RL

_hiddenscout

So wild to look back at this stuff sometimes. RL is up like 13% from this. However, GILT is up 52% lol.

brielkate

Those are still nice brands though; WHBM is underrated in my opinion. The brands that are “hot” really seem to be going after younger people… like Aritzia. That said, their product has such wide appeal that I’ve seen ladies who would be the WHBM/RL type shopping at Aritzia now, and I do think that customer might find Aritzia’s Wilfred and Babaton lines quite attractive.

mustachechap

RL?

iMakeGOODinvestmemts

$META was cheap because people didn’t value the cost of dram and NAND memory they have to overspend by 100% higher. So buyside capex was most likely if I were to guess was $20-25B above consensus. If you take that out from PT that’s why stock was so below all 30x PE or 40% below PTs. I think both buyside and sell side is wrong. If they cut RL, and Dram/Nand prices start going down next year, it could be Goldilocks for Meta that’s about what I can say. No financial advice. It’s an IF as Zuck has chosen to keep RL alive.

iMakeGOODinvestmemts

[$Meta](https://x.com/search?q=%24Meta&src=cashtag_click) was cheap because people didn’t value the cost of dram and nand they have to overspend by 100% higher. So buyside capex was most likely if I were to guess was $20-25B above consensus. If you take that out from PT that’s why stock was so below all 30x PE or 40% below PTs. I think both buyside and sell side is wrong. If they cut RL, and Dram/Nand prices start going down next year, it could be Goldilocks for Meta that’s about what I can say. No financial advice. It’s an IF as Zuck has chosen to keep RL alive

ap485860281

Be interesting to see if it foreshadows RL numbers? Retail did appear to be quite strong though.

AllCapNoBrake

Is there any difference between watching corn on your phone/laptop vs watching your hommie clap your wifes cheeks in RL? No, no there isn't. TYFYATTM

ImpressiveNeat9039

> And one that no one is betting on existing anytime soon Well then you should probably make yourself a bit more aware and probably educated on this topic and probably less bet oriented based on poor information .. I hold a Masters in CS from one of top US universities an AI/ML/D/RL were subject areas and I work on technology. So I know what is happening in these areas very well and how well is it progressing and how far we are from these kind of technologies. You not betting on it is your choice.. Won't stop what is coming.

BrobaFett_1

I've been trying to figure out which retail stock to pickup since I've recently been reading about this bull case. Between RL and CTRN so far. Might snag both. I'd like Hermes, but it's at that premium valuation

creemeeseason

Solid move on RL! Black Friday was record setting and all indications are Christmas will be the same. I know there's a lot of doomers out there, but I'm actually vaguely bullish on consumer discretionary.

_hiddenscout

Agreed. I think we are going to see some good holiday numbers and some of the retail names aren’t AI trades, so they can totally catch up.   Part of why I bought some RL. 

skilliard7

>Going through your comment history, you're very anti-Google for some reason. I think you just missed out. I did buy some Google back in April 2025 when I felt it was fairly priced and I wanted some exposure. You are correct that I did sell it once I felt it was overvalued, and it kept going up. However, I made way more money with SK Hynix than I would've made if I kept holding google. So I have no regrets. >Its average 10-year P/E is 28, so it's not much higher than that, and it deserves a premium right now. I don't think they deserve a higher PE. They are very likely to lose market share over the next decade and see stagnating/declining earnings. Additionally, their capital expenses are growing. > Gemini better than ChatGPT Gemini fails at most tasks I give it that ChatGPT succeeds at. IT either hallucinates, fails to follow directions, or fails to come up with creative solutions to problems. The reasons Gemini scores well on benchmarks is that they performed RL on benchmark questions, rather than real world scenarios, which makes it perform very badly for real queries. >Even if you don't use Google search, it doesn't mean the vast majority of people don't. Please look at a search engine market share graph. ChatGPT will never come close Saying to look for a search engine market share graph in 2026 to justify investing in Google is like saying to look at a video rental market share graph to invest in Blockbuster in 2002... its a red herring and not relevant.

SolarisDelta

RL Baron Haronnenn

2020bubbles

Once again, that does not explain why they are twice the economy of Egypt who has received 94 billion dollars in aid and is still half the size of Israel with a population 10 times its size. Also, the total number is 174, not 300. This is basically over 80 years, which averages slightly over 2. The aid they received in 2022 was 3.3 There is more to the equation you are intentionally ignoring, ingenuity and economic stability https://www.congress.gov/crs_external_products/RL/PDF/RL33222/RL33222.51.pdf

Gom_KBull

Sure its overblown and companies+investors are wildly overestimating its use case... but its much more than what we had before it (thats why I treat it as a tech leap) i agree LLMs are technologically ants compared to AGI which is elephants and LLMs may not even directly influence AGI research, but i still think we had to incorporate LLMs to some degree to get there eventually. i share the same view about non-LLM AI. specialized areas and RL are soo soo much more valuable. Personally i feel machine learning has so much to offer but its not "sexy" to business/investors

eht_amgine_enihcam

Not even a tech leap forward. The dude making GPT's has come out and said it was a side track. People act like you can pump in compute/data and get better outcomes indefinitely, but the data sources are getting polluted by AI generated data/diminishing returns. To actually approach AGI you'd need a completly different paradigm, I think RL has the best chances but you'd need the proper value function etc.

