ML

IslandExact429

Had 49ers ML Cashed out at a loss Didn’t feel it. Watched Q3 and thought Eagles would just play chop ball

randylush

GPUs are still the bottleneck fam. ML practitioners are not limited by SSDs. Those are a commodity.

Trans-Squatter

Money is never destroyed. It only changes hands. They buy gpus, energy, pay ML engineers, data. The people working in this sector are getting paid, then they go to the strip club, and the young single mom also gets paid. Monday changing hands is the lifeblood of the economy. What should they be doing it, hoarding it? putting it under their mattress? That's when the economy dies out. Oh but you say, if they used that money more productively, they would have made more money! And where would that money have come from I ask you? Their clients - other companies that somebody would point the finger to and say "Look at those idiots burning money paying Google" like the posts calling people who buy every new iphone stupid. is money. In the end the government prints more and whether google execs burn their money building datacenters, or sniffing cocaine of a down syndrome dwarf in Thailand - the outcome is always the same. Government is going to print that government is going to print, and that money will circulate in the economy. What changes is who gets to hold it. That's all. Money can never be spend badly. Money is good only when it is being spent in any way possible.

dirty_F0x

Brother you are missing something fundamental it seems like. You are trying to predict where IV will be (which is what your GARCH model seems to be doing) or describing if IV is expensive compared to its historical past. It is about having a view on whether volatility implied in the options today have a chance to exceed the subsequent realized volatility. That is the name of the game with options. And essentially it is the variance risk premium analysis. You start to see many ex pro traders working at reputable places talking about it in a much more eloquent way than me. You should check Ksander from Sharpe Two: he has a background in ML and ex trader, and he predicts exactly this. It feels the closest to what people would do at a firm. It is inaccessible to most retails traders because we do not have the data and the infrastructure he has built overtime, but he put all his trade in his substack and his option analytics platform is great and super convenient. Same idea with Kris from Moontower, although I feel like Kris is a little less practical from a trading standpoint. In any case, stay far far far away from technical analysis when trading options, your intuition is right: no one ever got paid big bucks at SIG or Citadel for the chart pattern abilities. But for advanced quantitative skills...? That's why they win so often at the first place.

Master___debator

It’s literally just linear regression slope over N days. No indicators, no ML, no magic. i asked GPT for help with the visualization https://preview.redd.it/d4mu0knv88cg1.png?width=1536&format=png&auto=webp&s=ed0747c15b112448df70d09ccada625a79ab3faa

EyeSea7923

I have an AI/ML degree and do some coding, but not technically the software engineer I think you are looking for

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.

Real-Introduction221

Iowa state ML and under total point tonight

Betteroffbroke

Still the buy. I’ve bought and sold RDDT three times and doubled my money every time. I’m holding shares and buying more on the next dip from AI fear mongering. I just read an article about Reddit being the 4th most visited site in the Uk beating out TikTok with the majority of its users being Gen Z (that’s the target market for social apps!). They only expanded into Europe a few years ago and are seeing big growth in India. The flywheel is gaining speed with new countries adopting due to AI/ML allowing for translation across languages, a subreddit for anyone about anything, and the massive opportunity for monetization - Reddit provides advertisers a unique opportunity to learn about what people are saying about their products + the ability to market their products to specific audiences based on their interests. That’s just my bias two cents.

LordFaquaad

Is this the similar thing as "Cuda" being used for ML applications so it essentially locks in the framework being used for developing autonomous vehicles? Wouldn't manufacturers still have to actually build out the model, understand how to incorporate into the car + sensors needed, etc.

cloud9ineteen

I don't know what you're smoking. Nvidia invested heavily into CUDA and made it the de facto standard for ML work. Even before the latest AI boom, Nvidia was pretty much the only game in town for ML. Now the circumstances went crazy beyond their wildest dreams but their vision absolutely set them up for this. AMD is up 138% in 5 years. NVDA is up 1300% in that time. One of these is not like the other. If you want to compare a company that was a bit more prepared for AI, Broadcom is up 700% in that time. I would say AMD utterly and completely failed to take advantage of the situation and underperformed the baseline. Regardless of the recent boom, AI/ML as a market for GPUs has been in the air for long and AMD pretty much failed completely to take advantage of this situation.

im_burning_cookies

Every life decision I’ve ever made has come down to this moment. Every options trade, every buy every sell, every paycheck collected. It all comes down to this. Putting it all down on something I KNOW will happen. Full port 14$. Illinois ML.

KuchKhaasHaiYNWA

My personal recommendation with a roughly 40 to 50 percent upside considering its current rate would be ServiceNow(NOW) stock that is simply pretty undervalued considering its fundamentals are super strong. They have next to no debt and have a pretty robust standing in the SaaS market which is only getting fuelled by AI/ML based solutions that the company offers along with its pivot to CyberSecurity sector with its recent acquisition Artemis. Bill Mcdermott is a proven leader and knows how to steer the ship when the tides are against us! Company also believes in him and have continued to invest in his leadership. I have personally invested 70 percent of my portfolio as the stock seems like a wonderful buying opportunity.

