Thursday, October 22, 2026

@TidefallCapital Oct 11 1 click

Trevor Scott on X

Eisman is long $NVDA by the way

@Fierce__beast Oct 11

Fierce_beast on X

$evlv adoption doesn't matter if it's arriving at bad economic terms. anything can be adopted if a company is subsidizing it for example. i care about at whether the adoption is economically profitable and if that economic profit has a long and sustainable runway it doesn't seem like that's the case with $evlv

@ParadisLabs Oct 11 1 click

Paradis on X

I think Gavin's correct. Open-weight models that compress frontier margins are positive for AI infra -- they don't shrink demand for compute. An example is after Deepseek R1, Tencent, Alibaba and Bytedance *significantly* increased H20 orders...and smaller healthcare & education firms started buying $NVDA H20's to run DeepSeek in-house. So we saw a broadening out of the customer base for the infra providers. In the West, I think $DELL is the best read on whether the buyer base is broadening beyond frontier labs. Hyperscalers mostly buy directly from Taiwanese ODMs, so Dell's AI customers are mostly neoclouds, sovereigns and enterprises. Dell's AI customer count went from >5,000 to >6,500 last quarter (30% growth in one quarter) which management say is mainly down to enterprise acceleration. Feels like that's one of the better public data points on how fast the self-hosting buyer base is growing. Which is a net-positive for AI infra. I don't understand how it can't be?

@StableBread Oct 11 2 clicks

Fajasy on X

AI labs are paying to close the gap on malware binaries their best models still can’t read. A ransomware nanocap I wrote up last month is now training its own model to read them, after data labeling companies approached its team. Even with far more capable models today, OpenAI and other top AI labs still pay experts to write and grade the problems their models get wrong. And that’s where data labeling companies play a role. They recruit experts in fields where models still make mistakes, like coding, finance, and medicine, and turn what those experts know into graded examples for the labs to train on. Here are just some of the valuations of the largest AI data labeling companies: → Scale AI: Valued at over $29B after $META paid $14.3B for a 49% stake in June 2025. → Surge AI: Was in talks to raise at a $25B valuation in July 2025. → Mercor: Was in talks to raise at a $20B valuation in July 2026. One of the skills the labs need help with is reading malware, the malicious software hackers use to break into or lock up computers. Malware often arrives as a binary, a compiled program in machine code, and many are obfuscated, or scrambled to hide their purpose. In August 2026, researchers led by Columbia University released SRE-Bench, a test of AI models on 262 binaries, and concluded that reverse engineering, or figuring out what a binary does, “remains largely unsolved.” According to Vals AI’s SRE-Bench leaderboard, OpenAI’s GPT-6 Astra claims the top spot, fully solving 56.9% of the binaries. But GPT-6 Astra still can’t fully solve the other 43.1%. Data labeling companies are now recruiting malware experts to train the labs’ models. Scale AI, for example, is hiring a product manager to build a cybersecurity product line, with “malware and binary analysis” among the skills it covers. Mercor also pays security experts $200-250/hour to evaluate AI-generated analyses, preferring people with reverse engineering or binary analysis backgrounds. That demand for malware skills has now reached the ransomware nanocap I wrote up last month. Its CEO said in a new interview that data labeling companies have asked the company to help label what each malware binary does, like whether it’s secretly encrypting files. Its software copies the encryption keys ransomware creates during an attack, so its engineers can build a decryptor (a program that reverses the encryption) and recover a victim’s files without paying the ransom. So is a company the market values at under $10M on track to be acquired by, or partner with, an AI lab or data labeling company? Or is binary analysis another AI plan that misses its targets, like the AI decryptor builder it has pitched since 2023? That's what I talk about in my ~3,500-word company update: https://t.co/IGkfgBX5hR

@Fierce__beast Oct 11

Fierce_beast on X

How frequently do restructurings at this scale actually work? Did it work for $STLA? https://t.co/TcKUd8hB3M

@Fierce__beast Oct 11

Fierce_beast on X

for $evlv unfortunately it seems the economics of their incremental customer wins are margin dilutive as they are trying to continue growing while competitors are catching up https://t.co/WOBgds8M4K