ML

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 Atlas being “manually coded” is outdated.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.

AverageUnited3237

You just proved you don't know what 'AI' actually means; Search ranking (BERT) and YouTube recommendations are the AI workloads running on those TPUs, so thanks for confirming you are completely out of your depth I copy/pasted Google's own words from their OWN BLOG highlighting how TPU facilitates Search and YouTube ML workloads. Google THEMSELVES are telling us where these TPU are used and you just choose to not believe them?

Melodic-Dolphin

Not all AI are equal, not all AI are ChatGPT. Most of the "specialized AIs" have nothing to do with LLMa at all, some of them what was previously called ML. Those are trained on relatively narrow, specific sets of data and they don't need all the pirated content. OpenAI and LLMs are different story, it's basically a pattern matching. They are not that good in any case, when a context is important.

snark42

I just support people doing ML, so I don't really know. I do know that CPUs with PyTorch are significantly slower for them and they migrated from custom CUDA code to PyTorch's abstraction layer that will supposedly easily work with more than just Nvidia/CUDA and didn't lose performance on Nvidia.

SantaSoul

I would not be entirely sure about this. I have heard many complaints that ML research at Google has stagnated and the bureaucracy is insufferable. Know plenty of colleagues that have left their research branch for greener pastures. But maybe it’s turning around again. It’s hard to be picky in this job market.

chennngiskhan

Until then Google wins! Well Google research might still win the research front since they have no lack of ML researchers.

no_frills_yo

Not so long ago, Google declared code red after gpt4 had launched. I feel sorry for the engineers trying to stir the ML training pot based on the whims and tantrums of their CEOs 😄

psssat

“Hard to do” in relation to llms is purely a money thing and data thing (and the data thing is actually a disguised money problem). The llm architecture for a model that can fit on your laptop versus a model that needs 8 h100s to run is exactly the same and is just some variant on the transformer. The only difference is an increase of parameters and more training data. Getting training data is also just a money problem too because it requires huge teams of engineers to scrap the whole web and huge teams of basically slaves to then add human touches to the data. Creating these multi trillion token datasets is such an insane task and it makes up for probably 90% of the work. And its a job that any technical staff member can work on even if they arent in ML. Nothing gemma did was revolutionary either. They have been using the tpu for years. All they did was curate a nice dataset and trained some generic model they had and were able to beat some benchmarks. But because so much money is required for this, it’s impossible to achieve this task unless you have google type of money. So then these companies like google and openai use this fact to make it seem like these companies are ran by genius savants who know more than anyone.

Primary-Debate-549

Well, Google's been at it for a decade. The irony is that the ML ecosystem for Nvidia ... was built by Facebook.

Prof_of_Wall_Street

Sorry to hear about he divorce, I can imagine that was a tough time. Looking at the numbers: Backing out returns on that, looks like 10.6% return = (12+113)/113 over a decade (the corresponding annual annual rate of return is CAGR ≈ 1.01% per year). As mentioned elsewhere, this is below inflation so the money has lost value in real (not nominal) terms. It's ok, let it go. The important question is what are you doing *now* to make sure that your future needs are being met by the capital that you have (and earning in your job). This means considering how to invest your capital now so that your future needs are met. If you need help, a fee-only financial advisor or an RIA might be the best way to go if you don't feel confident about where to invest and what the tradeoffs are among assets and asset classes (I'm unsure if your ML person has fiduciary duty or not).

VTSki001

There's a reason most of the old ML folks left after the firm was acquired by BoA ...

Deaths_Intern

Not only that, he's betting against the only hardware platform that can run ALL cutting edge ML models with ease. People outside the ML software industry don't *really* understand this, but Nvidia is completely unmatched because of how tightly integrated they are with the software stack for AI/ML models. Nvidia has a moat, and it is fucking enormous 

Maruman_man10

ML convinced me to start a managed account with a 100k. After 2 years it had made 2k. I dumped immediately. They have a long term approach while collecting their fees with a grin. I am not even sure who does the investing because it was so bad..interns?