NOT_MartinShkreli

49ers ML and Jags ML cuz I got now market plays is my move today lol

Asid_Phreak

I built my own from the ground up. I use IBKR and Databento as my only outside vendors. Everything besides that; code, logic, modules, back testing, execution is my work. I’m only running paper now but I’m at 83.6% success rate on scalping IWM AND SPY 0DTE. I’m integrating ML into it this week for strategy development. I have over 600TB of data patiently waiting to be replayed over and over again. Once a strategy is selected it’s sent out for verification, if it meets specs then and only then is it allowed a chance on the paper to prove itself. I’m aiming for 97% accuracy before going full prod.

dirty_F0x

Sorry for your loss brother. Too many people don't focus enough on understanding the true pnl driver when trading options and remain stuck in hyper leveraged directional bet, when options is an insurance contract on how much the underlying will move. If it moves more than what was implied in the contract, you will have to pay the claim (if you sold the option) and you will be entitled to the claim if you were long the option. That is why having a view on implied and realized volatility is so important. It is not the only factor obviously, otherwise it would be an easy game everybody on reddit would have mastered. I won't bore you more than that but if you want to go deeper, and still learn the correct way I can point you to two resources I find infinitely valuable, both have worked at reputable firms, not like the monkeys we find too often on youtube: check Kris from Moontower, he has so much free content about how pros think about options, I dont understand why people still bother with wheeling and stuff when this guy is out there, explaining how he would does it as a retail when before he worked at Susquehanna. Then you have Ksander from Sharpe Two. You should read his trade anatomy series on Substack, it is eye opening on how pros do pnl attribution to validate whether or not the thesis leading to the trade was correct or not. That mf hasn't missed in 6 months; he uses ML (he used to work at an AI company) to predict what is too expensive and ... well it works. They also both have analytics software: I find Ksander stuff really top notch as the ML models explain how they come up with decisions. That is so valuable to keep building your own trading intuition overtime.

dirty_F0x

If you are interested into vol strategies, there are now a few software out there targeting retail traders and get close to what you find quant traders use. I have tested all of the one below and will give you my honest opinion: \- UnusualWhales: Great idea when it came out, but it is impossible to make money out of that thing. I would stay clear if you actually look for edge, but they have nice viz and sometimes it's nice to spend a friday afternoon looking for weird flows on obscure tickers. \- Spotgamma: this one is one I don't believe why it is so popular. Purely focus on directional trading and more specifically 0dte. They have supposedly some prop measures to compute dealer exposure, but when you talk with pros and do your due diligence a little, they all tell you the same thing: this is a fantasy and the market doesn't work like this. Unless you trade billions and work as a flow trader, there is no edge for a retail trader here. But again, nice app, great content. One last thing: do not ask annoying question about showing edge and profitability over time, you will get banned. \- Moontower ai: great tool from Kris who has been writing so much about vol trading over the years. He has worked at SIG for many years and knows what he is doing. He is focused on vol strategies, particularly the VRP. Now, my honest take is his tool is confusing and if you do not have his level of expertise, you are still left "guessing" or using your own experience to find what is the best trade. \- Sharpe two: amazing tool by Ksander. He uses ML to score where you should short or long vol on many tickers. And ... well it works. The guy has a background in trading and ML and ... it shows: he writes on substack and hasn't had a losing trades in 6 months. The tool is easy to use once you understand the concept of probabilities. What I love is the model output the reason why it makes a prediction which is very handy to keep learning and not just follow a black box. The downside: it requires some reframing of how you think trading. He doesn't do directional trading at all and is almost exclusively in ETFs. Def worth checking. I'll finish with Predicting Alpha who wishes they were what Moontower and Sharpe Two is. Except ... they are not. I lost a lot of money with them because their data were not accurate, but also they do not have a trading background. And how much Sean can be a nice guy, when shit hits the fan, you want to be in the community of someone who knows what he is doing. That's why I prefer Kris and Ksander's stuff: I learn a ton with Kris, I make money with Ksander.

autumndrop

I think this frames Amazon a bit narrowly as a “product company” in the consumer sense. Amazon’s edge has historically been operational + platform DNA, not polished end-user products. In AI, that actually maps pretty well to infrastructure, tooling, and distribution — which is why AWS, custom silicon, and internal ML deployment matter more than a breakout consumer AI product. They may never “lead” AI the way Apple leads hardware or OpenAI leads models, but they don’t need to. If AI becomes embedded across commerce, logistics, cloud, and enterprise workflows, Amazon can still be one of the biggest beneficiaries without winning the narrative.

seanpuppy

Based on other comments here, Im guessing he owns the hardware he runs his backtests on. I have never written a trading algorithm but i've written several backtesting pipelines for ML models at each of my past jobs. Im guessing he wrote the meat and potatoes of the math in CUDA and in order to pump a ton of data through it. I think the biggest bottle neck isn't some massive supply of raw data, but trying lots of combos of parameters through some sort of monte carlo simulation - against all securities data. He said elsewhere he spends $1k/month on data electricity

mollusc_in_the_wind

False equivalence. ClickHouse/CatBoost are software projects (largely open-source) that can be great on their own merits. NBIS’ business model is renting compute capacity (GPUs + power + facilities) with an orchestration layer. Being able to build good ML/database software does not prove you can build a defensible neocloud with durable pricing power.

