CRM
Salesforce, Inc.
stock NYSE

Holding ORCL and CRM feels gayer than sucking a cock 😫

I would say QCOM or CRM but the plebs want overvalued

Background: I've spent twenty years doing data ontology work professionally — building the semantic structures that turn raw, ungoverned data into something usable, most recently at SurveyMonkey. On the side I've built a personal screener pulling 16 years of SEC XBRL data across roughly 1,700 tickers, normalizing inconsistent tags so true FCF (operating cash flow minus CapEx minus SBC) is comparable across companies. I'm posting this here specifically because I think the methodology question is more interesting than the stock picks, and this sub seems like the right place to have that argued with rather than just agreed with.
**The consensus trade and why I think it's incomplete**
Everyone agrees the AI infrastructure trade is the data platform layer — Snowflake, Databricks, Amplitude. Raw data storage, query, and governance tooling. The market has priced this consensus in fully; these names carry premium multiples on the "picks and shovels" thesis.
My argument: raw data infrastructure is closer to a commodity than people are pricing it as. SQL servers, data warehouses, analytics capture platforms — this category has been re-invented every decade with marginal differentiation, and the switching costs, while real, are mostly operational (migration pain) rather than epistemic (the new platform can do everything the old one could, eventually). What's scarce isn't the pipe. It's validated, structured, domain-specific content moving through the pipe.
**The taxonomy I'm using**
I split AI-relevant data companies into four categories:
Foundational language data — Reddit (RDDT) is the only name here. Granular subreddit classification plus upvote-based quality signal is genuinely unique training corpus for natural, idiomatic language. I don't own it — FCF yield too low for my framework, still in a cash-consuming growth phase — but the data moat argument is real.
Industry-specific contextual data — FactSet (FDS), Veeva (VEEV), Roper (ROP), S&P Global (SPGI). These companies have spent decades organizing messy, heavily regulated domain data into clean, structured ontologies: financial workflows, FDA-validated clinical trial records, county tax administration, credit ratings methodology. None of this is scrapeable. A general model trained on public web data has zero exposure to what a structured clinical trial submission or a properly normalized financial model actually looks like internally.
Workflow/usage data — Adobe (ADBE), Salesforce (CRM), SS&C (SSNC). The moat here is encoded human process rather than raw content. A Salesforce lead-to-contact-to-opportunity data model isn't bad design — it's encoding a specific sales workflow that took years to standardize across millions of companies. Replacing it means replicating not just the data but the process logic embedded in how that data gets created and transformed.
Data foundation platforms — Amplitude (AMPL), Snowflake (SNOW). The commodity layer described above.
**The valuation argument**
The names in categories 2 and 3 are trading at meaningfully better true FCF yields than the consensus infrastructure plays, despite (in my view) deeper and more durable moats — partly because the SaaSpocalypse selloff has lumped them in indiscriminately with software companies that genuinely do have weak, scrapeable moats. I think the market is pricing the wrong layer of the stack.
**The honest open question I'd actually like pushback on**
Is "irreplaceable context" really a durable moat, or just a temporary information asymmetry that AI labs close over time as they get better at synthetic data generation, data partnerships, or simply paying for licensing access to exactly this kind of structured content? If OpenAI or Anthropic can license FactSet's data outright, or if regulatory data eventually becomes more standardized and shareable industry-wide (think FDA pushing toward common data standards), does the moat compress faster than the multiple suggests it will? I think the moat holds longer than the market is currently pricing, but I'm genuinely less certain about the 10-year case than the 3-year case, and would like to hear from anyone closer to enterprise AI procurement or regulatory data standards on how real this risk is.
Full piece with the four-category breakdown and a true FCF yield comparison table is here, for anyone who wants the data: [https://cavemanscreener.substack.com/p/context-is-50-iq-points-part-ii-data](https://cavemanscreener.substack.com/p/context-is-50-iq-points-part-ii-data)
Disclosure: I own FDS and ADBE.

A bit too late to get into semis, these days I'd focus either on beaten down SaaS (NOW, CRM) or hyperscalers (MSFT, META, AMZN, except ORCL), since neither are going away anywhere.

I don't know about this one, I have been getting hard drilled on CRM, Too skeptical to left MSFT go deep in the mouth while CRM is going hard from behind.

Have you heard of NOW and CRM?

