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Sunday, October 18, 2026· 9297 stock ideas· 217 writers·
18,643 views· 8,650 clicks

From X

@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.

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