NVDAismyg0d

Bama ML. Turn $1500 into 5,000

fooish_moral

where the dude that guaranteed osu ML several days ago? thanks bro...

daishi55

Nope! You are simply incorrect. GenAI does learn, that’s what happens when they are training the model. > I have a masters in ML Then you need to get a refund. “The models just predict” that is what literally everything in machine learning is about. It is astounding that you could complete a masters in ML without learning that! Mind boggling. You should genuinely sue the university who wasted however many years of your life.

Hacking_the_Gibson

Only in the sense that machine learning is a broad enough term that it can encapsulate anything in this field.  Classic ML is really quite different from generative AI. Machine learning is what enables your phone to predict your battery usage and display the expected expiration. The applications of more stereotypical ML are a lot more bounded. Generative AI is a fucking free for all. 

One_Opportunity9167

Open new IRA's IMMEDIATELY. Put in the full amount...not just the check, but also what was withheld. Borrow if you have to. The ML withholding will come back to you once you file your 2025 taxes. If you fail to deposit the full amount into the new IRAs, the amount you're short will: \-Be taxable \-Probably have a penalty \-Will be gone from your tax-advantaged assets Mitigating factors: \-You can always withdraw the amount of the original Roth IRA contribution without penalty or taxes, just not the earnings. \-$22K is a lot of money when you're young, but it's not that much over your whole life. Stuff happens all the time and it's no fun when it happens, but keeping this in mind might help you sleep at night. If it were me: I would do what ML suggests and open new IRAs with them because it's faster. Put in the full amount, even if I had to borrow (short term). Then, in 2027, I would transfer all my IRAs to Fidelity or Vanguard. Who the hell puts this kind of crap on a customer the last week of the year?

BeneficialCoffee6589

This isn’t politics or PR — it’s about who actually builds the tech. After the U.S., Israel punches at the very top in exporting advanced technology because the breakthroughs are real. • Mellanox → ultra-low-latency networking that feeds AI clusters at scale; now core to NVIDIA’s data-center stack • Check Point → AI-driven threat prevention (ML models analyzing billions of events in real time, not signature-based junk) • Palo Alto Networks → AI/ML-native security platform (behavioral modeling, automated response, zero-trust at scale) • Wiz → AI graph analysis of cloud environments to predict attack paths before breaches happen Israel isn’t just “cyber” — it’s AI-first cyber, forged under real-world pressure. That’s why it’s becoming the new Taiwan of intelligence: not fabs, but the minds designing and defending the systems. NVDA isn’t virtue signaling — it’s doubling down where the engineering alpha already is. Believe it or not… calls 🟢🦍📈

Big_Seesaw_1792

Is this the way to fix the problem (Gemini)? # How to Fix It (The 60-Day Rule) **you have 60 days** from the date you received the funds to perform an **Indirect Rollover**. 1. **Open a New IRA:** Immediately open an account at another brokerage (like Fidelity, Vanguard, or Schwab). 2. **Deposit the Full Amount:** To avoid all taxes and penalties, you must deposit the **entire gross amount** that was in the account before liquidation. * *Example:* If your IRA had $10,000, ML likely sent you a check for $9,000 and sent $1,000 to the IRS. To "fix" this, you must deposit **$10,000** into your new IRA. 3. **The "Withholding" Gap:** You will have to come up with that 10% (the $1,000 in the example) out of your own pocket for now. When you file your taxes next year, you will report the rollover, and the 10% withheld by ML will be credited back to you as a tax refund.

aridneptune

Yeah, the reason was the CDD rule (as someone else pointed out). My wife just ignored the emails. Still, I would never have imagined that ML would just close the account. ML is refusing to do a custodial transfer.

LostAbbott

Dude.  You need to manage all of her stuff for her moving forward.  This isn't ML's fault it is your for not paying attention to your funds.  My wife hardly even knows her loginuch less what accounts she has where.  

aridneptune

Thanks! One question though…wouldn’t ML send two separate 1099-Rs?

aridneptune

ML is refusing to do that though!

Sov1245

ML = Merrill Lynch, not mother in law :D I read it the same quickly the first time.

Alexander_HamilDong

Curious - what was the mistake with ML?

Intelligent-You-6144

Trading View with Infiniti Algo or one of the other AI ML plug-ins.

bonsai-bro

When you said you "used AI" to analyze sentiment, what sentiment analysis models were you using? VADER? BERT? Or was it more classical ML methods like naive Bayes?

