Bullpen.News From Edwin Dorsey
Get Daily Recap Feedback

Friday, November 6, 2026

Back to the front page

Post Q3 EVC Update

EVC's Q3 update cites $30 million for a single station, a broadcast-business sale and stronger positioning for 2027.

EVC $832M
Nat Stewart - Stock Picking Newsletter 17 clicks

From X

@convequity 1 click

Convequity on X

$NVDA, $CBRS, and Huawei's Kirin 9050 Pro are three different bets on the same turning point: what the chip industry does when shrinking transistors stops paying. In AI chips, most energy goes into moving data, not into the calculation itself. In our view, as transistors shrink, calculations get cheaper much faster than the wires around them, so the industry's centre of gravity is shifting from ever-smaller transistors to shorter paths for data. That shift is why Huawei's logic folding — designing a circuit vertically to cut the distance data travels — looks potentially transformative and disruptive for the semiconductor industry to us. Here is the comparison in one line: one 16-bit floating-point calculation is the baseline, 1×, about 0.52 picojoules in NVIDIA's published GH200 energy model. Every figure below is the energy spent moving data, measured against that one calculation. 1. Moving data to local SRAM next to the computing units: Cerebras uses about 2× the baseline. NVIDIA's highly optimized local-memory path uses about 5–15× the baseline. 2. Moving data across the chip: we estimate Cerebras's core-to-core network at about 12–37× the baseline. NVIDIA's optimized path through L2, its shared on-chip cache, is about 60–90× the baseline. 3. Moving data off the chip to external memory: Cerebras's MemoryX path is about 330–800× the baseline. NVIDIA's optimized path to HBM, its external high-bandwidth memory, is about 230–290× the baseline. The energy cost is set by how far the data travels: a short hop costs single-digit multiples of one calculation, a trip across the chip costs tens of times, and a trip to external memory costs hundreds of times. If moving data is the real constraint, then shortening paths, which is what Huawei is doing with logic folding, matters more than another process node — for NVIDIA and Cerebras as much as for Huawei. These are engineering estimates, workload-dependent, not measurements of complete systems. The optimized block transfers behind NVIDIA's figures are already counted above; what remains is the physical cost of the wires and memory themselves. In our view, Cerebras's commercial case depends on whether its compiler can keep data close to computation across enough workloads. NVIDIA's advantage is a broader range of software, not a way around the energy cost of moving data.

Investor Letters

All letters

Entravision Q3 Update

Entravision’s Q3 ad-tech revenue reached $177.2 million, up 133% year over year, while total revenue rose 88% to $226.3 million and diluted…

EVC $832M
The Value Road 4 clicks

Axon 3Q26 Business Update

Axon cut full-year guidance after delayed federal Dedrone contracts slowed 3Q revenue growth to 28%, while software and services grew 43%.

AXON $31B
Speedwell Memos 2 clicks

AMD Q3 FY26, Annotated

AMD's Q3 revenue rose 45% to $13.4 billion as data center reached $8.5 billion, 64% of sales, driven by EPYC and Instinct ramps.

AMD $660B
Chipstrat 1 click

GREAT DEC-TECTIVE WORK

Diversified Energy reported $255.6 million of “true” nine-month free cash flow, while its $1.8 billion Birch acquisition would shift…

DEC $900M
The Oak Bloke 2 clicks

ARM Q2 FY3/27 Earnings Review

Arm reported Q2 revenue of $1.443 billion, up 27%, as licensing rose 50% while royalties grew 8%; Q3 guidance calls for $1.54 billion.

ARM $181B
Cestrian Capital Research

Publications we follow

Every publication on this site, hand-picked.

129 publications

Newsletters 109

YouTube 4

Blogs 15

Seeking Alpha 74 of 276

Nominate a publication

X accounts we follow

Every account on this site, hand-picked.

91 accounts
Nominate an account

Investor letters we follow

359 firms

Sectors

Ordered by how often our editor reaches for them.