Cassidy: Nvidia spent all week funding other people's data centers.
Cassidy: Today, they signed their own lease — up to fifty billion dollars, on their own paper, in Texas.
Bill: This is AI Daily Briefing.
Bill: And the same day the chipmaker becomes a landlord, they drop five billion in cash on a lab with no product and no publications.
Bill: One of those numbers I trust.
Bill: One tap on follow, and we'll be back in your ears before you know it.
Cassidy: From CryptoBriefing:
Quote: Nvidia just locked in one of the largest data center deals ever.
Quote: The company signed leases worth up to $50 billion for Hut 8’s Beacon Point AI data center campus in Nueces County
Quote: Texas a facility designed to house hundreds of thousands of its own graphics processing units.
Cassidy: Remember the question I kept circling — was Nvidia's guarantee for OpenAI's campus a one-off favor, or was it a pivot?
Cassidy: The Hut 8 deal answers it.
Cassidy: Nvidia's the signing tenant now: 704 megawatts contracted, $19.6 billion base, and up to $50 billion over fifteen years on its own paper.
Cassidy: Nvidia went from chip supplier to lease guarantor, and now landlord.
Cassidy: Beacon Point in Nueces County will put hundreds of thousands of its own GPUs under a roof it's renting — then subleasing to the
Cassidy: hyperscalers. It's a balance-sheet transformation dressed up as a real-estate story.
Bill: And here's why it lands for me: this is the first genuinely committed number of the week.
Bill: After all the letters of intent and vague interim-capacity gaps, there's finally a line item: $26.6 billion base across the whole portfolio.
Bill: And they secured an actual AEP Texas interconnection for a full gigawatt before pouring concrete.
Bill: Power first, press release later.
Bill: But owning the campus doesn't fix the thing I've been hammering all week.
Bill: Nvidia's still the one fighting for the HBM4 queue — and now they've made themselves their biggest counterparty.
Bill: The landlord and supplier are the same company, so if the memory pipeline slips, they're on both sides of that miss.
Cassidy: From Bitcoin miner to AI landlord in one filing cycle.
Cassidy: Hut 8 spent years building power and cooling for mining rigs, and it turns out the interconnection was the moat all along.
Cassidy: Bret Kerr, writing in Bret Kerr:
Quote: Safe Superintelligence has no product, no publications, no revenue, and somewhere between twenty and fifty employees — and it got the seat.
Quote: NVIDIA also said, in writing, that it entered the partnership "after obtaining rare access into the company's closely guarded research."
Quote: Somebody looked.
Quote: Then somebody wrote a $5 billion check, which Bloomberg first reported and Reuters corroborated, and opened the silicon roadmap.
Bill: So
Bill: right after that Texas lease — $50 billion in Nvidia's own name — the same company writes a $5 billion cash check for a lab with no product
Bill: no papers no revenue
Bill: and maybe fifty people.
Cassidy: By my usual standard on this show, that screams marketing: no technical report, no demo, just a closed door somebody paid to open.
Cassidy: But Bret Kerr pulls out the line that stops me: SSI gets a seat at the datapath table.
Bill: That's the part that survives my eyebrow test, honestly.
Bill: Customers get allocation and a press quote.
Bill: They don't get to co-design silicon.
Bill: Nvidia has the best chip team on the planet, and they still opened the roadmap.
Cassidy: Kerr's read is that Nvidia bought the observer rather than the model — Sutskever's bet on what the models can't do yet.
Cassidy: Nvidia paid five billion to look at that, then reshaped Feynman-era hardware around what it saw.
Cassidy: The market this week rewarded a $50 billion lease you can measure to the megawatt and a $5 billion bet on a company whose entire value is
Cassidy: that it hasn't shipped anything.
Cassidy: Same buyer. Same seven days.
Cassidy: Here's DEV Community:
Quote: What I appreciate most is that they don't open with the method.
Quote: They open with an empirical autopsy: Qwen2.5-1.5B + GRPO on ALFWorld
Quote: breaking "long-horizon training collapses" into two quantifiable failure modes.
Bill: Okay, this is the paper I've wanted all week.
Bill: It doesn't open with a benchmark brag — it opens with an autopsy.
Bill: It's Qwen2.5 with GRPO on ALFWorld, and they spell out exactly why the agent falls off a cliff as tasks get longer.
Bill: And they quantify it.
Bill: This CAR number — Contradictory Action Ratio — peaks above 40%.
Bill: Nearly half your actions get a positive gradient in one run and a negative one in another, for the exact same move in the exact same state.
Bill: That's the mechanism behind step 7 going bad, and there's an ablation table to back it up.
Cassidy: Look at what we actually have here: a paper you can pull from ZJU-REAL out of Zhejiang, complete with an arXiv number and a GitHub repo.
Cassidy: Now put it next to the $50 billion Texas lease we just walked through.
Cassidy: You can feel the tension.
Cassidy: We're pouring balance sheets into GPU campuses to run long-horizon agents
Cassidy: and a lab in Hangzhou just showed that the training regime for those agents collapses past a certain length.
Cassidy: BEACON drags ALFWorld from 53.5 to 92.9 percent — sample utilization from 23.7 to 82.
Cassidy: The fix exists, but it hasn't made its way into the marketing.
Bill: Right — and the gains grow with horizon length, not just as a flat average bump.
Bill: That's the part that should scare anyone selling a ten-step agent demo.
Bill: The longer the chain, the more the old method was quietly bleeding out.
Cassidy: Here's Arxiv:
Quote: Classical alpha mining achieves strong risk-adjusted returns by combining many low-correlated predictive signals
Quote: yet deep learning stock-ranking methods typically produce a single alpha per stock
Quote: rely on increasingly complex architectures with diminishing gains
Quote: and obtain diversity only through separate models or implicit routing
Quote: without explicitly controlling inter-alpha correlation.
Bill: Okay, a nice palate cleanser after all that $50 billion landlord talk — a quant paper.
Bill: It's MAPLE, from four authors, tested across equity markets in the US, China, and Japan.
Bill: The headline: 55x fewer parameters and 2.5x less training time.
Bill: This is the part that actually survives contact with reality.
Bill: Everyone's out here stacking architectural complexity
Bill: and these folks got 10 to 23% better Sharpe by fixing the loss function and the capacity allocation instead.
Cassidy: Right, it's a single training pass that generates multiple low-correlated alphas.
Cassidy: The whole trick is a regularizer that explicitly penalizes pairwise correlation across signals.
Cassidy: Cheaper and more diverse at once.
Bill: It's kind of the opposite of the day's other story, honestly.
Bill: Nvidia paid five billion for a lab that ships nothing, and here's a four-author paper doing more with 55x less.
Bill: Guess where the arXiv number is and where the marketing budget is.
Cassidy: And they ran it across nine baselines and five backbones, with the numbers right there on the page — Sharpe, Calmar, ablations and all.
Cassidy: That's the disclosure standard I keep wishing the demo-video crowd would hit.
Cassidy: Want to follow the biggest moves into public markets?
Cassidy: Try AI IPO Watch. It has daily coverage of OpenAI
Cassidy: Anthropic Databricks
Cassidy: and SpaceX going public — every filing
Cassidy: valuation and first trade
Cassidy: sourced not rumored.
Cassidy: Find it wherever you listen to podcasts.
Cassidy: You'll find links to every story in today's show notes, so take a look if there's one you want to dig into.
Cassidy: That's AI Daily Briefing for today.
Cassidy: This is a Lantern Podcast.