Dominetrix

Please read A Simple Path to Wealth by JL Collins. It's available as audiobook on Spotify. ML sucks. Move your money to charles Schwab or preferably fidelity. Learn how to budget. Start being more involved in your own life and finances.

DocInABox33

Whoa hold your horses and don’t do anything hastily and especially based on “advice” from Reddit. While what everyone here is saying is correct, ie be more active and find low cost index funds, THE WAY YOU EXECUTE the plan has to be well thought out. Because we only have limited information, WHAT YOU SHOULD DO depends on having a complete picture. As far as I can tell, you are in some kind of tax advantaged annuity so you really need to make sure you move out of it judiciously so avoid any potential capital gains tax or penalties. Unfortunately, you will need someone who has access to information about your funds and the knowledge base to recommend HOW TO MOVE your money. Especially given your lack of any financial literacy (not meant as an insult it’s just a statement of fact) you want someone like a FA to help you move the funds into an appropriate account type (I’m assuming this is a tax advantaged retirement fund so you’d need an IRA of some type) Again don’t do anything hastily because the money spent on an advisor to help you move the funds (or if you already pay for it through ML and they are fiduciaries) will be well worth it in terms of preventing a VERY costly taxable event and/or penalties!

Either_Letterhead_77

I work in companies where we have done training of ML software. Because of the importance of the dataset, generally, good care is taken while training it as ultimately, you must be sure the answers you're telling it to mimic are indeed accurate. More sarcastically: you ... don't already think the internet is full of garbage?

XYHopGuy

98% of posts in stock subs about this are circular and ignore public evidence, earning call transcripts, and financial statements. While also having zero context for ML and semis. It's not hard, they should try to rent a DGX A100 node and see how it goes.

skypnooo

NVDA has many technical problems to navigate now, which is why their exec team is starting to panic. GPUs are the equivalent of a Swiss army knife for many types of AI/ML training, where as TPUs are a precision tool for LLMs (which underpins most AGI efforts). NVDA is focused on more performance per token, where GOOG is more focused on token and context window optimization. NVDA, even when increasing performance to power ratios, is not solving the power supply challenge, whereas Google is investing in micro reactors. My prediction NVDA will fall off a cliff in late 26/early 27 when the market realizes there is not enough power in the world to achieve AGI using GPUs using NVDA's tech or anyone else's for that matter...

favicocool

> NVIDIA GPU: Training, inference, graphics, scientific computing – almost everything. It's CUDA + ecosystem: every AI/ML engineer and infra relies on it. AI apps, data centers and inference demand still makes NVIDIA hold its position. Isn’t all of the revenue in the inference and training use-cases though? Which, as you mentioned, TPUs are applicable to? That’s a sincere question, I’m not an NVDA investor - I couldn’t tell you off the top of my head what percentage of NVIDIA revenue is datacenter (inference and training) but I suspect it’s a large majority. I’m curious to know now, actually…

aayu08

AI / ML is literally just solving high degree polynomials on parallel threads to reduce computation time. LLM, ConvNet, LSTM etc all work on the same basic principle.

TJMBeav

I took them both ML and spread. Who gives 7 points to a 8-3 team of grown ass men? Also hit Cinncy and da Boys. Missed on the Lions. Dammit

warriorsReaper

“Before AI” show me a tool or tech which can mine the data of that scale before AI. No one is going to invest billions thinking some advances will be made in AI or ML god knows when in future. Hence, saving that sort of granular data is a liability than advantage

0uchmyballs

Well it uses tensor flow which has lots of ML applications is all I’m saying, including neural nets which are great at LLMs and vision detection.

Xelonima

TPUs were just a FUD, Nvidia ain't going anywhere. Even Google themselves would say it was nonsense, and some of their researchers in fact do. I remember using Nvidia's GPUs on Google's cloud years ago lmao. TPUs are useful for an important yet a comparably small fraction of ML applications. It's a shame that fields such as finance and economics are dominated by technologically (and scientifically) illiterate dumbasses mostly, so they penic söll shares like this. Then some clever funds will come and sweep it all from them, with Jensen buying himself another Rolex or leather jacket or smth idk.

