Weekly|GLM 5.2, Memory Crowding Out Others, LLM Deep Dive, MU Super-Cycle, NBIS Re-Rating, Optics Moat Intact, Orbital DC Reality Check
One of the questions that set the tone this week was whether China’s open-source models have started to hollow out frontier pricing power. We’d resist the framing. The recent risk-off across AI names traced a leverage unwind that rolled from Korea into Japan and then the US far more cleanly than it traced any GLM headline, and GLM has so far drawn a fraction of the hedge-fund attention DeepSeek once commanded. We also won’t wave the model away. On our internal data, GLM 5.2 lands around Opus 4.6, with a few benchmarks closer to 4.7–4.8, and the gap that actually matters has quietly compressed as third-party token routers turn cleaned inference traffic into RL datasets that open-source labs can finally buy. The cadence still reads as roughly six months behind the frontier, inside the market’s six-to-nine-month consensus, yet “good enough for the agent workload” is becoming the operative phrase for a widening set of tasks.
A more important narrative shift drew heavy market attention this week: rising memory prices have begun to squeeze the margins of other semiconductor and hardware names, including AI beneficiaries. The market ran this logic once before, in 4Q last year, but it stayed contained within consumer electronics. This time, it has spread across AI, semi, and hardware beyond memory itself. We still see the trend in LLM ARR growth as the variable that sets market confidence. Yet regulation is keeping the latest Anthropic and OpenAI models off the market, Meta and Google have been slow to show clear progress, and Chinese open-source models keep pressing in. On balance, we think the AI trade, which has run strong for months now, sits in a fragile window, and investors should take note. We will keep tracking both the LLM side and the supply chain to read where the sector turns next.
This Week’s Reports
H1 2026 LLM Update (Parts 1 & 2). Our latest read on the model layer: the Agent Scaling Law is the real story, and token maxxing has moved into its ROI-discipline phase. Part 1 frames the industry dynamics and why a token-price pullback doesn’t mean peak AI demand; Part 2 goes lab by lab across the frontier and Chinese model vendors, landing on compute as the binding constraint of the value chain.
Deep|LLM: 26H1 Update (Part 2): Frontier Labs, Chinese Model Vendors & the Compute Bottleneck
1. Leading-Company Updates: Frontier Labs and Chinese Model Vendors
Orbital Data Centers. Lower launch costs get the headlines, yet the orbital data-center case lives or dies on heat rejection, asset life, and replacement economics. We lay out why GW-scale stays a distant option and which engineering milestones actually de-risk it.
Deep|Space: Orbital Data Centers - Lower Launch Costs Do Not Make GW-Scale Commercialization Viable
Over the past six months, orbital data centers have moved from science-fiction framing into serious discussion among US equity investors. The reason is straightforward: terrestrial data centers are increasingly constrained by power interconnection, water, land permitting, noise, local opposition, and capex inflation, while Starship, reusable launch vehicles, and the LEO satellite supply chain have made large orbital infrastructure look less remote. The first layer of the bull case is valid. If launch cost and launch cadence continue to improve, many orbital infrastructure concepts that previously could not be financed will re-enter the investable discussion.
