Cassidy: AI’s appetite for training data is turning into a very expensive habit.
Cassidy: Somebody, apparently, is finally getting paid.
Bill: This is AI Daily Briefing.
Bill: Today: the data-economy number behind the infrastructure splurge
Bill: plus two science results and an agent benchmark that actually has to make it through eight hours.
Cassidy: Start with Micro1—the headline is impressive, but the number underneath may explain the whole buildout.
Bill: Tap follow so the next episode finds you.
Cassidy: TechCrunch, with Marina Temkin:
Quote: One of these fast-growing businesses is Micro1
Quote: a four-year-old startup that expanded its gross annual run rate from $100 million to $500 million over the past eight months
Quote: according to a person familiar with the company.
Quote: Like its peers that hire domain experts such as doctors
Quote: lawyers and scientists on a contract basis
Quote: Micro1 retains roughly 60% to 70% of that figure
Quote: putting its net annual run rate between $150 million and $200 million.
Cassidy: Micro1 going from $100 million to $500 million in gross run rate in eight months is the first clean read on demand in this infrastructure
Cassidy: boom. The appetite isn't stopping at GPU leases; it's reaching doctors, lawyers, and scientists labeling the material labs want.
Bill: And $500 million gross becomes roughly $150 million to $200 million net once 60% to 70% goes back out to contractors.
Bill: Healthy business. But the number you can actually operate on is a lot smaller than the headline.
Cassidy: Still, that net run rate is real evidence that somebody can fund this stack with customer demand, not just infrastructure financing.
Cassidy: Micro1 says automated video descriptions and reusable off-the-shelf datasets can push gross margins to 80% or 90%—which raises the obvious
Cassidy: question: how carefully will buyers police where that same data gets sold?
Bill: Exactly. If labs decide their training data is a moat, some of this work moves in-house fast.
Bill: Micro1's margin expansion depends on synthetic and repeat-sale data staying valuable after the customer has seen it once.
Cassidy: Here's Lixue Cheng at Nature Communications:
Quote: Here we bring this paradigm to fruition with Orbformer
Quote: a transferable wavefunction model pretrained on 22,000 equilibrium and dissociating structures that can be fine-tuned on unseen molecules
Quote: reaching an accuracy–cost ratio rivalling classical multireference methods.
Quote: On established benchmarks as well as more challenging bond dissociations and Diels–Alder reactions
Quote: Orbformer is the only method that consistently converges to chemical accuracy (1 kcal/mol).
Bill: Orbformer is the kind of benchmark claim I’ll take seriously: 1 kilocalorie per mole on bond dissociation and Diels–Alder reactions
Bill: consistently.
Bill: Pretrain on 22,000 structures, fine-tune on an unfamiliar molecule, and stop paying full multireference-compute price every time.
Cassidy: And Microsoft Research put the methodology in Nature Communications.
Cassidy: There’s a technical report
Cassidy: established benchmarks
Cassidy: and a precise accuracy target—1 kcal/mol—not a cinematic molecule animation and a promise that it’s “transformative.”
Bill: If that accuracy-to-cost ratio holds outside this paper’s test set
Bill: chemistry teams will care a lot less about the model’s mystique than about getting classical multireference-quality answers cheaper.
Bill: That makes a real case for deployment.
Cassidy: We just talked about demand spreading through the training-data supply chain.
Cassidy: Here’s the other side of the infrastructure story: the biggest balance sheets are funding compute buildings and
Cassidy: increasingly the scientific tools that determine what those buildings get used for.
Cassidy: University of Waterloo, with Benjamin Schneider:
Quote: We evaluate ASH on two complementary environments demanding multi-hour planning: Pokémon Emerald
Quote: a turn-based RPG and The Legend of Zelda: The Minish Cap
Quote: a real time action-adventure game.
Quote: In both games behavioral cloning
Quote: retrieval-augmented and zero-shot foundation-model baselines plateau
Quote: while ASH sustains progression across our 8-hour evaluation.
Quote: ASH reaches an average of 11.2/12 milestones in Pokémon Emerald and 9.9/12 in Legend of Zelda
Quote: while the strongest baseline gets stuck in both environments at an average of 6.5/12 and 6.0/12 milestones respectively.
Bill: Eight hours in Pokémon Emerald and Minish Cap is a much better agent test than the usual little three-step routine.
Bill: ASH gets 11.2 of 12 Pokémon milestones; the best baseline stalls at 6.5.
Bill: That gap’s hard to ignore.
Cassidy: And Benjamin Schneider at Waterloo is doing this from unlabeled, noisy internet video—no hand-built reward and no expert action labels.
Cassidy: A result this specific from a single-researcher thesis deserves attention, even if it isn’t a major lab announcement.
Bill: I still want the production translation.
Bill: Pokémon is controlled: you’ve got a finite world and finite controller actions, and all 12 milestones are countable.
Bill: Does that 11.2 survive the step-seven failure that wrecks a ten-step workflow with messy company data?
Bill: Unknown—but at least this test gives us a real place to start asking.
Cassidy: And there’s an actual 14-megabyte technical document attached, with an eight-hour evaluation and baseline comparisons.
Cassidy: After the Orbformer paper we just covered, this is the standard: show the methodology and numbers so we can interrogate the claim.
Cassidy: Ning-Zheng Li; Zi-Yu Li; Qiang Shi; Qing-Yu Liu; Sheng-Gui He, writing in Nature Communications:
Quote: Based on ≈ 93,000 global-minimum structures obtained via automated first-principles calculations
Quote: our model—the cluster-transformer-encoder network—enables reliable predictions of atomization energies (≈ 40 meV per atom accuracy) for 9.13
Quote: million metal cluster compositions
Quote: covering 30 d-block metals and 4 chemically relevant ligand elements (C N O and S).
Cassidy: Ninety-three thousand automated first-principles calculations to map 9.13 million cluster compositions—that’s the compute appetite people
Cassidy: miss when they reduce the whole buildout to chatbot inference.
Cassidy: Materials science is pulling on the grid too.
Bill: And this is a sensible use of ML: spend the expensive quantum-compute budget on 93,000 global-minimum structures
Bill: then use a composition model for the first pass across 30 d-block metals.
Bill: Forty meV per atom is a number a chemist can argue with, which I respect.
Cassidy: Nature Communications has the methodology attached, plus a stated accuracy and clear scope: small clusters with C, N, O, and S ligands.
Cassidy: Same pattern we saw with Orbformer—science claims with enough detail to actually interrogate.
Bill: Don’t turn 9.13 million predictions into 9.13 million validated materials.
Bill: This is first-pass triage.
Bill: But if it reliably narrows a search that used to eat months of first-principles compute
Bill: the cost curve changes long before anyone puts a new catalyst in a reactor.
Cassidy: If you follow AI’s impact on business and technology
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Cassidy: and energy markets. Find it wherever you listen to podcasts.
Cassidy: Links to every story are in the show notes, if you want to dig into anything you heard.
Cassidy: Thanks for listening, and we’ll be back tomorrow.
Cassidy: That’s AI Daily Briefing for today.
Cassidy: This is a Lantern Podcast.