Panda_Pam

Financial advisors, if they are from big firms like JPM, ML, etc., can give you access to alternative investments. I don't mean bitcoin or gold, I mean private equity, exchange funds, VC funds, etc. that you wouldn't be able to get in open market. Imagine if you were a VC invested Anthropic a couple of years ago, you would be banking right now! Alternative investments would be good for diversification, especially if you think AI bubble and recession will lead to a big correction in the market. One caveat is that private equity and exchange funds come with higher fees (I've seen up to 5%, they usually have performance fee on top of admin fees if the investment is doing well) , minimum size requirements (the smallest minimum investment that I've come accross is $50k), and for the most part less liquid. But if you're young, have excess money that you don't need for the next 7 to 10 years, it is not a big issue. With regard to fees, you can negotiate fee reduction as your portfolio grows. I've been able to get my fees reduced from 1% to 0.9%, to 0.7% over 10 years period, and should be able to get it down to 0.6% in the next year based on growth projections. Also, if you're interested in private equity or exchange funds, have your financial advisor waived the placement fee.

Engi-near

I think as technology progresses, older tech depreciates and the floor grows along with the ceiling.  For instance, we would have to regress pretty far to revert to an S&P price in the 200’s, like 1980’s era with no cell phones or laptops, or personal computers. We are past the dot com era w/r/t processing power, so the market floor is higher than 1500. Compute power is comparable but vastly superior to the 2010’s when I started building computers, so higher than 3000. Then there’s the COVID dip at 4000, when remote work kicked off. Track it up to ML models and LLM’s, which despite being in infancy are undoubtedly industry-changing. I don’t see the market crashing any bigger than it did for COVID, and I see a justification for continued growth.

bobthedonkeylurker

Considering that I work in Data Science/ML, no doubt we do. The slick front-end that ChatGPT delivered a few years ago that's driven the latest push certainly has pushed LLMs into the mainstream. But there were many models before that also functioned. This is not new technology.

team_3spread

>The index treats the 151 million workers as individual agents, each tagged with skills, tasks, occupation and location. It maps more than 32,000 skills across 923 occupations in 3,000 counties, then measures where current AI systems can already perform those skills. ... >The index is not a prediction engine about exactly when or where jobs will be lost, the researchers said. Instead, it’s meant to give a skills-centered snapshot of what today’s AI systems can already do, and give policymakers a structured way to explore what-if scenarios before they commit real money and legislation. Per [the article.](https://www.cnbc.com/2025/11/26/mit-study-finds-ai-can-already-replace-11point7percent-of-us-workforce.html) They are not stating that 11.7% of the workforce can or will be replaced, that are stating that 11.7% of the skills they have identified in the labor market can be done by AI, although they don't define what they mean by AI in the article so I assume this is just LLMs? The author just seems to be taking things out of context for the sake of making an article sound more exciting. Even [the actual paper published by the Iceberg team](https://arxiv.org/abs/2510.25137) does not state "11.7% of the labor market". They focus entirely on 'skills' that are identified as being core elements of different sectors of the labor market and what current technology can perform. Per the Iceberg paper: >Beyond technology occupations, AI capabilities extend to cognitive and administrative work. Tools developed for coding demonstrate technical capability in document processing, financial analysis, and routine administrative tasks - illustrating how capabilities demonstrated in technology contexts translate to other domains. Some adoption is already occurring: IBM reduced HR staff through AI automation \[[26](https://arxiv.org/html/2510.25137v1#bib.bib26)\], Salesforce froze hiring for non-technical roles \[[29](https://arxiv.org/html/2510.25137v1#bib.bib29)\], and McKinsey projects that 30% of financial tasks could be automated by 2030 \[[15](https://arxiv.org/html/2510.25137v1#bib.bib15)\]. >We apply the same skill-overlap methodology to administrative, financial, and professional service occupations beyond the technology sector. The Iceberg Index for digital AI shows values averaging 11.7%—five times larger than the 2.2% Surface Index. Unlike technology-sector exposure concentrated in coastal hubs, this broader skill overlap is geographically distributed. South Dakota, North Carolina, and Utah show higher Index values than California or Virginia. >Industrial states illustrate this pattern. Tennessee (11.6%) and Ohio (11.8%) show substantial Index values driven by administrative and coordination roles within factories and supply chains. These white-collar functions show technical exposure that maybe invisible to policymakers while states focus largely on physical automation. These patterns reveal where skill overlap extends beyond current visible adoption, though actual workforce impacts will depend on adoption decisions, quality thresholds, and organizational constraints (Figure [6](https://arxiv.org/html/2510.25137v1#S5.F6)(a)). The talk entirely of 'skills'. not replacing a certain percentage of the labor market. Just read the paper. The study does not at all discuss the infrastructure or energy requirements to facilitate the operation of LLMs (or other ML-systems) at the scale required to mass-replace labor. The study does not discuss or investigate whether or not current LLMs or other related technologies are actually capable of *replacing people.* None of this to say that the paper does not have merit, but the article (and this post) are undoubtedly blowing the information in the paper way of of proportion.