CurrentChannel5948

This is actually a solid thesis but I think you're being way too generous on the timeline here. ROCm is still hot garbage compared to CUDA and anyone who's actually tried to run ML workloads on AMD knows it's a pain in the ass The EPYC comparison is interesting but CPUs are way different from the AI accelerator market. Intel was literally asleep at the wheel for years while AMD was catching up. NVIDIA is actively investing billions into their moat That said, your depreciation point is spot on and those MI400 benchmarks could be spicy if they deliver

alecKarfonta

Sitting at +30% for the year. No options or margin just swing trading tech stock volatility. I am using a custom built analysis tool that combines traditional TA (momentum indicators and candlestick patterns) and an ensemble of machine learning models. I only make a trade when the TA, ML and my own intuition align.

AndAuri

Both reference the word "grokking" which had widespread use in the ML world even before AI was mainstream.

fooish_moral

canes ML it is... thanks wsb

NVDAismyg0d

Have way too much money on LSU ML. God help me 

TessTickols

Sounds like a 100% certified regard idea. Meanwhile I am using ML with macro and financial data to try to learn trading patterns, and there is no real edge so far after billions of trading years. I always seem to end up with a fairly respectable Sharpe, amazing bear market performance and bull underperformance.

drdecagon

I understand where you are coming coming from - but the videophile use case is still super niche. The trend is not that people want immersive experiences - they want access to a second screen. They want to flip through different content, different apps, for most of us our attention is shot and it's not getting better for younger generations. So, I think it's unlikely that immersive experiences will become VR's killer app. And AR, jeez, I can see business application clearly and some occasional consumer task application (and even that only materializing once there are additional advances and synergies with ML, machine vision, and AI) but I just can't imagine most people walking around with glasses with a bunch of additional notifications and information popping up non-stop in their field of vision. It might be close to sensory overload. Think about this - many cars nowadays have the HUD option - and yet, it is nowhere near a killer app - it's helpful, but not universally loved. I suspect that AR would start making a pronounced impact in cars first, but it hasn't yet in any meaningful way. There needs to be some kind of neuralink integration, where it's more integrated with our thoughts. But currently it's a solution looking for a problem (or really, for some form of a dramatic breakthrough).

shekurika

thats a different branch of AI/ML than the one thats important to the stockmarket (LLMs etc). its called bioinformatics and the idea is to more closely model the way the human brain works (whoch obviously is analogue as everything electrical in nature). but theyre in very early stages and itll take years of research to get anywhere

dirty_F0x

You should read some stuff from Euan Sinclair. If you like to get into the fundamentals of things, you can read Kris from Moontower. His stuff is really top notched. And recently I've liked a lot the very practical approach from Ksander, the guy who writes Sharpe Two, also that mf seems to not missing a trade lately. He is pretty open about ML and stuff, you should try to reach out.

Dry_Act6001

This doesn't even include the inevitable "How do I poison their dataset?" conversation in the world of adversarial LLMs and AI/ML

c-u-in-da-ballpit

I’m a software engineer focused on ML at a large-ish US company ($5 billion market cap) Everything in this post is uninformed horseshit

OUEngineer17

Move it wherever you want as long as you keep Platinum with ML, as that 2.6% cash back on everything CC is unbeatable.

lucun

Linked ML account balances count toward BoA premium tiers for people with higher balances with BoA. The premium tier credit card cash back boost is nice with their travel card. It becomes a generic 2.6%ish cash back anything card. BoA savings accounts are garbage though, but you can use the linked ML account to buy treasury mutual funds. I keep money and assets across many different banks/brokerages though

Nosemyfart

Yes, I particularly liked when they chatted about scaling up operations in the future on Azure. I'm a ML researcher and enjoy this sort of stuff outside of the investing potential.

RequirementTiny5823

Guys I HIGHLY RECOMMEND this course from company called UNLOX, it's crazy and i swear it's worth every single penny. Imagine training ML model on your tablet?? It's so crazy with edulet

Monkey_1505

I've been deep into AI since 2021, including doing my own training. A goodly portion of my x feed is me following ML researchers. There's actually a great deal of badly done research.

ThunderBobMajerle

Didn’t say you were, just pointing out betting the ML on heavy favorites is not a good strategy. they make the payout like 5%. It becomes an extremely bad bet because your payout is less than the odds they lose (10% in your case). That’s how the house wins.