My personal experience is basically the opposite of this.
I’m working as an external contractor doing exactly this right now: building an internal solution to replace a lesser-known SaaS in a niche category. And we did build it. Not as a “vibe-coded CRM in a weekend” thing, but as a real internal tool with direct user feedback, proper requirements, and the usual boring enterprise constraints.
The whole contract came out of frustration with the existing SaaS. The department using it basically had the same complaint I’ve heard a lot: tons of horizontal SaaS is bloated. Maybe 50–60% of the feature set is irrelevant to any one customer, while the specific things that customer actually needs are either missing, awkward, or require customization.
So the overlap between “what the SaaS offers” and “what this team actually needs” is often not that big. You pay for the whole product, including all the stuff you don’t use, and then you usually pay even more to some implementation partner or services team to bend the thing into something that sort of fits your workflow.
And to be fair, SaaS vendors almost have to be this way. They’re selling to thousands of customers, so they have to optimize for common use cases. They can’t deeply tailor the product around every weird internal process at every customer.
But that’s exactly where AI-assisted development changes the equation. You can now build only the features you actually need, in the way your users actually need them, with direct feedback from those users, at a price point that can be comparable to what you’d otherwise spend on SaaS licenses plus implementation/customization.
That’s the part I think people are underestimating. Even if the internal tool does not fully replace the SaaS, it becomes a credible next-best alternative. That alone puts pressure on pricing. The conversation with the vendor changes from “we have to pay whatever you charge” to “we can build a version of this internally for roughly what we’re paying you, so give us a reason not to.”
On the “SaaS with AI” point, I think that’s a slightly different argument. AI as a feature inside SaaS is not the same thing as AI-assisted development making it cheaper to replicate or replace parts of SaaS.
You can build an internal SaaS-like tool and include AI capabilities directly from the start. In some cases that might actually be easier than bolting AI onto a mature legacy product with a decade of existing workflows, permissions, integrations, and feature debt. Starting fresh has advantages.
I’m not saying this works for every category, and I’m definitely not saying every homegrown enterprise project will succeed. Plenty won’t. But I don’t buy the idea that internal builds are automatically several years behind SaaS. In a lot of cases, the SaaS is already several years away from what the customer actually needs.

I work at a big SaaS company so I’m super biased but the nuance that gets missed in the build it yourself track is that you’re usually trying to build what SaaS was before ai. SaaS is evolving with AI pretty damn fast so by the time you build out your CRM system using AI to hopefully speed it up (I’ve yet to see a vibe coded CRM actually get approval from InfoSec at an enterprise company tbh but I know several that are trying), what you’ve built may be several years out of date compared to buying the more up to date thing. I’m not overly confident in this POV but so far all the companies I speak to aren’t getting anywhere with their home grown projects. Tons of cool and exciting things getting created but none they could ever deploy.

So who at Phillips Securities had puts on CRM?

I can do better than that: CRM anyone?

SAP is essentially my top software pick. Incredible moat (much deeper than CRM), higher underlying growth rate than reported, well-run, well-diversified, and isn't running an unsustainable CapEx engine.

SAP dominates in ERP not in CRM. They have very little share in CRM. That's why I'm not very hopeful about Salesforce because CRM is definitely gonna penetrated by vibe coding.

Why buy Salesforce when you can build your own CRM?

The fear is that 5-10 years from now you just prompt "create a clone of salesforce CRM" and the AI does it. No need to license any software. We are not there, but who knows if we ever get there.

Guys I have tried searching, asking AI and try to do research but seems like I can’t get an answer.
Why SAAS is being hated by investor at the moment? I want to get some perspective. The answer I get is AI will replace these roles. Another answer I get is with layoffs, there will be lesser seats for enterprise to pay for. Let me breakdown bothe the point and please see what I am missing out.
Before I start, let’s look at some examples I am talking about specifically.
Adobe, Salesforce, Microsoft, Servicenow
1) AI will replace the need for SAAS
I am not sure how exactly this affects the SAAS business. All these companies has spent billions to integrate their enterprise softwares with the products mentioned above (to a lesser extent Adobe). But, let’s say Claude comes up with their own CRM software (for example), what it will take for enterprise to switch from existing to this new product? Wouldn’t the switching cost so expensive? Claude needs to provide so much of value for enterprise for them to switch. Wouldn’t it be easier to integrate AI within the existing software and achieve similar efficiency or productivity? Salesforce already doing it, other companies as well for their softwares. Servicenow is so embedded in my enterprise softwares, that for them to switch to a new software, it will take years to implement and get used to. Might as well to use the existing ones with AI integration. When Google introduce their enterprise suites of Google Docs, Sheet etc, everyone thought MSFT is done for. But they are still doing well. So I don’t understand the fear.
2) Number of users might decrease with layoffs
This one I agree but these are short term layoffs. Long term, new companies will come in creating more seat for the software user.
Help me to understand the severity of the situation.