FrankDrebinOnReddit

I'm not, but I'm perfectly qualified by being unqualified. And that's despite having a CS degree and having built some simple ML models using PyTorch for image classification and reinforcement learning games. ML is a specialized skill and deep transformer models are a specialized skill within a specialized skill.

hyzer_skip

Porting entire pipelines over is absolutely necessary. How is there any other way to move their years of research and model development to entirely new hardware with its own unique software framework requiring entirely different model architectures? For the records, I think TPUs are fucking sweet. They’re just too different to maximize from GPUs for the vast majority of top level AI researchers. I think Google will benefit just as much as Nvidia from the AI boom for different reasons. I’m invested heavily in both. I also work on Googles cloud platform everyday from their dev kit in ADK to ML models to deploying production agents in Agent Engine and with Gemini Enterprise endpoints. Their vertical stack is insane and allows them to have immense profits at every level. I also see how different their NN frameworks are even at my level as a senior data scientist and how that is a massive switching cost. That said, they will not significantly steal AI cloud customers from Nvidia for a very long time.

PatientBaker7172

NVIDIA GPU •Thousands of flexible CUDA cores •SIMD/SIMT architecture •Highly programmable •Supports FP8, FP16, BF16, TF32, FP32, FP64 (varies by generation) •Big L2 cache, high-bandwidth memory (HBM3/3e) •Tensor Cores accelerate matrix multiplies •Uses CUDA, the dominant AI software ecosystem Google TPU •Matrix multiplication units arranged into giant systolic arrays (e.g., 128×128 blocks) •Very limited instruction set •No graphics capability •Designed for maximum efficiency on fixed ML patterns •Uses HBM + interconnect optimized for Google’s internal workloads •Runs XLA compiler and is tied tightly to TensorFlow and JAX