PlayfulSurprise5237

Nvidia is and literally no other stock. But that's because they're selling the hardware for ML and LLMs being used in a FEW cases that is genuinely very valuable(medical field and robotics) You are so fucking coped out the wazoo if you think the AI pump is anything else. The pump you are seeing is based on the idea it's going to be some society collapsing fucking transcendental God, some fabric of reality changing shit straight out of a sci fi. You're so fucking delusional if you think it's going to be even a fraction of a fraction of that

NVDAismyg0d

Put $5,000 on BAMA ML. Pleaseeeeee

C130J_Darkstar

No, and 2024 called- it wants its talking points back. Department of Energy owns the process now, with the NRC signing off after the reactor is already built and running. Your point has no implications towards their timelines or potentially scalability- I recommend that you look into the May EOs and the new MOU between the NRC/DOE for streamlined licensing. https://www.nrc.gov/docs/ML2530/ML25303A288.pdf

dangered

Why are you so butthurt? I never even defended him. I don’t even know what quote of his you’re referring to. Did he say we are able to turn people into butterflies today? I disagree with that. I also completely disagree with gene editing research at its core on an ethical level. But I cannot deny that we could turn a person into a butterfly using this technology and it wouldn’t be all that hard. If it was a goal for a country to do it I’d give it 25 years before it was commercially available (that’s a very conservative estimate). Contrary to other developments, Genetic modification is simple in practice but the theoretical/research side is the limiting factor. Unfortunately ML models are incredibly good at identifying factors in genes faster than humanity as a whole so the theoretical side becomes increasingly simplified with every day that passes.

The_Rational_Gooner

if Huawei wasn't a threat, they would've have banned it on bs natsec excuses that are still unproven to this day. They'll do the same to EVs, they'll do the same to whatever China is ahead of the US in. As for "where is that innovation", I challenge you to take a look at the top papers for NeurIPS (the top ML conference) this year, particularly the names. Might open your eyes on things

LBCkook

Lmao wtf are you talking about? I live in LA there is plenty of construction that pops up or temporary road blocks, every single day. Waymo’s have no issue at all. Also referring to Lidar as a “crutch”. Yeah, that and radar are the crutches that allow the vehicle to operate safely in all necessary conditions. Every single other autonomous car company outside of Tesla, whether that is Waymo in the US or Weride and Autox in china, are using lidar in addition to cameras. It is allowing them to scale safely. If one day they no longer need it then fine, but Tesla is going to continue to run in to issues because cameras only see in 2D and are impacted by weather conditions. Here is what happened: Tesla chose camera only as a manufacturing decision. Not as a technical choice. They already had cameras in millions of cars, and adding lidar would require hardware retrofits (destroying the “your car will appreciate in value” promise). Musk publicly declared lidar “a fool’s errand” and walking that back would be admitting a massive strategic error. Now they’re stuck. Every new Tesla sold with camera-only hardware becomes another liability if the approach doesn’t work. They can’t pivot to lidar without admitting 10 years and billions in R&D were wrong. Without making millions of FSD-equipped vehicles obsolete, or without destroying their robotaxi narrative (the main bull case for the stock). They are essentially chasing their tale and people like you are just believing it because you don’t actually know what you’re talking about. You’re just believing the hype. Atlas being “manually coded” is outdated. Boston Dynamics uses ML for dynamic balance and motion planning. Atlas can do backflips and navigate complex terrain autonomously. Optimus…waves lol. That’s a major capability gap that isn’t going to be bridged in a year or two, if ever. Boston Dynamics has hundreds of Spot robots deployed in actual industrial/commercial use TODAY. Figure AI has units in BMW factories. Agility’s Digit is in Amazon warehouses. These aren’t demos, they’re generating revenue, so I don’t know what you mean when you pretend Tesla is unique in their manufacturing capabilities. I could name countless other companies out competing Tesla in robotics. Tesla’s advantage isn’t AI (Waymo uses Google’s Gemini, the most advanced LLM). It’s not data (they have zero deployed humanoid robots collecting real-world data). It’s manufacturing scale potential. IF the tech works. But they’re using the same camera-only approach that’s failing in FSD after 10 years. “Scales to entire planet”, with what working product? Optimus demos still show teleoperation questions. Meanwhile Boston Dynamics, Figure, Agility, and Chinese companies like Unitree ($16k humanoid, actually shipping) are deployed. Tesla is behind not ahead.

african_cheetah

Tesla is highly overvalued if they don’t crack autonomous-robots (Tesla w FSD is robot on wheels). Tesla is highly undervalued if they are first to crack and massively scale autonomy since they’re the only US industrial manufacturer with vertical supply chain of manufacturing cars, software, their own datacenter (collosus), ML engineers etc. Also including Elon having access to folks and resources at X, Grok, SpaceX. GM gave up on Cruise. Cruise could be 2nd behind Waymo. It is a massive gamble, but a non-zero probability Tesla hit their ambitions.

