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Wednesday, October 7, 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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Research Roundup Vol. LV

OpenAI’s enterprise privacy push and Synopsys’s Factory 2 royalty model anchor a technology roundup, alongside oil above $100 and Bitcoin’s…

SNPS $97B
Monte Independent Investment Research

New Position (ONON): Game is On

On Holding (ONON) is gaining sportswear share, expanding store space and launching soccer with Kylian Mbappé as investors debate U.S.

ONON $11B
Aurelion Research 3 clicks

When the Ads Come to the Data

Meta’s Muse could place ads inside its AI agent by letting a private VM select offers from advertisers without sharing user data with…

META $1.8T
MBI Deep Dives 5 clicks
VEEV · long

Veeva Systems INC

Veeva Systems is a vertical software company valued as if its best growth years are behind it.

VEEV $46B
Value Investors Club

Deep Dive: Intuit ($INTU)

Intuit’s $21.4 billion software franchise faces AI-driven pricing pressure in tax and accounting, even as QuickBooks and TurboTax retain…

INTU $79B
Rebound Capital 4 clicks

Canterra Minerals: Research Note

Canterra Minerals increased Lundberg indicated tonnage 55% to 26.1Mt, with 226.2Kt of contained copper equivalent, supporting a Buchans…

EQX $13B
Atrium Research - Equity Research

Kenorland Minerals: Research Flash

Franco-Nevada bought 10.0 million Kenorland shares, a 12.4% stake, for C$22.2 million in a secondary transaction that issued no new shares.

FNV $46B
Atrium Research - Equity Research 1 click

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