r/SecurityAnalysis

SK Hynix and the silicon cicada

SK Hynix lists on Nasdaq this week. $28 billion raise. Biggest foreign listing of its kind in years.

Most people are writing about accessibility. US funds finally get a way in. One analyst put it cleanly, 'the listing doesn't say anything new about SK Hynix, just removes a wall that was already transparent.'

Same day Bank of America publishes a note that bothered me more than any rating on this IPO. Not about SK Hynix. About the broader pattern. High-multiple stocks gapping up like this, historically, precedes snapbacks. BofA still calling for the S&P to close the year lower than where it sits today.

I keep staring at the capacity side of this. They're building new fabs, more NAND coming online, DRAM expansion all rolling in over the next two years. Rational if demand keeps climbing. I've seen this dance before in memory chips specifically.

First round back in 2018 when everyone was dumping into HBM and the rest of the market went straight to hell. Then 2023 when the whole industry got drunk on AI again and started breaking ground like nobody would ever slow down. Nobody adds a fab because the cycle's turning. Everybody adds one because it looks invincible. That's always been the tell.

Quantization shrunk models to a quarter of their stated RAM requirements. MoE architectures wake up a sliver of parameters per token instead of the whole model. KV-cache tricks cut inference memory further. None of these were planned when the GPUs shipped. They happened because someone got constrained enough to figure it out.

Efficiency tricks have hit DRAM and HBM before, quantization, MoE, KV-cache offload, and none of it has dented demand yet. Storage hasn't had its turn. Maybe it won't dent that either.

Here's what sticks with me from the last time I watched this. Everyone was drawing revenue projections based on raw memory growth in 2016. Then compression broke through and every vendor had to explain why their volume numbers weren't keeping up despite bigger models. The story changed overnight because the software layer stopped caring about the assumptions.

If inference keeps pushing toward longer context windows, bigger KV-cache offload, models sitting on disk between calls instead of fully in memory — NAND could become the next place someone gets clever about doing more with less. That means SK Hynix, Samsung, Micron are all building capacity for a demand curve that assumes the current way of using memory won't change.

That rarely holds.

The contrarian case here isn't just cycle timing. It's the industry capitalizing hardware at the exact moment software is most likely to route around needing as much of it. Both directions cut under the same capex bet. Hardware on one side, software on the other.

This doesn't mean Friday's IPO goes bad. Probably prices fine. Institutional demand is real. But two things can coexist: the accessibility discount gets removed today and the glut built during euphoria hits eighteen months from now. One's about this week. The other's about what happens after everyone who needed to buy has already bought.

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u/roll0ver — 7 hours ago
▲ 62 r/SecurityAnalysis+1 crossposts

Applying a data ontology framework to AI moat investing — why FactSet, Veeva, Roper, and SPGI may be mispriced relative to Snowflake/Databricks. Methodology and open question on durability inside.

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

Disclosure: I own FDS and ADBE.

u/JoeInOR — 6 days ago
▲ 25 r/SecurityAnalysis+1 crossposts

Last week I published a bear case DCF on Comcast with a "dream scenario" of an NBCUniversal spinoff at $55-75 per share. This morning they announced it. Stock is up 20% premarket. Here's the original math.

Last week I ran a bear case DCF on Comcast using normalized FCF of $16B, assumed 3% annual decline for 12 straight years, 10% discount rate, and explicitly stripped $89B in net debt from the terminal value.

Bear case fair value: $30-38 per share against a $22 price.

At the end of the piece I wrote a "dream scenario" section:

"A company that spun off Versant doesn't seem unlikely to eventually spin off other pieces - broadband infrastructure or Universal Studios as a standalone entity. According to my sum-of-parts analysis, a spinoff scenario could put CMCSA at $55-75 per share."

This morning Comcast announced exactly that. NBCUniversal and Sky spinning off into a separate publicly traded company. Broadband, wireless, and business services staying in the remaining Comcast. Stock up 20% in premarket.

The broadband rump - which produced 24x the adjusted EBITDA of the content business in Q1 2026 - is now going to trade as a pure-play infrastructure company. The content business gets its own multiple separately.

Even after the 20% move the stock is still below my bear case fair value of $30-38. The spinoff still needs regulatory approval and closes in about a year. The story isn't over.

Full DCF with the math, the debt analysis, and the buyback cannibalization model here: https://cavemanscreener.substack.com/p/buying-2-for-1-a-comcast-dcf-update

u/JoeInOR — 7 days ago