DFW_BjornFree

I almost shit my pants when I saw companies paying $50k for entry level CS / data analysts last night.  Been working as an ML engineer / data scientist for awhile, made $100k straight out of college.  If someone told me similar jobs would pay half what I was paid I'd probs go be an assistant manager at a QT gas station, at least them hoes make $80k

kialabearx

Aren't these stores money laundering fronts though? Candy cigs are cool but most American candy stores (run by Turkish) are deffo questionable and have allegations of ML on them which most often are true?

clingbat

You're half right. The thing is, Amazon and others have been working with Nvidia on pattern recognition and worker replacement tech and other similar tech for 5+ years now and are pretty far along with it. It does NOT require all this AI infrastructure investment to flesh out and operate. I've actually seen the demonstrations of exactly how Amazon is working w/ Nvidia to replace all their warehouse workers in Nvidia's "black box" at their HQ nearly five years ago, and then how it has come along last year in the same place. It was actually extremely depressing from a societal standpoint to watch, it definitely is going to put a lot of people out of jobs. The actual "bubble" is the absurd craving for compute to fuel these stupid LLM models right now. All the data center expansion at hundreds of MW and even GW scale for single sites and buying up accelerators with rapid depreciation curves. The bubble is not AI/ML itself more broadly for diverse applications, much of which has been in development for years outside the public eye. Anyone who is still truly convinced that there is anything path from LLM's to AGI is a fucking idiot. Most of these bets on massive data center infrastructure expansion are going to blow up in some of these debt heavy player's faces (OpenAI, Oracle, Coreweave, etc.). AWS, Google, and Microsoft are in the "race" to keep their stock prices rising, not caring if it's actually profitable or stupid because if nothing else, this boom will result in tons of new infrastructure for their traditional cloud services when they scoop up the data centers from those who go bust for 10 cents on the dollar. This boom busting is a win for them when they clean up on cheap infrastructure that was subsidized by this stupid broader FOMO investment cycle, so they have all the incentive in the world to egg it on so long as it's boosting stock prices.

Adventurous-Day1018

I just realized how dumb it is to play random ass YOLO earnings play calls. On a good call you maybe get a 2x but I am just about to 4x taking Spurs ML against OKC...

HasFiveVowels

Not at all. I first started writing ML algorithms in 2015

scruffman99

Interesting I also work in ML and AI and we are projecting 100M in AAR after 2026. Our products (used by car companies and dealerships across the world) is bringing tremendous value to our customers…get gud I guess bud.

DocHolidayPhD

I agree. It isn't that Oracle is offering anything exclusive to the AI and ML industry that isn't already being done elsewhere competitively.

Nickeless

Yeah bro I’ve worked using their software doing data engineering, ML work, and making dashboards for 2 years, but sure

Brokenandburnt

I wish no one ever started referring ML and LLM's as "AI". There is more intelligence in the enteric nervous system than those tools.\ Or in my dong for that matter.

glompulin

We get it. You bought stocks in AI companies. I work developing ML. It has genuinely stalled. 2 years ago my company said, “this is it. jobs are gone”, yet our model usage is decreasing. Humanity is rejecting AI

zennsunni

Try ML Scientist.

TheScourgeOfHumanity

cannot stop farting 😭 wife left to ML's house. God deliver me from this pain

kfpswf

Yes, the compute required for running large models is indeed mind numbing. But it's not like the technology is at a dead end with LLMs. The way I see it, these tech companies are running head-on towards an almost unattainable goal (of making humans redundant) and the exorbitant spending is in this pursuit. We are in a bubble in that regard, and we will go through a lot of pain as a global civilization when the bubble bursts. But the next generation of companies that will emerge will build their empires on the carcasses of these tech giants. Bottom line is, generative ML is useful! But not in the way these tech giants think. Some will perish in this pursuit, and better companies will emerge after that which will have the right use-case for this tech.

kfpswf

>If there is no money to be made from implementation, this would be the definition of a bubble, which concerns me. There is money to made, just not by replacing all workers to usher in some capitalist's wet dream. The current economic model is inherently unstable in the long run, and an expensive as shit technology like Generative ML (I refuse to call it AI) is adding fuel to the fire of crony capitalism. Generative AI will be useful to humanity one way or another in the coming years. It will be a revolution in every right, but it will not be the kind that the hucksters in Wall Street and the CEOs of these companies might want you to believe.

Keirtain

I don't disagree, but it's not like the inherent risk of those legacy ML models has suddenly changed. Regulating them into the dirt as part of a knee-jerk reaction related to LLMs doesn't really serve a necessary purpose.

Keirtain

I don't hate NIST, but as someone in the space, their framework has its own issues. The definition of AI in the framework is so broad that everything from LLMs, to 20-year old ML processes, to 30-year old excel macros are all suddenly arguably in scope. That would be bad enough, but everyone who supports or endorses the framework seems to view that as a feature and not a bug, and I find those people terrifying.

abcean

I haven't used facebook in over a decade so frankly I have no idea what the comments look like on there. It just struck me as very "how do you do fellow kids." I agree that reddit's gotten more trash as many of the highly knowledgeable people have left because as with any big, generalist system because people gravitate towards and push to the top content they can effortlessly understand. It used to be an audience of enthusiasts and now its not, it is what it is. Eternal september and all that, we ruined the internet for experts and now the internet is being ruined for us. I think some of that is confusion over terms. People hear AI and think chatgpt not building out a CNN to improve quality control. Marketing uses confusion over terms this to try to make people believe that every business is incorporating a genAI chatbot and seeing great returns so you need to buy their chatbot, and since that's easy there's a load of people doing it and thus is highly visible. There's a lot of obvious use-case for ML/AI, but when people are bitching they're mostly bitching about chatbots because that's the AI they engage with most frequently. And I kinda agree with them about the chatbots.