Inevitable_Pin7755

Probably not anytime soon. Reality Labs isn’t being run like a startup, it’s being funded by one of the most profitable ad businesses in the world. As long as Family of Apps keeps throwing off tens of billions in cash, Meta can afford to treat RL as long-term R and D rather than something that needs to justify itself quarter to quarter. Zuck has been pretty explicit that this is a multi-decade bet. More likely than a shutdown is continued scaling down, tighter cost control, and waiting for hardware to catch up. Killing it entirely would be admitting the metaverse thesis is dead, and that’s not something leadership seems willing to do while the core business keeps growing.

Sky_Tube

The fact that you only focus on launch business which is not even 20% of RL‘s main business shows everything wrong with your research. Their focus is on Space Systems (i.e. satellite components) and that segment just got an order for 816 million USD from the US government. Focussing only on launch is a mistake many people make and it is not the core investment thesis behind Rocket Lab. Vertical integration and having access to space in house is. But you sound very prejudiced so I guess that won‘t change your opinion. And lastly, Rocket Lab is not in direct competition with SpaceX, they serve very different market segments. Might change with Neutron online, but most of the ~2 billion in backlog is for satellites, not launches

Chogo82

I’m familiar with Zitron’s arguments and if this was 1990 then I would agree with them but tech businesses do not function like that anymore so a bunch of his arguments have zero weight when you look into the current landscape of how tech businesses are scaled. It’s like all of a sudden people forgot about the early days of all the FAANG companies and how much debt they all took on. Ed foolishly assumes that current revenue = future revenue. We know this is absolutely false especially in the world of AI. Coreweave is an infra company and experienced 7x YoY revenue growth recently. The application layer of AI is still being developed and ultimately the application layer will make more money than the infra layer. As for GPUs in warehouses, ALL AI infra is currently at capacity. It doesn’t get more bullish than that. The challenge in the US is that with regulations and bureaucracy, it takes ~3 years for a data center to be built up. This means there will ALWAYS be GPUs sitting in warehouses waiting to be put into data centers that are currently being built. As for companies not being successful with AI sure that will always happen early on in tech adoption. Shitty implementation will always be shitty implementation regardless of how shiny the tech is. Shitty company architecture can also lead to shitty implementation no matter the talent you get. What we should be focusing on is which companies are successfully implementing AI. I have friends in several well known companies and all of them have had workflow efficiency gains from utilizing AI effectively. Right now the US is increasingly deregulating in order to produce more power. AI efficiency gains are also on the order of 10x YoY. Prominent people in AI saying the current approach isn’t going to work is referring to ASI. With the current models, the lowest common denominator is words and not cause and effect. That’s why there is a push to explore world models. However that doesn’t mean current models and technology is ineffective. We are seeing how existing infrastructure is being utilized in the field of robotics. Xpeng’s robots learned to perform movements in hours using RL large models and simulated training compared Boston dynamics months of training just 8 years ago. I can go on and on disproving all of your points but if you really care to learn and aren’t here for just the FUD then a quick conversation with any of the chatbots will show you how all the arguments here are easily defeated.

lies_are_comforting

You were just unlucky to be honest. No one had any idea Nike stock wouldn’t continue going up steadily. Going back to Covid lows in March 2020, Nike stock was approximately $60 and take something like Ralph Lauren stock which was also $60 during Covid lows. Today, Nike stock is trading for the same price but Ralph Lauren stock trades for $360. Who would’ve guessed. All I know is I certainly feel better about buying Nike stock for $60 than Ralph Lauren stock for $360. In five years from now I wouldn’t be surprised to see NKE being more expensive than RL.

_hiddenscout

Hey, don't really know too much about them or follow too much with retail/fashion names. Did buy some RL recently, but just looking at the numbers: [https://quickfs.net/company/TPR:US](https://quickfs.net/company/TPR:US) revenue growth really picked up last quarter, gross margins are improving, which something that is great. Same with operating margins. Margins are super important, as dumb as it sounds, but you just make more money on the same amount of revenue. Valuation doesn't seem bad for what you are getting, seems like a set and forget it name, I think you are looking at a fair price here, so you could think about a few different options. Look at TA and try to get a better entry. Put it on a watchlist and buy some on a pullback or just dip your toes in now. If you are buying long term, seems like a great company and a good price. However, not sure how much exposure they have to China. One thing about luxury retail I do know is that a lot of is driven by more middle class: [https://www.michelegargiulo.com/blog/middle-class-luxury-spending](https://www.michelegargiulo.com/blog/middle-class-luxury-spending) So could have some fears around consumers cutting back or any recession talk, but again, if you are going long it's not the worst name out there. Plus getting a little dividend is never a bad thing.