After a long time, it's actually above 10%. My living costs were 2758 EUR for June 2026, and I think I can go even lower. I'm planning some investments around the house, but probably not in the following few months. So it's a good time to build reserves before that.
My dividends were 395 EUR, but mostly from the annual payer XTB.WA. I'm 36 years old, my market loss in June was -6.28% (it's fine, it was +12% in May).
To disclose my portfolios, here are my positions:
Growth: (9868.HK,ADBE,ADYEN.AS,ALGN,ALSTI.PA,AMZN,BABA,BIDU,BYDDY,BYND,CDR.WA,CELH,CROX,CRSP,CRSR,CRWD,DDOG,DUOL,ENPH,FB,FSLY,FTCV,GOOG,GOOGL,GRE.MC,HIMS,HUYA,IBKR,ILMN,INPST.AS,INT.ST,KER.WA,LMND,META,MQ,NET,NFLX,NIO,NOW,OKTA,ONDO.L,OTGLY,PLTR,PLUG,PYPL,QLYS,SEDG,SMR,SPIR,SPOT,TDOC,TSLA,TTCF,TTD,U,UBER,UPST,VFF,VYGVF,WISE.L,XPEV,ZNGA)
Dividend portfolio: (AAPL,ABBV,ADM,AFSI,ALLY,ASML.AS,B4B.DE,BA,BAC,BBWI,BKNG,BMY,BNS,BNS.TO,BTI,CAH,CL,CMI,COST,CP.TO,CRM,CTL,CVS,D,DAL,DG,DIN,DIS,EIX,EOAN.DE,EV,EVO.ST,F,FDS,FF,FL,FLO,GEO,GILD,GIS,GPC,HBH.DE,HRL,IBM,IIPR,INTC,ITW,JNJ,JPM,KHC,KMB,KMI,KO,KR,LB,LUMN,M,MA,MMM,MO,MPW,MS,MSFT,NEE,NHI,NKE,NOW,O,OHI,ONL,PEP,PG,PM,POOL,QCOM,SBUX,SKT,SO,SPG,STOR,STX,T,TGT,TSM,UNP,UPS,V,VER.VI,VFC,VZ,WBA,WBD,WEC,XEL,XTB.WA)
**What portion of your expenses would be covered by dividends? Include Age pls :)**

Margin debt at $1.4 Trillion
Margin interest almost \~10% ?
How long extremely high margin can hold, when Q2-ER on deck, ER-blackout going
Jumping from software $IGV $MSFT $META $CRM $NOW $ADBE $ACN to semi $SMH $MU $AMAT $LRCX $ASML $INTC $AMD $MRVL $SNDK $WDC rotational chair one after another by Algo scammers

Every idea my team has explored recently on are in one of two use cases:
1) problems that require AI on an ongoing basis *as the product*. These have the problem you describe. They are usually harder to make than expected and more expensive to run than expected. Totally agree with your point on this type of problem. Costs will rise.
2) problems where you can use AI to help skilled engineers write a deterministic product *once* that relies on legacy systems and doesn’t need AI on an ongoing basis. 
For problems in #2 we have seen tremendous success because all we are doing is increasing the output of our engineers to write “normal” code without recurring token cost. This makes each product cheaper to make, meaning we have more capacity and many more ideas make financial sense from an ROI perspective. 
To the original question of CRM platforms, I think that is mostly a #2 problem. But it’s not an easy one since SaaS products try to integrate so deep into all your business processes. Most businesses will still use SaaS so it definitely isn’t “dead”. But sophisticated companies will try to reduce license costs. So I do think there will be some revenue growth and margin headwinds for a lot SaaS companies, but unclear the magnitude. That’s why there are people passionately on each side of this debate. 

Why don’t nflx, CRM, Orcl pump with qqq holy shit? Serious answers only 😖😖😭😭

Good analysis. I think the area where you are optimistic (and why the market is pricing this lower than you are) is that there are significant uncertainty around revenue growth and margin expansion. You paint a rosy picture that these will both continue to trend favorably, and there is certainly a case for that to be true. 
But the bear case is that increasingly good AI coding tools make it feasible for more companies to code their own CRM in house, or potentially even bypass the idea of CRM entirely to have in-house operational databases by system of record rather than a 3rd party. Lots of benefits - I’m actually working on this in my company. Very difficult to do since SF is sticky. But if this becomes widespread, it would cause both revenue and margin concerns for $CRM