsistency. Enterprises don't need entirely new budgets to buy AI. They can redirect spending from software, outsourced services and the cost of doing work today. Enterprises still need to create value to justify AI spending, so it can't only be shifting existing budgets to AI. Imagine a company replaces a $10M operating expense with $3M of AI-enabled execution. There is room for enterprises and its vendors (hyperscalers, frontier labs, etc) to win. But... if compute, integration, etc push the real cost toward $10M, impressive adoption may produce very little economic benefit for the entperise 💸 Productivity gains can be real and still get absorbed by the cost of delivering them... which would be a problem in the long run cc @ajit_deshpande (last night's dinner topic)

At $300M are these still seed rounds... or Series As in disguise? .. meanwhile, 90% of pre-seed rounds are <$5M. Plenty of alpha here cc @cartainc

. @elonmusk quietly saying:

canary in the AI coal mine... except this one got too little gas 😜 cnbc.com/2026/09/24/oracle-d…

we will get to see ASIC performance & power metrics with FPGA-like design flexibility soon @JeffDean's point on automating the design loop that could make highly specialized silicon for each model architecture is fascinating.

I was lucky to back Mayank & Anand’s last company, which was acquired by @Gartner_inc @GatherHQs is a much bigger ambition: making customer behavior something companies can simulate before they bet on it. Proud to be an early investor again. 🚀🚀

Only in Bay Area 🔥

Don't miss this!