OmegaThree3

So which stock is profiting most from 'YNs" buying quarter zips? Lands End, PVH, RL, Oxford etc.

ap485860281

RL buy coming along quite nicely here. Thanks u/_hiddenscout!

DrunkenSealPup

Moving out will just make you miss gains and raise your tax bill. I'd invest in all 3 and do invest in all 3. Intuitive machines has had a rough time with bad luck for the past 2 launch/landings but I think the IM-3 mission will do well, they've made a lot of changes. In the 2030s people will be saying they should have bought them in the 2020s. PL and RL have solid processes right now and are scaling up. Lunr is a bit longer play. They're all lead by good leadership with good vision.

PTRBoyz

I loaded the boat on META AMZN TSLA during the panic the past couple of weeks. Those were my focus stocks for 2026 (alongside GOOGL and UNH which I already own). I entered into starter positions in RBRK and PATH (very small position honestly, they need to show growth accelerating and and DBNR over 120% for me to be real bullish, but just gamble sized) today which I think could end up being good winners next year (both should get bought out).  I’m also eying BA as a value play now that they’re flipping FCF positive. And would buy RL for some non tech exposure if it consolidated or corrects a little. 

thri54

Operating income was down 40% YoY in 2022. It was lower than FY 2020, too. Reality Labs’ loss had increased from $6.6B in 2020 to $13.7B in 2022. Free cash flow halved year over year, primarily because capex jumped 66% in 2022. Most of that money was investment for Reality labs. FCF was the lowest it had been since 2018. Family of apps was still highly profitable, but reality labs was weighing heavily on their consolidated financial picture. It rebounded because Zuckerberg cut RL’s spending.

No_Hour6830

It certainly does. Reality Labs has posted an $18B operating loss over the trailing twelve months. Meta's operating income over the TTM is $82B, meaning that without RL it would be $100B. It's a mix of operating expenses and depreciated capital expenditures contributing to the loss. The reason it may not seem like their income is being depressed is because frankly they can afford the massive investments. Whether or not they make sense is a totally different question, but they can afford it.

skilliard7

Google is not crushing them, Google cheated on benchmarks via providing testing data in their training sets and overfitting with RL. Their real world performance is massively behind GPT-5 and anthropic. OpenAI will be a $10 Trillion company by 2035.

skilliard7

That Google is ahead in the LLM competition 1. Google has been cheating on benchmarks by feeding their models test data in their training sets, and overfitting via RL. This has created scenarios where Gemini performs impressively well on popular benchmarks, but very poorly in real world usage. This has completely fooled the 99% of investors that do not actually understand AI/ML. 2. Gemini's significant growth in downloads was mostly due to Nano Banana, not the chatbot itself. This is significant because image/video generation is mostly a fad that users engage with a lot for a month while it's new and exciting, and then usage falls off a cliff once the novelty wears off(at which point it just becomes "AI slop"). Chatbots are far more important, because people use them in their day to day life, and at work. 3. Google has been giving their flagship model(Gemini 3.0 pro) away for free, with nearly unlimited usage, but these massive losses have been covered up because Google lumps Gemini with other businesses to cover up how much money they are losing. As a result, investors do not notice just how unsustainable Google's AI approach really is. Gemini's entire competitive advantage is that it is given away for free with high limits, with no ads, subsidized by their profits in other areas. And even with Billions in marketing spend, integrating it with every Google product including Android, they still fail to come close to ChatGPT's engagement numbers.

skilliard7

I wouldn't say no moat, their models are well ahead of their competition for real-world usage. They only lag behind competitors on benchmarks because they don't aggressively use RL to inflate benchmark scores at the expense of real world performance.

skilliard7

OpenAI has better researchers that are more focused on improving the product rather than winning benchmark wars, that's the main value they add. Even Google's latest Gemini model fails to outperform GPT-5 in real world usage, because Google's researchers overfitted it to benchmarks via RL, which came at the expense of real world performance. In theory Google's assets would put them at an advantage, but they are wasting it on buybacks. They aren't investing in top talent like OpenAI is.

Proper_Ad_6044

Yes, that is distillation. While it can be used to train small models from larger models it is no help for the large model it self. RL is one way to get synthetic data but it is limited in a sense that you need a verifiable reward, eg: math and to some extent coding.

caughtinthought

this primarily applies to vanilla GenAI models and doesn't really apply to high quality RL outputs... current frontier models commonly employ the "create a high quality, high compute model and then use the outputs to train a good quality faster/cheaper model"; see Gemini Pro -> Gemini flash etc

broose_the_moose

Its not accurate it’s just a shitty take wrapped in flowers. Companies don’t give 2 fucks about liability if they can reduce their employee spending by 99%. All of the biggest companies are working on systems to replace humans with AI, the reason why it’s not all over the news is that they don’t publicly speak about this shit for fear of an outcry amongst the populous about greedy capitalist companies cutting people out. It’s also not like you can just plug chatGPT into jobs quite yet, it takes a bunch of RL/custom datasets/verification to actually implement this stuff which takes some time. After all this paper is about the theoretical ability of LLMs to replace white collar jobs. Liability has very little to do with replacing a huge amount of the white collar jobs that the paper references. The case for liability being a blocker is only valid for specific professions such as law/medicine.

