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Monday, October 12, 2026

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@StableBread

Fajasy on X

If ur reading a write-up and they don't include a valuation range, more often than not: 1) They're lying to themselves. E.g. only pitching bull case. "The next 10x." $GRAB a couple years ago. 2) They're lying to you, the reader. E.g. not focusing on bear case enough. "It's asymmetric"... but actual downside if couple thing flops is 60%+. $FLNC just 2-3 months ago. 3) They have no clue and prob conclude by saying "position as small speculative bet" to be in a win-win scenario to gain subs. $CCXI today... Multiple based valuation is the simple/lazy route and easier to defend, yet less practical. Absolute valuations like DCF or Penman REV forces you to think more through scenarios and the business itself, which is what's actually useful because it's more subjective.
@convequity

Convequity on X

Whether AI can run security operations on its own comes down to one question: when an automated action is wrong, who is accountable for finding out what happened and fixing it? Our note borrows two levels from self-driving cars. At L2 the driver answers for mistakes. At L4 the car drives itself, so accountability shifts to the company behind the system. 1. At L2, a third-party AI agent mostly assists. It connects to a security platform over MCP, the protocol that lets an outside AI model pull data from a vendor's platform. It suggests next steps and drafts investigations, while the human and the security platform still hold the wheel. 2. At L4 or L5, the system acts with no human in the loop in the security operations centre. It quarantines a machine, kills a process, revokes access or blocks traffic. Any of those can be wrong. When one is, the customer needs a clear accountable party that will investigate and fix the problem. In our view $PANW can be that party. It is already inside the customer's systems, keeps audit trails, and its own products take those hard-to-reverse actions. AI model labs are poorly placed for that role. On our reading, their terms usually limit what they answer for, so when an outside agent acts wrongly, the customer is left to investigate and fix the problem alone. Outside agents compete most easily for the analyst's workspace, which is L2 work. That makes $PANW better placed than model labs to sell autonomy.

Investor Letters

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“Is Ralph Lauren Still Relevant?”

Ralph Lauren’s 2017 CEO change put Patrice Louvet in charge of reversing nine quarters of same-store sales declines and restoring the…

RL $22B
TSOH Investment Research 1 click

A temporary glitch?

Coupang’s November 2025 cybersecurity breach tests whether a selloff is temporary despite its 28% Korean e-commerce share, 14 million-plus…

CPNG $27B
Asian Century Stocks 9 clicks

TSOH Weekly Roundup (10/09/2026)

Paramount Skydance’s debt-heavy Warner Bros. Discovery acquisition, streaming bundle pricing and alcohol-industry diversification anchor…

WBD $78B
TSOH Investment Research
DB · long

Deutsche Bank AG

Deutsche Bank trades at 1.0x tangible book, with projected ROTCE above 13% and a favorable setup ahead of earnings.

DB $63B
Value Investors Club

Zscaler Powered By Agentic AI Growth

Zscaler's $3.8 billion ARR grew 25% year over year as AI-driven security demand and operating leverage support a path to doubling ARR by…

ZS $37B
Andres Cardenal, CFA

Boyz in the Hood

Robinhood’s AI agents, 24/7 equity trading and 10:1 crypto perpetual futures encourage higher retail trading velocity, leverage and…

HOOD $91B
WHAT ARE WE DOING? “Contrarians At The Gate” 1 click

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