The author argues the SaaS selloff creates a bifurcation between AI-protected Systems of Record/Castles and vulnerable point-solution Cottages, with specific long and avoid examples.
VEEV — LONG Veeva Systems is presented as a Castle protected by a trust/regulatory moat because it manages clinical trials and FDA software validation requirements. Pharma companies are unlikely to risk billion-dollar drug approvals on homemade AI scripts, so regulation acts as the protector. The author does not state a specific risk for Veeva.
Consider Veeva Systems (VEEV). It's a Castle. They manage clinical trials. The FDA requires software validation. A pharma company isn't going to risk a billion-dollar drug approval on a homemade AI script. Regulation is the protector.
CSU.TO — LONG Constellation Software is cited as a Castle because its vertical market software runs entire industries and is embedded as the workflow itself. Customers cannot rip it out without stopping the business, making switching risk too high. The author does not state a specific risk for Constellation.
Consider a Vertical Market Software (VMS) that runs entire industries (e.g., transit scheduling, club management) like Constellation Software. The software is the workflow. You can't rip it out without stopping the business. The risk for the business owner is too high to switch.
DOCU — AVOID DocuSign is described as a Cottage/point solution whose brand recognition may not protect it if Salesforce builds a native signing agent. In that scenario, DocuSign becomes a feature rather than a standalone company. The stated risk is Salesforce integrating e-signature natively.
On the other hand, consider DocuSign. It has brand recognition, but ultimately it's a point solution. If Salesforce builds a native "sign here" agent, DocuSign becomes a feature, not a company.
TTD — LONG The Trade Desk is listed as having a network-effect moat that AI code generation cannot easily replicate because users collaborate on the same platform. The author argues even if AI can build a design tool, it cannot recreate millions of users on the same file. The author does not state a specific risk for The Trade Desk.
Even if AI can write the code to build a design tool, it can't replicate the millions of users collaborating on the same file. The Trade Desk and Figma have this as protection.
INTU — LONG Intuit is named as a Castle that owns proprietary, non-public data, which the author says makes it AI-resistant. The mechanism is data gravity: using AI on that data makes the database stickier rather than commoditizing the software. The author does not state a specific risk for Intuit.
The Castle companies that own proprietary, non-public data (e.g., Salesforce, Intuit, Bloomberg).
ADBE — AVOID Adobe is called a Castle but flagged for a valuation trap and further share price downside from the business-model shift. Moving from seat-based to consumption-based revenue may hurt earnings in the short term even if the core business remains intact. The stated risk is short-term earnings pressure during the model transition.
Some "Castles" (like Adobe or Salesforce) may face further share price downside because of the business model shift, even if their core business models remain in tact. Moving from Seat-Based (linear, predictable revenue) to Consumption-Based (variable, lumpy revenue) may hurt earnings in the short term.
CRM — AVOID Salesforce is identified as a Castle due to proprietary data, but also flagged for further share price downside from shifting seat-based to consumption-based revenue. The author warns this model change may hurt earnings in the short term even if the core business stays intact. The stated risk is short-term earnings pressure during the transition.
Some "Castles" (like Adobe or Salesforce) may face further share price downside because of the business model shift, even if their core business models remain in tact. Moving from Seat-Based (linear, predictable revenue) to Consumption-Based (variable, lumpy revenue) may hurt earnings in the short term.
DUOL — AVOID Duolingo is used as an example of a Cottage whose value comes from wrapping public data in a nice UI. The author argues an AI agent can easily replicate that kind of offering, making it vulnerable to AI commoditization. The stated risk is AI replication of its public-data-based product.
If a company’s value comes from wrapping public data in a nice UI (e.g., Duolingo), an AI agent can easily replicate it.