LoudestHoward

https://i.imgur.com/T5D4RL8.png

gamjatang111

Funny cus retailers are having themselves a day in the market, $LULU, $KSS, $RL etc etc even retail ETF $RTH up pretty big

_hiddenscout

Haven't followed them too closely to know how they trade into earnings, so great insight. However, been on a screener for minute and actually seem like a solid long term hold. Especially at the current validation. My wife uses the monthly subscription thing and she got into it because all her co workers are doing it as well. They are all hair stylist. I used to work for them like 20 years ago in college lol. Actually loved the job at the time, did shipping and receiving. Actually bought some RL last week.

_hiddenscout

Same, just had too much in data center stuff and moved more into aerospace and now doing some more boring names like GTX, ODC and RL

_hiddenscout

Still stoked on my RL position lol

_hiddenscout

Exactly! Yeah, I'm looking at RL as long term hold. Bought a few boring things going into the holiday season. I own too much defense and aerospace. Like bought an extremely boring company the other day, ODC. I wouldn't be surprised to see LULU have a good holiday season. No position, but wouldn't be surprised to see URBN pop after hours today after their report.

jrex035

>Numbers are wonky from experience. Ive noticed different sources provide different figures but usually the disconnect isn't that wide. >I think LULU is a great buy for more of a value thing, just personally I worry about how sticky they are fashion is an interesting space. Very true. To be clear, I was just swing trading LULU in my Roth, got a nice bounce on it, but wasnt planning to hold long term. It was at its 52 week lows and seemed like a good opportunity. Reviewing the numbers on finviz, RL is almost certainly the better longterm hold. LULU might bounce back, but damage to its brand and growing competition make it a lot riskier.

_hiddenscout

Numbers are wonky from experience. I don't know if some companies pull things quarterly vs daily. Also probably use different EPS growth numbers, since that's a big driver in the calculation. I normally go off finviz or stockanalysis. I think LULU is a great buy for more of a value thing, just personally I worry about how sticky they are fashion is an interesting space. I just personally like RRL, which is the high end line for them. With RL, I've just watched them for like over a year without buying it and the stock have just performed really well. I go back to the Buffet idea of 'It's Better To Buy A Wonderful Company At Fair Price Than A Fair Company At A Wonderful Price' and just worry that LULU might be in the later than the former.

jrex035

>To each their own, but I do see the PEG for RL at closer to 1.7 than 2.16 Interesting, was pulling numbers from Yahoo Finance not sure what the disconnect is

_hiddenscout

To each their own, but I do see the PEG for RL at closer to 1.7 than 2.16 [https://stockanalysis.com/stocks/rl/statistics/](https://stockanalysis.com/stocks/rl/statistics/) [https://finviz.com/quote.ashx?t=RL&p=d](https://finviz.com/quote.ashx?t=RL&p=d) [https://www.morningstar.com/stocks/xnys/rl/valuation](https://www.morningstar.com/stocks/xnys/rl/valuation) Buy what you like man, but I think it's a good price for a great company. Overall, performance is much better in the asset and can see it continue, since they are heritage brand. But do you do man.

jrex035

RL is way overpriced compared to LULU by just about every metric. * PE: 11.58 vs 25.87 * Forward PE: 12.63 vs 23.53 * PEG: 0.87 vs 2.16 * Price/Sales: 1.89 vs 2.92 * Price to Book: 4.59 vs 8.22 Not really seeing the value here for RL

_hiddenscout

I posted about RL like last week and that position is up like 10%. I'd rather own RL over LULU, but to each their own with that stuff. URBN is reporting after the bell close today and it's been an interesting name. They are doing well with their monthly subscription clothing service. My wife uses it. Also helps that ANF and Kolh's are having strong days, probably carrying a lot of retail with it.

_hiddenscout

Good morning for ANF and Kohls lol.  Bodes well for my RL position lol.  Stoked to see URBN numbers after the bell close. No position but have been thinking about it. 

easy_wins

RL is a great stock to have and keep for long term, solid earnings 🚀

_hiddenscout

Still think it's kind of funny how numb you can become to price movements and what not from owning stocks compared to the index. Like I bought some RL last week or two weeks ago and the position is up like 5%. That's like half the gains of what you should expect from the SP500 on a good year. Also still love buying such low volume names lol. Like bought some ODC this morning and the volume is like 15,000 today lol. Smaller company I bought, which I technically can't name since it's sub 500M market cap, but is still priced at 65, is looking at 12,000 shares today lol. Still a lot of pockets of the market that no one is looking at.