Cowkaine

There's a difference between coding roles and operational roles. There's a huge difference between writing a small project and managing a whole companies codebase. Why do you think these "companies" that your referring to are replacing coders? The first jobs to go in a company will not be the ones who have knowledge to understand the code. There are multiple studies online that show progress is not meaningfully increased in real world scenarios. There may not be as many low level entry coding jobs, but as I said earlier, Ops engineering, devops, and ML roles will be increasing to accomodate the different tools required to debug, deploy, and maintain the codebase. You think these models will just run on their own, fix and deploy themselves? [https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/](https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/) [https://www.hashicorp.com/en/blog/ai-is-making-developers-faster-but-at-a-cost](https://www.hashicorp.com/en/blog/ai-is-making-developers-faster-but-at-a-cost)

anon1999666

Currently in the process of getting onboarded at a massive medical company for a remote AI/ML engineering role. The job is basically just automating 10,000 + data input roles over the next 6-7 months. It’s incredibly sad

Cowkaine

The idea that coding is going out the window when the experts in this field are literally some of the only people that can debug, deploy, and maintain these models is crazy. Roles in operational engineering, devops, and ML are all very sought after because of this.

PRISMTRADES

I was able to do it! there are some great ML studies and engines out there that I’m using in my ui and I’m getting 85%+ confidence signals on the next 30min/1hr/4hr move and after the 30 min I get a 98%+ accuracy hit on that predicted number 🫣🫣

PandaMcGee3

ML is hard to get right man. AI/LLMs are easier for me

SippieCup

Counterpoint: all the released papers point to the fact the scaling hasn’t reached a plateau with the data they have and additional training on the data available is the fastest way to improve LLMs right now. LLMs are big enough that there might never be an overfitting issue at all especially since every frontier model has a corpus of the entire internet stored locally to them. To put in perspective all business email ML Before gpt 3 was basically only trained on the Enron emails. This isn’t a not enough data issue. While higher quality data is always preferred, it’s just not necessary yet to produce better models. XAI has proven with grok that just throwing more compute is enough.

PRISMTRADES

I did the same type of thing but instead we used ML to learn from these behaviors, scatter the web for these type of trades and based on that it predicts the next move.

prcodes

Yes of course, this has always been true and known to AI/ML researchers. There are many stages to training an LLM and they are all important. The implication that compute is suddenly less important is wrong though. All else being equal (e.g., given the same high-quality datasets), a model trained with more compute will perform better than one with less compute. Its all important and if you want the best results you will make improvements to all stages in the training pipeline.

Hydr_AI

If you interested in the subject you should check [ML factor investing](http://www.mlfactor.com). Quite insightfull on factor construction and theoritical/academic background of the different premia.

boxesofcats

Definitely worth it. ML has a bad online interface but it’s fine for set it and forget it.   The CC bonus makes the Premium Rewards card one of the most valuable cards around, and you don’t have to concentrate hard on navigating bonus categories. It rivals my Chase Sapphire Reserve but BoA comes far ahead for everyday spend. 

SPAC_Time

[Freenome and Perceptive Capital Solutions Corp Announce Business Combination Agreement to Create a Publicly Listed Company Transforming Blood-Based Multi-Cancer Detection through an AI/ML-Enabled Multiomics Platform](https://www.prnewswire.com/news-releases/freenome-and-perceptive-capital-solutions-corp-announce-business-combination-agreement-to-create-a-publicly-listed-company-transforming-blood-based-multi-cancer-detection-through-an-aiml-enabled-multiomics-platform-302634039.html) \- PCSC [Investor Presentation](https://www.sec.gov/Archives/edgar/data/2017526/000114036125044461/ef20060706_ex99-2.htm)

Hefty-Ad2809

do i YOLOOOO everything into cowboys ML?

TherealCarbunc

The government will be backing quantum defense and it will be ramping up over the next 2-5 years. I'd rather buy and hold this for 2-5 years at these evaluations than chasing it when it's $10+. yes it is currently unprofitable but if you review their news trends, they are positioning themselves very well to be a player in the market Governments aim: # Phases of the Migration Strategy # 1. Standardization (Complete/Ongoing) * **Target:** Select and standardize quantum-resistant algorithms. * **Status:** **Complete/Ongoing.** The National Institute of Standards and Technology (**NIST**) has finalized the first set of PQC standards, including: * **ML-KEM** (Module-Lattice-Based Key-Encapsulation Mechanism, replacing key exchange algorithms like Diffie-Hellman). * **ML-DSA** (Module-Lattice-Based Digital Signature Algorithm, replacing digital signature algorithms like ECDSA). * **SLH-DSA** (A hash-based digital signature, intended as a secondary option). * **NSA's Role:** The NSA's **CNSA 2.0** suite requires the use of these NIST-selected algorithms, confirming the government's official cryptographic direction. # 2. Inventory and Pilot Deployment (Current Phase) * **Target:** Federal agencies must conduct a **comprehensive cryptographic inventory** to identify all systems using vulnerable public-key cryptography. * **Timeline:** * **Immediate:** Agencies must create quantum-readiness roadmaps and begin identifying systems that are vulnerable or will not be able to support PQC. * **2025 (CNSA 2.0):** New software, firmware, web servers, and cloud services for NSS must **support and prefer** CNSA 2.0 algorithms. # 3. Implementation and Enforcement (2027 Onward) * **Target:** Full transition to hybrid and then exclusively PQC algorithms. * **Key Milestones (CNSA 2.0):** * **January 1, 2027:** All **new acquisitions** for National Security Systems must be CNSA 2.0 compliant by default. * **2030:** All deployed equipment and services in NSS that cannot support CNSA 2.0 must be **phased out**. * **2031:** Full enforcement begins across most NSS cryptographic implementations.