Electronic_Muffin218

Yes, the codebase has to change if folks have hard-coded to CUDA (presumably any of the larger NVIDIA customers do this to maximize ROI, but they are also the most well-positioned to rewrite to TensorFlow or whatever is the new hotness for TPU use in Google Cloud). TensorFlow continues to work on NVIDIA, but I have no idea how optimal it is or not. The general advantage to the TPUs is going to be cost over time - less expensive per unit/work for Google to build, and they design and deploy a new generation roughly every year that delivers better efficiency per unit power. Yes, NVIDIA will continue to produce higher-density chips over time, too - but I don't believe they are as efficient at comparable tasks and the gap will continue to widen - but IANAMLP. I suspect Google will have to discount TPU pricing vs. comparable NVIDIA pricing to attract customers afraid of vendor lock-in to TensorFlow, but their cost of goods to deliver those units of processing has got to be much lower. Presumably some tasks are more suited to CUDA (See [Google docs here](https://docs.cloud.google.com/tpu/docs/intro-to-tpu) for a list of tasks that aren't optimal on TPUs). I have a feeling larger companies will move to multivendor ML/GenAI provider sourcing for all of the same reasons they do so for general cloud compute today - price leverage. Yes, there is pain in having to write to N different APIs. There are some solution providers who abstract that away, but you have to pay a price for those software layers. Here's how adoption goes for the little guys: \- startup founders DIY for a time on rented cloud AI, nudged toward one vendor by their benevolent VC advisers for Kairetsu purposes \- eventually, the company scales so much that they negotiate a deal to get preferred bulk pricing from any one of the big vendors \- eventually, the company gets bent over so badly by that one vendor that they immediately rewrite on some sort of intermediate abstraction layer and pay the price to get access to deployment on the other cloud vendors, so they get some pricing leverage back \- eventually, the company gets big enough to make it worthwhile to rewrite directly to each cloud vendors' APIs and make their own abstraction layer At any point along the way, the little guys may die, get acquired, or stall out at a size where it doesn't make sense to go to the next stage. Here's how adoption goes for the big-sized guys whose primary competency is not computer systems: \- endless RFPs for years handheld by consultants, eventually deal is inked, consultants get paid handsomely to start moving workloads into the cloud \- the solution gets rebuilt a few times over the ensuing years, never quite working as advertised, but well enough to claim some victories for director and VP promptions

Fevorkillzz

Because models come in all different sizes and use different tensor operations. At the end of the day you need to 1.) software where kernels are tailored to your PEs 2.) lots of HBM 3.) them to have a sensible programming model There’s a million other issues but ML workloads aren’t as fixed function as people might think.

paloaltothrowaway

You can still do non-LLM ML workloads 

No_Location_3339

Google is a long term hold. One of the biggest tech companies with the widest range of expertise. Good management and excellent leadership especially in ML and AI (Demis Hassabis).

scotty_dont

Firstly, it is months not years. Secondly as has already been pointed out to you there are not huge amounts of engineers at this level of the tech stack. Third, you think the XLA developers can’t debug an XLA error? I can’t even. How long does it take a decent researcher to learn Jax? Well I hope for fucks sake they already know NumPy or they don’t belong in the field. XLA is not an unreliable dumpster fire and most engineers are not spending their time on weird custom ops that hit some undiscovered bug. Yes, every company is quite comfortable with “relying” on external engineering departments. They do so constantly and everywhere. My god, I’m relying on Apples engineering department to write this message, who are relying on ARM, who are relying on… > If you wish to make an ~~apple pie~~ ML tech stack from scratch, you must first invent the universe Carl Sagan

PolishTar

AI video models can easily run on TPUs. Google has [explicitly confirmed](https://cloud.google.com/blog/products/compute/ironwood-tpus-and-new-axion-based-vms-for-your-ai-workloads) that Veo (their line of video models) runs on TPUs. Video models don't use the rasterization pipeline and instead use the same operations as any other large transformer based ML model: a ton of matrix multiplies + a little bit of vector processing for nonlinear activations + a moderate amount of shuffling data around. Sure, a TPU doesn't have specialized graphics units like raytracing cores or ROPs, but those aren't useful for video models anyways since they don't even touch the traditional rasterization pipeline. Even Nvidia has been cutting these from their datacenter AI GPUs to minimize wasted space and maximize perf/mm2. Technically there are still a few vestigal ROPs on the GB100 for firmware compatibility reasons, but they've been cutting them down every generation and they're likely to be removed entirely soon.

melodyze

As an ML person, I care because none of.the optimizations I want to use exist unless I'm targeting CUDA, and writing those optimization myself is immensely painful and a different skillset than what I do.