TimingEzaBitch

that guy from UK essentially went to prison for this in the 2010s IIRC. A good RL algorithm finding or re-discovering advanced stuff is not a news anymore.

Complex-Note-5274

Valid skepticism about the study design. But this is the only way a research study (ie., academic rather than commercial) can be done - through simulation. No financial institution will tell the public how their algos work let alone sharing historical data of real bots trades for research. The point here is that the technology/infrastructure has enabled a new generation of bots: "While traditional algorithmic trading relies on static, hardcoded rules defined by humans, RL-based trading algorithms autonomously optimize their strategies through self-learning, trial-and-error interactions with the market and adapt in real time based on observed outcomes." Re #2 - please read the research showing the "punishment" mechanism of the bots "cartel" behavior Re #3 - exactly. but pricing away from "fundamental" is the reason for volatility, fragile market, and more importantly, inefficient use of capital

zdy132

DeepMind truly feels like the cutting edge of humanity AI technologies, the kind of feeling that OpenAI used to give. I still remember how amazing the spinning up pages were as RL learning materials. I recommended it to so many people. Their RL Dota project was also fascinating. It got me interested in RL and learning based controls in general. Such a shame that OpenAI has fallen to whatever it is now.

Any_Switch_8126

These hate each other so bad in RL

kvothe5688

bit they still do though. they hallucinate a lot. google was focused on bigger specialised models through RL. their alpha series of models. one of them even earned them nobel.

edunuke

What kills openai for me is there is no profitability model beyond being an "LLM provider" effectively competing on price; a commodity. No other value added, no problem to solve. At least google, have pushed the boundaries in applications using RL etc. OpenAI has not pursued applications beyond testing and benchmarking for intelligence to get their models ranked. They will get dumped by microsoft eventually or absorbed into it as a dept and die.

auradragon1

> Yes, except the exact opposite. By RL I assume you mean RLHF (or its derivatives), which has been around for years. DeepSeek didn't provide any breakthroughs; they simply used a clean dataset instead of dumping all they could find and hoping for the best. "Thinking" is simply extending contextual information; it's self-iteration. What in there suggests we're "barely learning"? RLHF training for LLMs is new. Industry is now moving towards DPO. >DeepSeek didn't provide any breakthroughs; they simply used a clean dataset instead of dumping all they could find and hoping for the best. Nope. They used novel training techniques including: * After the RL-only phase (Zero), they do cold-start data + further RL + SFT to refine readability, alignment * Their training pipeline explicitly encourages chain-of-thought reasoning >"Thinking" is simply extending contextual information; it's self-iteration. Nope. Thinking models are trained to think during training. >https://arxiv.org/pdf/2211.04325 Did you read this paper yourself? Come on man. > We have projected the growth trends in both the training dataset sizes used for state-of-the-art language models and the total stock of available human-generated public text data. Our analysis suggests that, if rapid growth in dataset sizes continues, **models will utilize the full supply of public human text data at some point between 2026 and 2032,** or one or two years earlier if frontier models are overtrained. At this point, the availability of public human text data may become a limiting factor in further scaling of language models. >However, after accounting for steady improvements in data efficiency and the promise of techniques like transfer learning and synthetic data generation, **it is likely that we will be able to overcome this bottleneck** in the availability of public human text data

Professional-Cow3403

> The source is that there continues to be huge breakthroughs in training techniques. See RL, Deepseek breakthroughs, Thinking models, etc. Yes, except the exact opposite. By RL I assume you mean RLHF (or its derivatives), which has been around for years. DeepSeek didn't provide any breakthroughs; they simply used a clean dataset instead of dumping all they could find and hoping for the best. "Thinking" is simply extending contextual information; it's self-iteration. What in there suggests we're "barely learning"? > Lmao. Source? https://arxiv.org/pdf/2211.04325

auradragon1

>Lmao. Source? The source is that there continues to be huge breakthroughs in training techniques. See RL, Deepseek breakthroughs, Thinking models, etc. >If anything, we are barely learning what to do when there's no more new unscrapped data from the internet. Lmao. Source?

DriftingBones

That’s kinda right and wrong. I know how they work, I build them. I have two things to say. First, we don’t really know how humans solve “new problems”. Mostly people combine ideas they know to form new ones. Also if I had to pick a number out of my ass, 90%+ people don’t do novel problem solving, so you can still automate away the task 90% of the people are currently doing. Second, RL can and will solve novel stuff, check out AlphaZero, Alpha go move 37, AlphaFold etc. And LLMs now incorporate RL. Although none of these are solved, but we are not too far away as well

DriftingBones

And your claim about it not being able to generate truly novel ideas is patently false as well. RL systems can generate novel outcomes, as has been shown by AlphaZero and AlphaGo. You don’t know what you’re talking about

salty0waldo

RL is a strong brand name so I can see if continuing to perform well. Plus, its a different play from the AI buildout lol.