LymelightTO

> Intel can't even fabricate their own chips, why would Apple have confidence that Intel could manage theirs? Well, firstly, that's hyperbolic, Intel fabricates some of its chips, and outsources some of its chips to TSMC, and the proportion that it outsources looks likely to shrink in the future. Secondly, that's a retrospective analysis of past decisions, based on what everyone already knows, which is that TSMC *was* the process leader. The strategy of IFS is to leapfrog TSMC for process leadership *in the future*, so we have to look at what may happen in 2027, 2028, etc. as a result of the nodes IFS has in the pipeline, not just say, "Well, TSMC has been the process leader in the recent past, so this will continue to be the case". > If you're running a multi billion dollar business, why would you have the least competent potential partner making your most critical products? Even *if* TSMC remains the process leader, and 14A has poor yields or gets delayed or something, there's still significant value for Apple in diversifying its supply chain, and having a viable plan to switch some or all of its M-series chips out of Taiwan fabs, particularly given the geopolitical issues over Taiwan that look poised to come to a head in 2027, and the general tenor of the US administration. Also, it's not necessarily clear that Apple *needs* to "go with the best" for its mobile device chips, particularly if hyperscalers want to start a bidding war over TSMC fab space for the best chips for ML use cases.

aedes

For more context… Most of the driver for spending on AI has been motivated by an opinion piece from a few years ago that suggested AI abilities would follow scaling laws.  If you’re not familiar with that, basically, the notion was that as long as you made them “big” enough, they could do anything.  This is why companies were spending trillions of dollars on this stuff.  The big problem happened in the past 12 months or so when more recent ML research showed that **they don’t actually follow scaling laws,** and that in many applications, we are already at or near the maximum theoretical ability possible. This is why you’re not hearing people talk about AGI incessantly anymore. And why hype over agentic AI is fading as well.  TLDR the technology turned out not to follow scaling laws. This was not expected and most spending has been made assuming it would.

Pretend-Paper4137

God of the gaps reasoning is crazy in AI- "Yeah but it can't do x" over and over and over as it keeps being able to do the previous xs faster and faster and faster. Compared to when I started in ML, there have been a SHIT TON of things that it couldn't do well that are now trivial. The progress has been insane, and accelerating.  Like, other humans are going to use it to our disadvantage,  but that's the main problem with every technology. Calls.

Polus43

This. Been working in the ML world for a long time and the success of new models tends to come from how well defined (think rigid) the business process. Financial services is so regulated, e.g. UDAAP, larger institutions have spent a decade removing decision-making from points of consumer customer contact. GenAI will simply remove the "robotic" feel and AI Agents can likely take over the well defined tasks.

bigkoi

Call centers have been doing automation for years. Either automated as rule based flows , ML or now AI. It is the perfect use case for ML and AI. Chatbots wet the original target for call center / customer experience.

SpezLuvsNazis

ML enabled autocorrect is one of those things where the ceiling is amazing but the floor is just so much worse than the more mathematical based ones. Also apparently my phone thinks I’m a pirate because it always likes to autocorrect to “thar”

breakonthrough65

"People outside the ML software industry don't *really* understand this" And are you on the inside of the ML industry?

jarkon-anderslammer

My ML model just recommended a frozen potato company. I think it had too much Tylenol. 

zitrored

You are correct. AI is not ML based products. Terms get interchanged unfortunately.

Cadet_Stimpy

Difficult to say. The rush to implement and master AI (and robotics it seems now) in my opinion leads to an economic collapse specially for the working class, or potentially a major technical catastrophe due to lack of forethought when rapidly implementing AI to replace humans. My guess is that if things go to plan, the market will continue to rise, USD may likely continue to falter, inflation continues, and the working class struggles into a potential depression type scenario. But if AI/ML isn’t properly overseen (regulated), we could eventually see a major infrastructural collapse. That would have the potential to hinder across all socioeconomic classes. Of course, many billionaires have been perfecting their bunkers in between space races, so I’d imagine they’d again fair well in this situation. It’s a toss up for most of us if I had to guess.

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.