hyzer_skip

I’ll detail it for you. duh, most people don’t code CUDA by hand. Thats the whole point. CUDA isn’t about the syntax or code, it’s the entire kernel/tooling ecosystem underneath PyTorch and TF. You can abstract it away, but you can’t replace it. That’s why AMD, AWS, Google, etc. all have to build their own backend compilers just to get in the same ballpark. Yeah, PyTorch “runs” on TPUs, but performance, kernels, debugging, fused ops, all the shit that actually matters at scale still lives in CUDA land. That’s why every major lab, including Anthropic, still trains their SOTA models on NVIDIA even if they sprinkle inference on other hardware. The CUDA moat isn’t devs writing CUDA. It’s that the entire industry’s ML stack is built around it. Google can afford to live inside their own TPU world. Everyone else can’t and will run on CUDA.

PerfunctoryComments

The ASIC nonsense is a ridiculous differentiation, and nvidia's rather pathetic cope statement is trying to feed into misinformation. Like, the core thing ML is using in large deployments is tensor cores. Basically ASICs custom built for MAC/FMA. Just massive matrices being fuse multiplied and biases added, trillions of times. Which is precisely what a TPU does. Indeed, a TPU has a pretty robust CISC instruction set, and them has an ARM64 orchestrator, and basically the entire imaginary "we're general and they're an ASIC" difference disappears.

PerfunctoryComments

"Sure and why do you think AMD gpu adoption for AI/ML is so abysmal. " Because AMD had *dogshit* contributions to the ML framework for years. Not only did they contribute little, they then tied it to very specific pieces of hardware. Where nvidia knew how important it was and contributed heavily to these projects to make them effortless on almost any nvidia hardware, including laptops, low end graphics cards, etc. But now everyone realizes how important this is. Google added Pytorch/XLA to make running models on TPUs relatively straightforward. As the other person said, the moat basically got filled in.

rgbhfg

Sure and why do you think AMD gpu adoption for AI/ML is so abysmal. It’s because PyTorch et al are perf optimized for CUDA and the AMD drivers and support isn’t anywhere near as mature

hyzer_skip

Job postings are meant to cast as wide a net as possible when trying to attract specific talent, not sure if that’s necessarily the best indicator of actual market share. Also, we aren’t talking about our average ML job applicants. The software engineers actually programming the bleeding edge LLMs and GenAI architectures at places outside of Google are the very top level mathematicians and scientists that got to where they are because of their highly specialized expertise in the architectures behind the popular models. None of these architectures are JAX. Llama 4, Anthropic Claude, OpenAI, Deepseek, you name it, are all CUDA. You do not risk retraining these experts.

rgbhfg

Their GPUs are basically ASICs at this point. They have “tensor” cores that are purpose designed for ML The other challenge is CUDA as the software moat is very high.

alteraltissimo

Come on JAX is mentioned in like 80% of professional ML job ads

kotarel

TPUs aren't new. AI changes too quick for ASICS to stay relevant long enough without having to redesign them. If they do create something that can adapt or some kind of framework for new LLM/ML that reduces that obsolscence, then yes they will outscale GPUs. It's the same kind of principle as with Bitcoin miners. ASICS far outperform GPUs but can only do one thing (SHA256). If Google creates TPUs for their own model and only that, they can def destroy the competition as they are far more cost efficient than GPUs and it will force people to go with Google as the TPUs will only work with their models. Sure is a threat to OpenAI as they have no edge.

jpk195

Here’s one for the ML needs - if Meta picks up TPUs, is it PyTorch or Tensorflow?