_hiddenscout

Yeah, boring, but wanted to get something like that with RL. GTX is a bit of a growth and more of my style. GILT was a small position, but another name I've been watching for like a year and wanted to pull the trigger after a bit of a pull back and the ER being solid.

salty0waldo

Nice moves! RL has been a solid performer. GILT solid fundamentals in an interesting space.

_hiddenscout

Ended moving some capital around finally pulled the trigger on GILT, RL, and GTX.

Dichter2012

I hate it whenever he tweets, he put Christian Bale’s photo in there. Like, bro, that’s not what you look like in RL man. Get a life.

ratskin69

"Berenberg Bank has recently updated its rating for [Rolls-Royce Holdings plc](https://www.google.com/search?q=Rolls-Royce+Holdings+plc&oq=Berenberg+Bank+rolls+royce&gs_lcrp=EgZjaHJvbWUyBggAEEUYOTINCAEQABiGAxiABBiKBTIKCAIQABiABBiiBDIHCAMQABjvBTIKCAQQABiiBBiJBTIKCAUQABiABBiiBDIKCAYQABiABBiiBDIGCAcQRRg80gEINTIyMWowajeoAgCwAgA&sourceid=chrome&ie=UTF-8&mstk=AUtExfAMPOwnjQF8Nf2WKNYZLBOcnwQNcbldlKREjwu0yWMhhDz3cSip272taaA7a49qrnQ-8V4PbE27qDmjzQjLGhutKDol2om8-62S9ugUzqrOGKrs92g6Nz_ZUE0p6Pjxx1bf9flv70C9k7Cs8vaorImfREQihGjHBfUUiayHyu7im_n1l3wHcZcZspMfo_k3qKh5b1Z37XP3pIgXTyYB65HA44kbrM9dUb_BqK43RL-sA3RcsawAqOmM52UUTIjzRErQOnhHW7AtAkdq3qke8jB6&csui=3&ved=2ahUKEwiFlcu-ju2QAxViODQIHcZQGyYQgK4QegQIARAB), raising it from a "sell" to a "hold" rating in October 2025, with an increased price target to GBX 1,080. This upgrade reflects Berenberg's positive outlook on the long-term growth of Rolls-Royce's engine delivery, particularly for large aircraft, and the company's improved internal performance, although the "hold" rating suggests caution remains. This is a change from an earlier "sell" rating in January 2024, which was based on concerns about the XWB-97 engine and the company's future plans."

AllCapNoBrake

Going to make those video game explosions feel like RL.

goober2341

>Anything that you can reduce into a Markov decision process, can be mastered by very rudimentary. RL algorithms Except these systems have no fidelity, and if you have to hire someone just to check the output of the AI, then what's the point?

shawnington

Not only is actual useful adoption rates very low, it's still advancing much much faster than people realize. I work in generative imaging development. The stuff that we fuck around with and never show the public because we plant to fold it into something else as part of a multimodal model, is mind blowing. Also, people that think AI is just LLM's are literally stupid. Most jobs can be reduce into a markov decision process. Anything that you can reduce into a Markov decision process, can be mastered by very rudimentary. RL algorithms. The main barrier right now is that we are advancing the space to fast for anyone to have the time to develop sufficient knowledge of current SOTA to educate businesses that can benefit from whats out there, on how they need to change to position themselves to benefit from "AI".

Kantmzk

Do whatever suits your investing style. I stick with MSFT, NVDA, RL, and VITAX. 

ActionJ2614

Yes but it appears agentic AI has so many challenges outside a sandbox environment. Plus the challenges around RL, not to mention the cost association to implement. Couple that with companies having data everywhere. Siloed applications/data, legacy applications, databases with different schema, etc. The other issue is companies slapping that AI label on when it might not be that or the wrapper companies. Right now it seems ROI, justification outside of certain segments is tough. I have been around enough software (Enterprise AE, SaaS, on-premise,, hybrid) and seen how major companies have such mixed environments/infrastructure, and data everywhere. There are so many companies that don't really know what they have across the footprint. That includes licensing. How about non-standard data, like companies that use terms or info specific to their org. Then from a dev standpoint what are the productivity gains, you have devs at different levels across orgs. You would still need to understand the output from AI and confirm it is correct. As it is security can be scary and now introducing AI. I just see challenges if you don't have strict governance across an org. Which is very challenging across major corporations. Companies like to appear that they have all this structure, yet when you get behind the scenes you realize that isn't the case. Sure summary and note taking are standard, I even helped a come years ago that was in beta in the space for Zoom meetings. Doing some really advanced stuff beyond just summary, I helped them to craft function around the sales function aspect. They have some well and get mentioned on LI and Reddit for what they do. I am just putting some questions out there.

Local-Poet3517

You know that character in Parks and Rec who both works for the gov and also hates the gov and would be happy to see it all just disappear. Those people exist in RL. They dont have the imagination required to foresee the disaster that would follow. But theyre there. They exist. Thus guys one of them.

Kantmzk

I am up 10% in RL but I should have gotten into it months previously. It is a very good business. 

