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Tuesday, November 3, 2026

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AMD Earnings Review

AMD reported $13.4 billion in Q3 revenue, with Data Center sales up 97% to $8.5 billion, and guided Q4 revenue to $17 billion.

AMD $798B
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$BFLY earnings thoughts Overall very pleased. Numbers were excellent double beat and raise with record gross margins --- This despite weakness in int'l sales due to lumpiness and weakness from sales team but happy to hear on the call they are very aggressively working to switch their int'l sales model to the us based one which focuses mainly on large enterprise deals . The fact they had such strong numbers despite int'l weakness is promising. embedded rev which is the real growth driver is ramping - went from practically zero to over 11 million but they didnt disclose exact contribution to that was from mid journey 15 new contracts in the last Q for compass AI ten 7 figure deals five 6 figure deals ! also most noteable for the data moat compass in getting up to 80% of all scans in some hospitals vastly improving the ai data moat All the near term catalysts are on track home + CC rev is already starting in Q4 w/partners already wanting to expand to bladder and spesis scans. P5.1 next gen chip/probe on track to start shipping early 27 as well expanding into icu and cardiology verticals highlighted near term opportunities non medical / wearables already shipping to embedded partners some really interesting commentary on fed ramp approval and some very large potential opportunities. most interesting thing for me personally on the call was they referenced around 7 times that the next big "mid-journey" type of deal was coming soon. thesis fully intact on this one of one. $SILC earnings thoughts Overall solid but disappointed by lack of color on the cyber security refresh cycle. Solid EPS beat slight revenue beat + raise return to profitability earlier than forecasted and mentioned on call expect that to accelerate in Q4 seems like a interesting upcoming catalyst "in final discussions with a new customer on a “major evolving use case” and expect agreement imminently" design wins ahead of schedule although bearish flag that one inference partner left due to a change in strategy which i think is main cause for weakness today but if this next deal is as imminent as they imply investor confidence should return. other bearish flag is inventory remains quite elevated at 73M but they did mention on call they expect refreshes coming soon from core cyber security biz unfortunate that there was only one analyst in the q + a so didn't get a ton of color i really would of loved to know more about this cyber security refresh and it was only adressed in passing due to the analyst question but in general with near term catalyst as well as continued healthy rev growth and return to non-gaap profitability stock feels cheap here
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The Artificial Analysis (AA) Intelligence Index update is interesting, and the right reading is more nuanced than its one headline number. AA is an amateur score, aimed mostly at non-specialists. It is not the most authoritative benchmark, the most professional one, or the most accurate one, partly because of how AA sets up its own tests. On FrontierCode-AA, GPT-6 Luna (medium) is the language model that grades the answers of frontier models including GPT-6 Astra, Claude Fable and Claude Opus. AA uses that judge on FrontierCode 1.1 and 2.0. On FrontierCode 2.0, Luna scores about 25, while Claude Opus 5.5 scores about 46, GPT-6 Astra about 42 and Claude Fable 5.1 about 36. So the judge scores below each of the frontier models it grades. People still use AA because it turns "which model is better" into one number. That number is easy to quote, so labs put it in launch announcements and buyers use it to compare models quickly. Many labs intentionally overfit: they train a model on the answers and the tests from the benchmarks that made up AA's score, so it scores well on those tests without getting better at the work. About 60% or more of the benchmarks that constituted that score were public. On a public test, the questions and answers are published, so a lab can put them into training data and the model can memorize them. The published score then rises because the model has already seen that test, and that score is the number the market will quote. Labs under pressure to show a strong result overfit those public tests hard. The result is high scores on the tests and low scores in actual use. Real work brings new code, documents and problems the model has never seen, so memorized test items do not carry over. So far, $META's Muse, $GOOGL's Gemini and Moonshot's Kimi seem to be the most aggressive. Meta and Google face the most pressure to show they are back on top. They gamed the tests heavily, and their actual performance remains weak. A performance claim is now easy to check in actual use. Right after Opus 5.5 shipped, Anthropic's token use jumped and many people moved back to Claude Code, while Muse Code and Google Antigravity remain weak. Token spend and the coding product people actually open show what customers do with their own work, and a model trained on a public test cannot produce that shift. If Muse and Gemini had the lead their test scores implied, use would have moved toward Muse Code and Antigravity, and it did not. Benchmarking remains one of everyone's biggest problems. Weak benchmarks saturate. A weak test has a ceiling, and once the top models all reach the maximum score, the ranking stops separating them, even if one model is still better at the work. Strong benchmarks with public questions get gamed, because labs can train on the published questions. Closed benchmarks keep the questions hidden, which makes them harder to train on. The cost is weak transparency and reproducibility, and they are harder to treat as independent. A buyer cannot see the items, rerun the test from those questions, or check that the lab and the scorer stayed apart, so the result is less trustworthy. AA's new v5 index is 60% private or held-out, meaning the questions in that 60% are kept out of public view, and this is the first time a majority of the index is private. That private share is a different figure from the 60% or more of the earlier tests that were public. Even with that cost, I think AA made two right moves. It now uses more closed benchmarks, and it dropped its own poorly designed special benchmarks: GDPval-AA v2.1, SciCode and AA-LCR v1.1. Apart from the single headline number, the leaders on each test are still informative: Real code-repository development (FrontierCode): Claude Opus 5.5, 46% on FrontierCode 2.0 at max effort, the same result as above Terminal engineering tasks (Terminal-Bench): Kimi K3.1 Pro, 67% on Terminal-Bench 4.0 Terminal scientific research (Terminal-Bench-Science): GPT-6 Astra, 63% at max effort Complex physics research questions (CritPt): GPT-6 Astra, 72% on the private hard subset at high effort Enterprise knowledge work (AA-Briefcase): Claude Sonnet 5.5, 1,812 Elo Enterprise SaaS automation (AutomationBench): Gemini 4 Argon, 78% Complex PDF understanding (GDP.pdf): Muse Wonder Full, 33% all-pass HLE-Diamond, the refined hard-exam set: Claude Opus 5.5, 68% at max effort Knowledge reliability (AA-Omniscience): Claude Opus 5.5, 48 on AA-Omniscience v1.1

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Stevanato down 30%. Yikes.

Stevanato fell 30% after forecasting low-single-digit 2027 growth as lower Nexa syringe volumes from a GLP-1 customer offset solid Q3…

STVN $5.4B
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Bending Spoons Spa

Bending Spoons is a software and digital-assets holding company that has completed more than 50 acquisitions since 2013.

BSP $29B
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