Remarkable-Ear-1592

because they think they're ML architects now

PeteyPab305

You all really think AI doesn't have use cases? LMFAO I have bad news for you. That entire argument about "sheer momentum" is missing the point. AI isn't some vaporware running on hopes and dream, it's a massive, efficiency engine already deployed in nearly every sector of the economy. We're talking about present-day results, not future speculation: Amazon uses it for warehouse robotics and logistics, Palantir and defense sectors rely on it for predictive intelligence and threat modeling, and in medicine, it's already beating humans at diagnosing specific cancers from MRIs. It's maximizing throughput, cutting labor costs, and saving billions in R&D. The money being invested isn't just investors doubling down on a hope-fueled bubble, they're scaling deployment for a technology that's already proven it can generate trillions in marginal profit. Every industry, from algorithmic trading in finance to customer service bots is now reliant on ML models. Sure, monetary tightening will pop some speculative valuations, but it won't kill the essential technology that's keeping the lights on in modern business operations. The use cases are already here, and they are demonstrably producing ROI.

Nosemyfart

Sure. But that's the nature of business. Thermofisher scientific still makes money when failing companies with no future buy products to conduct laboratory research. That doesn't mean TMO isn't also supplying a rockstar in the making with a fantastic drug in the pipeline. Same thing with Nvidia, as long as there is a general use case for AI and ML theirs and others shovels will continue selling. Dot com bust also left phoenixes rising from the ashes to become some of the largest companies in the world.

TomatoSpecialist6879

Am I just attracting shitty AI bots powered by garbage ML today or some shit? Who the fuck would even put MU in the same category as pharma/biotech? It's up 156% this ytd and you think it has very little upside when the demand for memory chips barely begun? Are you retarded?

Charming_Squirrel_13

this. ML algorithms are nothing new. LLMs don’t seem that useful to scientific discovery tbh 

sarhoshamiral

Assistance with ML is very different. Both VS code and VS has ML assisted completions for example. For me written by AI means using agent modes to produce code and push it.

TheUltimateCatArmy

lol all the engineers at Nvidia code in Cursor. I worked at FAANG this summer and my boss estimates 80% of code is written with the assistance of ML.

elite5472

1. Tensor cores are a rebrand of CUDA cores and the main addition was stuff for upscaling and raytracing. That's why older cards with lots of VRAM are actually pretty good for AI work. 2. ML/AI is just the computation of billions of sigmoid functions in big matrixes. This is something GPUs are basically built for, there's no "oh but they weren't built for AI" nonsense here. The fastest AI processors are still NVIDIA cards. 3. Google's TPUs are not commercially available, have the driver/support infrastructure of GPUs, and have no resale value because you can't use them for something else. The real risk for NVIDIA is its own used products flooding the market if the bubble pops and all these startups/datacenters find themselves insolvent, much like what happened with crypto, but 100x worse. Consumers can't absorb datacenter GPUs like hobbysts could with intel servers. Can't game on an H100.

alderson710

>GPUs were NOT custom built to handle machine learning. GPUs are designed towards solving physics problems and generating dense graphics. Wrong. Certain Nvidia GPUs are designed specifically for ML pipelines. You are mixing it up the consumer GPU. > For machine learning models you don't NEED GPUs anymore. How so? Google TPU are not even available for sale? And even though they were, do you think you can cover the entire world's demand for compute? No way... not even NVidia can handle that at the moment: 2 year backlog. > NVIDIA also has 70-80% margins on their chips. That margin is now in question. This is you opinion and there is nothing that would suggest that at he moment. > A lot of their customers are developing their own custom chips. Which customers? Google is one of the biggest Nvidia customer. Even though they use it for the Cloud business. Everyone else is securing compute whether directly with NVidia or using proxy neo cloud companies. You got it all wrong. I agree that Google is a very good bet at the moment, but this has nothing to do with Nvidia.

FreshLiterature

Tensor chips were custom built for machine learning workloads. That's what an LLM is. GPUs were NOT custom built to handle machine learning. They are very good at doing math which is why they are being used to handle ML work. GPUs are designed towards solving physics problems and generating dense graphics. For machine learning models you don't NEED GPUs anymore. That's what Google has proved out. NVIDIA also has 70-80% margins on their chips. That margin is now in question. Will GPUs still be used? Sure. Will they be NVIDIA GPUs? Maybe, maybe not. A lot of their customers are developing their own custom chips.