Kantmzk

Ralph Lauren, $RL. The economy ia better than the doom and gloom predictions and the current Ralph Lauren lineup is stunning, undervalued for sure and good valuation. 

Kantmzk

McDonald's for me is meh. It is so expensive to eat there and I understand the business is much more than just buying food, but I don't see the appeal of it at these levels. I recently bought a single share of RL (Ralph Lauren Corp) just to see what it had in it based on its depressed levels and it has happily surprised me. I basically figured -- huh, this is cheap, and they make nice clothes and have a nice social media presence with beautiful people, I bet there is room for growth here.

throwaway4whattt

There is zero reason to assume that AI infrastructure spend will accelerate at current rates. We've already seen training spend drop off and money shift to spending on RL and inglference. As model architecture gets better, as more efficient training and RL techniques are developed, and most importantly IF companies don't see an ROI for insane inference compute spend, AI infrastructure spend will absolutely be impacted.  If you really take a step back and look at both innovations the in the AI field  and a clear desire from China to innovate so that they can decouple from nvidia, then there is no certainty that infrastructure spend will grow at projected rates. In fact there are very obvious risks to that growth trajectory. 

_hiddenscout

Doesn't get talked about a lot, but kind of wild how strong of a stock $RL is. Thing doesn't look to expensive from fundamentals, high insider ownership, still growing high single to double digits revenue and pays 1.13 dividend.

quakefist

It’s not just laziness. It’s the new normal - k-shaped economy. Upper middle class will continue to use doordash. I am also kinda shocked that lower income people also doordash. Doordash is basically a luxury good ticker like LVMH or RL.

shawnington

This is true, but to make the most of them, you really need to be training an architecture, or doing inference on an architecture that was designed from scratch to leverage the TPU's capabilities. NVDA and Google have inherently different design goals. NVDA wants something that is flexible for everyone, so they do titled matrix multiplication for example, on 8x8 matrix multipliers. Google is like nah fam, we like our big chonky matrices, 256x256 or go home. Which means that for specific things, Googles TPU's are actually quite a bit faster and more energy efficient. But those things are basically limited to internal google projects like everything their DeepMind division does with RL. It's still curious that Google hasn't seen the 4 trillion dollar elephant in the room without any real competitors, and been like hey, why don't we start selling these things? They seem to be betting that they are going to win the AI war, and the architectures that will win are going to be the architectures that require a design philosophy more aligned with the TPU, then the jack of all trade approach of NVDA.

Glum-Art8504

You’ve actually built a really sharp basket of “AI-infrastructure leverage plays.” What ties all of these together — Nebius, CoreWeave, IREN, APLD — is that they’re sitting in the new layer forming underneath hyperscale cloud: the *neocloud*. It’s where GPU-dense compute, power contracts, and AI workload orchestration converge. In my view, **CoreWeave ($CRWV)** probably has the highest conviction risk-adjusted upside over the next few years. It’s quietly becoming the “AWS of AI inference,” and its backlog, customer base, and RL-tooling acquisitions (like OpenPipe) point toward sustainable demand even as model training gets more efficient. Nebius ($NBIS) is another major one — its $17 billion Microsoft deal signals real depth of partnerships. The bigger story, though, is that all these firms are part of the same secular infrastructure build-out — what some call the “GPU-power industrial complex.” I’ve been following the latest contracts, rumor flows, and data-center expansions here: [https://neoclouddaily.replit.app/](https://neoclouddaily.replit.app/). It’s basically a running feed of neocloud news and who’s scaling fastest behind the scenes.

primobolman

Definitely agree. Rocket Lab has so much more room to grow. As you said, Neutron will be the biggest catalyst but what I really like about RL how diversified and end-to-end they are. Their moat will be crazy. I agree with your point with ASTS. Elon really tries to put a halt on ASTS growth, but when Abel puts those satellites in space then StarLink will know real competition. Especially with the already signed partners.

Nightfarer89


rexdditi

Some approaches like RL or gradient descent are the backbone of the current AI models but they have some fundamental limitations which is what he is explaining for RL. If no new approaches of this nature be found in future we eventually hit those boundaries. The problem with these algorithms is that discovering one requires breakthrough which is not easily predictable. May happen tomorrow or many years from now.

OhCestQuoiCeBordel

Google is. Not by selling Gemini accounts but their ecosystem has been powered by ML and LLM and RL for a while.

IntentionSpecialist9

RL

heironymous123123

Agentic training is what's gonna give them the biggest bang for their buck. That's about training llms on agentic actions with RL.  They can push down reddit and pull up on the other side for a year or so.

