Author:Select-Leading-4542
Compiled by: Shenchao TechFlow
Recently, the Reddit stock market community r/stocks has ignited a heated discussion – as AI infrastructure stocks represented by Nvidia (NVDA) have completed their major rally, more and more investors are starting to focus on application-layer companies that truly convert AI into profits, believing a new round of rotation is quietly unfolding.
Frequently mentioned targets in this round of discussion include:
Original Post:
NVDA and all AI infrastructure stocks have obviously completed their massive run-up.
I'm starting to wonder if capital is finally rotating to those companies that are actually using AI to enhance their own profit margins.
Currently mainly watching RDDT, SNOW, NOW, and SHOP.
RDDT is clearly positioned centrally as a data provider, fundamentals look very strong indeed. SNOW's crazy spike after earnings shows the market really bought into its new AI products. NOW and SHOP are both heavily integrating AI into their platforms – purely from a chart perspective, both look like good setups for a bounce.
What other targets on your watchlists fit this logic? Any worth deeper research?
Some Representative Replies from the Comments:
DeathStar_81 (10 hours ago): RDDT is literally breaking out right now. Fundamentals are too strong to suppress. 70% revenue growth, 90% gross margins, PEG ratio below 1.
Ambitious_Traffic530 (11 hours ago): Reddit has risen a lot these past few days, is it still worth buying now or wait for a pullback?
tobybells: Reddit has been trading in the same range – down from 120-130, consolidating around 140-150, then running to 160-170. Buying anywhere in this range is fine. I'm long RDDT, 2000 shares, cost basis 170, so you're buying cheaper than me now.
ShowerMotor (12 hours ago): Call me conservative, but I think the second wave is still semiconductors, the third wave will be the hyperscale cloud providers and the Mag7... boring stuff. I plan to move most of my portfolio into Nasdaq 100 next year and hold until who knows when.
AloneStaff5051 (11 hours ago): Adding context: All LLM models are trained on Reddit data. Anthropic and Perplexity haven't paid, there's obviously a lawsuit ongoing against them right now.
PotatoAjacent104937 (12 hours ago): If you're using this logic, Palantir should be on your list. I hold Palantir, but feel their adoption of government contracts is slowing. Last earnings: Government revenue up 84% YoY, Commercial revenue up 133% YoY.
Last year it felt like there were Palantir new contract headlines every day, but the numbers don't lie!
Zipski577: Defense/AI spending increases every year, and Palantir's share increases year after year. I used to think the commercial side was the biggest opportunity, thought it was severely overvalued, but after deep diving into government contracts and historical data to remodel, a $200+ price target looks very realistic.
Hoosier2016: META is too. Their AI-assisted ad targeting is already extremely profitable.
🔴 Bull Camp Summary: RDDT is the Strongest Logic in This Wave
Discussion around Reddit (RDDT) is the most heated within the community, with bullish views centered on its data moat – almost all mainstream Large Language Models (LLMs) have used Reddit data for training, and companies like Anthropic, Perplexity have not yet paid, with related lawsuits in progress. Supporters believe:
- Revenue growth of 70% YoY, gross margin as high as 90%, PEG ratio near or even below 1, valuation remains severely undervalued
- As LLMs penetrate e-commerce scenarios, Reddit's value as the "trust layer of authentic human feedback" will continue to rise
- The stock price is currently consolidating in the 140-170 range, with technical signals pointing to a potential breakout
Point of Contention: How Deep is Reddit's Data Moat Really?
Some investors remain skeptical, arguing: Lots of data doesn't equal high quality, many new models have shifted to fine-tuning small language models (SLMs) on existing datasets, the reliability of Reddit content itself is questionable, and its bargaining power against big tech is overestimated.
For example:
TyrannosPyros (8 hours ago): I've completely sold out of RDDT because it was underperforming and not letting me put more money into AMD and TSMC. The data moat is severely exaggerated. Most new models are created by fine-tuning LLMs on existing datasets. Their ad revenue is fine, but I don't think they have much bargaining power against big tech.
Fireballsdude: I really don't understand why people think that just because LLMs have scraped the existing Reddit dataset, a continuous supply of fresh data isn't important. LLMs won't just be enterprise-facing, they'll do e-commerce too, as another monetization avenue for these huge investments.
🟢 Views on Other Popular Targets
- META: AI-assisted ad targeting is already significantly improving monetization efficiency. Some investors believe the market has over-penalized it for Metaverse failures and high CapEx, presenting an undervalued opportunity.
- Palantir (PLTR): Latest earnings show government revenue +84% YoY, commercial revenue +133% YoY, strong numbers, but some investors perceive a disconnect from news hype.
- Snowflake (SNOW): Surged 30%+ in a single day post-earnings, AI data products gaining market recognition, but some lament "getting on the train too late."
- Semiconductors & Hyperscale Cloud Providers: Some traditional investors believe the second wave is still semiconductors, and the third wave will be Google, Apple, etc. (Mag7), suggesting simply buying and holding Nasdaq 100 long-term.
Professional Perspective: How Does the Options Market View This Rotation?
A user in the comments offered a more professional analysis from the volatility surface angle: Infrastructure stocks (like Nvidia, Dell) saw volatility compression after earnings, as the market has reached consensus on the direction of CapEx expansion;
However, the uncertainty for application-layer targets (RDDT, SNOW, SHOP) is two-sided, with implied volatility structures not skewed upward like infrastructure stocks. Therefore, rather than using options to lever up on applications, it might be cleaner to simply buy the stocks directly.
This discussion reflects the core market divergence right now: The easy money in AI infrastructure has been made, where is the next ten-bagger?
Most participants tend to believe the monetization logic of the application layer is gradually becoming clearer, but catalysts are not yet fully realized. RDDT has become the most watched target due to its unique data assets, while META and Palantir gain more fundamental support from their already-deployed AI monetization capabilities.