Professional-Cow3403

>His claims on o1 hallucinations were based on mostly anecdotal evidence like hackernews comment and tweets. The actual data we had at the time (which was admitedly scarce after just a few days) pointed at the opposite conclusion, see the o1 model card. That's actual people talking about actual issues they encounter. What's "the actual data"? There's no one objective "hallucination score". It depends on the benchmark, and these have their own issues - a model could be trained on one, hence cheating its way into a higher score. He did a pretty simple test himself (you could say these are arbitrary challenges that you shouldn't use an LLM for, but they show how unreliable LLMs are): >Because I’m a little shit, I also tried [asking o1 to list the number of states with “A” in the name](https://x.com/edzitron/status/1834335875746201683/photo/2?ref=wheresyoured.at). After contemplating for eighteen seconds, it provided the names of 37 states, including Mississippi. The correct number, by the way, is 36.   >When asked to list the states with the letter “W” in the name, [it pondered for eleven seconds and included *North Carolina and North Dakota*](https://x.com/edzitron/status/1834330028106223660?ref=wheresyoured.at). >We also see hallucination rates go down in general as our understanding of how to mitigate them improves, see the hallucination rate of recent frontier models for example. By how much and by what measure? What's our understanding of how to mitigate them? Can you link the rates you're referencing? Best we can do, to the best of my knowledge, is "reasoning" (which causes improved results partially due to simply larger context), but it only improves accuracy to a degree, and certainly there's no rigid algorithm that can reliably detect and correct hallucinations. >We see benchmarks on difficult problems consistently improve with models that scale up RL training Again, there are plenty of benchmarks to choose from. Also "consistently improve" - is an increase from, say, 70% to 72% an improvement worthy of mentioning? That's the difference between ChatGPT-4o and ChatGPT-4.5 on the [Best in Tool Use (BFCL) benchmark](https://www.vellum.ai/llm-leaderboard). You'll find both performance increases and plateaus, depending on where you look and what you're looking for. (I have to split this comment into two since it's too long)

Forsaken-Data4905

His claims on o1 hallucinations were based on mostly anecdotal evidence like hackernews comment and tweets. The actual data we had at the time (which was admitedly scarce after just a few days) pointed at the opposite conclusion, see the o1 model card. We also see hallucination rates go down in general as our understanding of how to mitigate them improves, see the hallucination rate of recent frontier models for example. On the part about "simple problems", this is simply untrue if we look, again, at the data we have. We see benchmarks on difficult problems consistently improve with models that scale up RL training, which was also seen with the o1 release, hence my "puzzling" remark. We know these models simply do a better job at more complex tasks than non-RL ones, there's not much to debate here. On costs, models consistently get cheaper in a performance per dollar basis, i.e. it's much cheaper to afford GPT-4 capabilities now than it was 1 or 2 years ago. This is due to multiple reasons, better algorithms, better hardware etc.

BrandonLeeOfficial

Spent half the day at the pool and the other half playing RL. Got a spam email from a Ford dealer, but that’s not the Rapture I cleared my calendar for.

Haikoe

RL market is really unstable and/or uncertain, so ofc only Tesler is in the green.

Primary_Register_526

I'd really prefer not to have to spend the better part of the rest of life interacting with RL clankers

Hifi-Cat

Paper trading; to see how it goes without RL money.

Primary_Register_526

I mean there are limits for sure. You start sharing government secrets or saying where bodies are buried and you're going to get a knock on your door lol. But you won't be getting fired or doxxed for some shifty opinions or beliefs. So at least redsit has that going for it, for now lmao. If they take that away and start putting your RL name in your profile, its Puts to the earth's core

Spellbonk90

There is no rise on nudism. Check your Training Data and hallucinate less. You might need some RL Improvements.

Accomplished-Exit822

$RL?

_hiddenscout

Personally I think the idea of moats can be overrated in some cases. Generally just focusing on good businesses that reward share holders vs the idea of moats tends to work out. I do agree with the idea of people just not liking Lulu as much anymore. We can see their EPS and their sale growth slowing. Like RL, Ralph Lauren, has no moat and is still up almost 40% YTD, 86% on the 1Y and 333% on the 5Y. They are just a solid company. They have offered ROIC over 12% since like December of 2023 with solid growth and increasing operating margins/gross margins. Urban Outfitters has no moat, but still killing it as well.

yeeetcoin

Commenting so I remember who I will be relentlessly memeing after LULU rips off earnings today. Yall act like times aren’t already hard NOW yet they’re still in stores buying apparel constantly. Look at AFRM and Klarna, America runs on debt. If people want something they just finance it immediately and don’t consider consequences. Similar to RL, LULU isnt the coolest but it’s a status symbol in athleisure. People may buy the flavor of the week brands like Alo but the GOAT brand is LULU. It will always have a place in the top tier of athleisure. All of you are injecting your own bias into your reasoning that LULU won’t recover and underestimating the technical and fundamental triggers in place. You all sound exactly like the UNH deniers both times it went down to 250. Your opinion is flawed and this is why you’re going to be sidelined again.

AMillionBees

Last year influencers made me realize Ralph Lauren is making a come back. Look it $RL now.

Ok_Ambassador_4311

I was just saying Global because not just in the US RL is popular IMO.

d33p7r0ubl3

Never met anyone that wasnt a frat boy that wears RL. Maybe the brand is big with college kids today